Baseline Whole-Gland MRI Radiomics for Risk Stratification After Suspicious mpMRI and Negative Biopsy: A MULTIPROS Sub-Analysis.
Authors
Affiliations (5)
Affiliations (5)
- Division of Imaging Sciences and Technology, School of Medicine, University of Dundee, Ninewells Hospital, Dundee DD1 9SY, UK.
- Department of Radiological Sciences, College of Applied Medical Sciences, Taif University, Taif 21944, Saudi Arabia.
- Department of Clinical Radiology, Ninewells Hospital and Medical School, Dundee DD1 9SY, UK.
- Department of Computer Science, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia.
- Division of Respiratory Medicine and Gastroenterology, School of Medicine, University of Dundee, Ninewells Hospital, Dundee DD1 9SY, UK.
Abstract
<b>Background/Objectives</b>: Patients with suspicious prostate mpMRI but negative baseline biopsy remain challenging to manage, as some are later diagnosed with clinically significant prostate cancer (csPCa). This study evaluated whether baseline whole-gland radiomic features from T2-weighted imaging and apparent diffusion coefficient (ADC) maps could predict subsequent csPCa detection. <b>Methods</b>: This retrospective radiomics sub-analysis of the MULTIPROS trial included men with suspicious baseline mpMRI findings (PI-RADS v2.0 categories 3-5) and no csPCa detected at baseline biopsy. The primary outcome was subsequent csPCa detection during follow-up, defined as ISUP Grade Group ≥2. Whole-gland prostate segmentation was performed on baseline T2-weighted images using a semi-automated approach with manual refinement, and the resulting T2-derived masks were applied to the spatially corresponding ADC maps. Radiomic features were extracted using PyRadiomics version 3.1.0a2 after standardised preprocessing. Feature robustness was assessed in a 20-patient reproducibility subset using intra- and inter-reader intraclass correlation coefficients, followed by correlation filtering and LASSO-based feature prioritisation. Radiomics-only, clinicoradiological-only, and clinicoradiological-radiomic logistic regression models were evaluated using stratified five-fold cross-validation. <b>Results</b>: T2-based modelling included 151 patients, of whom 36/151 (23.8%) had subsequent csPCa detected; the median time to csPCa detection or last follow-up was 64 months. ADC analyses included 144 patients, of whom 34/144 (23.6%) had subsequent csPCa detected. The T2 clinicoradiological-radiomic model achieved a mean AUC of 0.696 ± 0.060, compared with 0.713 ± 0.072 for the T2 clinicoradiological-only model and 0.456 ± 0.091 for the T2 radiomics-only model. In the ADC-available cohort, ADC radiomics-only, ADC clinicoradiological-radiomic, and combined T2 + ADC clinicoradiological-radiomic models achieved mean AUCs of 0.684 ± 0.052, 0.635 ± 0.157, and 0.663 ± 0.106, respectively. Calibration was imperfect, with deviations at higher predicted probabilities and overprediction in the highest predicted-risk bin. <b>Conclusions</b>: Baseline whole-gland MRI radiomics showed limited and inconsistent predictive value for subsequent csPCa detection after negative biopsy and did not demonstrate consistent incremental improvement over clinicoradiological predictors. These findings are exploratory and hypothesis-generating, and external validation is required before clinical use.