The Primacy of High B-Value 3T-DWI Radiomics in the Prediction of Clinically Significant Prostate Cancer

Predicting clinically significant prostate cancer (csPCa) is crucial in PCa management. 3T-magnetic resonance (MR) systems may have a novel role in quantitative imaging and early csPCa prediction, accordingly. In this study, we develop a radiomic model for predicting csPCa based solely on native b2000 diffusion weighted imaging (DWIb2000) and debate the effectiveness of apparent diffusion coefficient (ADC) in the same task. In total, 105 patients were retrospectively enrolled between January-November 2020, with confirmed csPCa or ncsPCa based on biopsy. DWIb2000 and ADC images acquired with a 3T-MRI were analyzed by computing 84 local first-order radiomic features (RFs). Two predictive models were built based on DWIb2000 and ADC, separately. Relevant RFs were selected through LASSO, a support vector machine (SVM) classifier was trained using repeated 3-fold cross validation (CV) and validated on a holdout set. The SVM models rely on a single couple of uncorrelated RFs (rho < 0.15) selected through Wilcoxon rank-sum test (p <= 0.05) with Holm-Bonferroni correction. On the holdout set, while the ADC model yielded AUC = 0.76 (95% CI, 0.63-0.96), the DWIb2000 model reached AUC = 0.84 (95% CI, 0.63-0.90), with specificity = 75%, sensitivity = 90%, and informedness = 0.65. This study establishes the primary role of 3T-DWIb2000 in PCa quantitative analyses, whilst ADC can remain the leading sequence for detection.

Automated summary

This study developed a radiomic model based on native b2000 diffusion weighted imaging for predicting clinically significant prostate cancer, with a focus on the efficacy compared to apparent diffusion coefficient. The support vector machine models rely on carefully selected local first-order radiomic features for analysis and prediction of prostate cancer.