A machine learning radiomics model based on bpMRI to predict bone metastasis in newly diagnosed prostate cancer patients.

Abstract

ObjectivesTo develop and evaluate a machine learning radiomics model based on bpMRI to predict bone metastasis (BM) status in newly diagnosed prostate cancer (PCa) patients. MethodsWe retrospectively analyzed biparametric magnetic resonance imaging MRI (bpMRI) scans of PCa patients from multiple centers between January 2016 and October 2021. 348 PCa patients were recruited from two institutions for this study. The first institution contributed 284 patients, stratified and randomly divided into training and internal validation cohorts at a 7:3 ratio. The remaining 64 patients were sourced from the second institution and comprised the external validation cohort. Radiomics features were extracted from axial T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) tumor regions. We developed the radiomics prediction model for BM in the training cohort and validated it in the internal and external validation cohorts. As a benchmark, we trained the logistic regression model with lasso feature reduction (LFR-LRM) in the training cohort and further compared it with Naive Bayes, eXtreme Gradient Boosting (XGboost), Random Forest (RF), GBDT, SVM, Adaboost, and KNN algorithms and validated in both the internal and external cohorts. The performance of several predictive models was assessed by receiver operating characteristic (ROC). ResultsThe LFR-LRM model achieved an area under the receiver operating characteristic curve (AUC) of 0.89 (95% CI: 0.822–0.974) and an accuracy of 0.828 (95% CI: 0.713–0.911). The AUC and accuracy in external validation were 0.866 (95% CI: 0.784–0.948) and 0.769 (95% CI: 0.648–0.864), respectively. The RF and XGBoost models outperformed the LFR-LRM, with AUCs of 0.907 (95% CI: 0.863–0.949) and 0.928 (95% CI: 0.882–0.974) and accuracies of 0.831 (95% CI: 0.727–0.907) and 0.884 (95% CI: 0.792–0.946). External validation for these models yielded AUCs and accuracies of 0.911 (95% CI: 0.861–0.966), 0.921 (95% CI: 0.889–0.953), and 0.846 (95% CI: 0.735–0.923) and 0.876 (95% CI: 0.771–0.945), respectively. ConclusionsThe XGboost machine learning model is more accurate than LFR-LRM for predicting BM in patients with newly confirmed PCa.