International Journal of Radiation Research

en Research on the application of multi-parametric MRI radiomics based on different regions of interest in the evaluation of benign and malignant prostate lesions Radiation Biology Radiation Biology تحقيق بديع Original Research <div style="text-align: justify;">Background: The aim was to examine the diagnostic utility of radiomics via multi-parametric magnetic resonance imaging (mpMRI) in distinguishing between benign and malignant prostate lesions when features are extracted from different regions of interest (ROIs). Materials and Methods: A retrospective cohort of 99 individuals having mpMRI (T2WI, DCE, ADC) with histopathological confirmation was analyzed and split into training (n = 69) and validation (n = 30) at random as 7:3. Radiomics characteristics were taken from two ROIs: the lesion area (Set 1) and the whole-prostate area (Set 2). After choice of features by least absolute shrinkage and selection operator (LASSO), logistic regression (LR) was utilized to generate three models: Model 1 (lesion-based radiomics), Model 2 (prostate-based radiomics), and Model 3 (combined radiomics plus clinical factors). Receiver operating characteristic (ROC) was utilized to evaluate diagnostic results, and inter-model comparisons were conducted with DeLong’s test. Results: Of the 99 patients, 59 had benign and 40 had malignant lesions. In the training set, the area under the curve (AUC) values of Models 1, 2, and 3 were 0.997 (0.991–1.000), 0.969 (0.937–1.000), and 0.999 (0.997–1.000), while the corresponding validation AUCs were 0.920 (0.829–1.000), 0.916 (0.820–1.000), and 0.929 (0.843–1.000). There were no statistically significant variations among the three models. Conclusion: Radiomics models derived from mpMRI, regardless of whether features are extracted from the lesion area or from the entire prostate, demonstrate strong diagnostic capability in differentiating between prostate tumors that are benign and cancerous, suggesting substantial potential for clinical application.</div> Prostate cancer, radiomics, machine learning, multiparametric magnetic resonance imaging. 1181 1187 http://ijrr.com/browse.php?a_code=A-10-1-1631&slc_lang=en&sid=1 W. Zhao 7900319475328460035137 7900319475328460035137 No Department of Radiology, Huaibei People's Hospital, Huaibei, Anhui, China L. Zhang 452473418@qq.com 7900319475328460035138 7900319475328460035138 Yes Department of Radiology, Huaibei People's Hospital, Huaibei, Anhui, China L. Wang 7900319475328460035139 7900319475328460035139 No Department of Radiology, Huaibei People's Hospital, Huaibei, Anhui, China L. Chen 7900319475328460035140 7900319475328460035140 No Department of Radiology, Huaibei People's Hospital, Huaibei, Anhui, China D. Li 7900319475328460035141 7900319475328460035141 No Department of Radiology, Huaibei People's Hospital, Huaibei, Anhui, China