Authors
MadsRyøJochumsenMD, Ph.D.
1,2,5✉Phone0045 78456270Emailmadsjoch@rm.dk JensSörensen1,2,3
NanaLouiseChristensen1,2
MargitHaislund1
MichaelBorre2,4
KirstenBouchelouche1,2
LarsPoulsenTolbod1,2
1Department of Nuclear MedicineAarhus University HospitalAarhusDenmark
2Department of Clinical MedicineAarhus UniversityAarhusDenmark
3Department of Surgical SciencesUppsala University HospitalUppsalaSweden
4Department of UrologyAarhus University HospitalAarhusDenmark
5Department of Nuclear MedicineAarhus University HospitalPalle Juul-Jensens Boulevard 1658200Aarhus NDenmark, Denmark
Mads Ryø Jochumsen1,2, Jens Sörensen1, 2,3 Nana Louise Christensen1,2, Margit Haislund1, Michael Borre2,4, Kirsten Bouchelouche1,2, and Lars Poulsen Tolbod1,2
1. Department of Nuclear Medicine, Aarhus University Hospital, Aarhus, Denmark
2. Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
3. Department of Surgical Sciences, Uppsala University Hospital, Uppsala, Sweden
4. Department of Urology, Aarhus University Hospital, Aarhus, Denmark
Corresponding and first Author
Mads Ryø Jochumsen, MD, Ph.D., ORCID: 0000-0002-7676-2322
Department of Nuclear Medicine, Aarhus University Hospital, Denmark
Palle Juul-Jensens Boulevard 165, 8200 Aarhus N, Denmark
Telephone Number: 0045 78456270
Email: madsjoch@rm.dk
ICC intraclass correlation coefficient
LE leading edge
MRI magnetic resonance imaging
OSEM ordered subset expectation maximization
PET positron emission tomography
PIDIF input function from pelvic arteries
PSF point-spread-function
TAC time-activity-curves
TOF time-of-flight
VOI volume of interest
Abstract
Background:
Tumour perfusion is a universal cancer biomarker with potential value in characterizing primary prostate tumours and longitudinal measurements for evaluation of treatment response. To evaluate whether a change in perfusion is significant, the reproducibility of the measurement must be known. [15O]H2O positron emission tomography (PET) is the gold standard for non-invasive quantitative perfusion imaging, however no repeatability data on prostate cancer exist. Hence, the aim of the present study is to determine the repeatability of [15O]H2O tumour perfusion in prostate cancer.
Results:
Thirteen well-defined MRI PI-RADS lesions from ten patients were studied. The repeatability of [15O]H2O K1 was 30% using both parametric image calculation and volume of interest (VOI)-based analysis. Intraclass correlation coefficient (ICC) was 0.89 and 0.91 for parametric image calculation and VOI-based analysis, respectively. A study sample size of 10 patients should be sufficient for detecting a relative change of 20% over time.
Conclusions:
[15O]H2O tumour perfusion in localized prostate cancer can be measured with a high repeatability, showing comparable performance when using parametric K1 perfusion maps and VOI-based analysis. For longitudinal evaluation, changes above 30% are likely to represent actual changes in tumour perfusion, for example as response to a specific treatment.
Key Words:
Tumour blood flow
perfusion
prostate cancer
[15O]H2O
test-retest
repeatability
sample size calculation
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Background
Tumour perfusion is a universal cancer biomarker (1), with potential value in characterizing the aggressiveness of primary prostate tumours and separating significant prostate cancer from insignificant cancer (2–5). [15O]H2O positron emission tomography (PET) is the gold standard for non-invasive quantitative perfusion imaging. A potential application for tumour perfusion imaging could be for evaluation of treatment response through serial measurements before, during and after a specific treatment. To evaluate whether a change in perfusion is significant or not, the reproducibility of the measurement must be known. Previous test-retest studies found a high reproducibility of perfusion measurements with [15O]H2O PET in various non-prostatic tumours (6–8). Previously, our group assessed the repeatability of perfusion measurements in primary prostate cancer using 82Rb PET (9), however no data on [15O]H2O PET in prostate cancer exists. Hence, the aim of the present study is to determine the repeatability of [15O]H2O tumour perfusion in prostate cancer.
Methods
Patient Population
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Ten patients with localized prostate cancer were recruited immediately before radical prostatectomy. All patients had a prior clinical prostate multiparametric magnetic resonance imaging (MRI) performed and histologically verified prostate cancer. Patients were excluded if they had hip alloplastic or contraindications for MRI scan such as magnetic metallic implants, claustrophobia etc., and patients should be able to lie in the scanner for the extended scan duration.
Imaging
All patients underwent two scan sessions, each consisting of a 5-minute dynamic pelvic [15O]H2O PET scan and a dynamic [15O]H2O PET heart scan for obtaining an image-derived input function. All scans were performed on a 3 Tesla GE Signa PET/MRI Quant Works scanner (GE Healthcare, Waukesha, Wisconsin, USA).
A standardized 400 MBq [15O]H2O bolus was injected via an automatic injection pump at the beginning of each bed position, followed by 5 minutes dynamic PET acquisition.
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Frame structure 1x10s, 1x5s, 15x3s, 5x5s, 2x10s, 5x15s, 4x30s. Voxel size 2.8x2.8x2.8 mm3. Reconstruction algorithm ordered subset expectation maximization (OSEM) with point-spread-function (PSF) and time-of-flight (ToF) (VuePoint FX SharpIR). The images were filtered with a 3 mm transaxial Gaussian filter and a light axial filter [1:6:1].
Image Analysis
Tumour volumes of interest (VOIs) of thirteen well-defined MRI PI-RADS lesions from ten patients were drawn directly on the T2 MRI sequence by visual guidance, taking all available sequences into account. All VOIs were drawn using Hermes Affinity viewer version 3.0.1 (Hermes Medical Solutions, Stockholm, Sweden).
Tumour VOIs were subsequently transferred to the parametric [15O]H2O PET K1 images and [15O]H2O PET image series for direct reading of tumour perfusion and extraction of time-activity-curves (TACs), respectively.
Heart image-derived input functions (HIDIF) were extracted automatically from the separate dynamic [15O]H2O cardiac scan series by cluster analysis to identify venous and arterial clusters (10). These blood input functions were used for both parametric image calculation and VOI-based analysis. VOI-based analysis was performed with correction for delay and both with and without correction for dispersion (estimated using the image-derived input function from pelvic arteries (PIDIF) (5) (Table 1).
Parametric K1 images were constructed using either HIDIF with correction for delay and dispersion or using HIDIF with voxel wise delay calculated from the Leading-Edge method (11) (Table 1).
[15O]H2O wash-in (K1) (mL/min/mL) and wash-out (k2) (mL/min/mL) was calculated using a single-tissue compartment model. Kinetic analyses and blood input function extractions were performed using the aQuant Research Package (MedTrace, Hørsholm, Denmark).
Table 1
Definitions of different reconstructions and corrections applied. VOI = volume of interest, PIDIF = input function from pelvic arteries, HIDIF = heart image-derived input functions, Param = parametric, LE = leading edge, TACs = time-activity-curves,
VOI_PIDIF | Fit of TACs from VOI, including delay. Input is HIDIF with delay and dispersion correction from PIDIF. |
|---|
VOI_HIDIF | Fit of TACs from VOI, including delay. Input is HIDIF. |
Param_PIDIF | Mean of parametric values. Input is HIDIF with delay and dispersion correction from PIDIF. |
Param_LE | Mean of parametric values. Input is HIDIF with voxel wise delay calculated from the Leading-Edge method. |
Statistical Analysis
The data were visually inspected for normality using QQ-plots and based on Bland-Altman plots the variation between measurements do not seem to be depending on the average. The repeatability of the method was calculated by the method described by Bland and Altman (12). The within-patient / within-lesion coefficient of variance, repeatability, and intraclass coefficients (ICCs) were calculated for K1 and k2. We used the same statistical parameters and formulas as described in detail in Lodge et al. (7). Sample size calculations for potential future studies were performed to detect relative changes in tumour perfusion of -20%, -30%, and − 50% using a 2-sided significance test of no difference for paired log-normally distributed data with a significance level of 5% and a power of 95%.
Study data were collected and managed using REDCap (Vanderbilt University Medical Centre, Nashville, Tennessee, USA) electronic data capture tools, hosted at Aarhus University (13).
Statistical analysis was performed in MATLAB (MATLAB, MathWorks, Natick, MA) and Stata version 15.1 (StataCorp LLC, College Station, Texas, USA).
Results
Patient characteristics are presented in Table 2, while test and retest measurements using different reconstructions and corrections are found in Table 3.
Table 2
Patient characteristics. Three patients (2, 4 and 7) had two PI-RADS lesions in which tumour perfusion was assessed. Summary statistics are given as mean ± standard deviation for normally distributed continuous variables and median with range for ordinal variables.
Patient | Age | PSA | Gleason Grade Group (Prostatectomy) | Gleason Grade Group (lesion) | Lesion Size (cm3) | PI-RADS | Zone |
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1 | 60 | 8.2 | 1 | 1 | 2.7 | 4 | TZ |
2 | 64 | 17.2 | 3 | 1 | 3.0 | 4 | PZ |
1 | 0.9 | 4 | PZ |
3 | 64 | 5.7 | 4 | 4 | 1.2 | 4 | PZ |
4 | 69 | 3.7 | 2 | 2 | 1.6 | 5 | PZ |
1 | 1.3 | 5 | PZ |
5 | 59 | 16.0 | 2 | 1 | 2.9 | 5 | TZ |
6 | 65 | 18.3 | 2 | 2 | 3.2 | 5 | PZ |
7 | 58 | 7.6 | 2 | 1 | 0.73 | 4 | PZ |
1 | 0.54 | 4 | PZ |
8 | 68 | 10.7 | 2 | 2 | 0.83 | 4 | PZ |
9 | 75 | 10.4 | 5 | 3 | 4.2 | 4 | PZ/TZ |
10 | 72 | 12.6 | 2 | 1 | 2.7 | 4 | TZ |
Mean Median | 65.4 ± 5.62 | 11.04 ± 4.95 | 2 [1 ; 5] | 1 [1 ; 4] | 1.98 ± 1.18 | 4 [4 ; 5] | |
Table 3
Test and retest tumour perfusion measurements (K1) using different reconstructions and corrections.
| | | | Test (K1) | | | | | Retest (K1) | | |
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Patient | Tumour Size (cm3) | VOI_PIDIF | VOI_HIDIF | Param_PIDIF | Param_LE | | VOI_PIDIF | VOI_HIDIF | Param_PIDIF | Param_LE |
1 | 2.7 | 0.073 | 0.075 | 0.094 | 0.083 | | 0.096 | 0.095 | 0.119 | 0.112 |
2 | 3.0 | 0.208 | 0.198 | 0.231 | 0.197 | | 0.201 | 0.184 | 0.227 | 0.206 |
0.9 | 0.168 | 0.165 | 0.190 | 0.176 | | 0.197 | 0.189 | 0.217 | 0.216 |
3 | 1.2 | 0.331 | 0.305 | 0.343 | 0.254 | | 0.239 | 0.259 | 0.263 | 0.280 |
4 | 1.6 | 0.219 | 0.243 | 0.234 | 0.206 | | 0.257 | 0.256 | 0.257 | 0.232 |
1.3 | 0.212 | 0.247 | 0.234 | 0.197 | | 0.214 | 0.213 | 0.217 | 0.236 |
5 | 2.9 | 0.328 | 0.327 | 0.357 | 0.328 | | 0.305 | 0.275 | 0.310 | 0.229 |
6 | 3.2 | 0.237 | 0.235 | 0.263 | 0.201 | | 0.201 | 0.188 | 0.220 | 0.185 |
7 | 0.73 | 0.204 | 0.204 | 0.247 | 0.240 | | 0.190 | 0.197 | 0.232 | 0.234 |
0.54 | 0.158 | 0.157 | 0.170 | 0.177 | | 0.157 | 0.158 | 0.183 | 0.197 |
8 | 0.83 | 0.315 | 0.290 | 0.308 | 0.283 | | 0.263 | 0.266 | 0.268 | 0.270 |
9 | 4.2 | 0.236 | 0.236 | 0.241 | 0.266 | | 0.220 | 0.205 | 0.232 | 0.216 |
10 | 2.7 | 0.255 | 0.242 | 0.273 | 0.250 | | 0.297 | 0.290 | 0.310 | 0.299 |
In general, the different reconstructions performed almost equally and correlated excellently both between VOI methods and between VOI and parametric methods. There was a tendency towards larger variation when applying the correction for dispersion (VOI_PIDIF) for the VOI method and when using the Leading-Edge method for delay correction in the parametric images (Param_LE) (Fig. 2).
Measures of ICC and repeatability coefficients are listed in Table 4. Repeatability for k2 was poor (repeatability coefficient 67.9% – 71.4%), probably explained by poor signal to noise ratio in small lesions with relatively low perfusion (Supplementary Fig. 1).
Sample size calculations for a longitudinal study on change in tumour perfusion based on the repeatability from the present study are found in Table 5.
Table 4
ICC and Repeatability coefficient for various parameters.
Measure | ICC | Repeatability (%) |
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VOI_PIDIF | 0.90 | 34.48 |
VOI_HIDIF | 0.91 | 30.32 |
Param_PIDIF | 0.89 | 29.92 |
Param_LE | 0.81 | 38.05 |
Table 5
Sample size calculations for longitudinal study on change in tumour perfusion based on the repeatability from the present study. Sample size needed to detect relative changes of -20%, -30%, and − 50% were calculated using a 2-sided significance test for paired data with a significance level of 5% and a power of 95%.
| | K1 (VOI PIDIF) | K1 (VOI HIDIF) | K1 (Param_PIDIF | K1 (Param_LE) |
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Sample Size (N) at -20% change | 10 | 8 | 8 | 11 |
Sample Size (N) at -30% change | 6 | 5 | 5 | 6 |
Sample Size (N) at -50% change | 4 | 3 | 3 | 4 |
Discussion
The main result of the present study is that tumour perfusion in primary prostate cancer can be measured with a repeatability of around 30%, meaning that increase or decrease above 30% in the individual patient is likely to represent actual changes in tumour physiology.
As tumour perfusion correlated to International Society of Urological Pathology (ISUP) grade and hence aggressiveness in previous studies it is a relevant measure in prostate cancer (2–5). Tumour perfusion is a marker of nutrient agnostic growth potential; hence it might represent a more relevant aspect of tumour biology compared to prostate-specific membrane antigen (PSMA) expression. FDG is not an obvious alternative as prostate cancer is often not FDG-avid.
Potential clinical applications could be for monitoring small tumours with low ISUP grade followed by urologists in active surveillance, as a more aggressive treatment approach might be considered for tumours with high K1. Besides, repeated tumour perfusion measurement might be a good method for monitoring the effect of local treatment of prostate cancer or for assessing effects of systemic neoadjuvant therapy prior to local treatment.
As these potential indications usually concern small tumours, it is crucial to show that tumour perfusion can be measured with high repeatability even in small tumours below 1 cm3.
In contrast to our previous repeatability study on 82Rb PET, this study assessed the repeatability of the method unaffected by the day-to-day variability, which will inevitably affect longitudinal clinical scans during treatment. The patients were scanned on PET/MRI and instructed not to move between scans to ensure optimal alignment between MRI-derived VOIs and perfusion images. The present cohort of 10 patients with 13 primary lesions is comparable with most previous repeatability studies on PET tumour perfusion (8, 9, 14), and half the size of the cohort examined by Lodge et al. (7).
The repeatability of 30% found in the present study is comparable with those from Lodge et al. (7) and our previous study on 82Rb PET (9), while the studies from de Langen et al. (14) and van der Veldt et al. (8) found somewhat lower repeatability of 16–18%. Explanations for this deviation could be the method of input function and that we included much smaller tumours than previous studies, even below 1 cm3.
Lodge et al. discussed the possibility of reducing the repeatability to approximately 26% by averaging two repeated measurements at each time point (7). Another possibility to increase repeatability could be to utilise the superior sensitivity of long axial field-of-view PET scanners, which would also obviate the need for a separate heart scan. However, the repeatability of [15O]H2O tumour perfusion on long axial field-of-view PET scanners remains to be determined. Using PET/MRI has the obvious advantage of localising the tumour anatomically, which is needed for VOI definition in subjects with concurrent benign hyperplasia.
Future perspectives
As gold standard for non-invasive measurement of tumour perfusion, [15O]H2O PET is a robust tool for assessing tumour biology. With increasing availability of [15O]H2O generator systems and other perfusion agents, routine PET-assessment of quantitative tumour blood flow is now within reach at multiple PET-centres worldwide.
Regarding primary prostate tumours, benign hyperplastic nodules with increased perfusion is one of the main challenges as shown in Fig. 1, row B. This illustrates both that PET perfusion is merely suited for tumour characterization than for tumour detection and the importance of a separate modality for VOI definition.
Monitoring of oncological treatment response in patients with metastatic prostate cancer could potentially be a promising application, especially with the introduction of long axial field-of-view PET scanners that allow quantitative assessment of tumour perfusion in patients with metastatic disease (11). On this topic, the repeatability of [15O]H2O perfusion measurements in prostate cancer metastases is also unknown.
The potential clinical applications of PET tumour perfusion imaging for characterization and monitoring prostate cancer patients needs to be explored in future studies.
Conclusions
[15O]H2O perfusion PET is a repeatable method for measurement of tumour perfusion in localized prostate cancer, showing comparable performance when using parametric K1 perfusion maps and VOI-based analysis. For longitudinal evaluation, changes above 30% are likely to represent actual changes in tumour perfusion, for example as treatment response.
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Electronic Supplementary Material
Below is the link to the electronic supplementary material
References
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All subjects signed an informed consent form.
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Availability of data and material
The datasets used in the current study are available from the corresponding author on reasonable request.
Acknowledgements
The authors would like to thank the research technicians at The Department of Urology for help with recruitment and all colleagues at The Department of Nuclear Medicine and PET-Centre, especially the staff at the PET/MRI scanner Louise Forsmann Grønnemark and Jesper Bjærre.
The study was financially supported by Tømrermester Jørgen Holm og hustru Elisa f. Hansens Mindelegat, P.A. Messerschmidt og hustrus fond, NEYE-Fonden, Fabrikant Einar Willumsens Mindelegat, and Højmosegårdlegatet granted to Mads Ryø Jochumsen.