Predicting the pathological subdiagnosis of benign prostatic hyperplasia with MRI radiomics: A noninvasive approach
Abstract
To predict the pathological subdiagnosis of benign prostatic hyperplasia (BPH‐PS) with the overarching aim of mitigating finasteride overtreatment, we conducted a study integrating clinical, pathological, and radiomic data from BPH patients at our center. The study comprised a retrospective analysis of data and a prospective cohort of BPH patients undergoing pharmacological treatment for six months. Sixty patients (mean age: 68.5 ± 7.1 years) with 360 histopathological whole‐slide images (WSIs) were included in the retrospective cohort. Three gland recognition models were developed and trained to identify glandular structures in WSIs, facilitating the calculation of gland ratios. The optimal model, based on Artificial Neural Network—Multilayer Perceptron (ANN‐MLP), achieved an intraclass correlation coefficient (ICC) of .948 (p < .001) compared to manual annotation. Radiomic features and habitat analysis were extracted from apparent diffusion coefficient (ADC). Patients were categorized into two groups, BPH with predominantly glandular hyperplasia (BPH‐G) and BPH with predominantly stromal hyperplasia (BPH‐S). An optimal ADC cut‐off value of 1.110×10−3 mm2/s was determined to calculate the Habitat Score. The Habitat Score demonstrated superior predictive performance for BPH‐PS, with an area under the curve (AUC) value of .902. A total of 161 patients (mean age: 64.3 ± 7.0 years) were enrolled in a prospective cohort study. By comparing the changes in prostate volume under…
How strong is the evidence?
Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 161 participants or records.
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