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Peer-reviewedCC BY 4.0Frontiers in Oncology

Artificial intelligence in esophageal cancer: a narrative review from a thoracic surgical perspective

  • Dongrong Peng1
  • Wenyi Liu1,2
  • Baihua Zhang1
  1. 1Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
  2. 2Department of Thoracic Surgery, Fujian Medical University Union Hospital
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Abstract

Background: Artificial intelligence (AI) has potential applications across the esophageal cancer (EC) care pathway, but reported retrospective performance often has not translated into clinically validated decision support. We review the evidence through the lens of thoracic surgical decisions, where the consequences of prediction errors are asymmetric.

Scope: This narrative review examines AI studies relevant to thoracic surgical decisions, with an emphasis on esophageal squamous cell carcinoma (ESCC). Selected reports were mapped to clinical tasks, analysis units, and validation settings. Supplementary PubMed and Web of Science searches informed the review; studies were selected purposively to illustrate clinical tasks and validation problems, not to represent the entire field.

Key observations: A randomized endoscopy trial supports improved lesion detection in its study setting. Selected retrospective studies report external testing for nodal risk or pathological response, whereas surgical video and complication models mainly provide internal evaluation. Image-level results, patient-level predictions, and clinical outcomes are distinct. Calibration and prospective pathway evaluation remain necessary before treatment allocation can be based on model output.

Conclusions: AI in EC should presently be considered an investigational decision-support layer rather than a replacement for endoscopic, imaging-based, or multidisciplinary assessment. Translation requires patient-level data partitioning, transparent reporting of calibration and clinical utility, independent external validation, and prospective studies using prespecified decision thresholds.

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Cite

BibTeXRIS
BibTeX
@article{peng2026artificial,
  title = {{Artificial intelligence in esophageal cancer: a narrative review from a thoracic surgical perspective}},
  author = {Dongrong Peng and Wenyi Liu and Baihua Zhang},
  journal = {Frontiers in Oncology},
  year = {2026},
  month = oct,
  doi = {10.3389/fonc.2026.1917622},
  url = {https://www.frontiersin.org/articles/10.3389/fonc.2026.1917622/full},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Artificial intelligence in esophageal cancer: a narrative review from a thoracic surgical perspective
AU  - Dongrong Peng
AU  - Wenyi Liu
AU  - Baihua Zhang
PY  - 2026
DA  - 2026/10/20
JO  - Frontiers in Oncology
DO  - 10.3389/fonc.2026.1917622
UR  - https://www.frontiersin.org/articles/10.3389/fonc.2026.1917622/full
AB  - Background: Artificial intelligence (AI) has potential applications across the esophageal cancer (EC) care pathway, but reported retrospective performance often has not translated into clinically validated decision support. We review the evidence through the lens of thoracic surgical decisions, where the consequences of prediction errors are asymmetric.  Scope: This narrative review examines AI studies relevant to thoracic surgical decisions, with an emphasis on esophageal squamous cell carcinoma (ESCC). Selected reports were mapped to clinical tasks, analysis units, and validation settings. Supplementary PubMed and Web of Science searches informed the review; studies were selected purposively to illustrate clinical tasks and validation problems, not to represent the entire field.  Key observations: A randomized endoscopy trial supports improved lesion detection in its study setting. Selected retrospective studies report external testing for nodal risk or pathological response, whereas surgical video and complication models mainly provide internal evaluation. Image-level results, patient-level predictions, and clinical outcomes are distinct. Calibration and prospective pathway evaluation remain necessary before treatment allocation can be based on model output.  Conclusions: AI in EC should presently be considered an investigational decision-support layer rather than a replacement for endoscopic, imaging-based, or multidisciplinary assessment. Translation requires patient-level data partitioning, transparent reporting of calibration and clinical utility, independent external validation, and prospective studies using prespecified decision thresholds.
SN  - 2234-943X
ER  -