开发一个多机构预测模型，基于放射组学和剂量学特征，估计食管鳞状细胞癌(ESCC)接受放射治疗后的局部反应。局部反应被分为两组(不完全和完全)。提出了一个外部验证模型和一个混合模型，其中两个机构的患者被随机混合。包括2012年至2017年接受放化疗的I-IV期ESCC患者，并且具有超过5年的随访时间。排除接受姑息性或术前放疗且无FDG PET图像的患者。分割包括在治疗计划中使用的GTV、CTV和PTV。此外，还创建了收缩、扩张和外壳区域。从CT、FDG PET图像和剂量分布中提取了放射组学和剂量组学特征。使用决策树、支持向量机、k最近邻(kNN)算法和神经网络(NN)分类器开发了基于机器学习的预测模型。共有116名患者在中心1接受了入组治疗，中心2有26名患者。外部验证模型在基于CT的放射组学方面表现出最高的准确率，为65.4%，基于PET的放射组学方面为77.9%，基于NN分类器的放射组学方面为72.1%。混合模型在基于CT的放射组学方面表现出最高的准确率，为84.4%，基于kNN分类器，基于PET的放射组学方面为86.0%，基于NN分类器的剂量组学方面为79.0%。所提出的混合模型在ESCC患者的局部放射治疗预测中表现出了有希望的预测性能。预测食管癌患者的完全反应可能有助于改善总生存率。混合模型有潜力提高预测性能，超过了传统提出的外部验证模型。•使用放射组学和剂量组学预测接受食管癌放射治疗患者的反应。•基于PET的放射组学的混合模型使用神经网络分类器，提高了8.1%的预测准确率。•混合模型有潜力提高预测性能。©2023年。作者(们)，在欧洲放射学学会的独家许可下。

To develop a multi-institutional prediction model to estimate the local response to oesophageal squamous cell carcinoma (ESCC) treated with definitive radiotherapy based on radiomics and dosiomics features.The local responses were categorised into two groups (incomplete and complete). An external validation model and a hybrid model that the patients from two institutions were mixed randomly were proposed. The ESCC patients at stages I-IV who underwent chemoradiotherapy from 2012 to 2017 and had follow-up duration of more than 5 years were included. The patients who received palliative or pre-operable radiotherapy and had no FDG PET images were excluded. The segmentations included the GTV, CTV, and PTV which are used in treatment planning. In addition, shrinkage, expansion, and shell regions were created. Radiomic and dosiomic features were extracted from CT, FDG PET images, and dose distribution. Machine learning-based prediction models were developed using decision tree, support vector machine, k-nearest neighbour (kNN) algorithm, and neural network (NN) classifiers.A total of 116 and 26 patients enrolled at Centre 1 and Centre 2, respectively. The external validation model exhibited the highest accuracy with 65.4% for CT-based radiomics, 77.9% for PET-based radiomics, and 72.1% for dosiomics based on the NN classifiers. The hybrid model exhibited the highest accuracy of 84.4% for CT-based radiomics based on the kNN classifier, 86.0% for PET-based radiomics, and 79.0% for dosiomics based on the NN classifiers.The proposed hybrid model exhibited promising predictive performance for the local response to definitive radiotherapy in ESCC patients.The prediction of the complete response for oesophageal cancer patients may contribute to improving overall survival. The hybrid model has the potential to improve prediction performance than the external validation model that was conventionally proposed.• Radiomics and dosiomics used to predict response in patients with oesophageal cancer receiving definitive radiotherapy. • Hybrid model with neural network classifier of PET-based radiomics improved prediction accuracy by 8.1%. • The hybrid model has the potential to improve prediction performance.© 2023. The Author(s), under exclusive licence to European Society of Radiology.