Clinical Radiomics Nomogram Based on Ultrasound

Objectives This study aims to develop a noninvasive preoperative predictive model utilizing ultrasound radiomics combined with clinical characteristics to differentiate uterine sarcoma from leiomyoma. Methods This study included 212 patients with uterine mesenchymal lesions (102 sarcomas and 110 leiomyomas). Clinical characteristics were systematically selected through both univariate and multivariate logistic regression analyses. A clinical model was constructed using the selected clinical characteristics. Radiomics features were extracted from transvaginal ultrasound images, and 6 machine learning algorithms were used to construct radiomics models. Then, a clinical radiomics nomogram was developed integrating clinical characteristics with radiomics signature. The effectiveness of these models in predicting uterine sarcoma was thoroughly evaluated. The area under the curve (AUC) was used to compare the predictive efficacy of the different models. Results The AUC of the clinical model was 0.835 (95% confidence interval CI: 0.761–0.883) and 0.791 (95% CI: 0.652–0.869) in the training and testing sets, respectively. The logistic regression model performed best in the radiomics model construction, with AUC values of 0.878 (95% CI: 0.811–0.918) and 0.818 (95% CI: 0.681–0.895) in the training and testing sets, respectively. The clinical radiomics nomogram performed well in differentiation, with AUC values of 0.955 (95% CI: 0.911–0.973) and 0.882 (95% CI: 0.767–0.936) in the training and testing sets, respectively. Conclusions The clinical radiomics nomogram can provide more comprehensive and personalized diagnostic information, which is highly important for selecting treatment strategies and ultimately improving patient outcomes in the management of uterine mesenchymal tumors.