Machine Learning-Based Radiomics Model to Predict Benign and Malignant PI-RADS v2.1 Category 3 lesions : A Retrospective Multi-center Study

Abstract

Purpose: To develop machine learning-based prediction models derive from different MRI sequences for distinction between benign and malignant PI-RADS 3 lesions before intervention, and to cross-institution validate the generalization ability of the models. Methods: The pre-biopsy MRI datas of 463 patients diagnosed as PI-RADS 3 lesions were collected from 4 medical institutions. 2347 radiomics features were extracted from the VOI of T2WI, DWI and ADC maps. The ANOVA feature ranking method and support vector machine (SVM) classifier were used to construct 3 single-sequence models and 1 integrated model combined with the features of three sequences. All the models were established in the training set and independently verified in the internal test and external validation set. The area under the receiver operating characteristic curve (AUC) was used to quantify the predictive performance of each model. Hosmer-lemeshow test was used to evaluate the degree of fitting between prediction probability and pathological results. Non-inferiority test was used to check generalization performance of the integrated model. Results: T2WI-model with the mean AUC of 0.717 for predicting clinically significant prostate cancer (csPCa) (internal test AUC = 0.738 vs. external validation AUC = 0.695, P = 0.264) and 0.624 for predicting all cancer (internal test AUC = 0.678 vs. external validation AUC = 0.589, P = 0.547). DWI-model with the mean AUC of 0.658 for predicting csPCa (internal test AUC = 0.635 vs. external validation AUC = 0.681, P = 0.086) and 0.655 for predicting all cancer (internal test AUC = 0.712 vs. external validation AUC = 0.598, P = 0.437). ADC-model with the mean AUC of 0.746 for predicting csPCa (internal test AUC = 0.767 vs. external validation AUC = 0.724, P = 0.269) and 0.645 for predicting all cancer (internal test AUC = 0.650 vs. external validation AUC = 0.640, P = 0.848). Integrated model with the mean AUC of 0.803 for predicting csPCa (internal test AUC = 0.804 vs. external validation AUC = 0.801, P = 0.019) and 0.763 for predicting all cancer (internal test AUC = 0.801 vs. external validation AUC = 0.754, P = 0.047). Conclusions: The radiomics model based on mechine learning has the potential to be a non-invasive tool to distinguish cancerous, noncancerous and csPCa in PI-RADS 3 lesions and the generalization ability between different date set.

Research on texture images and radiomics in urology: a review of urological MR imaging applications

“…More specifically, studies focused on the prediction of csCaP in case of PIRADS 3 lesions [14 ▪▪ ,15,16]. Indeed, in a multiinstitutional study of 463 patients, Jin et al observed that their integrated model (3 single-sequence radiomics) had the best performance compared to each single sequence and to PSAD, in predicting csCaP (internal test AUC = 0.804 vs. external validation AUC = 0.801) [14 ▪▪ ].…”