Large-scale Models of Esophageal Cancer and Related Research

Condition(s) studied

Investigational drug(s) / intervention(s)

Observational study; no assigned intervention. Participants receive routine esophageal cancer management (endoscopy, imaging, pathology, clinical follow-up) as standard care.: Routine esophageal cancer management including endoscopy, imaging, pathology, and clinical follow-up as per standard clinical practice. No additional, experimental, or assigned intervention is administered. The AI large language model processes de-identified data from routine care for comparative analysis against standard care benchmarks over 3 years.

Study summary

The goal of this observational study is to learn about the clinical utility of an artificial intelligence (AI) large language model in patients undergoing screening, diagnosis, treatment, and prognosis assessment for esophageal cancer. The main question it aims to answer is:

Does the AI model improve early detection rate, diagnostic accuracy, treatment personalization, and prognostic prediction for esophageal cancer compared to standard care? Participants already receiving routine esophageal cancer management (including endoscopy, imaging, pathology, and clinical follow-up) as part of their regular medical care will have their de-identified data processed by the AI model; researchers will compare model-based recommendations and outcomes with standard care benchmarks over 3 years.

Last updated on Oct 31, 2027

Eligibility

Primary outcome measure(s)

• Area under the ROC curve (AUC) of the multimodal model for diagnosing esophageal cancer, calculated by ROC analysis using pathological biopsy as the gold standard, based on 5-fold cross-validation on the internal validation set. — 1 year and 5 months
  The AUC ranges from 0.5 to 1.0, with higher values indicating better diagnostic performance.
• Overall accuracy (proportion of correct classifications) of the multimodal model for diagnosing esophageal cancer, derived from the confusion matrix of the model's predictions on the internal validation set, with pathological biopsy as the gold standard. — Up to 3 years
  The accuracy ranges from 0% to 100%, with higher percentages indicating better classification performance.
• Concordance index (C-index) of the multimodal model for predicting overall survival and progression-free survival, derived from Cox proportional hazards model on time-to-event data. — Up to 3 years
  The C-index ranges from 0.5 to 1.0, where 0.5 indicates random chance and 1.0 indicates perfect prediction; higher values indicate better predictive discrimination.

Trial sites (3)

More The First Affiliated Hospital of Henan University of Science and Technology trials in China

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