AUTHOR=Li Chunxiang , Qiao Ge , Li Jinghan , Qi Lisha , Wei Xueqing , Zhang Tan , Li Xing , Deng Shu , Wei Xi , Ma Wenjuan 

TITLE=An Ultrasonic-Based Radiomics Nomogram for Distinguishing Between Benign and Malignant Solid Renal Masses

JOURNAL=Frontiers in Oncology

VOLUME=12

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.847805

DOI=10.3389/fonc.2022.847805

ISSN=2234-943X

ABSTRACT=<sec><title>Objectives</title><p>This study was conducted in order to develop and validate an ultrasonic-based radiomics nomogram for diagnosing solid renal masses.</p></sec><sec><title>Methods</title><p>Six hundred renal solid masses with benign renal lesions (<italic>n</italic> = 204) and malignant renal tumors (<italic>n</italic> = 396) were divided into a training set (<italic>n</italic> = 480) and a validation set (<italic>n</italic> = 120). Radiomics features were extracted from ultrasound (US) images preoperatively and then a radiomics score (RadScore) was calculated. By integrating the RadScore and independent clinical factors, a radiomics nomogram was constructed. The diagnostic performance of junior physician, senior physician, RadScore, and radiomics nomogram in identifying benign from malignant solid renal masses was evaluated based on the area under the receiver operating characteristic curve (ROC) in both the training and validation sets. The clinical usefulness of the nomogram was assessed using decision curve analysis (DCA).</p></sec><sec><title>Results</title><p>The radiomics signature model showed satisfactory discrimination in the training set [area under the ROC (AUC), 0.887; 95% confidence interval (CI), 0.860–0.915] and the validation set (AUC, 0.874; 95% CI, 0.816–0.932). The radiomics nomogram also demonstrated good calibration and discrimination in the training set (AUC, 0.911; 95% CI, 0.886–0.936) and the validation set (AUC, 0.861; 95% CI, 0.802–0.921). In addition, the radiomics nomogram model showed higher accuracy in discriminating benign and malignant renal masses compared with the evaluations by junior physician (DeLong <italic>p</italic> = 0.004), and the model also showed significantly higher specificity than the senior and junior physicians (0.93 vs. 0.57 vs. 0.46).</p></sec><sec><title>Conclusions</title><p>The ultrasonic-based radiomics nomogram shows favorable predictive efficacy in differentiating solid renal masses.</p></sec>