Application Value of Radiomic Nomogram in the Differential Diagnosis of Prostate Cancer and Hyperplasia - Gui, Shaogao; Lan, Min; Wang, Chaoxiong; Nie, Si; Fan, Bing (Frontiers in Oncology, 2022)
Objective: Prostate cancer and hyperplasia require different treatment strategies and have completely different outcomes; thus, preoperative identification of prostate cancer and hyperplasia is very important. The purpose of this study was to evaluate the application value of magnetic resonance imaging (MRI) radiomic nomogram based on T2-weighted (T2WI) images in differentiating prostate cancer and hyperplasia. Materials and Methods: 146 patients (66 cases of prostate cancer and 80 cases of prostate hyperplasia) who were confirmed by surgical pathology between September 2019 and September 2019 were selected.  We manually delineated T2WI images of all patients using ITK-SNAP software and radiomic analysis using Analysis Kit (AK) software. A total of 396 tumor texture features were extracted. Subsequently, the effective features were selected used the LASSO algorithm and the radiomic feature model was constructed. Next, combined with independent clinical risk factors, a multivariate Logistic regression model was used to establish radiomic nomogram. The receiver operator characteristic (ROC) curve was used to evaluate the prediction performance of the radiomic nomogram.  Finally, the clinical application value of the nomogram was evaluated by decision curve analysis. Results: The PSA and the selected imaging features were significantly correlated with the differential diagnosis of prostate cancer and hyperplasia. The radiomic model had good discrimination efficiency for prostate cancer and hyperplasia. The training set (AUC=0.85, 95% CI: 0.77-0.92) and testing set (AUC=0.84, 95% CI: 0.72-0.96) were effective. The radiomic nomogram, combined with the radiomic characteristics of MRI and independent clinical risk factors, showed better differentiation efficiency in the training set (AUC=0. 91, 95% CI: 0. 85-0.97) and testing set (AUC = 0. 90, 95% CI: 0. 81-0.99). The decision curve showed the clinical application value of the radiomic nomogram. Conclusion: The radiomic nomogram of T2-MRI combined with clinical risk factors can easily identify prostate cancer and hyperplasia, It also and provide suggestions for further clinical events.