Artificial intelligence in esophageal cancer radiotherapy: a literature review.
- 1Department of Intervention, The Second People's Hospital of Baiyin City, Baiyin, China.
- 2Jiangsu Hengrui Pharmaceuticals Co., Ltd., Shanghai, China.
Abstract
Radiotherapy is a key component in the management of esophageal cancer (EC). Artificial intelligence (AI) has emerged as a transformative tool for optimizing radiotherapy workflows. Numerous AI-based tools are being developed to improve efficiency and facilitate the personalization of radiation oncology. This study aimed to provide an up-to-date review of the literature on recent advances in AI applications across the EC radiotherapy workflow. The PubMed, Embase, and Web of Science databases were searched to retrieve original English-language articles published from January 1, 2020 to May 26, 2026. The following search terms were used: "Esophageal neoplasms", "esophageal cancer", "oesophageal cancer" AND "artificial intelligence", "AI" AND "radiation oncology", "radiotherapy", "radiation", "chemoradiotherapy". The included studies covered different steps in the radiotherapy workflow; that is, initial treatment decision-making, treatment planning and preparation, treatment delivery, and post-treatment follow-up. A total of 105 studies covering the different stages of the radiotherapy workflow were included in the review. Most studies focused on initial treatment decision-making, primarily benefit-risk stratification through predictive modeling of treatment outcomes and treatment-related toxicities, followed by treatment planning and preparation, particularly automated segmentation and treatment planning. Conversely, few studies investigated AI applications in treatment plan approval and quality assurance, or treatment delivery. Notably, most of the included studies were retrospective, with substantial heterogeneity in study designs, methodologies, and reporting. AI demonstrates substantial potential to facilitate clinical decision-making while improving the efficiency and accuracy of radiotherapy workflows. Future research should prioritize multicenter prospective studies, standardized methodological and reporting frameworks, and explainable AI (XAI) to improve the reliability, transparency, and clinical translation of AI models.
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BibTeX
@article{wang2026artificial,
title = {{Artificial intelligence in esophageal cancer radiotherapy: a literature review.}},
author = {Guangcai Wang and Gengshen Bai and Shiwen Huo},
journal = {Journal of thoracic disease},
year = {2026},
month = oct,
doi = {10.21037/jtd-2026-2873},
url = {https://europepmc.org/article/MED/42996985},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Artificial intelligence in esophageal cancer radiotherapy: a literature review.
AU - Guangcai Wang
AU - Gengshen Bai
AU - Shiwen Huo
PY - 2026
DA - 2026/10/27
JO - Journal of thoracic disease
DO - 10.21037/jtd-2026-2873
UR - https://europepmc.org/article/MED/42996985
AB - Radiotherapy is a key component in the management of esophageal cancer (EC). Artificial intelligence (AI) has emerged as a transformative tool for optimizing radiotherapy workflows. Numerous AI-based tools are being developed to improve efficiency and facilitate the personalization of radiation oncology. This study aimed to provide an up-to-date review of the literature on recent advances in AI applications across the EC radiotherapy workflow. The PubMed, Embase, and Web of Science databases were searched to retrieve original English-language articles published from January 1, 2020 to May 26, 2026. The following search terms were used: "Esophageal neoplasms", "esophageal cancer", "oesophageal cancer" AND "artificial intelligence", "AI" AND "radiation oncology", "radiotherapy", "radiation", "chemoradiotherapy". The included studies covered different steps in the radiotherapy workflow; that is, initial treatment decision-making, treatment planning and preparation, treatment delivery, and post-treatment follow-up. A total of 105 studies covering the different stages of the radiotherapy workflow were included in the review. Most studies focused on initial treatment decision-making, primarily benefit-risk stratification through predictive modeling of treatment outcomes and treatment-related toxicities, followed by treatment planning and preparation, particularly automated segmentation and treatment planning. Conversely, few studies investigated AI applications in treatment plan approval and quality assurance, or treatment delivery. Notably, most of the included studies were retrospective, with substantial heterogeneity in study designs, methodologies, and reporting. AI demonstrates substantial potential to facilitate clinical decision-making while improving the efficiency and accuracy of radiotherapy workflows. Future research should prioritize multicenter prospective studies, standardized methodological and reporting frameworks, and explainable AI (XAI) to improve the reliability, transparency, and clinical translation of AI models.
SN - 2072-1439
SN - 2077-6624
ER -