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.

中文翻译：

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MR放射组学预测食管鳞状细胞癌新辅助放化疗后病理完全缓解：一项多中心研究

超过40%的可切除食管鳞状细胞癌（ESCC）患者在新辅助放化疗（nCRT）后达到病理完全缓解（pCR），预后良好，可能受益于器官保存策略。我们的研究旨在开发和验证基于 MR 放射组学的机器学习模型，以准确预测 ESCC 患者 nCRT 后的 pCR。在这项回顾性多中心研究中，2014 年 9 月至 2022 年 9 月期间在机构 1（训练集）以及 2017 年 12 月至 2021 年 8 月期间在机构 2 纳入了接受基线 MR（T2 加权成像）和 nCRT 加手术的符合条件的 ESCC 患者（测试集）。使用基于临床因素和 MR 放射组学的机器学习算法构建模型来预测 nCRT 后的 pCR。曲线下面积（AUC）和截止分析用于评估模型性能。本研究共有 155 名患者入组，其中训练组 82 名，测试组 73 名。放射组学模型是基于两个放射组学特征构建的，训练集中的 AUC 为 0.968 (95%CI 0.933–0.992)，测试集中的 AUC 为 0.885 (95%CI 0.800-0.958)。截止分析的准确度为 82.2% (95%CI 72.6-90.4%)，敏感性为 75.0% (95%CI 58.3-91.7%)，特异性为 85.7% (95%CI 75.5-96.0%)在测试集中。开发并验证了基于 MR 放射组学的机器学习模型，可准确预测 ESCC 患者 nCRT 后的 pCR。