Yang Xianyue, Gao Chuanfen, Sun Nian, Qin Xiachuan, Liu Xiaoling, Zhang Chaoxue
Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
Department of Ultrasound, Anhui Provincial Maternity and Child Health Hospital, Hefei, Anhui, China.
Front Oncol. 2024 May 23;14:1353780. doi: 10.3389/fonc.2024.1353780. eCollection 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.
A total of 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 taken as the external validation set. The clinical independent predictors were selected by univariate and multivariate logistic regression analyses. 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 the radiomics model. Next, the ability of the clinical, US-radiomics, and clinical US-radiomics combined model was compared to diagnose stage I CC. Finally, the Shapley additive explanations (SHAP) method was used to explain the contribution of each feature.
Long diameter of the cervical lesion (L) and squamous cell carcinoma-associated antigen (SCCa) were independent clinical predictors of stage I CC. The eXtreme Gradient Boosting (Xgboost) model performed the best among the six ML radiomics models, with area under the curve (AUC) values in the training, internal validation, and external validation sets being 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 sets being 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 non-invasive prediction method may be used for the preoperative identification of patients with stage I CC.
本回顾性研究旨在建立一种基于超声（US）影像组学和临床因素的联合模型，用于术前预测I期宫颈癌（CC）患者。
回顾性分析安徽医科大学第一附属医院经阴道超声（TVS）发现宫颈病变的209例CC患者，将患者分为训练集（n = 146）和内部验证集（n = 63），并将来自安徽省妇幼保健院和南充市中心医院的52例CC患者作为外部验证集。通过单因素和多因素逻辑回归分析选择临床独立预测因素。从US图像中提取US影像组学特征。通过单因素分析、Spearman相关性分析和最小绝对收缩和选择算子（LASSO）算法选择最显著特征后，使用六种机器学习（ML）算法构建影像组学模型。接下来，比较临床、US影像组学和临床-US影像组学联合模型诊断I期CC的能力。最后，使用Shapley加性解释（SHAP）方法解释每个特征的贡献。
宫颈病变长径（L）和鳞状细胞癌相关抗原（SCCa）是I期CC的独立临床预测因素。在六种ML影像组学模型中，极端梯度提升（Xgboost）模型表现最佳，训练集、内部验证集和外部验证集的曲线下面积（AUC）值分别为0.778、0.751和0.751。在最终的三个模型中，基于临床特征和rad-score的联合模型显示出良好的鉴别能力，训练集、内部验证集和外部验证集的AUC值分别为0.837、0.828和0.839。决策曲线分析验证了联合列线图的临床实用性。SHAP算法说明了联合模型中每个特征的贡献。
我们建立了一种可解释的联合模型来预测I期CC。这种非侵入性预测方法可用于I期CC患者的术前识别。
