Department of Radiation Oncology, National Clinical Research Center for Cancer/Cancer Hospital, National Cancer Center, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli #17, Chaoyang District, Beijing, China.
Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, No.55.Section 4, South Renmin Road, Chengdu, 610042, China.
Cancer Imaging. 2024 Jan 23;24(1):16. doi: 10.1186/s40644-024-00659-x.
More than 40% of patients with resectable esophageal squamous cell cancer (ESCC) achieve pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT), who have favorable prognosis and may benefit from an organ-preservation strategy. Our study aims to develop and validate a machine learning model based on MR radiomics to accurately predict the pCR of ESCC patients after nCRT.
In this retrospective multicenter study, eligible patients with ESCC who underwent baseline MR (T2-weighted imaging) and nCRT plus surgery were enrolled between September 2014 and September 2022 at institution 1 (training set) and between December 2017 and August 2021 at institution 2 (testing set). Models were constructed using machine learning algorithms based on clinical factors and MR radiomics to predict pCR after nCRT. The area under the curve (AUC) and cutoff analysis were used to evaluate model performance.
A total of 155 patients were enrolled in this study, 82 in the training set and 73 in the testing set. The radiomics model was constructed based on two radiomics features, achieving AUCs of 0.968 (95%CI 0.933-0.992) in the training set and 0.885 (95%CI 0.800-0.958) in the testing set. The cutoff analysis resulted in an accuracy of 82.2% (95%CI 72.6-90.4%), a sensitivity of 75.0% (95%CI 58.3-91.7%), and a specificity of 85.7% (95%CI 75.5-96.0%) in the testing set.
A machine learning model based on MR radiomics was developed and validated to accurately predict pCR after nCRT in patients with ESCC.
超过 40%的可切除食管鳞癌（ESCC）患者在新辅助放化疗（nCRT）后达到病理完全缓解（pCR），这些患者预后良好，可能受益于器官保留策略。我们的研究旨在开发和验证一种基于磁共振影像组学的机器学习模型，以准确预测 ESCC 患者 nCRT 后的 pCR。
本回顾性多中心研究纳入了 2014 年 9 月至 2022 年 9 月在机构 1（训练集）接受基线磁共振（T2 加权成像）和 nCRT 加手术的符合条件的 ESCC 患者，以及 2017 年 12 月至 2021 年 8 月在机构 2（测试集）接受 nCRT 加手术的符合条件的 ESCC 患者。使用机器学习算法基于临床因素和磁共振影像组学构建模型，以预测 nCRT 后的 pCR。使用曲线下面积（AUC）和截断分析评估模型性能。
这项研究共纳入了 155 名患者，其中 82 名患者在训练集，73 名患者在测试集。基于两个影像组学特征构建了影像组学模型，在训练集和测试集中的 AUC 分别为 0.968（95%CI 0.933-0.992）和 0.885（95%CI 0.800-0.958）。截断分析得出测试集的准确率为 82.2%（95%CI 72.6-90.4%）、敏感度为 75.0%（95%CI 58.3-91.7%）和特异度为 85.7%（95%CI 75.5-96.0%）。
基于磁共振影像组学的机器学习模型被开发和验证，以准确预测 ESCC 患者 nCRT 后的 pCR。
