AimTo study the feasibility of radiomic analysis of baseline [F-18]fluoromethylcholine positron emission tomography/computed tomography (PET/CT) for the prediction of biochemical recurrence (BCR) in a cohort of intermediate and high-risk prostate cancer (PCa) patients.Material and methodsSeventy-four patients were prospectively collected. We analyzed three prostate gland (PG) segmentations (i.e., PG(whole): whole PG; PG(41%): prostate having standardized uptake value - SUV > 0.41*SUVmax; PG(2.5): prostate having SUV > 2.5) together with three SUV discretization steps (i.e., 0.2, 0.4, and 0.6). For each segmentation/discretization step, we trained a logistic regression model to predict BCR using radiomic and/or clinical features.ResultsThe median baseline prostate-specific antigen was 11 ng/mL, the Gleason score was > 7 for 54% of patients, and the clinical stage was T1/T2 for 89% and T3 for 9% of patients. The baseline clinical model achieved an area under the receiver operating characteristic curve (AUC) of 0.73. Performances improved when clinical data were combined with radiomic features, in particular for PG(2.5) and 0.4 discretization, for which the median test AUC was 0.78.ConclusionRadiomics reinforces clinical parameters in predicting BCR in intermediate and high-risk PCa patients. These first data strongly encourage further investigations on the use of radiomic analysis to identify patients at risk of BCR.

Role of radiomic analysis of [18F]fluoromethylcholine PET/CT in predicting biochemical recurrence in a cohort of intermediate and high risk prostate cancer patients at initial staging

Guglielmo, Priscilla;Evangelista, Laura
2023-01-01

Abstract

AimTo study the feasibility of radiomic analysis of baseline [F-18]fluoromethylcholine positron emission tomography/computed tomography (PET/CT) for the prediction of biochemical recurrence (BCR) in a cohort of intermediate and high-risk prostate cancer (PCa) patients.Material and methodsSeventy-four patients were prospectively collected. We analyzed three prostate gland (PG) segmentations (i.e., PG(whole): whole PG; PG(41%): prostate having standardized uptake value - SUV > 0.41*SUVmax; PG(2.5): prostate having SUV > 2.5) together with three SUV discretization steps (i.e., 0.2, 0.4, and 0.6). For each segmentation/discretization step, we trained a logistic regression model to predict BCR using radiomic and/or clinical features.ResultsThe median baseline prostate-specific antigen was 11 ng/mL, the Gleason score was > 7 for 54% of patients, and the clinical stage was T1/T2 for 89% and T3 for 9% of patients. The baseline clinical model achieved an area under the receiver operating characteristic curve (AUC) of 0.73. Performances improved when clinical data were combined with radiomic features, in particular for PG(2.5) and 0.4 discretization, for which the median test AUC was 0.78.ConclusionRadiomics reinforces clinical parameters in predicting BCR in intermediate and high-risk PCa patients. These first data strongly encourage further investigations on the use of radiomic analysis to identify patients at risk of BCR.
2023
Artificial intelligence
Fluorocholine
Positron emission tomography computed tomography
Prostatic neoplasms
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11699/82355
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