Purpose: We constructed a prediction model to predict a 2-year locoregional recurrence based on the clinical features and radiomic features extracted from the machine learning method using computed tomography (CT) before definite chemoradiotherapy (dCRT) in locally advanced esophageal cancer.Patients and methods: A total of 264 patients (156 in Beijing, 87 in Tianjin, and 21 in Jiangsu) were included in this study. All those locally advanced esophageal cancer patients received definite radiotherapy and were randomly divided into five subgroups with a similar number and divided into training groups and validation groups by five cross-validations. The esophageal tumor and extratumoral esophagus were segmented to extract radiomic features from the gross tumor volume (GTV) drawn by radiation therapists before radiotherapy, and six clinical features associated with prognosis were added. T stage, N stage, M stage, total TNM stage, GTV, and GTVnd volume were included to construct a prediction model to predict the 2-year locoregional recurrence of patients after definitive radiotherapy.Results: A total of 264 patients were enrolled from August 2012 to April 2018, with a median age of 62 years and 81% were males. The 2-year locoregional recurrence rate was 52.6%, and the 2-year overall survival rate was 45.6%. About 66% of patients received concurrent chemotherapy. In total, we extracted 786 radiomic features from CT images and the Principal Component Analysis (PCA) method was used to screen out the maximum 30 features. Finally, the Support Vector Machine (SVM) method was used to construct the integrated prediction model combining radiomics and clinical features. In the five training groups for predicting locoregional recurrence, the mean value of C-index was 0.9841 (95%CI, 0.9809–0.9873), and in the five validation groups, the mean value was 0.744 (95%CI, 0.7437–0.7443).Conclusions: The integrated radiomics model could predict the 2-year locoregional recurrence after dCRT. The model showed promising results and could help guide treatment decisions by identifying high-risk patients and enabling strategies to prevent early recurrence.
目的：本研究旨在构建一个预测模型，用于预测局部晚期食管癌患者在确定性放化疗（dCRT）前，基于临床特征及通过机器学习方法从计算机断层扫描（CT）图像中提取的影像组学特征的2年局部区域复发风险。 患者与方法：本研究共纳入264例患者（北京156例、天津87例、江苏21例）。所有局部晚期食管癌患者均接受确定性放疗，并按近似数量随机分为五个亚组，通过五折交叉验证划分为训练组和验证组。研究对食管肿瘤及瘤外食管区域进行分割，从放疗前放射治疗师勾画的肿瘤靶区（GTV）中提取影像组学特征，并加入六项与预后相关的临床特征。纳入T分期、N分期、M分期、总TNM分期、GTV及GTVnd体积等参数，构建预测患者放疗后2年局部区域复发的预测模型。 结果：2012年8月至2018年4月期间共入组264例患者，中位年龄62岁，男性占81%。2年局部区域复发率为52.6%，2年总生存率为45.6%。约66%的患者接受了同步化疗。从CT图像中总共提取了786个影像组学特征，采用主成分分析（PCA）方法筛选出最多30个特征。最终使用支持向量机（SVM）方法构建了结合影像组学与临床特征的集成预测模型。在预测局部区域复发的五个训练组中，C-index平均值为0.9841（95%CI，0.9809–0.9873）；在五个验证组中，平均值为0.744（95%CI，0.7437–0.7443）。 结论：集成影像组学模型能够预测dCRT后2年局部区域复发风险。该模型显示出良好的预测效能，有助于通过识别高危患者并制定预防早期复发的策略来指导治疗决策。
肿瘤(癌症)患者之家