Uni and Multi-Modal Radiomic Features for the Predicting Prostate Cancer Aggressiveness

The use of quantitative radiomic features of MRI to predict the aggressiveness of prostate cancer has attracted increasing amounts of attention due to its potential as a non-invasive biomarker for prostate cancer. In this study, to predict prostate cancer aggressiveness, we investigate the usefulness of multi-modal radiomic features according to combination method such as concatenation or averaging and compare multi-modal radiomic features to uni-modal radiomic features. To define the prostate cancer region of T2wMR based on ground truth pathology, a radiologist manually segmented prostate cancer referring to a fusion result of registration of histopathology image and T2wMR. The prostate cancer region of the ADC is then defined as the same region as the T2wMR through registration of the ADC on the T2wMR. To extract radiomic features to predict prostate cancer aggressiveness, total 68 features are calculated for each region of T2wMR and ADC. To predict the aggressiveness of prostate cancer, a random forest classifier is trained for each region in T2wMR and ADC. The prostate cancer regions were categorized as Low GS Group (GS <= 3+4) and High GS Group (GS >= 4+3). As results, the sensitivity of combined features was the highest at 82.0% which is higher 2.4%, 2.7% and 4.8% than ADC, T2wMR, and average combined features. Experiment results showed that the possibility of determining the aggressiveness of prostate cancer through the multi-modal radiomic features of T2wMR and ADC.