A Machine Learning Method to Predict Pathological Complete Response of Esophageal Cancer after Neoadjuvant Chemoradiotherapy with Clinicohematological Markers and MR Radiomics: A Multi-Center Study

Abstract

Nearly 30% of patients with local advanced esophageal cancer achieved pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT), who may benefit from organ-preservation strategy under accurate prediction of pCR. We aimed to develop and validate machine learning models based on clinicohematological markers and MR radiomics to accurately predict pCR of esophageal cancer after nCRT. In this multi-center study, eligible patients with esophageal cancer who received baseline MR scan (T2-weighted image) 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). Pre-nCRT and post-nCRT blood test results were collected to calculate hematological markers. Models were constructed by machine learning based on clinicohematological markers and MR radiomics to predict pCR. Area under the curve (AUC) and cut-off analysis were used to evaluate model performances. Totally 154 patients (81 in the training set and 73 in the testing set) were enrolled. The combined model integrating pre-nCRT monocyte-to-lymphocyte ratio and 6 radiomics features achieved AUC of 0.800 (95% CI 0.671-0.918) in the testing set, with sensitivity of 79.2% (95% CI 62.5%-95.8%), specificity of 83.7% (95% CI 73.5%-93.9%), positive predictive value of 76.0% (95% CI 62.5%-90.0%), and negative predictive value of 89.6% (95% CI 82.0%-95.8%). A machine learning model based on clinicohematological markers and MR radiomics to predict pCR after nCRT for patients with esophageal cancer was developed and validated, providing a novel tool for personalized treatment. It is necessary to further validate in more large datasets.