Investigation of radiomic features on MRI images to identify extraprostatic extension in prostate cancer

Background and objective: Detection of extraprostatic extension (EPE) preoperatively is of critical importance in the context of prostate cancer (PCa) management and outcomes. This study aimed to characterize the radiomic features of malignant prostate lesions based on multi-paramagnetic magnetic resonance imaging (mpMRI).

Methods: We analyzed 20 patients who underwent mpMRI followed by radical prostatectomy. Two experienced radiologists manually segmented the 3D lesions using the T2-weighted (T2WI) and Apparent Diffusion Coefficient (ADC) imaging sequences. A total of 210 radiomic features were extracted from each lesion. We used the Recursive Feature Elimination with Cross-Validation to select key features. Using the selected radiomic features, we developed a Multilayer Perceptron (MLP) neural network to classify the EPE and non-EPE lesions. The pathology results were accepted as gold standard for EPE. We measured the performance of the classifier, calculating the area-under-curve (AUC), sensitivity, and specificity.

Results: A total of 25 lesions were segmented, including 12 lesions with EPE and 13 lesions without EPE, based on the pathology reports. We selected 18 radiomic features (18/210). The MLP classifier using these features provided a good sensitivity (0.75), specificity (0.79), and AUC of 0.82, 95 % CL [0.59 - 0.96] in identifying the EPE lesions.

Conclusions: This pilot study presents 18 radiomic features derived from T2-weighted and ADC images and demonstrates their potential in the preoperative prediction of EPE in PCa using an MLP model.