Predicting Tumor Perineural Invasion Status in High-Grade Prostate Cancer Based on a Clinical–Radiomics Model Incorporating T2-Weighted and Diffusion-Weighted Magnetic Resonance Images

SIMPLE SUMMARY: Perineural invasion (PNI) is present in 17–75% of prostate cancer patients and is an important mechanism for cancer progression, leading to poor prognoses. An optimized preoperative technique is needed to detect PNI in prostate cancer patients and administer the best treatment. The aim of our retrospective study was to develop a model based on high-throughput radiomic features of bi-parametric MRI combined with clinical factors that can predict PNI status in high-grade prostate cancers. In total, 183 high-grade PCa patients were included in this retrospective study, and the radiomics model based on 13 selected features of bi-parametric MRI showed better discrimination than did the conventional model in the test cohort (area under the curve (AUC): 0.908). Discrimination efficiency improved when the radiomics and clinical models were combined (AUC: 0.947). This improved model may help predict PNI in prostate cancer patients and allow more personalized clinical decision-making. ABSTRACT: Purpose: To explore the role of bi-parametric MRI radiomics features in identifying PNI in high-grade PCa and to further develop a combined nomogram with clinical information. Methods: 183 high-grade PCa patients were included in this retrospective study. Tumor regions of interest (ROIs) were manually delineated on T2WI and DWI images. Radiomics features were extracted from lesion area segmented images obtained. Univariate logistic regression analysis and the least absolute shrinkage and selection operator (LASSO) method were used for feature selection. A clinical model, a radiomics model, and a combined model were developed to predict PNI positive. Predictive performance was estimated using receiver operating characteristic (ROC) curves, calibration curves, and decision curves. Results: The differential diagnostic efficiency of the clinical model had no statistical difference compared with the radiomics model (area under the curve (AUC) values were 0.766 and 0.823 in the train and test group, respectively). The radiomics model showed better discrimination in both the train cohort and test cohort (train AUC: 0.879 and test AUC: 0.908) than each subcategory image (T2WI train AUC: 0.813 and test AUC: 0.827; DWI train AUC: 0.749 and test AUC: 0.734). The discrimination efficiency improved when combining the radiomics and clinical models (train AUC: 0.906 and test AUC: 0.947). Conclusion: The model including radiomics signatures and clinical factors can accurately predict PNI positive in high-grade PCa patients.