Author:
Liu Yunsong,Wang Yi,Wang Xin,Xue Liyan,Zhang Huan,Ma Zeliang,Deng Heping,Yang Zhaoyang,Sun Xujie,Men Yu,Ye Feng,Men Kuo,Qin Jianjun,Bi Nan,Wang Qifeng,Hui Zhouguang
Abstract
Abstract
Background
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.
Methods
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.
Results
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.
Conclusion
A machine learning model based on MR radiomics was developed and validated to accurately predict pCR after nCRT in patients with ESCC.
Funder
Beijing Hope Run Special Fund of Cancer Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Radiology, Nuclear Medicine and imaging,Oncology,General Medicine,Radiological and Ultrasound Technology