Keywords: Diagnosis/Prediction, Prostate, magnetic resonance imaging, radiomics, tumor-stroma ratio, tumor microenvironment
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.
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