Diagnostic nomogram based on intralesional and perilesional radiomics features and clinical factors of clinically significant prostate cancer

Abstract Previous studies on the value of radiomics for diagnosing clinically significant prostate cancer (csPCa) only utilized intralesional features. However, the role of tumor microenvironment is important in tumor generation and progression. The aim of this study is to build and validate a nomogram based on perilesional and intralesional radiomics features and clinical factors for csPCa. This is a retrospective study, which included 140 patients who underwent prostate magnetic resonance imaging (MRI). This study used 3. 0T T2 ‐weighted imaging, apparent diffusion coefficient maps (derived from diffusion‐weighted images), and dynamic contrast‐enhanced MRI . Region of interest (ROI)s were segmented by two radiologists. Intralesional and combined radiomics signatures were built based on radiomics features extracted from intralesional and the combination of radiomics features extracted from intralesional and perilesional volumes. Serum total prostate‐specific antigen level and combined radiomics signature scores were used to construct a diagnostic nomogram. Intraclass correlation efficient analysis was used to test intra‐ and inter‐rater agreement of radiomics features. Min‐max scalar was used for normalization. One‐way analysis of variance or the Mann–Whitney U ‐test was used for univariate analysis. Receiver operating characteristic curve analysis, accuracy, balanced accuracy, and F1‐score were used to evaluate radiomics signatures and the nomogram. Also, the nomogram was evaluated using decision curve analysis in testing cohort. Delong test was used to compare area under the curves (AUCs). Statistical significance was set at p < 0.05. In testing cohort, AUC, accuracy, balanced accuracy, and F1‐score of combined radiomics signature (0.94, 0.83, 0.80, and 0.87, respectively) were all higher than that of intralesional radiomics signature (0.90, 0.77, 0.74, and 0.83, respectively). The difference between AUCs was insignificant ( p of 0.19). AUC, accuracy, balanced accuracy, and F1‐score of the nomogram were 0.96, 0.94, 0.95, and 0.95, respectively. Nomogram was clinically useful when threshold probability of a patient is higher than 0.06. Perilesional radiomics features improved the discrimination ability of the radiomics signature. Diagnostic nomogram had a good performance. Level of Evidence 3. Technical Efficacy Stage 2.