Prediction of Genomic Signature of Prostate Lesion Radiosensitivity by mpMRI Radiomics and Machine Learning

Genomic classifiers, such as PORTOS, have shown great promise in the prediction of prostate cancer radiosensitivity. However, the spatial heterogeneity of prostate cancer may confound genomic assessment due to tumor sampling error. We aimed to develop a model predictive of PORTOS genomic signature using multiparametric MRI (mpMRI) radiomics features and machine learning. Lesions were localized based on Habitat Risk Score maps. Eight radiomic features were selected (out of 167) including T2, ADC, high B-value intensity and texture variables and used to build logistic regression models through cross-validation. Our analysis shows association between the radiomics profile and prostate lesion radiosensitivity phenotype.