Journal of magnetic resonance imaging : JMRI · 2025
Biparametric MRI-Based Habitat Analysis Integrated With Deep Learning for Predicting Clinically Significant Prostate Cancer in PI-RADS Category 3 Lesions.
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Deng Shuitang, Hu Jinwen, Wang Hui, Han Xiaoyu, Ao Weiqun
Аннотация
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.
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.