Related Experiment Video
Updated: Nov 14, 2025
A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
Published on: March 21, 2025
Radiomics Models Based on Apparent Diffusion Coefficient Maps for the Prediction of High-Grade Prostate Cancer at
Chao Han1, Shuai Ma1, Xiang Liu1
1Department of Radiology, Peking University First Hospital, Beijing, China.
Background:
It is feasible to use magnetic resonance (MR)-based radiomics to distinguish high-grade from low-grade prostate cancer (PCa), but radiomics model performance based on fully automated segmentation remains unknown.
Purpose:
To develop and test radiomics models based on manually or automatically gained masks on apparent diffusion coefficient (ADC) maps to predict high-grade (Gleason score ≥ 4 + 3) PCa at radical prostatectomy (RP).
Study Type:
Retrospective.
Population:
A total of 176 patients (94 high-grade PCa and 82 low-grade PCa) with complete RP, preoperative biopsy, and multiparametric magnetic resonance imaging (mpMRI) were retrospectively recruited and randomly divided into training (N = 123) and test (N = 53) cohorts.
Field Strength/Sequence:
Using a 3.0-T MR scanner, ADC maps were calculated from diffusion-weighted imaging (b values = 0, 1400 s/mm2 , echo planar imaging).
Assessment:
Two radiologists segmented the whole prostate gland and the most index prostate lesion. Automatic segmentation of the prostate and the lesion were performed. Four radiomics models were constructed using four masks (manual/automatic prostate gland/PCa lesion segmentation). According to the standard reference of the RP histopathologic assessment, the performance of each radiomics models was compared with that of biopsy and Prostate Imaging Reporting and Data System version 2.1 (PI-RADS) assessment.
Statistical Tests:
A receiver operating characteristic curve analysis was employed to estimate the area under the curve (AUC) values of the models. The AUCs of the four models, biopsy, and PI-RADS assessment were compared using the DeLong test.
Results:
The four radiomics models yielded AUCs of 0.710, 0.731, 0.726, and 0.709 in the test cohort, respectively; biopsy and PI-RADS assessment yielded AUCs of 0.793 and 0.680, respectively. No significant differences were found among model, biopsy, and PI-RADS assessment comparisons (P = 0.132-0.988).
Data Conclusion:
To distinguish high-grade from low-grade PCa, radiomics models based on automatic segmentation on ADC maps exhibit approximately the same diagnostic efficacy as manual segmentation and biopsy, highlighting the possibility of a fully automatic workflow combining automated segmentation with radiomics analysis.
Evidence Level:
4 TECHNICAL EFFICACY: Stage 2.
