MRI-based habitat radiomics analysis for predicting clinically significant prostate cancer: a retrospective...

This study aims to evaluate the performance of habitat radiomics based on multi-parameter magnetic resonance (mpMRI) in predicting clinically significant prostate cancer (csPCa).

This retrospective study included 642 patients with pathology-proven prostate cancer (PCa) and benign prostatic hyperplasia (BPH) from two institutions. Center 1 was split into a training and internal test set, while Center 2 served as the external test set. Tumor regions of interest (ROI) were divided into three spatial habitats by K-means clustering. The classical radiomics score (Crad-score) and habitat radiomics score (Hrad-score) were developed following feature selection and dimensionality reduction. Multivariable analysis was conducted to ascertain independent predictors. The performance of these models was evaluated using receiver operating characteristic (ROC) curves. The Delong test was used to compare the area under the curves(AUCs) between models. The best-performing radiomics model was integrated with clinical data to develop a combined model, presented as a nomogram. Moreover, we further investigated whether the combined model provided additional value for improving the predictive accuracy of csPCa detection in PI-RADS 3-4 lesions.

The Hrad-score showed better performance than the Crad-score, achieving AUCs of 0.931, 0.907, and 0.893 in the training, internal test, and external test sets, respectively. The CH Combined model yielded optimal performance in all three sets, with AUCs of 0.966, 0.954, and 0.954, respectively. Additionally, in the subgroup of patients with PI-RADS 3-4 lesions, the CH combined model outperformed other single models in predicting csPCa, with AUCs of 0.933, 0.869 and 0.962 across the three sets.

Habitat analysis could act as a non-invasive auxiliary tool for predicting csPCa, and furthermore, it could reduce unnecessary biopsies and assist physicians in making clinical decisions.