ORIGINAL RESEARCH article
Front. Oncol.
Sec. Surgical Oncology
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1590448
This article is part of the Research TopicArtificial Intelligence in Clinical Oncology: Enhancements in Tumor ManagementView all articles
Assessing Response in Endoscopy Images of Esophageal Cancer Treated with Total Neoadjuvant Therapy via Hybrid-Architecture Ensemble Deep Learning
Provisionally accepted
- 1Beijing Cancer Hospital, Peking University, Beijing, Beijing Municipality, China
- 2Endoscopy Center, Beijing Cancer Hospital, Peking University, Beijing, Beijing Municipality, China
- 3Peking University, Beijing, China
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Background and Aims: Esophageal cancer (EC) patients may achieve pathological complete response (pCR) after receiving total neoadjuvant therapy (TNT), which allows them to avoid surgery and preserve organs. We aimed to benchmark the performance of existing artificial intelligence (AI) methods and develop a more accurate model for evaluating EC patients' response after TNT.: We built the Beijing-EC-TNT dataset, consisting of 7,359 images from 300 EC patients who underwent TNT at Beijing Cancer Hospital. The dataset was divided into Cohort1 (4,561 images, 209 patients) for cross-validation and Cohort 2 (2,798 images, 91 patients) for external evaluation. Patients and endoscopic images were labeled as either pCR or non-pCR based on postoperative pathology results. We systematically evaluated mainstream AI models and proposed EC-HAENet, a hybridarchitecture ensembled deep learning model.In image-level classification, EC-HAENet achieved an area under the curve of 0.98 in Cohort 1 and 0.99 in Cohort 2. In patient-level classification, the accuracy of EC-HAENet was significantly higher than that of endoscopic biopsy in both Cohorts 1 and 2 (accuracy, 0.93 vs. 0.78, P<0.0001 and 0.93 vs. 0.71, P<0.0001).EC-HAENet can assist endoscopists in accurately evaluating the response of EC patients after TNT.
Keywords: esophageal cancer, Endoscopy, Total neoadjuvant therapy, Pathological complete response, deep learning
Received: 09 Mar 2025; Accepted: 14 Apr 2025.
Copyright: © 2025 Yuan, Liu, He, Dai, Wu, Chen, Wu and Lu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Ke-Neng Chen, Beijing Cancer Hospital, Peking University, Beijing, 100142, Beijing Municipality, China
Qi Wu, Beijing Cancer Hospital, Peking University, Beijing, 100142, Beijing Municipality, China
Yanye Lu, Peking University, Beijing, China
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