Development and internal validation of transrectal ultrasound radiomics models for patient- and needle-level prostate cancer prediction in biopsy-naive men
Year: 2026
Type: article
Study design: Observational Study
Abstract: Background: Current prostate cancer (PCa) assessment uses clinical findings, prostate-specific antigen (PSA), and multiparametric magnetic resonance imaging (mp-MRI), while transrectal ultrasound (TRUS) mainly provides real-time biopsy guidance. We developed zone-specific TRUS radiomics models for patient-level and planned needle-level PCa prediction. Methods: This retrospective single-center study included 247 biopsy-naive men undergoing freehand transperineal 10-core systematic biopsy from August 2023 to April 2024. Each patient contributed two transition zone (TZ) cores and eight peripheral zone (PZ) cores. A blinded sonographer delineated whole-zone and pre-firing needle-target regions of interest (ROIs), visually matched to post-firing images acquired seconds later. From each ROI, 513 radiomic features were extracted, sparsely selected, and classified with a two-layer RNN. Models were evaluated using an 80:20 split. A patient or core was positive when pathology showed Gleason score (GS) ≥6. Results: The cohort included 116 patients with PCa and 131 with benign findings; 545 of 2,470 cores were positive. Without PSA, the combined TZ + PZ patient-level model had the highest patient-level area under the receiver operating characteristic curve (AUC) (0.828), whereas needle-level AUCs were 0.699, 0.698, and 0.680 for TZ, PZ, and TZ + PZ models. With PSA, the combined patient-level model reached an AUC of 0.876, and the needle-level TZ + PSA model reached 0.826. Conclusions: The findings support further investigation of zone-specific TRUS radiomics, particularly for patient-level prediction. Needle-level results are exploratory and do not establish real-time biopsy guidance. External validation, confidence intervals, comparator models, prospective registration, and patient-grouped evaluation are required before clinical use.
Institutions: Fudan University, Huashan Hospital
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7223787984