Multimodal AI for Precision Diagnosis of Esophageal Cancer

Fudan University

Status

Active, not recruiting

Conditions

Esophageal Cancer

Treatments

Other: surgery

Other: ESD

Other: NAT

Study type

Observational

Funder types

Other

Identifiers

NCT07629921

B2025-145(2)

Details and patient eligibility

About

This study intends to construct two multimodal deep learning models: one for the diagnosis of esophageal cancer and the prediction of invasive depth to assess suitability for endoscopic resection; the other model, based on this, classifies endoscopic non-resectable patients into different degrees of invasion to further explore the differences in the sensitivity and survival of AI-predicted benign and malignant tumors in patients' responses to NAT, thereby providing reliable decision support for precise individualized treatment. This aspect has rarely been addressed in previous studies.

Enrollment

264 patients

Sex

All

Ages

18 to 80 years old

Volunteers

No Healthy Volunteers

Inclusion criteria

Age ≥18 years.
Histologically confirmed or clinically suspected esophageal squamous cell carcinoma (ESCC).
Availability of pre-treatment endoscopic images and contrast-enhanced chest/upper abdominal CT scans.
Availability of complete clinical and pathological data.
Patients who underwent endoscopic resection (ESD/EMR) or esophagectomy with pathological assessment of tumor invasion depth.
Adequate image quality for analysis.
Written informed consent (for prospective cohorts, if applicable).

Exclusion criteria

Histology other than squamous cell carcinoma.
Prior treatment for esophageal cancer before baseline imaging, including chemotherapy, radiotherapy, immunotherapy, or endoscopic resection.
Distant metastatic disease at diagnosis.
Incomplete clinical, imaging, or pathological data.
Poor-quality CT or endoscopic images unsuitable for analysis.
History of another active malignancy within the past 5 years.
Recurrent esophageal cancer.