TL;DR
ADC radiomics provided complementary information for differentiating benign and malignant PI-RADS 3-5 prostate lesions, but its incremental value beyond the clinical model was limited.
The apparent diffusion coefficient-based radiomics model effectively detected csPCa in PI-RADS 3 lesions in the training set and demonstrated moderate performance in the external validation set.
A minimal-feature radiomics model derived exclusively from ADC images showed promising diagnostic performance for differentiating malignant from benign prostate lesions, and may provide a practical, reproducible, and resource-efficient strategy for supporting prostate cancer diagnosis.
The PSAD + D nomogram achieves diagnostic performance comparable to PI-RADS + PSAD for predicting csPCa while eliminating reader dependency through objective, highly reproducible parameters (intraclass correlation coefficients).
The combined model, incorporating ISUP grade, MRI-EPE score, and peritumoral ADC histogram features (peri1_Minimum and peri3_Minimum) enables robust prediction of EPE in PI-RADS 4–5 prostate cancer.
Background and purpose Primary central nervous system lymphoma (PCNSL) and high-grade glioma (HGG) may share overlapping conventional MRI appearances. We developed and internally validated a parsimonious model combining tumor apparent diffusion coefficient (ADC) and the lipid-to-creatine ratio (Lip/Cr), compared it wit...
Objectives To characterize recurrence heterogeneity among patients with VI-RADS 2 non-muscle-invasive bladder cancer (NMIBC) and to develop and internally validate a recurrence-free survival (RFS) prediction model incorporating quantitative MRI features. Methods This retrospective study included 342 patients with patho...
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