Magnetic Resonance Imaging Radiomics-Based Machine Learning Prediction of Clinically Significant Prostate Cancer in Equivocal PI-RADS 3 Lesions

Background While Prostate Imaging Reporting and Data System (PI-RADS) 4 and 5 lesions typically warrant prostate biopsy and PI-RADS 1 and 2 lesions may be safely observed, PI-RADS 3 lesions are equivocal. Purpose To construct and cross-validate a machine learning model based on radiomics features from T-2-weighted imaging (T2WI) of PI-RADS 3 lesions to identify clinically significant prostate cancer (csPCa), that is, pathological Grade Group >= 2. Study type Single-center retrospective study. Population A total of 240 patients were included (training cohort, n = 188, age range 43-82 years; test cohort, n = 52, age range 41-79 years). Eligibility criteria were 1) magnetic resonance imaging (MRI)-targeted biopsy between 2015 and 2020; 2) PI-RADS 3 index lesion identified on multiparametric MRI; (3) biopsy performed within 1 year of MRI. The percentages of csPCa lesions were 10.6% and 15.4% in the training and test cohorts, respectively. Field strength/sequence A 3 T; T2WI turbo-spin echo, diffusion-weighted spin-echo echo planar imaging, dynamic contrast-enhanced MRI with time-resolved T1-weighted imaging. Assessment Multislice volumes-of-interest (VOIs) were drawn in the PI-RADS 3 index lesions on T2WI. A total of 107 radiomics features (first-order histogram and second-order texture) were extracted from the segmented lesions. Statistical Tests A random forest classifier using the radiomics features as input was trained and validated for prediction of csPCa. The performance of the machine learning classifier, prostate specific antigen (PSA) density, and prostate volume for csPCa prediction was evaluated using receiver operating characteristic (ROC) analysis. Results The trained random forest classifier constructed from the T2WI radiomics features good and statistically significant area-under-the-curves (AUCs) of 0.76 (P = 0.022) for prediction of csPCa in the test set. Prostate volume and PSA density showed moderate and nonsignificant performance (AUC 0.62, P = 0.275 and 0.61, P = 0.348, respectively) for csPCa prediction in the test set. Conclusion The machine learning classifier based on T2WI radiomic features demonstrated good performance for prediction of csPCa in PI-RADS 3 lesions. Evidence Level 4 Technical Efficacy 2

Automated summary

A machine learning model based on T2WI radiomics features was constructed to identify clinically significant prostate cancer in PI-RADS 3 lesions, showing good performance with statistically significant results.