An interpretable clinical ultrasound-radiomics combined model for diagnosis of stage I cervical cancer - Yang, Xianyue; Gao, Chuanfen; Sun, Nian; Qin, Xiachuan; Liu, Xiaoling; Zhang, Chaoxue (Frontiers in Oncology, 2024)
The purpose of this retrospective study was to establish a combined model based on ultrasound (US)-radiomics and clinical factors to predict patients with stage I cervical cancer (CC) before surgery.209 CC patients who had cervical lesions found by transvaginal sonography (TVS) from the First Affiliated Hospital of Anhui Medical University were retrospectively reviewed, patients were divided into the training set (n=146) and internal validation set (n=63), and 52 CC patients from Anhui Provincial Maternity and Child Health Hospital and Nanchong Central Hospital were as the external validation set. The clinical independent predictors were selected by the univariate and multivariate logistic regression analysis. US-radiomics features were extracted from US images. After selecting the most significant features by univariate analysis, spearman's correlation analysis, and the least absolute shrinkage and selection operator (LASSO) algorithm, six machine learning (ML) algorithms were used to build radiomics model. Next, we compared the ability of the clinical, USradiomics, and clinical US-radiomics combined model to diagnose stage I CC. Finally, the Shapley additive interpretation (SHAP) method was used to explain the contribution of each feature. Results Long diameter of the cervical lesion (L) and squamous cell carcinoma-associated antigen (SCCa) were independent clinical predictors of stage I CC. The Xgboost model performed the best among the six ML radiomics models, with AUC values in the training, internal validation and external validation set were 0.778, 0.751 and 0.751, respectively. In the final three models, the combined model based on clinical features and rad-score showed good discriminative power, with AUC values in the training, internal validation and external validation set were 0.837, 0.828 and 0.839, respectively. The decision curve analysis validated the clinical utility of the combined nomogram.The SHAP algorithm illustrates the contribution of each feature in the combined model.We established an interpretable combined model to predict stage I CC. This noninvasive prediction method may be used for the preoperative identification of patients with stage I CC.