Repeatability of tumour perfusion measurement with [15O]H2O PET in prostate cancer

Repeatability of tumour perfusion measurement with [15O]H2O PET in prostate cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Repeatability of tumour perfusion measurement with [ 15 O]H 2 O PET in prostate cancer Mads Ryø Jochumsen, Jens Sörensen, Nana Louise Christensen, Margit Haislund, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7908120/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Jan, 2026 Read the published version in EJNMMI Research → Version 1 posted 5 You are reading this latest preprint version 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. [ 15 O]H 2 O 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 [ 15 O]H 2 O tumour perfusion in prostate cancer. Results: Thirteen well-defined MRI PI-RADS lesions from ten patients were studied. The repeatability of [ 15 O]H 2 O K 1 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: [ 15 O]H 2 O tumour perfusion in localized prostate cancer can be measured with a high repeatability, showing comparable performance when using parametric K 1 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. Tumour blood flow perfusion prostate cancer [15O]H2O test-retest repeatability sample size calculation Figures Figure 1 Figure 2 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 ). [ 15 O]H 2 O 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 [ 15 O]H 2 O PET in various non-prostatic tumours ( 6 – 8 ). Previously, our group assessed the repeatability of perfusion measurements in primary prostate cancer using 82 Rb PET ( 9 ), however no data on [ 15 O]H 2 O PET in prostate cancer exists. Hence, the aim of the present study is to determine the repeatability of [ 15 O]H 2 O tumour perfusion in prostate cancer. Methods Patient Population 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 [ 15 O]H 2 O PET scan and a dynamic [ 15 O]H 2 O 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 [ 15 O]H 2 O bolus was injected via an automatic injection pump at the beginning of each bed position, followed by 5 minutes dynamic PET acquisition. 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 [ 15 O]H 2 O PET K 1 images and [ 15 O]H 2 O 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 [ 15 O]H 2 O 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 K 1 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 ). [ 15 O]H 2 O wash-in (K 1 ) (mL/min/mL) and wash-out (k 2 ) (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 K 1 and k 2 . 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 (cm 3 ) PI-RADS Zone 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 (K 1 ) using different reconstructions and corrections. Test (K 1 ) Retest (K 1 ) Patient Tumour Size (cm 3 ) 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 k 2 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 (%) 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%. K 1 (VOI PIDIF) K 1 (VOI HIDIF) K 1 (Param_PIDIF K 1 (Param_LE) 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 K 1 . 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 cm 3 . In contrast to our previous repeatability study on 82 Rb 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 82 Rb 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 cm 3 . 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 [ 15 O]H 2 O 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, [ 15 O]H 2 O PET is a robust tool for assessing tumour biology. With increasing availability of [ 15 O]H 2 O 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 [ 15 O]H 2 O 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 [ 15 O]H 2 O perfusion PET is a repeatable method for measurement of tumour perfusion in localized prostate cancer, showing comparable performance when using parametric K 1 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. Abbreviations HIDIF heart image-derived input functions 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 Declarations Ethics approval The study was approved by the institutional review board (Central Denmark Region Committees on Health Research Ethics (1-10-72-238-19)) and performed in accordance with the 1964 Helsinki Declaration and its later amendments. Consent to participate and publication All subjects signed an informed consent form. Availability of data and material The datasets used in the current study are available from the corresponding author on reasonable request. Competing Interests MRJ, NLC, MH and MB declare that they have no competing interests. JS and LPT hold shares in MedTrace, Hørsholm, Denmark. Funding 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. The funding sources had no role in the scientific work. Authors´ Contribution All authors (MRJ, NLC, JS, MH, KB, MB, LPT) contributed to the study conception and design. Funding was obtained by MRJ. Patient recruitment and data collection were performed by MRJ, MH and NLC. Scan and data analysis and writing of the first draft of the manuscript was performed by MRJ and LPT and all authors (MRJ, NLC, JS, MH, KB, MB, LPT) commented on previous versions of the manuscript, and have read and approved of the final manuscript. 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. References Johnson GB, Harms HJ, Johnson DR, Jacobson MS. PET Imaging of Tumor Perfusion: A Potential Cancer Biomarker? Seminars in nuclear medicine. 2020;50(6):549–61. Jochumsen MR, Sörensen J, Pedersen BG, Nyengaard JR, Krag SRP, Frøkiær J et al. Tumour blood flow for prediction of human prostate cancer aggressiveness: a study with Rubidium-82 PET, MRI and Na(+)/K(+)-ATPase-density. Eur J Nucl Med Mol Imaging. 2020. 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Reproducibility of tumor blood flow quantification with 15O-water PET. Journal of nuclear medicine: official publication. Soc Nuclear Med. 2008;49(10):1620–7. van der Veldt AA, Hendrikse NH, Harms HJ, Comans EF, Postmus PE, Smit EF, et al. Quantitative parametric perfusion images using 15O-labeled water and a clinical PET/CT scanner: test-retest variability in lung cancer. J nuclear medicine: official publication Soc Nuclear Med. 2010;51(11):1684–90. Jochumsen MR, Bouchelouche K, Nielsen KB, Frokiaer J, Borre M, Sorensen J, et al. Repeatability of tumor blood flow quantification with (82)Rubidium PET/CT in prostate cancer - a test-retest study. EJNMMI Res. 2019;9(1):58. Harms HJ, Knaapen P, de Haan S, Halbmeijer R, Lammertsma AA, Lubberink M. Automatic generation of absolute myocardial blood flow images using [15O]H2O and a clinical PET/CT scanner. Eur J Nucl Med Mol Imaging. 2011;38(5):930–9. Jochumsen MR, Christensen NL, Iversen P, Gormsen LC, Sørensen J, Tolbod LP. Whole-body parametric mapping of tumour perfusion in metastatic prostate cancer using long axial field-of-view [(15)O]H(2)O PET. Eur J Nucl Med Mol Imaging. 2024;51(13):4134–40. Bland JM, Altman DG. Measuring agreement in method comparison studies. Stat Methods Med Res. 1999;8(2):135–60. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377–81. de Langen AJ, Lubberink M, Boellaard R, Spreeuwenberg MD, Smit EF, Hoekstra OS, et al. Reproducibility of tumor perfusion measurements using 15O-labeled water and PET. Journal of nuclear medicine: official publication. Soc Nuclear Med. 2008;49(11):1763–8. 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10:01:26","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":37446,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7908120/v1/a2035c63b5c310d14532cd02.png"},{"id":95340454,"identity":"14c7d693-8c8e-430b-ada8-c6178627a781","added_by":"auto","created_at":"2025-11-07 01:28:17","extension":"xml","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":79297,"visible":true,"origin":"","legend":"","description":"","filename":"EJRED25004460structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7908120/v1/145324c136fd06729d7601ef.xml"},{"id":95524168,"identity":"23650aba-0941-4651-83b7-fac84752b7c8","added_by":"auto","created_at":"2025-11-10 10:02:25","extension":"html","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":86567,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7908120/v1/9e1a382f6dfc37fd5aae657e.html"},{"id":95340440,"identity":"abe6a8ce-1b44-43b0-8af2-33502d5500cb","added_by":"auto","created_at":"2025-11-07 01:28:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":366734,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTest and retest scan images of two patients in the study. Row A is patient nine with a PI-RADS 4 lesion involving both transitional and peripheral zone and row B is patient six with a PI-RADS 5 peripheral zone lesion. Both had tumours with high perfusion and significant prostate cancer, patient nine (row A) had biopsy Gleason Grade Group 3 and postprostatectomy Gleason Grade Group 5 and patient six (row B) had both biopsy and postprostatectomy Gleason Grade Group 2.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7908120/v1/3279999655a8d23bbc9482fb.png"},{"id":95340446,"identity":"123d55f5-3656-4d47-b6e6-d2e7e94b1103","added_by":"auto","created_at":"2025-11-07 01:28:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":149130,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eRepeated K\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e measures are plotted against each other for VOI_PIDIF, VOI_HIDIF, Param_PIDIF and Param_LE (abbreviations explained in Table 1). Grey dashed line represents y = x, whereas solid black line is the linear fit. Linear equations and ICC are shown. Bland Altman plots for K\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e for VOI_PIDIF, VOI_HIDIF, Param_PIDIF and Param_LE. Black solid line is mean difference between measurement 2 and 1. Black dotted lines are 95% upper and lower 95% limits of agreement (1.96 x sd). ICC: intraclass correlation. RPC: repeatability coefficient.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7908120/v1/b773e3569655ff761ffdd1c7.png"},{"id":100069492,"identity":"608efb92-2bc1-4583-832e-64ae8bef25cc","added_by":"auto","created_at":"2026-01-12 16:14:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1457063,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7908120/v1/800ff255-4e72-4f49-b3e3-34dec75b9721.pdf"},{"id":95524741,"identity":"b5b1a928-213d-4040-8223-58adae191846","added_by":"auto","created_at":"2025-11-10 10:03:22","extension":"tif","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":3622962,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-7908120/v1/45fd6ee0da6336cb7ca80f01.tif"}],"financialInterests":"","formattedTitle":"\u003cp\u003eRepeatability of tumour perfusion measurement with [\u003csup\u003e15\u003c/sup\u003eO]H\u003csub\u003e2\u003c/sub\u003eO PET in prostate cancer\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eTumour perfusion is a universal cancer biomarker (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), with potential value in characterizing the aggressiveness of primary prostate tumours and separating significant prostate cancer from insignificant cancer (\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). [\u003csup\u003e15\u003c/sup\u003eO]H\u003csub\u003e2\u003c/sub\u003eO 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 [\u003csup\u003e15\u003c/sup\u003eO]H\u003csub\u003e2\u003c/sub\u003eO PET in various non-prostatic tumours (\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Previously, our group assessed the repeatability of perfusion measurements in primary prostate cancer using \u003csup\u003e82\u003c/sup\u003eRb PET (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), however no data on [\u003csup\u003e15\u003c/sup\u003eO]H\u003csub\u003e2\u003c/sub\u003eO PET in prostate cancer exists. Hence, the aim of the present study is to determine the repeatability of [\u003csup\u003e15\u003c/sup\u003eO]H\u003csub\u003e2\u003c/sub\u003eO tumour perfusion in prostate cancer.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePatient Population\u003c/h2\u003e\u003cp\u003e 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.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eImaging\u003c/h3\u003e\n\u003cp\u003eAll patients underwent two scan sessions, each consisting of a 5-minute dynamic pelvic [\u003csup\u003e15\u003c/sup\u003eO]H\u003csub\u003e2\u003c/sub\u003eO PET scan and a dynamic [\u003csup\u003e15\u003c/sup\u003eO]H\u003csub\u003e2\u003c/sub\u003eO 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).\u003c/p\u003e\u003cp\u003eA standardized 400 MBq [\u003csup\u003e15\u003c/sup\u003eO]H\u003csub\u003e2\u003c/sub\u003eO bolus was injected via an automatic injection pump at the beginning of each bed position, followed by 5 minutes dynamic PET acquisition.\u003c/p\u003e\u003cp\u003eFrame 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].\u003c/p\u003e\n\u003ch3\u003eImage Analysis\u003c/h3\u003e\n\u003cp\u003eTumour 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).\u003c/p\u003e\u003cp\u003eTumour VOIs were subsequently transferred to the parametric [\u003csup\u003e15\u003c/sup\u003eO]H\u003csub\u003e2\u003c/sub\u003eO PET K\u003csub\u003e1\u003c/sub\u003e images and [\u003csup\u003e15\u003c/sup\u003eO]H\u003csub\u003e2\u003c/sub\u003eO PET image series for direct reading of tumour perfusion and extraction of time-activity-curves (TACs), respectively.\u003c/p\u003e\u003cp\u003eHeart image-derived input functions (HIDIF) were extracted automatically from the separate dynamic [\u003csup\u003e15\u003c/sup\u003eO]H\u003csub\u003e2\u003c/sub\u003eO cardiac scan series by cluster analysis to identify venous and arterial clusters (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). 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) (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eParametric K\u003csub\u003e1\u003c/sub\u003e 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 (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e[\u003csup\u003e15\u003c/sup\u003eO]H\u003csub\u003e2\u003c/sub\u003eO wash-in (K\u003csub\u003e1\u003c/sub\u003e) (mL/min/mL) and wash-out (k\u003csub\u003e2\u003c/sub\u003e) (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, H\u0026oslash;rsholm, Denmark).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eDefinitions of different reconstructions and corrections applied. VOI\u0026thinsp;=\u0026thinsp;volume of interest, PIDIF\u0026thinsp;=\u0026thinsp;input function from pelvic arteries, HIDIF\u0026thinsp;=\u0026thinsp;heart image-derived input functions, Param\u0026thinsp;=\u0026thinsp;parametric, LE\u0026thinsp;=\u0026thinsp;leading edge, TACs\u0026thinsp;=\u0026thinsp;time-activity-curves\u003c/em\u003e,\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVOI_PIDIF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFit of TACs from VOI, including delay.\u003c/p\u003e\u003cp\u003eInput is HIDIF with delay and dispersion correction from PIDIF.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVOI_HIDIF\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFit of TACs from VOI, including delay.\u003c/p\u003e\u003cp\u003eInput is HIDIF.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eParam_PIDIF\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean of parametric values.\u003c/p\u003e\u003cp\u003eInput is HIDIF with delay and dispersion correction from PIDIF.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eParam_LE\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean of parametric values.\u003c/p\u003e\u003cp\u003eInput is HIDIF with voxel wise delay calculated from the Leading-Edge method.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eThe 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 (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). The within-patient / within-lesion coefficient of variance, repeatability, and intraclass coefficients (ICCs) were calculated for K\u003csub\u003e1\u003c/sub\u003e and k\u003csub\u003e2\u003c/sub\u003e. We used the same statistical parameters and formulas as described in detail in Lodge et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Sample size calculations for potential future studies were performed to detect relative changes in tumour perfusion of -20%, -30%, and \u0026minus;\u0026thinsp;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%.\u003c/p\u003e\u003cp\u003eStudy data were collected and managed using REDCap (Vanderbilt University Medical Centre, Nashville, Tennessee, USA) electronic data capture tools, hosted at Aarhus University (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eStatistical analysis was performed in MATLAB (MATLAB, MathWorks, Natick, MA) and Stata version 15.1 (StataCorp LLC, College Station, Texas, USA).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003ePatient characteristics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, while test and retest measurements using different reconstructions and corrections are found in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePatient characteristics. Three patients (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e and \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) had two PI-RADS lesions in which tumour perfusion was assessed. Summary statistics are given as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation for normally distributed continuous variables and median with range for ordinal variables.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePSA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGleason Grade Group\u003c/p\u003e\u003cp\u003e(Prostatectomy)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGleason Grade Group\u003c/p\u003e\u003cp\u003e(lesion)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLesion Size (cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePI-RADS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eZone\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTZ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e17.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePZ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePZ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePZ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePZ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePZ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTZ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePZ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e7.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePZ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePZ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePZ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePZ/TZ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTZ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.04\u0026thinsp;\u0026plusmn;\u0026thinsp;4.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 [1 ; 5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 [1 ; 4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4 [4 ; 5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTest and retest tumour perfusion measurements (K\u003csub\u003e1\u003c/sub\u003e) using different reconstructions and corrections.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTest (K\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRetest (K\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTumour Size (cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVOI_PIDIF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVOI_HIDIF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eParam_PIDIF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eParam_LE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eVOI_PIDIF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eVOI_HIDIF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eParam_PIDIF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eParam_LE\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.094\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.112\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.206\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.216\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.331\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.305\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.254\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.259\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.263\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.280\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.257\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.257\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.232\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.247\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.236\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.327\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.305\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.229\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.235\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.263\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.185\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.247\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.234\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.177\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.197\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.290\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.308\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.283\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.263\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.270\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.241\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.216\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.273\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.297\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.290\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.299\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn 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.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMeasures of ICC and repeatability coefficients are listed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Repeatability for k\u003csub\u003e2\u003c/sub\u003e was poor (repeatability coefficient 67.9% \u0026ndash; 71.4%), probably explained by poor signal to noise ratio in small lesions with relatively low perfusion (Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e\u003cp\u003eSample size calculations for a longitudinal study on change in tumour perfusion based on the repeatability from the present study are found in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eICC and Repeatability coefficient for various parameters.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeasure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eICC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRepeatability (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVOI_PIDIF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVOI_HIDIF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParam_PIDIF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParam_LE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSample 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 \u0026minus;\u0026thinsp;50% were calculated using a 2-sided significance test for paired data with a significance level of 5% and a power of 95%.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eK\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003cp\u003e(VOI PIDIF)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eK\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003cp\u003e(VOI HIDIF)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eK\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003cp\u003e(Param_PIDIF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eK\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003cp\u003e(Param_LE)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSample Size (N) at -20% change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSample Size (N) at -30% change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSample Size (N) at -50% change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe 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.\u003c/p\u003e\u003cp\u003eAs 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 (\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). 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.\u003c/p\u003e\u003cp\u003ePotential 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 K\u003csub\u003e1\u003c/sub\u003e. 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.\u003c/p\u003e\u003cp\u003eAs 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 cm\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn contrast to our previous repeatability study on \u003csup\u003e82\u003c/sup\u003eRb 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 (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), and half the size of the cohort examined by Lodge et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe repeatability of 30% found in the present study is comparable with those from Lodge et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) and our previous study on \u003csup\u003e82\u003c/sup\u003eRb PET (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), while the studies from de Langen et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) and van der Veldt et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) found somewhat lower repeatability of 16\u0026ndash;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 cm\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eLodge et al. discussed the possibility of reducing the repeatability to approximately 26% by averaging two repeated measurements at each time point (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). 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 [\u003csup\u003e15\u003c/sup\u003eO]H\u003csub\u003e2\u003c/sub\u003eO 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.\u003c/p\u003e\n\u003ch3\u003eFuture perspectives\u003c/h3\u003e\n\u003cp\u003eAs gold standard for non-invasive measurement of tumour perfusion, [\u003csup\u003e15\u003c/sup\u003eO]H\u003csub\u003e2\u003c/sub\u003eO PET is a robust tool for assessing tumour biology. With increasing availability of [\u003csup\u003e15\u003c/sup\u003eO]H\u003csub\u003e2\u003c/sub\u003eO generator systems and other perfusion agents, routine PET-assessment of quantitative tumour blood flow is now within reach at multiple PET-centres worldwide.\u003c/p\u003e\u003cp\u003eRegarding primary prostate tumours, benign hyperplastic nodules with increased perfusion is one of the main challenges as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, 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.\u003c/p\u003e\u003cp\u003eMonitoring 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 (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). On this topic, the repeatability of [\u003csup\u003e15\u003c/sup\u003eO]H\u003csub\u003e2\u003c/sub\u003eO perfusion measurements in prostate cancer metastases is also unknown.\u003c/p\u003e\u003cp\u003eThe potential clinical applications of PET tumour perfusion imaging for characterization and monitoring prostate cancer patients needs to be explored in future studies.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003e[\u003csup\u003e15\u003c/sup\u003eO]H\u003csub\u003e2\u003c/sub\u003eO perfusion PET is a repeatable method for measurement of tumour perfusion in localized prostate cancer, showing comparable performance when using parametric K\u003csub\u003e1\u003c/sub\u003e 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.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eHIDIF \u0026nbsp; \u0026nbsp;heart image-derived input functions \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eICC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;intraclass correlation coefficient\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLE\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;leading edge\u003c/p\u003e\n\u003cp\u003eMRI \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;magnetic resonance imaging\u003c/p\u003e\n\u003cp\u003eOSEM \u0026nbsp;\u0026nbsp;ordered subset expectation maximization\u003c/p\u003e\n\u003cp\u003ePET \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;positron emission tomography\u003c/p\u003e\n\u003cp\u003ePIDIF \u0026nbsp; \u0026nbsp;input function from pelvic arteries \u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePSF\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;point-spread-function\u003c/p\u003e\n\u003cp\u003eTAC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;time-activity-curves\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTOF\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;time-of-flight\u003c/p\u003e\n\u003cp\u003eVOI \u0026nbsp; \u0026nbsp; \u0026nbsp; volume of interest \u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the institutional review board (Central Denmark Region Committees on Health Research Ethics (1-10-72-238-19)) and performed in accordance with the 1964 Helsinki Declaration and its later amendments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate and publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll subjects signed an informed consent form.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used in the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMRJ, NLC, MH and MB declare that they have no competing interests. JS and LPT hold shares in MedTrace, Hørsholm, Denmark.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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. The funding sources had no role in the scientific work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors´ Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors (MRJ, NLC, JS, MH, KB, MB, LPT) contributed to the study conception and design. Funding was obtained by MRJ. Patient recruitment and data collection were performed by MRJ, MH and NLC. Scan and data analysis and writing of the first draft of the manuscript was performed by MRJ and LPT and all authors (MRJ, NLC, JS, MH, KB, MB, LPT) commented on previous versions of the manuscript, and have read and approved of the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e\n\u003cp\u003eThe 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.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJohnson GB, Harms HJ, Johnson DR, Jacobson MS. PET Imaging of Tumor Perfusion: A Potential Cancer Biomarker? Seminars in nuclear medicine. 2020;50(6):549\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJochumsen MR, S\u0026ouml;rensen J, Pedersen BG, Nyengaard JR, Krag SRP, Fr\u0026oslash;ki\u0026aelig;r J et al. Tumour blood flow for prediction of human prostate cancer aggressiveness: a study with Rubidium-82 PET, MRI and Na(+)/K(+)-ATPase-density. Eur J Nucl Med Mol Imaging. 2020.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJochumsen MR, S\u0026ouml;rensen J, Tolbod LP, Pedersen BG, Fr\u0026oslash;ki\u0026aelig;r J, Borre M, et al. Potential synergy between PSMA uptake and tumour blood flow for prediction of human prostate cancer aggressiveness. EJNMMI Res. 2021;11(1):12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJochumsen MR, Tolbod LP, Pedersen BG, Nielsen MM, Hoyer S, Frokiaer J, et al. Quantitative Tumor Perfusion Imaging with (82)Rb PET/CT in Prostate Cancer: Analytic and Clinical Validation. Journal of nuclear medicine: official publication. Soc Nuclear Med. 2019;60(8):1059\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTolbod LP, Nielsen MM, Pedersen BG, Hoyer S, Harms HJ, Borre M, et al. Non-invasive quantification of tumor blood flow in prostate cancer using (15)O-H2O PET/CT. Am J Nucl Med Mol Imaging. 2018;8(5):292\u0026ndash;302.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLodge MA, Carson RE, Carrasquillo JA, Whatley M, Libutti SK, Bacharach SL. Parametric images of blood flow in oncology PET studies using [15O]water. J nuclear medicine: official publication Soc Nuclear Med. 2000;41(11):1784\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLodge MA, Jacene HA, Pili R, Wahl RL. Reproducibility of tumor blood flow quantification with 15O-water PET. Journal of nuclear medicine: official publication. Soc Nuclear Med. 2008;49(10):1620\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003evan der Veldt AA, Hendrikse NH, Harms HJ, Comans EF, Postmus PE, Smit EF, et al. Quantitative parametric perfusion images using 15O-labeled water and a clinical PET/CT scanner: test-retest variability in lung cancer. J nuclear medicine: official publication Soc Nuclear Med. 2010;51(11):1684\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJochumsen MR, Bouchelouche K, Nielsen KB, Frokiaer J, Borre M, Sorensen J, et al. Repeatability of tumor blood flow quantification with (82)Rubidium PET/CT in prostate cancer - a test-retest study. EJNMMI Res. 2019;9(1):58.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHarms HJ, Knaapen P, de Haan S, Halbmeijer R, Lammertsma AA, Lubberink M. Automatic generation of absolute myocardial blood flow images using [15O]H2O and a clinical PET/CT scanner. Eur J Nucl Med Mol Imaging. 2011;38(5):930\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJochumsen MR, Christensen NL, Iversen P, Gormsen LC, S\u0026oslash;rensen J, Tolbod LP. Whole-body parametric mapping of tumour perfusion in metastatic prostate cancer using long axial field-of-view [(15)O]H(2)O PET. Eur J Nucl Med Mol Imaging. 2024;51(13):4134\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBland JM, Altman DG. Measuring agreement in method comparison studies. Stat Methods Med Res. 1999;8(2):135\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHarris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ede Langen AJ, Lubberink M, Boellaard R, Spreeuwenberg MD, Smit EF, Hoekstra OS, et al. Reproducibility of tumor perfusion measurements using 15O-labeled water and PET. Journal of nuclear medicine: official publication. Soc Nuclear Med. 2008;49(11):1763\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"ejnmmi-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejre","sideBox":"Learn more about [EJNMMI Research](http://ejnmmires.springeropen.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ejre/default.aspx","title":"EJNMMI Research","twitterHandle":"@officialEANM","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Tumour blood flow, perfusion, prostate cancer, [15O]H2O, test-retest, repeatability, sample size calculation","lastPublishedDoi":"10.21203/rs.3.rs-7908120/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7908120/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e\u003cp\u003eTumour 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. [\u003csup\u003e15\u003c/sup\u003eO]H\u003csub\u003e2\u003c/sub\u003eO 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 [\u003csup\u003e15\u003c/sup\u003eO]H\u003csub\u003e2\u003c/sub\u003eO tumour perfusion in prostate cancer.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eThirteen well-defined MRI PI-RADS lesions from ten patients were studied. The repeatability of [\u003csup\u003e15\u003c/sup\u003eO]H\u003csub\u003e2\u003c/sub\u003eO K\u003csub\u003e1\u003c/sub\u003e 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.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e\u003cp\u003e[\u003csup\u003e15\u003c/sup\u003eO]H\u003csub\u003e2\u003c/sub\u003eO tumour perfusion in localized prostate cancer can be measured with a high repeatability, showing comparable performance when using parametric K\u003csub\u003e1\u003c/sub\u003e 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.\u003c/p\u003e","manuscriptTitle":"Repeatability of tumour perfusion measurement with [15O]H2O PET in prostate cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-07 01:28:11","doi":"10.21203/rs.3.rs-7908120/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-11-04T18:17:19+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-27T16:44:07+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"EJNMMI Research","date":"2025-10-25T06:25:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-24T12:18:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"EJNMMI Research","date":"2025-10-23T06:22:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"ejnmmi-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejre","sideBox":"Learn more about [EJNMMI Research](http://ejnmmires.springeropen.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ejre/default.aspx","title":"EJNMMI Research","twitterHandle":"@officialEANM","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1c2549c6-d581-4ebe-af52-07bbb2e5a66d","owner":[],"postedDate":"November 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T16:06:58+00:00","versionOfRecord":{"articleIdentity":"rs-7908120","link":"https://doi.org/10.1186/s13550-026-01375-2","journal":{"identity":"ejnmmi-research","isVorOnly":false,"title":"EJNMMI Research"},"publishedOn":"2026-01-08 15:59:31","publishedOnDateReadable":"January 8th, 2026"},"versionCreatedAt":"2025-11-07 01:28:11","video":"","vorDoi":"10.1186/s13550-026-01375-2","vorDoiUrl":"https://doi.org/10.1186/s13550-026-01375-2","workflowStages":[]},"version":"v1","identity":"rs-7908120","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7908120","identity":"rs-7908120","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}