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Published on: March 21, 2025
Biparametric MRI-Based Habitat Analysis Integrated With Deep Learning for Predicting Clinically Significant Prostate
Shuitang Deng1, Jinwen Hu2, Hui Wang1
1Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang Province, China.
Background:
Detection of clinically significant prostate cancer (csPCa) within PI-RADS category 3 lesions remains a major diagnostic challenge.
Purpose:
To develop and validate a biparametric MRI (bpMRI)-based habitat analysis model integrating deep learning features for predicting csPCa in PI-RADS 3 lesions using dual-center data.
Study Type:
Retrospective.
Population:
This study included 551 patients with MRI-identified PI-RADS category 3 lesions and histopathological confirmation. A total of 439 patients from Center 1 were randomly assigned to a training set (n = 328) and an internal validation (in-vad) set (n = 111), while an external validation (ex-vad) set (n = 112) was obtained from Center 2.
Field Strength/Sequence:
3 T/1.5 T. T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) sequences.
Assessment:
Lesions were manually segmented on preoperative T2WI and DWI, and tumor subregions were determined using k-means clustering. Deep learning features were obtained from each habitat subregion, and habitat-based models were built based on selected features. A habitat whole-tumor (Habitat W) model was subsequently derived by integrating all subregions. Recursive feature elimination (RFE) was applied to select the optimal predictors from the clinical and habitat-derived features; the clinical model was constructed using the selected clinical features, while the combined model incorporated all selected features.
Statistical Tests:
Student's t-test, Mann-Whitney U tests, Chi-squared tests, LASSO, areas under the curve (AUC), decision curve analysis (DCA), calibration curves, RFE, SHapley Additive exPlanations (SHAP). Statistical significance was defined as p-value < 0.05.
Results:
In the training, in-vad and ex-vad sets, the clinical model demonstrated AUC values of 0.893, 0.844, and 0.837, respectively. The habitat models (habitat 1, 2,3 and -W) achieved AUCs ranging from 0.857 to 0.952. The combined model yielded AUCs of 0.959, 0.963, and 0.949, respectively.
Data Conclusion:
The bpMRI-based deep learning Habitat W and combined model enables accurate assessment of csPCa in PI-RADS 3 lesions.
Technical Efficacy Stage:
3.
