Dataset Information

Non-invasive quantification of tumor blood flow in prostate cancer using 15O-H2O PET/CT.

ABSTRACT: Tumor blood flow (TBF) measurements in prostate cancer (PCa) provide an integrative index of tumor growth, which could be important for primary diagnosis and therapy response evaluation. 15O-water PET is the non-invasive gold standard but is technically demanding. The aim of this study was to compare the accuracy of three different non-invasive strategies with an invasively measured arterial input function (BSIF): Using image-derived input functions (IDIF) from either 1) a separate heart scan or 2) the pelvic scan or 3) a populations-based input function (PBIF). Nine patients with biopsy-verified PCa scheduled for prostatectomy were included. All patients were characterized with serum levels of PSA (s-PSA), multiparametric magnetic resonance imaging (mpMRI) and post-surgical histopathology Gleason Grade. Dynamic 15O-water was performed of the heart and the pelvic area 15 minutes apart. TBF estimated from both wash-in (K1) and wash-out (k2) constants was calculated using a one-compartmental model. Results: Mean (range) s-PSA was 12 (3-27) ng/mL, Gleason Grade Group was 2.9 (1-5), k2 was 0.44 (0.007-1.2), and K1 was 0.24 (0.07-0.55) mL/mL/min. k2 (BSIF) correlated with s-PSA (r=0.86, P<0.01) and Gleason Grade Group (rho=0.78, P=0.01). BSIF, heart-IDIF and PBIF provided near-identical k2 and K1 (r>0.95, P<0.001) with slopes near unity. The correlations of BSIF and pelvic-IDIF rate constants were good (r>0.95, P<0.001), but individual errors high. In conclusion, non-invasive protocols for 15O-water PET with IDIF or PBIF accurately measures perfusion in prostate cancer and might be useful for evaluation of tumor aggressiveness and treatment response.

SUBMITTER: Tolbod LP

PROVIDER: S-EPMC6261877 | biostudies-other | 2018

REPOSITORIES: biostudies-other

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Non-invasive quantification of tumor blood flow in prostate cancer using 15O-H2O PET/CT.

Tolbod Lars P LP Nielsen Maria M MM Pedersen Bodil G BG Høyer Søren S Harms Hendrik J HJ Borre Michael M Borghammer Per P Bouchelouche Kirsten K Frøkiær Jørgen J Sørensen Jens J

American journal of nuclear medicine and molecular imaging 20181020 5

Tumor blood flow (TBF) measurements in prostate cancer (PCa) provide an integrative index of tumor growth, which could be important for primary diagnosis and therapy response evaluation. 15O-water PET is the non-invasive gold standard but is technically demanding. The aim of this study was to compare the accuracy of three different non-invasive strategies with an invasively measured arterial input function (BSIF): Using image-derived input functions (IDIF) from either 1) a separate he ...[more]

PMID: 30510847

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Project description:PurposeRegional cerebral blood flow (rCBF) quantification using 123I-N-isopropyl-p-iodoamphetamine (123I-IMP) requires an invasive, one-time-only arterial blood sampling for measuring the 123I-IMP arterial blood radioactivity concentration (Ca10). The purpose of this study was to estimate Ca10 by machine learning (ML) using artificial neural network (ANN) regression analysis and consequently calculating rCBF and cerebral vascular reactivity (CVR) in the dual-table autoradiography (DTARG) method.Materials and methodsThis retrospective study included 294 patients who underwent rCBF measurements through the 123I-IMP DTARG. In the ML, the objective variable was defined by the measured Ca10, whereas the explanatory variables included 28 numeric parameters, such as patient characteristic values, total injection 123I-IMP radiation dose, cross-calibration factor, and the distribution of 123I-IMP count in the first scan. ML was performed with training (n = 235) and testing (n = 59) sets. Ca10 was estimated in testing set by our proposing model. Alternatively, the estimated Ca10 was also calculated via the conventional method. Subsequently, rCBF and CVR were calculated using estimated Ca10. Pearson's correlation coefficient (r-value) for the goodness of fit and the Bland-Altman analysis for assessing the potential agreement and bias were performed between the measured and estimated values.ResultsThe r-value of Ca10 estimated by our proposed model was higher compared with the conventional method (0.81 and 0.66, respectively). In the Bland-Altman analysis, mean differences of 4.7 (95% limits of agreement (LoA): -18-27) and 4.1 (95% LoA: -35-43) were observed using proposed model and the conventional method, respectively. The r-values of rCBF at rest, rCBF after the acetazolamide challenge, and CVR calculated using the Ca10 estimated by our proposed model were 0.83, 0.80 and 0.95, respectively.ConclusionOur proposed ANN-based model could accurately estimate the Ca10, rCBF, and CVR in DTARG. These results would enable non-invasive rCBF quantification in DTARG.

Project description:BACKGROUND:To determine the relative abilities of compartment models to describe time-courses of 18F-fluoromisonidazole (FMISO) tumor uptake in patients with advanced stage non-small cell lung cancer (NSCLC) imaged using dynamic positron emission tomography (dPET), and study correlations between values of the blood flow-related parameter K1 obtained from fits of the models and an independent blood flow measure obtained from perfusion CT (pCT). NSCLC patients had a 45-min dynamic FMISO PET/CT scan followed by two static PET/CT acquisitions at 2 and 4-h post-injection. Perfusion CT scanning was then performed consisting of a 45-s cine CT. Reversible and irreversible two-, three- and four-tissue compartment models were fitted to 30 time-activity-curves (TACs) obtained for 15 whole tumor structures in 9 patients, each imaged twice. Descriptions of the TACs provided by the models were compared using the Akaike and Bayesian information criteria (AIC and BIC) and leave-one-out cross-validation. The precision with which fitted model parameters estimated ground-truth uptake kinetics was determined using statistical simulation techniques. Blood flow from pCT was correlated with K1 from PET kinetic models in addition to FMISO uptake levels. RESULTS:An irreversible three-tissue compartment model provided the best description of whole tumor FMISO uptake time-courses according to AIC, BIC, and cross-validation scores totaled across the TACs. The simulation study indicated that this model also provided more precise estimates of FMISO uptake kinetics than other two- and three-tissue models. The K1 values obtained from fits of the irreversible three-tissue model correlated strongly with independent blood flow measurements obtained from pCT (Pearson r coefficient = 0.81). The correlation from the irreversible three-tissue model (r?=?0.81) was stronger than that from than K1 values obtained from fits of a two-tissue compartment model (r?=?0.68), or FMISO uptake levels in static images taken at time-points from tracer injection through to 4 h later (maximum at 2 min, r?=?0.70). CONCLUSIONS:Time-courses of whole tumor FMISO uptake by advanced stage NSCLC are described best by an irreversible three-tissue compartment model. The K1 values obtained from fits of the irreversible three-tissue model correlated strongly with independent blood flow measurements obtained from perfusion CT (r?=?0.81).

Dataset Information

Non-invasive quantification of tumor blood flow in prostate cancer using 15O-H2O PET/CT.

Publications

Non-invasive quantification of tumor blood flow in prostate cancer using <sup>15</sup>O-H<sub>2</sub>O PET/CT.

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