Prediction of Tumor-Stroma Ratio in Prostate Cancer using multiparametric MRI-Based Radiomics Mode

Motivation: Tumor stroma is considered one of the key participants in prostate cancer development, progression, and even treatment resistance as an independent predictor, is associated with aggressiveness in a variety of malignancies. Goal(s): We would like to apply the value of stroma cells in clinical practice for assessing the aggressiveness of PCa. Approach: Five multiparametric magnetic resonance imaging (mp- MRI) radiomics feature-based machine learning models were developed and assessed to predict the tumor-stroma ratio (TSR) of PCa. Results: The developed Multi-Layer Perception model showed excellent performance at predictive the TSR in prostate cancer with the area under the ROC curve (AUC) at 0.860. Impact: This study constructed a mp-MRI-based radiomics model which is capable of accurately predicting the TSR of PCa and may serve as a complementary tool for assisting in risk stratification and guiding treatment decisions.