Yin Xiaoyan, Cui Yongbin, Liu Tonghai, Li Zhenjiang, Liu Huiling, Ma Xingmin, Sha Xue, Ma Changsheng, Han Dali, Yin Yong
Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, China.
Department of Radiation Oncology, Affiliated Cancer Hospital, The Third Affiliated Teaching Hospital of Xinjiang Medical University, Urumuqi, China.
BMC Gastroenterol. 2025 Apr 29;25(1):313. doi: 10.1186/s12876-025-03899-8.
This study is aimed to develop and validate a machine learning model, which combined radiomics and clinical characteristics to predicting the definitive chemoradiotherapy (dCRT) treatment response in esophageal squamous cell carcinoma (ESCC) patients.
204 advanced ESCC patients were included who underwent dCRT at our hospital. Patients were randomly divided into training cohort and validation cohort with a ratio of 7:3. The radiomics features were selected by LASSO algorithm. The clinical features were selected by multivariate logistics analysis (p < 0.05). Subsequently, a combined radiomics and clinical model was established and validated to predict the treatment response in ESCC patients by logistic regression model. The performance of the model was evaluated by receiver operating characteristic (ROC) curve, decision curve analysis (DCA), nomogram, and calibration curve.
Total of 944 radiomics features were extracted from the pre-treatment contrasted enhanced CT images (CECT). After feature selection, 3 radiomics features and 3 clinical features were identified as the most predictive variables. The combined model shows better prediction performance among radiomics model or clinical model. The radiomics model's AUC values in training and validation cohort are 0.71,0.69. As for clinical model the AUC values were 0.74,0.75 in training and validation cohort. However, the AUC values in combined model are 0.79, 0.78 in training cohort and validation cohort, respectively. DCA and calibration curve also demonstrated good performance for the combined model.
The radiomics combined clinical features model demonstrates superior treatment response prediction ability for ESCC patients received dCRT. This model has the potential to assist clinicians in identifying non-responsive patients before treatment and guide individualized therapy for advanced ESCC patients.
本研究旨在开发并验证一种机器学习模型，该模型结合放射组学和临床特征来预测食管鳞状细胞癌（ESCC）患者的确定性放化疗（dCRT）治疗反应。
纳入204例在我院接受dCRT的晚期ESCC患者。患者按7:3的比例随机分为训练队列和验证队列。通过LASSO算法选择放射组学特征。通过多因素逻辑分析选择临床特征（p<0.05）。随后，建立并验证了一个结合放射组学和临床的模型，以通过逻辑回归模型预测ESCC患者的治疗反应。通过受试者工作特征（ROC）曲线、决策曲线分析（DCA）、列线图和校准曲线评估模型的性能。
从治疗前对比增强CT图像（CECT）中提取了总共944个放射组学特征。经过特征选择，确定3个放射组学特征和3个临床特征为最具预测性的变量。联合模型在放射组学模型或临床模型中表现出更好的预测性能。放射组学模型在训练队列和验证队列中的AUC值分别为0.71、0.69。临床模型在训练队列和验证队列中的AUC值分别为0.74、0.75。然而，联合模型在训练队列和验证队列中的AUC值分别为0.79、0.78。DCA和校准曲线也证明了联合模型的良好性能。
放射组学联合临床特征模型对接受dCRT的ESCC患者表现出卓越的治疗反应预测能力。该模型有可能帮助临床医生在治疗前识别无反应患者，并指导晚期ESCC患者的个体化治疗。
