TY  - GEN
AU  - Zheng, Fangjieyi
AU  - Chen, Kening
AU  - Zhang, Xiaoqian
AU  - Wang, Qiong
AU  - Zhang, Zhixin
AU  - Niu, Wenquan
PY  - 2025/1/1, 2025
TI  - Prevalence of malnutrition and associated factors in Chinese children and adolescents aged 3-14 years using machine learning algorithms
BT  - Journal of global health
VL  - 15
SP  - 04204
SN  - 2047-2978
N1  - 40689479
M3  - 10.7189/jogh.15.04204
N1  - 2026/9/3 10:39:00
N1  - J Glob Health
KW  - Child Nutrition Disorders; Machine Learning
N2  - &lt;h4&gt;Background&lt;/h4&gt;Child malnutrition represents a critical global public health issue and it is characterised by high prevalence and severe long-term consequences for growth and development. A better understanding of its contributory factors is essential to inform the design of targeted prevention strategies and evidence-based interventions. We aimed to estimate the prevalence of malnutrition in children and adolescents aged 3-14 years, and further to identify promising factors associated with child malnutrition using machine learning algorithms.&lt;h4&gt;Methods&lt;/h4&gt;Thirty kindergartens and 26 schools were randomly selected from Beijing and Tangshan. Child malnutrition was defined according to WHO standards. Factors for child malnutrition were selected by Logistic regression and three ensemble learning algorithms. An open-access web platform was developed to facilitate calculating probabilities of child malnutrition.&lt;h4&gt;Results&lt;/h4&gt;Total 18 503 children and adolescents were surveyed, and 10.93% (n?=?2022) of them were found to be malnourished. Random forest emerged as the best model, as it carried the highest area under the receiver operating characteristic curve (AUROC) at 0.929. Under the implementation of random forest, top eight factors that formed the optimal set for child malnutrition prediction were identified, including age, frequency of fast food intake, frequency of late-night snacking, family history of diabetes, duration of breastfeeding, sedentary time, and parental body mass index. Further Logistic regression analyses confirmed the predictive significance of these individual factors.&lt;h4&gt;Conclusions&lt;/h4&gt;We have identified eight contributory factors for malnutrition in 3-14-year-old children and adolescents in Beijing and Tangshan, with their prediction performance optimal under random forest. More studies among independent populations are warranted to validate our findings.
N1  - J Glob Health
AD  - Centre for Evidence-Based Medicine, Capital Institute of Paediatrics, Beijing, People's Republic of China.
UR  - http://europepmc.org/abstract/MED/40689479
https://doi.org/10.7189/jogh.15.04204
https://europepmc.org/articles/PMC12278689
https://europepmc.org/articles/PMC12278689?pdf=render
ID  - 1925
ER  - 

TY  - JOUR
AU  - Zhang, Zhihong
AU  - Wan, Zhong
AU  - Zhu, Yu
AU  - Zhang, Lijuan
AU  - Zhang, Lili
AU  - Wan, Hongwei
PY  - 2021
TI  - Prevalence of malnutrition comparing NRS2002, MUST, and PG-SGA with the GLIM criteria in adults with cancer: A multi-center study
SP  - 111072
N1  - 2021/3/1
JF  - Nutrition
VL  - 83
SN  - 0899-9007
M3  - https://doi.org/10.1016/j.nut.2020.111072
N1  - 2026/9/3 10:42:00
N1  - ScienceDirect
KW  - Cancer;Malnutrition;Screening;Nutrition assessment;Diagnostic criteria
N2  - Objectives
UR  - https://www.sciencedirect.com/science/article/pii/S0899900720303555
DO - https://doi.org/10.1016/j.nut.2020.111072
ID  - 1928
ER  - 

TY  - JOUR
AU  - Zeyauddin, Md
AU  - Abidin, Shafiqul
AU  - Khan, Imran
AU  - Bokhari, Mohammad Ubaidullah
AU  - Siddiqui, Md Ashraf
AU  - Ahmad, Ausaf
AU  - Alam, Shadab
PY  - 2025
TI  - Clinical predictive fusion network for accurate disease prediction in patient cohorts.
SP  - 3626
JF  - Scientific reports
VL  - 16
IS  - 1
SN  - 2045-2322 
N1  - 41449223
M3  - 10.1038/s41598-025-33645-9
N1  - 2026/9/3 10:47:00
N1  - PubMed
N1  - English
KW  - AI in healthcare;Clinical decision system;Ensemble learning;Predictive analysis;Treatment therapy prediction
N2  - The increasing complexity of healthcare data demands predictive models that are both accurate and interpretable. This study presents the Clinical Predictive Fusion Network (CPFN). This adaptive ensemble learning framework integrates Logistic Regression, Random Forest, and Support Vector Machine classifiers through a validation-driven weighted fusion strategy. The model's adaptive weighting enables it to learn the relative reliability of base classifiers across multimodal patient datasets. CPFN was evaluated using 10-fold stratified cross-validation on disease-specific (cardiology, neurology, diabetes, pulmonology, and oncology) and a synthetically fused multi-disease dataset, achieving up to 93.0??0.4% accuracy on individual datasets and 95.5??0.3% on the combined dataset. Other metrics included a recall of 92.0??0.5%, F1-score of 92.5??0.4%, and ROC-AUC ranging from 0.95 to 0.975 (95% CI, bootstrap 1000 resamples). These results demonstrate that CPFN maintains consistent and generalizable performance across heterogeneous data sources. The model's transparent fusion design and detailed pseudocode enhance reproducibility and clinical applicability, positioning CPFN as a scalable, data-driven decision-support framework for next-generation predictive healthcare systems.
AD  - Department of Computer Science , Aligarh Muslim University , Aligarh, India.;Department of Computer Science , Aligarh Muslim University , Aligarh, India.;School of Computer Science, UPES, Dehradun, India. imran.khan1@ddn.upes.ac.in.;Department of Computer Science , Aligarh Muslim University , Aligarh, India.;Department of Computer Science , Aligarh Muslim University , Aligarh, India.;Department of Computer Application , Integral University , Lucknow, India.;Department of Computer Science , Jazan University , Jazan, Saudi Arabia.
UR  - https://www.ncbi.nlm.nih.gov/pubmed/41449223
DO - 10.1038/s41598-025-33645-9
ID  - 1936
ER  - 

TY  - JOUR
AU  - Yan, Mingfang
AU  - Lin, Zhenmeng
AU  - Chen, Rong
AU  - Liu, Ying
AU  - Jian, Jinliang
AU  - Zhuo, Changhua
PY  - 2025
TI  - Identification of postoperative weight loss trajectories and development of a machine learning-based tool for predicting malnutrition in gastric cancer patients.
SP  - 1678879
JF  - Frontiers in nutrition
VL  - 12
SN  - 2296-861X 
N1  - 41041132
M3  - 10.3389/fnut.2025.1678879
N1  - 2026/9/3 11:02:00
N1  - PubMed
N1  - English
KW  - gastric cancer;machine learning;malnutrition;nomogram;weight loss trajectories
N2  - BACKGROUND: Significant postoperative weight loss and malnutrition represent common and serious complications following radical gastrectomy for gastric cancer. Early identification of distinct weight loss trajectories and prediction of malnutrition risk may facilitate targeted interventions. METHODS: This prospective, observational longitudinal study enrolled 312 gastric adenocarcinoma patients undergoing radical gastrectomy. Participants were assessed preoperatively (T0) and at 3, 6, 9, and 12?months postoperatively (T1-T4). Percentage weight loss was calculated at each postoperative time point. Latent growth mixture modeling (GMM) identified distinct weight loss trajectories. Eight machine learning algorithms (XGBoost, SVM, RF, NB, KNN, MLP, GBM, PLS) were trained using predictors selected by LASSO regression and the Boruta algorithm to predict GLIM-defined malnutrition at 6?months postoperatively (T2, the peak malnutrition timepoint). Additionally, a multivariable logistic regression-derived nomogram was developed and validated, with assessments of discrimination, calibration, and clinical utility. RESULTS: GMM identified three distinct 12-month postoperative weight loss trajectories: severe (11.9%), moderate (36.2%), and minimal (51.9%). The prevalence of GLIM-defined malnutrition peaked at 51.6% at 6?months (T2). Among the eight machine learning models, XGBoost achieved the best performance in predicting 6-month malnutrition. The final nomogram, which incorporated age 65?years, preoperative underweight status, preoperative reduced muscle mass, and total gastrectomy, showed excellent discrimination, calibration, and clinical utility. DeLong's test indicated no significant difference in AUC between the XGBoost model and the nomogram (p?=?0.121). CONCLUSION: This study delineates distinct postoperative weight loss trajectories in gastric cancer patients. We developed and validated both an advanced ML model (XGBoost) and a clinically interpretable nomogram for accurately predicting 6-month postoperative malnutrition risk.
AD  - Department of Anesthesiology Surgery, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian, China.;Department of Gastrointestinal Surgery, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian, China.;College of Animal Science, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China.;Department of Gastrointestinal Surgery, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian, China.;Department of Gastrointestinal Surgery, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian, China.;Department of Gastrointestinal Surgery, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian, China.
UR  - https://www.ncbi.nlm.nih.gov/pubmed/41041132
DO - 10.3389/fnut.2025.1678879
ID  - 1942
ER  - 

TY  - JOUR
AU  - Waters, John K.
AU  - Reznik, Scott I.
PY  - 2022
TI  - Update on Management of Squamous Cell Esophageal Cancer.
SP  - 375-385
JF  - Current oncology reports
VL  - 24
IS  - 3
SN  - 1523-3790 
N1  - 35142974
M3  - 10.1007/s11912-021-01153-4
N1  - 2026/9/3 11:06:00
N1  - PubMed
N1  - English
KW  - Checkpoint inhibitor;Chemoradiation;Esophageal cancer;Esophageal cancer staging;Neoadjuvant therapy;Squamous cell carcinoma
N2  - PURPOSE OF THE REVIEW: Esophageal cancer is the sixth most common cause of cancer death globally. Squamous cell carcinoma of the esophagus (ESCC) is the predominant histologic type in the world. Treatment strategies have evolved in the last decade and new paradigms are replacing traditional approaches at all stages of cancer. This review will summarize the epidemiology, diagnosis, staging, and treatment of esophageal squamous cell carcinoma. RECENT FINDINGS: Novel approaches to screening may be cost-effective in regions with a high incidence of ESCC. Multi-disciplinary evaluation and treatment has become the standard of care. Endoscopic resection may be an option for early stage ESCC. Minimally invasive esophagectomy can be performed safely as a primary therapy or after-induction chemoradiation. Several recent studies have found a survival benefit to immunotherapy for patients with metastatic or persistent disease. Multi-disciplinary evaluation and multi-modal therapy including cytotoxic chemotherapy, radiation, surgery, and immunotherapy have improved survival compared to surgery alone.
AD  - Division of Thoracic Surgery, Department of Cardiovascular and Thoracic Surgery, 5323 Harry Hines Boulevard, MC 8879, Dallas, TX, 75390-8879, USA.;Division of Thoracic Surgery, Department of Cardiovascular and Thoracic Surgery, 5323 Harry Hines Boulevard, MC 8879, Dallas, TX, 75390-8879, USA. Scott.reznik@utsouthwestern.edu.
UR  - https://www.ncbi.nlm.nih.gov/pubmed/35142974
DO - 10.1007/s11912-021-01153-4
ID  - 1947
ER  - 

TY  - JOUR
AU  - Wang, Xue
AU  - Yang, Fengchun
AU  - Zhu, Mingwei
AU  - Cui, Hongyuan
AU  - Wei, Junmin
AU  - Li, Jiao
AU  - Chen, Wei
PY  - 2023
TI  - Development and Assessment of Assisted Diagnosis Models Using Machine Learning for Identifying Elderly Patients With Malnutrition: Cohort Study.
SP  - e42435
JF  - Journal of medical Internet research
VL  - 25
SN  - 1438-8871 
N1  - 36917167
M3  - 10.2196/42435
N1  - 2026/9/3 11:03:00
N1  - PubMed
N1  - English
KW  - GLIM;SHAP;Shapley additive explanation;XGBoost;algorithm;diagnose;diagnosis;diagnostic;disease-related malnutrition;elder;global leadership initiative on malnutrition;machine learning;malnutrition;model;nutrition;older adult;older inpatients;risk;visualization
N2  - BACKGROUND: Older patients are at an increased risk of malnutrition due to many factors related to poor clinical outcomes. OBJECTIVE: This study aims to develop an assisted diagnosis model using machine learning (ML) for identifying older patients with malnutrition and providing the focus of individualized treatment. METHODS: We reanalyzed a multicenter, observational cohort study including 2660 older patients. Baseline malnutrition was defined using the global leadership initiative on malnutrition (GLIM) criteria, and the study population was randomly divided into a derivation group (2128/2660, 80%) and a validation group (532/2660, 20%). We applied 5 ML algorithms and further explored the relationship between features and the risk of malnutrition by using the Shapley additive explanations visualization method. RESULTS: The proposed ML models were capable to identify older patients with malnutrition. In the external validation cohort, the top 3 models by the area under the receiver operating characteristic curve were light gradient boosting machine (92.1%), extreme gradient boosting (91.9%), and the random forest model (91.5%). Additionally, the analysis of the importance of features revealed that BMI, weight loss, and calf circumference were the strongest predictors to affect GLIM. A BMI of below 21 kg/m2 was associated with a higher risk of GLIM in older people. CONCLUSIONS: We developed ML models for assisting diagnosis of malnutrition based on the GLIM criteria. The cutoff values of laboratory tests generated by Shapley additive explanations could provide references for the identification of malnutrition. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR-EPC-14005253; https://www.chictr.org.cn/showproj.aspx?proj=9542.
AD  - Department of Clinical Nutrition, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.;Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.;Department of General Surgery, Beijing Hospital, National Center of Gerontology, Beijing, China.;Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.;Department of Gastrointestinal Surgery, Beijing Hospital, National Center of Gerontology, Beijing, China.;Department of General Surgery, Beijing Hospital, National Center of Gerontology, Beijing, China.;Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.;Department of Gastrointestinal Surgery, Beijing Hospital, National Center of Gerontology, Beijing, China.;Department of General Surgery, Beijing Hospital, National Center of Gerontology, Beijing, China.;Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.;Department of Gastrointestinal Surgery, Beijing Hospital, National Center of Gerontology, Beijing, China.;Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.;Department of Clinical Nutrition, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
UR  - https://www.ncbi.nlm.nih.gov/pubmed/36917167
DO - 10.2196/42435
ID  - 1944
ER  - 

TY  - JOUR
AU  - Wang, Xinyuan
AU  - Li, Chenyu
AU  - Xu, Lingyu
AU  - Jiang, Siqi
AU  - Guan, Chen
AU  - Che, Lin
AU  - Wang, Yanfei
AU  - Man, Xiaofei
AU  - Xu, Yan
PY  - 2025
TI  - Construction and validation of prognostic models for acute kidney disease and  mortality in patients at risk of malnutrition: an interpretable machine learning  approach.
SP  - sfaf080
JF  - Clinical kidney journal
VL  - 18
IS  - 4
SN  - 2048-8505 
N1  - 40236512
M3  - 10.1093/ckj/sfaf080
N1  - 2026/9/3 10:40:00
N1  - PubMed
N1  - English
KW  - acute kidney disease;hospital mortality;machine learning;patients at risk of malnutrition;risk prediction
N2  - BACKGROUND: Acute kidney injury (AKI) is a prevalent complication in patients at  risk of malnutrition, elevating the risks of acute kidney disease (AKD) and  mortality. AKD reflects the adverse events developing after AKI. This study aimed  to develop and validate machine learning (ML) models for predicting the  occurrence of AKD, AKI and mortality in patients at risk of malnutrition.  METHODS: We retrospectively reviewed the medical records of patients at risk of  malnutrition. Eight ML algorithms were employed to predict AKD, AKI and  mortality. The performance of the best model was evaluated using various metrics  and interpreted using the SHapley Additive exPlanation (SHAP) method. An  artificial intelligence (AI)-driven web application was also created based on the  best model. RESULTS: A total of 13?395 patients were included in our study. Among  them, 1751 (13.07%) developed subacute AKD, 1253 (9.35%) were transient AKI, and  1455 (10.86%) met both AKI and AKD criteria. The incidence rate of mortality was  6.74%. The light gradient boosting machine (LGBM) outperformed other models in  predicting AKD, AKI and mortality, with area under curve values of 0.763, 0.801  and 0.881, respectively. The SHAP method revealed that AKI stage, lactate  dehydrogenase, albumin, aspirin usage and serum creatinine were the top five  predictors of AKD. An online prediction website for AKI, AKD and mortality was  developed based on the final models. CONCLUSIONS: The LGBM models provide an  effective method for predicting AKD, AKI and mortality at an early stage in  patients at risk of malnutrition, enabling prompt interventions. Compared with  the AKD model, the models for predicting AKI and mortality perform better. The  AI-driven web application can significantly aid in creating personalized  preventive measures. Future work will aim to expand the application to larger,  more diverse populations, incorporate additional biomarkers and refine ML  algorithms to improve predictive accuracy and clinical utility.
AD  - Department of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao,;Department of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao,;Department of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao,;Department of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao,;Department of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao,;Department of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao,;Department of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao,;Department of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao,;Department of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao,
UR  - https://www.ncbi.nlm.nih.gov/pubmed/40236512
DO - 10.1093/ckj/sfaf080
ID  - 1926
ER  - 

TY  - JOUR
AU  - Sung, Hyuna
AU  - Ferlay, Jacques
AU  - Siegel, Rebecca L.
AU  - Laversanne, Mathieu
AU  - Soerjomataram, Isabelle
AU  - Jemal, Ahmedin
AU  - Bray, Freddie
PY  - 2021
TI  - Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries.
SP  - 209-249
JF  - CA: a cancer journal for clinicians
VL  - 71
IS  - 3
SN  - 0007-9235 
N1  - 33538338
M3  - 10.3322/caac.21660
N1  - 2026/9/3 10:34:00
N1  - PubMed
N1  - English
KW  - burden;cancer;epidemiology;incidence;mortality
N2  - This article provides an update on the global cancer burden using the GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer. Worldwide, an estimated 19.3 million new cancer cases (18.1 million excluding nonmelanoma skin cancer) and almost 10.0 million cancer deaths (9.9 million excluding nonmelanoma skin cancer) occurred in 2020. Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), followed by lung (11.4%), colorectal (10.0 %), prostate (7.3%), and stomach (5.6%) cancers. Lung cancer remained the leading cause of cancer death, with an estimated 1.8 million deaths (18%), followed by colorectal (9.4%), liver (8.3%), stomach (7.7%), and female breast (6.9%) cancers. Overall incidence was from 2-fold to 3-fold higher in transitioned versus transitioning countries for both sexes, whereas mortality varied <2-fold for men and little for women. Death rates for female breast and cervical cancers, however, were considerably higher in transitioning versus transitioned countries (15.0 vs 12.8 per 100,000 and 12.4 vs 5.2 per 100,000, respectively). The global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020, with a larger increase in transitioning (64% to 95%) versus transitioned (32% to 56%) countries due to demographic changes, although this may be further exacerbated by increasing risk factors associated with globalization and a growing economy. Efforts to build a sustainable infrastructure for the dissemination of cancer prevention measures and provision of cancer care in transitioning countries is critical for global cancer control.
AD  - Surveillance and Health Equity Science, American Cancer Society, Atlanta, Georgia.;Section of Cancer Surveillance, International Agency for Research on Cancer, Lyon, France.;Surveillance and Health Equity Science, American Cancer Society, Atlanta, Georgia.;Section of Cancer Surveillance, International Agency for Research on Cancer, Lyon, France.;Section of Cancer Surveillance, International Agency for Research on Cancer, Lyon, France.;Surveillance and Health Equity Science, American Cancer Society, Atlanta, Georgia.;Section of Cancer Surveillance, International Agency for Research on Cancer, Lyon, France.
UR  - https://www.ncbi.nlm.nih.gov/pubmed/33538338
DO - 10.3322/caac.21660
ID  - 1920
ER  - 

TY  - JOUR
AU  - Stoltzfus, Jill C.
PY  - 2011
TI  - Logistic regression: a brief primer.
SP  - 1099-104
JF  - Academic emergency medicine : official journal of the Society for Academic Emergency Medicine
VL  - 18
IS  - 10
SN  - 1069-6563 
N1  - 21996075
M3  - 10.1111/j.1553-2712.2011.01185.x
N1  - 2026/9/3 10:45:00
N1  - PubMed
N1  - English
N2  - Regression techniques are versatile in their application to medical research because they can measure associations, predict outcomes, and control for confounding variable effects. As one such technique, logistic regression is an efficient and powerful way to analyze the effect of a group of independent variables on a binary outcome by quantifying each independent variable's unique contribution. Using components of linear regression reflected in the logit scale, logistic regression iteratively identifies the strongest linear combination of variables with the greatest probability of detecting the observed outcome. Important considerations when conducting logistic regression include selecting independent variables, ensuring that relevant assumptions are met, and choosing an appropriate model building strategy. For independent variable selection, one should be guided by such factors as accepted theory, previous empirical investigations, clinical considerations, and univariate statistical analyses, with acknowledgement of potential confounding variables that should be accounted for. Basic assumptions that must be met for logistic regression include independence of errors, linearity in the logit for continuous variables, absence of multicollinearity, and lack of strongly influential outliers. Additionally, there should be an adequate number of events per independent variable to avoid an overfit model, with commonly recommended minimum "rules of thumb" ranging from 10 to 20 events per covariate. Regarding model building strategies, the three general types are direct/standard, sequential/hierarchical, and stepwise/statistical, with each having a different emphasis and purpose. Before reaching definitive conclusions from the results of any of these methods, one should formally quantify the model's internal validity (i.e., replicability within the same data set) and external validity (i.e., generalizability beyond the current sample). The resulting logistic regression model's overall fit to the sample data is assessed using various goodness-of-fit measures, with better fit characterized by a smaller difference between observed and model-predicted values. Use of diagnostic statistics is also recommended to further assess the adequacy of the model. Finally, results for independent variables are typically reported as odds ratios (ORs) with 95% confidence intervals (CIs).
AD  - Research Institute, St. Luke's Hospital and Health Network, Bethlehem, PA, USA. StoltzJ@slhn.org
UR  - https://www.ncbi.nlm.nih.gov/pubmed/21996075
DO - 10.1111/j.1553-2712.2011.01185.x
ID  - 1932
ER  - 

TY  - JOUR
AU  - Song, Chunhua
AU  - Cao, Jingjing
AU  - Zhang, Feng
AU  - Wang, Chang
AU  - Guo, Zengqing
AU  - Lin, Yuan
AU  - Shi, Yingying
AU  - Hu, Wen
AU  - Ba, Yi
AU  - Xu, Hongxia
AU  - Li, Wei
AU  - Shi, Hanping
PY  - 2019
TI  - Nutritional Risk Assessment by Scored Patient-Generated Subjective Global Assessment Associated with Demographic Characteristics in 23,904 Common Malignant Tumors Patients
SP  - 50-60
N1  - 2019/1/2
JF  - Nutrition and Cancer
VL  - 71
IS  - 1
SN  - 0163-5581
M3  - 10.1080/01635581.2019.1566478
N1  - Routledge
N1  - 2026/9/3 10:37:00
N1  - Taylor & Francis Online
N2  - Malnutrition is a problem affecting tumor patients greatly. This study aims to investigate whether demographic characteristics are related to the malnutrition of cancer patients. Twenty-three thousand nine hundred and four (23,904) patients with 16 common malignant tumors were enrolled in the study. Patient Generated Subjective Global Assessment (PG-SGA) was used as a screening tool to assess the nutritional risk of patients and analysis of variance was used to compare PG-SGA scores of patients. Correlations between PG-SGA scores and demographic characteristics were evaluated by correlation analysis. We observed that 57.88% tumor patients had some degree of malnutrition (score 4) and only 20.61% were well-nourished (score 0?1). Screening scores were higher among older patients for most of the tumors. PG-SGA scores showed the significant difference between females and males in some tumors. In addition, the PG-SGA scores of some tumors were significantly different in various types of medical insurances, education levels, occupations, regions, and nationalities. Correlation analysis indicated the existence of associations between PG-SGA scores and demographic characteristics. Understanding the distribution of nutritional risk of tumor patients and the correlations between the PG-SGA scores and demographic characteristics could help identify subgroups who may benefit from targeted interventions to improve the effect of clinical treatment and the quality of life for oncology patients.
UR  - https://doi.org/10.1080/01635581.2019.1566478
DO - 10.1080/01635581.2019.1566478
ID  - 1923
ER  - 

TY  - JOUR
AU  - Shi, Hui
AU  - Yang, Dong
AU  - Tang, Kaichen
AU  - Hu, Chunmei
AU  - Li, Lijuan
AU  - Zhang, Linfang
AU  - Gong, Ting
AU  - Cui, Yanqin
PY  - 2022
TI  - Explainable machine learning model for predicting the occurrence of postoperative malnutrition in children with congenital heart disease
SP  - 202-210
N1  - 2022/1/1
JF  - Clinical Nutrition
VL  - 41
IS  - 1
SN  - 0261-5614
M3  - https://doi.org/10.1016/j.clnu.2021.11.006
N1  - 2026/9/3 10:43:00
N1  - ScienceDirect
KW  - Malnutrition;Congenital heart disease;Machine learning;Children;Interpretability
N2  - Summary
UR  - https://www.sciencedirect.com/science/article/pii/S0261561421005070
DO - https://doi.org/10.1016/j.clnu.2021.11.006
ID  - 1930
ER  - 

TY  - JOUR
AU  - Sguanci, Marco
AU  - Palomares, Sara Morales
AU  - Cangelosi, Giovanni
AU  - Petrelli, Fabio
AU  - Sandri, Elena
AU  - Ferrara, Gaetano
AU  - Mancin, Stefano
PY  - 2025
TI  - Artificial Intelligence in the Management of Malnutrition in Cancer Patients: A  Systematic Review.
SP  - 100438
JF  - Advances in nutrition (Bethesda, Md.)
VL  - 16
IS  - 7
SN  - 2161-8313 
N1  - 40334987
M3  - 10.1016/j.advnut.2025.100438
N1  - 2026/9/3 10:41:00
N1  - PubMed
N1  - English
KW  - artificial intelligence;body composition;cachexia;cancer patients;clinical outcomes;deep learning;machine learning;malnutrition;nutritional assessment;oncology
N2  - Malnutrition is a critical complication among cancer patients, affecting 80% of  individuals depending on cancer type, stage, and treatment. Artificial  intelligence (AI) has emerged as a promising tool in healthcare, with potential  applications in nutritional management to improve early detection, risk  stratification, and personalized interventions. This systematic review evaluated  the role of AI in identifying and managing malnutrition in cancer patients,  focusing on its effectiveness in nutritional status assessment, prediction,  clinical outcomes, and body composition monitoring. A systematic search was  conducted across PubMed, Cochrane Library, Cumulative Index to Nursing and Allied  Health Literature, and Excerpta Medica Database from June to July 2024, following  the Preferred Reporting Items for Systematic Reviews and Meta-Analyses  guidelines. Quantitative primary studies investigating AI-based interventions for  malnutrition detection, body composition analysis, and nutritional optimization  in oncology were included. Study quality was assessed using the Joanna Briggs  Institute Critical Appraisal Tools, and evidence certainty was evaluated with the  Oxford Centre for Evidence-Based Medicine framework. Eleven studies (n = 52,228  patients) met the inclusion criteria and were categorized into 3 overarching  domains: nutritional status assessment and prediction, clinical and functional  outcomes, and body composition and cachexia monitoring. AI-based models  demonstrated high predictive accuracy in malnutrition detection (area under the  curve >0.80). Machine learning algorithms, including decision trees, random  forests, and support vector machines, outperformed conventional screening tools.  Deep learning models applied to medical imaging achieved high segmentation  accuracy (Dice similarity coefficient: 0.92-0.94), enabling early cachexia  detection. AI-driven virtual dietitian systems improved dietary adherence (84%)  and reduced unplanned hospitalizations. AI-enhanced workflows streamlined  dietitian referrals, reducing referral times by 2.4 d. AI demonstrates  significant potential in optimizing malnutrition screening, body composition  monitoring, and personalized nutritional interventions for cancer patients. Its  integration into oncology nutrition care could enhance patient outcomes and  optimize healthcare resource allocation. Further research is necessary to  standardize AI models and ensure clinical applicability. This systematic review  followed a protocol registered prospectively on Open Science Framework  (https://doi.org/10.17605/OSF.IO/A259M).
AD  - A.O. Polyclinic San Martino Hospital, Genova, Italy.;Department of Pharmacy, Health and Nutritional Sciences (DFSSN), University of;School of Pharmacy, Polo Medicina Sperimentale e Sanit Pubblica "Stefania;School of Pharmacy, Polo Medicina Sperimentale e Sanit Pubblica "Stefania;Faculty of Medicine and Health Sciences, Catholic University of Valencia San;Nephrology and Dialysis Unit, Ramazzini Hospital, Carpi, Italy.;IRCCS Humanitas Research Hospital, via Manzoni 56, 20089, Rozzano, Milan, Italy.
UR  - https://www.ncbi.nlm.nih.gov/pubmed/40334987
DO - 10.1016/j.advnut.2025.100438
ID  - 1927
ER  - 

TY  - JOUR
AU  - Norman, Kristina
AU  - Ha?, Ulrike
AU  - Pirlich, Matthias
PY  - 2021
TI  - Malnutrition in Older Adults-Recent Advances and Remaining Challenges.
JF  - Nutrients
VL  - 13
IS  - 8
SN  - 2072-6643 
N1  - 34444924
M3  - 10.3390/nu13082764
N1  - 2026/9/3 11:04:00
N1  - PubMed
N1  - English
KW  - DoMAP;GLIM criteria;ageing;anorexia of aging;inflammaging;malnutrition;micronutrients;sarcopenia
N2  - Malnutrition in older adults has been recognised as a challenging health concern associated with not only increased mortality and morbidity, but also with physical decline, which has wide ranging acute implications for activities of daily living and quality of life in general. Malnutrition is common and may also contribute to the development of the geriatric syndromes in older adults. Malnutrition in the old is reflected by either involuntary weight loss or low body mass index, but hidden deficiencies such as micronutrient deficiencies are more difficult to assess and therefore frequently overlooked in the community-dwelling old. In developed countries, the most cited cause of malnutrition is disease, as both acute and chronic disorders have the potential to result in or aggravate malnutrition. Therefore, as higher age is one risk factor for developing disease, older adults have the highest risk of being at nutritional risk or becoming malnourished. However, the aetiology of malnutrition is complex and multifactorial, and the development of malnutrition in the old is most likely also facilitated by ageing processes. This comprehensive narrative review summarizes current evidence on the prevalence and determinants of malnutrition in old adults spanning from age-related changes to disease-associated risk factors, and outlines remaining challenges in the understanding, identification as well as treatment of malnutrition, which in some cases may include targeted supplementation of macro- and/or micronutrients, when diet alone is not sufficient to meet age-specific requirements.
FAU - Norman, Kristina
AD  - Department of Geriatrics and Medical Gerontology, Charit-Universit?tsmedizin Berlin, Corporate Member of Freie Universit?t Berlin and Humboldt-Universit?t zu Berlin, 13347 Berlin, Germany.;Department of Nutrition and Gerontology, German Institute of Human Nutrition Potsdam-Rehbrcke, 14458 Nuthetal, Germany.;Institute of Nutritional Science, University of Potsdam, 14458 Nuthetal, Germany.;German Center for Cardiovascular Research (DZHK), Partner Site Berlin, 10115 Berlin, Germany.;Department of Nutrition and Gerontology, German Institute of Human Nutrition Potsdam-Rehbrcke, 14458 Nuthetal, Germany.;Institute of Nutritional Science, University of Potsdam, 14458 Nuthetal, Germany.;Imperial Oak Outpatient Clinic, Endocrinology, Gastroenterology & Clinical Nutrition, 12159 Berlin, Germany.
UR  - https://www.ncbi.nlm.nih.gov/pubmed/34444924
DO - 10.3390/nu13082764
ID  - 1945
ER  - 

TY  - JOUR
AU  - Morgan, Eileen
AU  - Soerjomataram, Isabelle
AU  - Rumgay, Harriet
AU  - Coleman, Helen G.
AU  - Thrift, Aaron P.
AU  - Vignat, Jr?me
AU  - Laversanne, Mathieu
AU  - Ferlay, Jacques
AU  - Arnold, Melina
PY  - 2022
TI  - The Global Landscape of Esophageal Squamous Cell Carcinoma and Esophageal Adenocarcinoma Incidence and Mortality in 2020 and Projections to 2040: New Estimates From GLOBOCAN 2020
SP  - 649-658.e2
N1  - 2022/9/1
JF  - Gastroenterology
VL  - 163
IS  - 3
SN  - 0016-5085
M3  - https://doi.org/10.1053/j.gastro.2022.05.054
N1  - 2026/9/3 10:35:00
N1  - ScienceDirect
KW  - Esophageal Cancer;Epidemiology;Cancer Prevention
N2  - Background & Aims
UR  - https://www.sciencedirect.com/science/article/pii/S0016508522006084
DO - https://doi.org/10.1053/j.gastro.2022.05.054
ID  - 1921
ER  - 

TY  - JOUR
AU  - Loh, Hui Wen
AU  - Ooi, Chui Ping
AU  - Seoni, Silvia
AU  - Barua, Prabal Datta
AU  - Molinari, Filippo
AU  - Acharya, U. Rajendra
PY  - 2022
TI  - Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011-2022).
SP  - 107161
JF  - Computer methods and programs in biomedicine
VL  - 226
SN  - 0169-2607 
N1  - 36228495
M3  - 10.1016/j.cmpb.2022.107161
N1  - 2026/9/3 11:00:00
N1  - PubMed
N1  - English
KW  - Attention mechanism;CBR;Deep learning;EBM;Expert system;Explainable artificial intelligence (XAI);GradCAM;Healthcare;LIME;LRP;Machine learning;PRISMA;Rule-based;SHAP;Saliency map
N2  - BACKGROUND AND OBJECTIVES: Artificial intelligence (AI) has branched out to various applications in healthcare, such as health services management, predictive medicine, clinical decision-making, and patient data and diagnostics. Although AI models have achieved human-like performance, their use is still limited because they are seen as a black box. This lack of trust remains the main reason for their low use in practice, especially in healthcare. Hence, explainable artificial intelligence (XAI) has been introduced as a technique that can provide confidence in the model's prediction by explaining how the prediction is derived, thereby encouraging the use of AI systems in healthcare. The primary goal of this review is to provide areas of healthcare that require more attention from the XAI research community. METHODS: Multiple journal databases were thoroughly searched using PRISMA guidelines 2020. Studies that do not appear in Q1 journals, which are highly credible, were excluded. RESULTS: In this review, we surveyed 99 Q1 articles covering the following XAI techniques: SHAP, LIME, GradCAM, LRP, Fuzzy classifier, EBM, CBR, rule-based systems, and others. CONCLUSION: We discovered that detecting abnormalities in 1D biosignals and identifying key text in clinical notes are areas that require more attention from the XAI research community. We hope this is review will encourage the development of a holistic cloud system for a smart city.
AD  - School of Science and Technology, Singapore University of Social Sciences, Singapore.;School of Science and Technology, Singapore University of Social Sciences, Singapore.;Department of Electronics and Telecommunications, Biolab, Politecnico di Torino, Torino 10129, Italy.;Faculty of Engineering and Information Technology, University of Technology Sydney, Australia; School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Australia.;Department of Electronics and Telecommunications, Biolab, Politecnico di Torino, Torino 10129, Italy.;School of Science and Technology, Singapore University of Social Sciences, Singapore; School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Australia; School of Engineering, Ngee Ann Polytechnic, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan; Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan. Electronic address: aru@np.edu.sg.
UR  - https://www.ncbi.nlm.nih.gov/pubmed/36228495
DO - 10.1016/j.cmpb.2022.107161
ID  - 1938
ER  - 

TY  - JOUR
AU  - Liu, Yi
AU  - Xu, Yehua
AU  - Guo, Lixia
AU  - Chen, Zhongbin
AU  - Xia, Xueqin
AU  - Chen, Feng
AU  - Tang, Li
AU  - Jiang, Hua
AU  - Xie, Caixia
PY  - 2025
TI  - Development and external validation of machine learning models for the early prediction of malnutrition in critically ill patients: a prospective observational study.
SP  - 248
JF  - BMC medical informatics and decision making
VL  - 25
IS  - 1
SN  - 1472-6947 
N1  - 40611154
M3  - 10.1186/s12911-025-03082-9
N1  - 2026/9/3 11:01:00
N1  - PubMed
N1  - English
KW  - Critically ill patients;Machine learning;Malnutrition;Prediction model
N2  - BACKGROUND: Early detection of malnutrition in critically ill patients is crucial for timely intervention and improved clinical outcomes. However, identifying individuals at risk remains challenging due to the complexity and variability of patient conditions. This study aimed to develop and externally validate machine learning models for predicting malnutrition within 24?h of intensive care unit (ICU) admission, culminating in a web-based malnutrition prediction tool for clinical decision support. METHODS: A total of 1006 critically ill adult patients (aged??18 years) were included in the model development group, and 300 adult patients comprised the external validation group. The development data were partitioned into training (80%) and testing (20%) sets. Hyperparameters were optimized via 5-fold cross-validation on the training set, eliminating the need for a separate validation set while ensuring internal validation. External validation was performed on an independent group to assess generalizability. Predictors were selected using random forest recursive feature elimination; seven machine learning models-Extreme Gradient Boosting (XGBoost), random forest, decision tree, support vector machine (SVM), Gaussian naive Bayes, k-nearest neighbor (k-NN), and logistic regression-were trained and evaluated for accuracy, precision, recall, F1 score, Area Under the Receiver Operating Characteristic Curve (AUC-ROC), Area Under the Precision-Recall Curve (AUC-PR). Model interpretability was analyzed using SHapley Additive exPlanations (SHAP) to quantify feature contributions. RESULTS: In the development phase, among 1006 patients, 34.0% had moderate malnutrition and 17.9% severe malnutrition. The XGBoost model achieved superior predictive accuracy with an accuracy of 0.90 (95% CI?=?0.86-0.94), precision of 0.92 (95% CI?=?0.88-0.95), recall of 0.92 (95% CI?=?0.89-0.95), F1 score of 0.92 (95% CI?=?0.89-0.95), AUC-ROC of 0.98 (95% CI?=?0.96-0.99), and AUC-PR of 0.97 (95% CI?=?0.95-0.99) on the testing set. External validation confirmed robust performance with an accuracy of 0.75 (95% CI: 0.70-0.79), precision of 0.79 (95% CI: 0.75-0.83), recall of 0.75 (95% CI: 0.70-0.79), F1 score of 0.74 (95% CI: 0.69-0.78), AUC-ROC of 0.88 (95% CI: 0.86-0.91), and AUC-PR of 0.77 (95% CI: 0.73-0.80). CONCLUSIONS: Machine learning models, particularly XGBoost, demonstrated promising performance in early malnutrition prediction in ICU settings. The resultant web-based tool offers valuable resource for clinical decision support. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2200058286 ( https://www.chictr.org.cn/bin/project/edit? pid=248690 ). Registered 4th April 2022. Prospectively registered.
AD  - Department of Nursing, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.;Department of Emergency Intensive Care Unit, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.;Department of Neurology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.;Department of Surgical Intensive Care Unit, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.;Department of Gynecology and Obstetrics, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.;Department of Oncology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.;Department of Operating Room Nursing, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.;Department of Emergency Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China. jianghua@uestc.edu.cn.;Department of Nursing, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China. caixiaxie@uestc.edu.cn.
UR  - https://www.ncbi.nlm.nih.gov/pubmed/40611154
DO - 10.1186/s12911-025-03082-9
ID  - 1940
ER  - 

TY  - JOUR
AU  - Lin, Heng-Yi
AU  - Feng, Hsin-Chun
AU  - Wang, Yu-Jen
PY  - 2025
TI  - Trajectory of weight loss and nutritional decline in head and neck cancer patients undergoing concurrent chemoradiotherapy: a longitudinal observational study.
SP  - 991
JF  - Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
VL  - 33
IS  - 11
SN  - 0941-4355 
N1  - 41160186
M3  - 10.1007/s00520-025-10051-5
N1  - 2026/9/3 11:02:00
N1  - PubMed
N1  - English
KW  - Head and neck cancer (HNC);Nutrition assessment;Nutrition knowledge;Nutrition screening tool
N2  - PURPOSE: Nutritional decline is a common challenge in head and neck cancer (HNC) patients undergoing concurrent chemoradiotherapy (CCRT). This study aimed to evaluate weekly trends in body weight and nutritional status to identify the timing and progression of malnutrition risk during treatment, thereby informing the timing of potential nutritional interventions. METHODS: This observational longitudinal study enrolled 23 newly diagnosed HNC patients (87% male, median age 56.7?years) receiving radiotherapy, with or without concurrent chemotherapy. Weekly weight measurements and Mini-Nutritional Assessment (MNA) scores were recorded throughout the 7-week treatment period. Nutritional counseling and general dietary advice were routinely provided as part of standard clinical care. RESULTS: Significant weight loss began in the 5th week of treatment, with an average total weight loss of 5.9% by the end of CCRT. MNA scores showed a clear decline from week 4 onward, and by week 7, most patients had entered the malnourished category. In parallel, 76% of patients required soft or liquid diets by the end of treatment, and the use of oral nutritional supplements increased from 10 to 96%. CONCLUSION: The results demonstrate that the risk of malnutrition increases rapidly during CCRT, particularly after the 4th week of treatment. Regular nutritional monitoring may be essential to detect early deterioration and guide the timing of nutritional support. Further studies are needed to evaluate the efficacy of structured nutritional interventions initiated early in the treatment course.
AD  - Department of Radiation Oncology, Fu Jen Catholic University Hospital, New Taipei City, Taiwan.;Department of Dietetics, National Taiwan University Hospital, Taipei, Taiwan.;Department of Radiation Oncology, Fu Jen Catholic University Hospital, New Taipei City, Taiwan. yujen.wang@gmail.com.;School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan. yujen.wang@gmail.com.
UR  - https://www.ncbi.nlm.nih.gov/pubmed/41160186
DO - 10.1007/s00520-025-10051-5
ID  - 1943
ER  - 

TY  - JOUR
AU  - Kong, Xueyuan
AU  - Liu, Ping
AU  - Wang, Guotian
AU  - Sun, Shiqing
AU  - Li, Ling
PY  - 2025
TI  - Methods for diagnosing malnutrition in patients with esophageal cancer, and the association with nutritional and inflammatory indices: A cross?sectional study.
SP  - 223
JF  - Oncology letters
VL  - 29
IS  - 5
SN  - 1792-1074 
N1  - 40110582
M3  - 10.3892/ol.2025.14969
N1  - 2026/9/3 11:00:00
N1  - PubMed
N1  - English
KW  - Global Leadership Initiative on Malnutrition;Patient-Generated Subjective Global Assessment;esophageal cancer;inflammation;tumor nutrition
N2  - Clinically, it is important to diagnose malnutrition for the treatment and prognosis of cancer patients; however, at present, there are no established standards. The present study evaluated the consistency of malnutrition diagnostic tools in association with relevant nutritional and inflammatory markers in patients with advanced esophageal cancer. Specifically, the Patient-Generated Subjective Global Assessment (PG-SGA) and Global Leadership Initiative on Malnutrition (GLIM) tools were assessed. Patients with a new diagnosis of esophageal cancer at Tengzhou Central People's Hospital (Tengzhou, China) between January 2023 and December 2023 were evaluated within 24 h of admission using Nutritional Risk Screening 2002 (NRS2002), GLIM and PG-SGA. Additionally, relevant physical examinations and laboratory data were collected. The malnutrition occurrence rates based on PG-SGA, GLIM and GLIM with NRS2002 screening (NRS2002-GLIM) were 75.01, 51.88 and 41.25%, respectively. The agreement between PG-SGA and GLIM, and between PG-SGA and NRS2002-GLIM diagnoses was weak (=0.379, P<0.001; and =0.376, P<0.001, respectively). PG-SGA showed a moderate negative correlation with body mass index (BMI) (rs=-0.460), weak positive correlations with age (r(s)=0.234) and IL-6 (r(s)=0.249), and very weak negative correlations with albumin (r(s)=-0.178) and PNI (r(s)=-0.168). While the indicators correlated with GLIM and PG-SGA were consistent, the strength of correlation varied slightly. Logistic regression analysis of PG-SGA and GLIM indicated that age and BMI were independent risk factors for malnutrition. In addition, PG-SGA also showed that the neutrophil count was an independent risk factor for malnutrition. Overall, patients with esophageal cancer exhibit a high incidence of malnutrition, and different diagnostic methods provide varying results. Malnutrition is closely associated with age, inflammatory markers and BMI, suggesting their potential utility in guiding nutritional interventions for patients with esophageal cancer.
AD  - Department of Oncology, Tengzhou Central People's Hospital, Tengzhou, Zaozhuang, Shandong 277500, P.R. China.;Department of Oncology, Tengzhou Central People's Hospital, Tengzhou, Zaozhuang, Shandong 277500, P.R. China.;Department of Oncology, Tengzhou Central People's Hospital, Tengzhou, Zaozhuang, Shandong 277500, P.R. China.;Department of Oncology, Tengzhou Central People's Hospital, Tengzhou, Zaozhuang, Shandong 277500, P.R. China.;Department of Oncology, Tengzhou Central People's Hospital, Tengzhou, Zaozhuang, Shandong 277500, P.R. China.
UR  - https://www.ncbi.nlm.nih.gov/pubmed/40110582
DO - 10.3892/ol.2025.14969
ID  - 1939
ER  - 

TY  - JOUR
AU  - Jordan, Taja
AU  - Mastnak, Denis Mlakar
AU  - Palamar, Nizra
AU  - Kozjek, Nada Rotovnik
PY  - 2018
TI  - Nutritional Therapy for Patients with Esophageal Cancer
SP  - 23-29
N1  - 2018/1/2
JF  - Nutrition and Cancer
VL  - 70
IS  - 1
SN  - 0163-5581
M3  - 10.1080/01635581.2017.1374417
N1  - Routledge
N1  - 2026/9/3 10:36:00
N1  - Taylor & Francis Online
N2  - Malnutrition develops in 79% patients with esophageal cancer. Thus, these patients represent a group of cancer patients, which is the most nutritionally compromised. Dysphagia and more than 10% loss of body weight are already present at the time of diagnosis. Treatments for esophageal cancer contribute significantly to the development of malnutrition. This paper describes the nutritional treatment of patients and nutritional strategies in patients with dysphagia and other nutritional problems that accompany the treatment of patients with esophageal cancer. Here are shown the types and methods of nutritional support, which are suitable for this group of patients. Nutritional support of patients with esophageal cancer is performed as a parallel therapeutic route.
UR  - https://doi.org/10.1080/01635581.2017.1374417
DO - 10.1080/01635581.2017.1374417
ID  - 1922
ER  - 

TY  - JOUR
AU  - Jiang, Wei
AU  - Zhang, Bo
AU  - Xu, Jiaqi
AU  - Xue, Liyan
AU  - Wang, Luhua
PY  - 2025
TI  - Current status and perspectives of esophageal cancer: a comprehensive review.
SP  - 281-331
JF  - Cancer communications (London, England)
VL  - 45
IS  - 3
SN  - 2523-3548 
N1  - 39723635
M3  - 10.1002/cac2.12645
N1  - 2026/9/3 11:05:00
N1  - PubMed
N1  - English
KW  - Comprehensive review;Esophageal cancer;Pathological characteristics;Prevention and Screening;Treatment advancements
N2  - Esophageal cancer (EC) continues to be a significant global health concern, with two main subtypes: esophageal squamous cell carcinoma and esophageal adenocarcinoma. Prevention and changes in etiology, improvements in early detection, and refinements in the treatment have led to remarkable progress in the outcomes of EC patients in the past two decades. This seminar provides an in-depth analysis of advances in the epidemiology, disease biology, screening, diagnosis, and treatment landscape of esophageal cancer, focusing on the ongoing debate surrounding multimodality therapy. Despite significant advancements, EC remains a deadly disease, underscoring the need for continued research into early detection methods, understanding the molecular mechanisms, and developing effective treatments.
AD  - Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, Guangdong, P. R. China.;Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P. R. China.;Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P. R. China.;Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P. R. China.;Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, Guangdong, P. R. China.
UR  - https://www.ncbi.nlm.nih.gov/pubmed/39723635
DO - 10.1002/cac2.12645
ID  - 1946
ER  - 

TY  - JOUR
AU  - Huang, Weijia
AU  - Wang, Congjun
AU  - Wang, Ye
AU  - Yu, Zhu
AU  - Wang, Shengyu
AU  - Yang, Jian
AU  - Lu, Shunzu
AU  - Zhou, Chunyi
AU  - Wu, Erlv
AU  - Chen, Junqiang
PY  - 2024
TI  - Predicting malnutrition in gastric cancer patients using computed tomography(CT) deep learning features and clinical data.
SP  - 881-891
JF  - Clinical nutrition (Edinburgh, Scotland)
VL  - 43
IS  - 3
SN  - 0261-5614 
N1  - 38377634
M3  - 10.1016/j.clnu.2024.02.005
N1  - 2026/9/3 11:01:00
N1  - PubMed
N1  - English
KW  - Computed tomography;Deep learning;Gastric cancer;Malnutrition;Psoas major
N2  - OBJECTIVE: The aim of this study is using clinical factors and non-enhanced computed tomography (CT) deep features of the psoas muscles at third lumbar vertebral (L3) level to construct a model to predict malnutrition in gastric cancer before surgery, and to provide a new nutritional status assessment and survival assessment tool for gastric cancer patients. METHODS: A retrospective analysis of 312 patients of gastric cancer were divided into malnutrition group and normal group based on Nutrition Risk Screening 2002(NRS-2002). 312 regions of interest (ROI) of the psoas muscles at L3 level of non-enhanced CT were delineated. Deep learning (DL) features were extracted from the ROI using a deep migration model and were screened by principal component analysis (PCA) and least-squares operator (LASSO). The clinical predictors included Body Mass Index (BMI), lymphocyte and albumin. Both deep learning model (including deep learning features) and mixed model (including selected deep learning features and selected clinical predictors) were constructed by 11 classifiers. The model was evaluated and selected by calculating receiver operating characteristic (ROC), area under curve (AUC), accuracy, sensitivity and specificity, calibration curve and decision curve analysis (DCA). The Cohen's Kappa coefficient () was using to compare the diagnostic agreement for malnutrition between the mixed model and the GLIM in gastric cancer patients. RESULT: The results of logistics multivariate analysis showed that BMI [OR?=?0.569 (95% CI 0.491-0.660)], lymphocyte [OR?=?0.638 (95% CI 0.408-0.998)], and albumin [OR?=?0.924 (95% CI 0.859-0.994)] were clinically independent malnutrition of gastric cancer predictor(P?<?0.05). Among the 11 classifiers, the Multilayer Perceptron (MLP)were selected as the best classifier. The AUC of the training and test sets for deep learning model were 0.806 (95% CI 0.7485-0.8635) and 0.769 (95% CI 0.673-0.863) and with accuracies were 0.734 and 0.766, respectively. The AUC of the training and test sets for the mixed model were 0.909 (95% CI 0.869-0.948) and 0.857 (95% CI 0.782-0.931) and with accuracies of 0.845 and 0.861, respectively. The DCA confirmed the clinical benefit of the both models. The Cohen's Kappa coefficient () was 0.647 (P?<?0.001). Diagnostic agreement for malnutrition between the mixed model and GLIM criteria was good. The mixed model was used to calculate the predicted probability of malnutrition in gastric cancer patients, which was divided into high-risk and low-risk groups by median, and the survival analysis showed that the overall survival time of the high-risk group was significantly lower than that of the low-risk group (P?=?0.005). CONCLUSION: Deep learning based on mixed model may be a potential tool for predicting malnutrition in gastric cancer patients.
AD  - Department of Gastrointestinal Gland Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China; Guangxi Key Laboratory of Enhanced Recovery after Surgery for Gastrointestinal Cancer, Nanning, China; Guangxi Clinical Research Center for Enhanced Recovery after Surgery, Nanning, China; Guangxi Zhuang Autonomous Region Engineering Research Center for Artificial Intelligence Analysis of Multimodal Tumor Images, Nanning, China.;Department of Gastrointestinal Gland Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China; Guangxi Key Laboratory of Enhanced Recovery after Surgery for Gastrointestinal Cancer, Nanning, China; Guangxi Clinical Research Center for Enhanced Recovery after Surgery, Nanning, China; Guangxi Zhuang Autonomous Region Engineering Research Center for Artificial Intelligence Analysis of Multimodal Tumor Images, Nanning, China.;Department of Gastrointestinal Gland Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China; Guangxi Key Laboratory of Enhanced Recovery after Surgery for Gastrointestinal Cancer, Nanning, China; Guangxi Clinical Research Center for Enhanced Recovery after Surgery, Nanning, China; Guangxi Zhuang Autonomous Region Engineering Research Center for Artificial Intelligence Analysis of Multimodal Tumor Images, Nanning, China.;Department of Gastrointestinal Gland Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China; Guangxi Key Laboratory of Enhanced Recovery after Surgery for Gastrointestinal Cancer, Nanning, China; Guangxi Clinical Research Center for Enhanced Recovery after Surgery, Nanning, China; Guangxi Zhuang Autonomous Region Engineering Research Center for Artificial Intelligence Analysis of Multimodal Tumor Images, Nanning, China.;Department of Gastrointestinal Gland Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China; Guangxi Key Laboratory of Enhanced Recovery after Surgery for Gastrointestinal Cancer, Nanning, China; Guangxi Clinical Research Center for Enhanced Recovery after Surgery, Nanning, China; Guangxi Zhuang Autonomous Region Engineering Research Center for Artificial Intelligence Analysis of Multimodal Tumor Images, Nanning, China.;Department of Gastrointestinal Gland Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China; Guangxi Key Laboratory of Enhanced Recovery after Surgery for Gastrointestinal Cancer, Nanning, China; Guangxi Clinical Research Center for Enhanced Recovery after Surgery, Nanning, China; Guangxi Zhuang Autonomous Region Engineering Research Center for Artificial Intelligence Analysis of Multimodal Tumor Images, Nanning, China.;Guangxi Zhuang Autonomous Region Engineering Research Center for Artificial Intelligence Analysis of Multimodal Tumor Images, Nanning, China.;Guangxi Zhuang Autonomous Region Engineering Research Center for Artificial Intelligence Analysis of Multimodal Tumor Images, Nanning, China.;Guangxi Zhuang Autonomous Region Engineering Research Center for Artificial Intelligence Analysis of Multimodal Tumor Images, Nanning, China.;Department of Gastrointestinal Gland Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China; Guangxi Key Laboratory of Enhanced Recovery after Surgery for Gastrointestinal Cancer, Nanning, China; Guangxi Clinical Research Center for Enhanced Recovery after Surgery, Nanning, China; Guangxi Zhuang Autonomous Region Engineering Research Center for Artificial Intelligence Analysis of Multimodal Tumor Images, Nanning, China. Electronic address: chenjunqiang@gxmu.edu.cn.
UR  - https://www.ncbi.nlm.nih.gov/pubmed/38377634
DO - 10.1016/j.clnu.2024.02.005
ID  - 1941
ER  - 

TY  - JOUR
AU  - Huang, Shujun
AU  - Cai, Nianguang
AU  - Pacheco, Pedro Penzuti
AU  - Narrandes, Shavira
AU  - Wang, Yang
AU  - Xu, Wayne
PY  - 2018
TI  - Applications of Support Vector Machine (SVM) Learning in Cancer Genomics.
SP  - 41-51
JF  - Cancer genomics & proteomics
VL  - 15
IS  - 1
SN  - 1109-6535 
N1  - 29275361
N1  - 2026/9/3 10:45:00
N1  - PubMed
N1  - English
KW  - Machine learning (ML);biomarker discovery;cancer classification;classifier;driver gene;drug discovery;gene expression;gene selection;gene-gene interaction;genomics;kernel function;review;support vector machine (SVM)
N2  - Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that has been used for cancer genomic classification or subtyping. Today, as advancements in high-throughput technologies lead to production of large amounts of genomic and epigenomic data, the classification feature of SVMs is expanding its use in cancer genomics, leading to the discovery of new biomarkers, new drug targets, and a better understanding of cancer driver genes. Herein we reviewed the recent progress of SVMs in cancer genomic studies. We intend to comprehend the strength of the SVM learning and its future perspective in cancer genomic applications.
AD  - College of Pharmacy, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Canada.;Research Institute of Oncology and Hematology, CancerCare Manitoba, Winnipeg, Canada.;Research Institute of Oncology and Hematology, CancerCare Manitoba, Winnipeg, Canada.;Research Institute of Oncology and Hematology, CancerCare Manitoba, Winnipeg, Canada.;Research Institute of Oncology and Hematology, CancerCare Manitoba, Winnipeg, Canada.;Departments of Biochemistry and Medical Genetics, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Canada.;Department of Computer Science, Faculty of Sciences, University of Manitoba, Winnipeg, Canada.;Research Institute of Oncology and Hematology, CancerCare Manitoba, Winnipeg, Canada.;Departments of Biochemistry and Medical Genetics, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Canada.;College of Pharmacy, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Canada wayne.xu@umanitoba.ca.
UR  - https://www.ncbi.nlm.nih.gov/pubmed/29275361
ID  - 1933
ER  - 

TY  - JOUR
AU  - Hu, Jianchang
AU  - Szymczak, Silke
PY  - 2023
TI  - A review on longitudinal data analysis with random forest.
JF  - Briefings in bioinformatics
VL  - 24
IS  - 2
SN  - 1467-5463 
N1  - 36653905
M3  - 10.1093/bib/bbad002
N1  - 2026/9/3 10:47:00
N1  - PubMed
N1  - English
KW  - clustered data;longitudinal data;machine learning;multivariate response;repeated measurements
N2  - In longitudinal studies variables are measured repeatedly over time, leading to clustered and correlated observations. If the goal of the study is to develop prediction models, machine learning approaches such as the powerful random forest (RF) are often promising alternatives to standard statistical methods, especially in the context of high-dimensional data. In this paper, we review extensions of the standard RF method for the purpose of longitudinal data analysis. Extension methods are categorized according to the data structures for which they are designed. We consider both univariate and multivariate response longitudinal data and further categorize the repeated measurements according to whether the time effect is relevant. Even though most extensions are proposed for low-dimensional data, some can be applied to high-dimensional data. Information of available software implementations of the reviewed extensions is also given. We conclude with discussions on the limitations of our review and some future research directions.
AD  - Institute of Medical Biometry and Statistics, University of Lbeck, Ratzeburger Allee 160, 23562, Lbeck, Germany.;Institute of Medical Biometry and Statistics, University of Lbeck, Ratzeburger Allee 160, 23562, Lbeck, Germany.
UR  - https://www.ncbi.nlm.nih.gov/pubmed/36653905
DO - 10.1093/bib/bbad002
ID  - 1935
ER  - 

TY  - JOUR
AU  - Che, Dongsheng
AU  - Liu, Qi
AU  - Rasheed, Khaled
AU  - Tao, Xiuping
PY  - 2011
TI  - Decision tree and ensemble learning algorithms with their applications in bioinformatics.
SP  - 191-9
JF  - Advances in experimental medicine and biology
VL  - 696
SN  - 0065-2598 
N1  - 21431559
M3  - 10.1007/978-1-4419-7046-6_19
N1  - 2026/9/3 10:46:00
N1  - PubMed
N1  - English
N2  - Machine learning approaches have wide applications in bioinformatics, and decision tree is one of the successful approaches applied in this field. In this chapter, we briefly review decision tree and related ensemble algorithms and show the successful applications of such approaches on solving biological problems. We hope that by learning the algorithms of decision trees and ensemble classifiers, biologists can get the basic ideas of how machine learning algorithms work. On the other hand, by being exposed to the applications of decision trees and ensemble algorithms in bioinformatics, computer scientists can get better ideas of which bioinformatics topics they may work on in their future research directions. We aim to provide a platform to bridge the gap between biologists and computer scientists.
FAU - Che, Dongsheng
AD  - Department of Computer Science, East Stroudsburg University, East Stroudsburg, PA 18301, USA. dche@po-box.esu.edu
UR  - https://www.ncbi.nlm.nih.gov/pubmed/21431559
DO - 10.1007/978-1-4419-7046-6_19
ID  - 1934
ER  - 

TY  - JOUR
AU  - Cao, Jingjing
AU  - Xu, Hongxia
AU  - Li, Wei
AU  - Guo, Zengqing
AU  - Lin, Yuan
AU  - Shi, Yingying
AU  - Hu, Wen
AU  - Ba, Yi
AU  - Li, Suyi
AU  - Li, Zengning
AU  - Wang, Kunhua
AU  - Wu, Jing
AU  - He, Ying
AU  - Yang, Jiajun
AU  - Xie, Conghua
AU  - Zhou, Fuxiang
AU  - Song, Xinxia
AU  - Chen, Gongyan
AU  - Ma, Wenjun
AU  - Luo, Suxia
AU  - Chen, Zihua
AU  - Cong, Minghua
AU  - Ma, Hu
AU  - Zhou, Chunling
AU  - Wang, Wei
AU  - Luo, Qi
AU  - Shi, Yongmei
AU  - Qi, Yumei
AU  - Jiang, Haiping
AU  - Guan, Wenxian
AU  - Chen, Junqiang
AU  - Chen, Jiaxin
AU  - Fang, Yu
AU  - Zhou, Lan
AU  - Feng, Yongdong
AU  - Tan, Rongshao
AU  - Ou, Junwen
AU  - Zhao, Qingchuan
AU  - Wu, Jianxiong
AU  - Lin, Xin
AU  - Yang, Liuqing
AU  - Fu, Zhenming
AU  - Wang, Chang
AU  - Deng, Li
AU  - Li, Tao
AU  - Song, Chunhua
AU  - Shi, Hanping
PY  - 2021
TI  - Nutritional assessment and risk factors associated to malnutrition in patients with esophageal cancer.
SP  - 100638
JF  - Current problems in cancer
VL  - 45
IS  - 1
SN  - 0147-0272 
N1  - 32829957
M3  - 10.1016/j.currproblcancer.2020.100638
N1  - 2026/9/3 10:59:00
N1  - PubMed
N1  - English
KW  - Esophageal cancer;Nutrition status;Nutritional support;PG-SGA;Risk factor
N2  - INTRODUCTION: Esophageal cancer is the fourth most common cause of cancer death in China. Patients with esophageal cancer are more likely to suffer from malnutrition. The purpose of this study is to assess nutritional status of patients with esophageal cancer from multiple perspectives and analyze the risk factors. METHODS: A total of 1482 esophageal cancer patients were enrolled in the study. We investigated the Scored Patient Generated Subjective Global Assessment (PG-SGA) scores, NRS-2002 scores, Karnofsky performance status scores, anthropometric, and laboratory indicators of patients. Unconditional logistic regression analysis was applied to identify the risk factors of nutritional status. RESULTS: PG-SGA (4) and NRS-2002 (3) showed the incidence of malnutrition were 76% and 50%, respectively. In the patients with PG-SGA score 4, the proportion of patients who did not receive any nutritional support was 60%. The incidence of malnutrition in females was significantly higher than that in males. Besides, abnormality rates of Red blood cell (P < 0.001), MAC (P?=?0.037), and MAMC (P < 0.001) in males was significantly higher than that in females, while abnormality rates of TSF (P < 0.001) was lower than that in females. After adjusted with the other potential risk factors listed, unconditional logistic regression analysis indicated smoking (odds ratio: 2.868, 95% confidence interval: 1.660-4.954), drinking (OR: 1.726, 95% CI: 1.099-2.712), family history (OR: 1.840, 95% CI: 1.132-2.992), radiotherapy or chemotherapy (OR: 1.594, 95% CI: 1.065-2.387), and pathological stage (OR: 2.263, 95% CI: 1.084-4.726) might be the risk factors of nutritional status, while nutritional support can reduce the risk of malnutrition. CONCLUSION: Effective nutritional risk assessment methods and nutritional intervention measures can be adopted according to the research data to improve quality of life of esophageal cancer patients.
AD  - Department of Preventive Medicine, Heze Medical College, Heze, Shandong 274000, China.;Department of Nutrition, Daping Hospital & Research Institute of Surgery, Third Military Medical University, Chongqing 400042, China.;Cancer Center of the First Hospital of Jilin University, Changchun, Jilin 130021 China.;Department of Medical Oncology, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou, Fujian 350014, China.;Department of Gastrointestinal Surgery, Affiliated Tumor Hospital of Guangxi Medical University, Nanning, Guangxi 530021, China.;Department of Surgery, The First Affiliated Hospital of SunYat-sen University, Guangzhou, Guangdong 510080, China.;Department of Clinical Nutrition, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China.;Department of Gastrointestinal Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin 300060, China.;Department of Nutrition and Metabolism of Oncology, Affiliated Provincial Hospital of Anhui Medical University, Hefei, Anhui 230031, China.;Department of Clinical Nutrition, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei 050031, China.;Department of Gastrointestinal Surgery, Institute of Gastroenterology, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650032, China.;Zhongshan Chenxinghai Hospital (Affiliated Zhongshan Chenxinghai Hospital of Guangdong Medical University), Zhongshan, Guangdong 528400, China; Department of Clinical Nutrition, The First People's Hospital of Kashi, Xinjiang 844000, China.;Department of Clinical Nutrition, Chongqing General Hospital, Chongqing 400014, China.;Department of Colorectal and Anal Surgery, Huizhou Municipal Central Hospital, Huizhou, Guangdong 516001, China.;Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei 430071, China.;Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei 430071, China.;Department of Oncology, Xingtai People's Hospital, Hebei Medical University, Xingtai, Hebei 054031, China.;The First Department of the Tumor Hospital of Harbin Medical University, Harbin, Heilongjiang 150085, China.;Department of Nutrition, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong 510080, China.;Department of Oncology, Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, Henan 450008, China.;Department of General Surgery, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China.;Comprehensive Oncology Department, Cancer Hospital, Chinese Academy of Medical Sciences, Beijing 100021, China.;Department of Oncology, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou 563000, China.;The Fourth Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang 150001, China.;Cancer Center, The First People's Hospital of Foshan, Foshan, Guangdong 528000, China.;Department of Gastrointestinal Tumor Surgery, The First Affiliated Hospital, Xiamen University, Xiamen, Fujian 361003, China.;Department of Nutrition, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.;Department of Nutrition, Tianjin Third Central Hospital, Tianjin 300170, China.;Department of Surgery, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong 510632, China.;Department of General Surgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu 210008, China.;Department of Gastrointestinal Surgery, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, China.;Department of Radiation and Medical Oncology, People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi 530021, China.;Department of Clinical Nutrition, Peking University Cancer Hospital and Institute, Beijing 100142, China.;Department of Nutrition, Third Affiliated Hospital of Kunming Medical College, Tumor Hospital of Yunnan Province, Kunming, Yunnan 650118, China.;Department of Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.;Department of Nutrition, Guangzhou Red Cross Hospital, Guangzhou, Guangdong 510220, China.;Department of Clinical Nutrition, Clifford Hospital, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510632, China.;Department of Digestive Diseases, Xijing Hospital, Fourth Military Medical University, Xi'an, Shanxi 710032, China.;Department of Hepatobiliary Surgery, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.;Department of Nutrition, Daping Hospital & Research Institute of Surgery, Third Military Medical University, Chongqing 400042, China.;Department of Gastrointestinal Surgery/Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, The 9th Clinical College, PKU. No.10 Tieyi Road, Haidian Dist, Beijing 100038, China.;Cancer Center, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, China.;Cancer Center of the First Hospital of Jilin University, Changchun, Jilin 130021 China.;Cancer Center of the First Hospital of Jilin University, Changchun, Jilin 130021 China.;Department of Radiotherapy, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan 610041, China. Electronic address: litaoxmf@126.com.;Department of Epidemiology and Statistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, Henan, PR China. Electronic address: sch16@zzu.edu.cn.;Department of Gastrointestinal Surgery/Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, The 9th Clinical College, PKU. No. 10 Tieyi Road, Haidian Dist, Beijing 100038, China. Electronic address: shihp@ccmu.edu.cn.
UR  - https://www.ncbi.nlm.nih.gov/pubmed/32829957
DO - 10.1016/j.currproblcancer.2020.100638
ID  - 1937
ER  - 

TY  - JOUR
AU  - Bray, Freddie
AU  - Laversanne, Mathieu
AU  - Sung, Hyuna
AU  - Ferlay, Jacques
AU  - Siegel, Rebecca L.
AU  - Soerjomataram, Isabelle
AU  - Jemal, Ahmedin
PY  - 2024
TI  - Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.
SP  - 229-263
JF  - CA: a cancer journal for clinicians
VL  - 74
IS  - 3
SN  - 0007-9235 
N1  - 38572751
M3  - 10.3322/caac.21834
N1  - 2026/9/3 10:32:00
N1  - PubMed
N1  - English
KW  - cancer burden;cancer control;epidemiology;incidence;mortality
N2  - This article presents global cancer statistics by world region for the year 2022 based on updated estimates from the International Agency for Research on Cancer (IARC). There were close to 20 million new cases of cancer in the year 2022 (including nonmelanoma skin cancers [NMSCs]) alongside 9.7 million deaths from cancer (including NMSC). The estimates suggest that approximately one in five men or women develop cancer in a lifetime, whereas around one in nine men and one in 12 women die from it. Lung cancer was the most frequently diagnosed cancer in 2022, responsible for almost 2.5 million new cases, or one in eight cancers worldwide (12.4% of all cancers globally), followed by cancers of the female breast (11.6%), colorectum (9.6%), prostate (7.3%), and stomach (4.9%). Lung cancer was also the leading cause of cancer death, with an estimated 1.8 million deaths (18.7%), followed by colorectal (9.3%), liver (7.8%), female breast (6.9%), and stomach (6.8%) cancers. Breast cancer and lung cancer were the most frequent cancers in women and men, respectively (both cases and deaths). Incidence rates (including NMSC) varied from four-fold to five-fold across world regions, from over 500 in Australia/New Zealand (507.9 per 100,000) to under 100 in Western Africa (97.1 per 100,000) among men, and from over 400 in Australia/New Zealand (410.5 per 100,000) to close to 100 in South-Central Asia (103.3 per 100,000) among women. The authors examine the geographic variability across 20 world regions for the 10 leading cancer types, discussing recent trends, the underlying determinants, and the prospects for global cancer prevention and control. With demographics-based predictions indicating that the number of new cases of cancer will reach 35 million by 2050, investments in prevention, including the targeting of key risk factors for cancer (including smoking, overweight and obesity, and infection), could avert millions of future cancer diagnoses and save many lives worldwide, bringing huge economic as well as societal dividends to countries over the forthcoming decades.
AD  - Cancer Surveillance Branch, International Agency for Research on Cancer, Lyon, France.;Cancer Surveillance Branch, International Agency for Research on Cancer, Lyon, France.;Surveillance and Health Equity Science, American Cancer Society, Atlanta, Georgia, USA.;Cancer Surveillance Branch, International Agency for Research on Cancer, Lyon, France.;Surveillance and Health Equity Science, American Cancer Society, Atlanta, Georgia, USA.;Cancer Surveillance Branch, International Agency for Research on Cancer, Lyon, France.;Surveillance and Health Equity Science, American Cancer Society, Atlanta, Georgia, USA.
UR  - https://www.ncbi.nlm.nih.gov/pubmed/38572751
DO - 10.3322/caac.21834
ID  - 1919
ER  - 

TY  - JOUR
AU  - Aoyama, Toru
AU  - Kazama, Keisuke
AU  - Maezawa, Yukio
AU  - Hara, Kentaro
PY  - 2023
TI  - Usefulness of Nutrition and Inflammation Assessment Tools in Esophageal Cancer Treatment.
SP  - 22-35
JF  - In vivo (Athens, Greece)
VL  - 37
IS  - 1
SN  - 0258-851X 
N1  - 36593006
M3  - 10.21873/invivo.13051
N1  - 2026/9/3 11:07:00
N1  - PubMed
N1  - English
KW  - CAR;GPS;LCR;NLR;Nutritional assessment;esophageal cancer;inflammation assessment;review
N2  - Multidisciplinary treatment for esophageal cancer leads to nutritional and inflammatory changes. Recent studies showed that nutritional and inflammatory changes during multidisciplinary treatment affect both short and long-term oncological outcomes in esophageal cancer treatment. Therefore, evaluation of the nutritional and inflammatory status during treatment is necessary in order to optimize and utilize multidisciplinary therapy for esophageal cancer. If patients with esophageal cancer are able to determine their nutritional and inflammatory status, they will be able to select the optimal esophageal cancer, anti-inflammation, and nutritional treatments. Various types of nutrition and inflammation assessment tools have been developed and reported for esophageal cancer, with each tool having its own clinical characteristics, which must be understood before being applied in clinical practice. This review summarizes the background, current status, and future perspectives on the application of nutrition and inflammation assessment tools in esophageal cancer treatment.
AD  - Department of Surgery, Yokohama City University, Yokohama, Japan; t-aoyama@lilac.plala.or.jp.;Department of Gastrointestinal Surgery, Kanagawa Cancer Center, Kanagawa, Japan.;Department of Surgery, Yokohama City University, Yokohama, Japan.;Department of Gastrointestinal Surgery, Kanagawa Cancer Center, Kanagawa, Japan.;Department of Surgery, Yokohama City University, Yokohama, Japan.;Department of Gastrointestinal Surgery, Kanagawa Cancer Center, Kanagawa, Japan.;Department of Surgery, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, Tokyo, Japan.;Department of Surgery, Yokohama City University, Yokohama, Japan.;Department of Gastrointestinal Surgery, Kanagawa Cancer Center, Kanagawa, Japan.;Department of Surgery, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, Tokyo, Japan.
UR  - https://www.ncbi.nlm.nih.gov/pubmed/36593006
DO - 10.21873/invivo.13051
ID  - 1948
ER  - 

TY  - GEN
AU  - Zhang, Liyan
AU  - Lu, Yuhan
AU  - And
AU  - Fang, Yu
2014
TI  - Nutritional status and related factors of patients with advanced gastrointestinal cancer | British Journal of Nutrition | Cambridge Core
VL  - 2026
SP  - Nutritional status and related factors of patients with advanced gastrointestinal cancer - Volume 111 Issue 7
Y2  - 2026/9/3 11:07:00
N1  - 2026/9/3 11:08:00
N1  - Cambridge Core
N1  - Chinese
UR  - https://www.cambridge.org/core/journals/british-journal-of-nutrition/article/nutritional-status-and-related-factors-of-patients-with-advanced-gastrointestinal-cancer/88BC5091F7150A91C7B71CBB6A8758F1
ID  - 1924
ER  - 

TY  - GEN
AU  - Tibshirani, Robert
2018
TI  - Regression Shrinkage and Selection Via the Lasso
VL  - 2026
SP  - SUMMARY. We propose a new method for estimation in linear models. The lasso minimizes the residual sum of squares subject to the sum of the absolute valu
Y2  - 2026/9/3 11:08:00
N1  - 2026/9/3 11:09:00
N1  - OUP Academic
N1  - Chinese
UR  - https://dx.doi.org/10.1111/j.2517-6161.1996.tb02080.x
ID  - 1929
ER  - 

TY  - GEN
AU  - Xu, Hongxia
2020
TI  - 
		Investigation on nutrition status and clinical outcome of patients with common cancers in Chinese patients: a multicenter prospective study protocol
							| International Journal of Clinical Trials
			
VL  - 2026
Y2  - 2026/9/3 11:10:00
N1  - 2026/9/3 11:11:00
N1  - Chinese
UR  - https://www.ijclinicaltrials.com/index.php/ijct/article/view/401
ID  - 1931
ER  - 
