Multimodal Al for Precision Diagnosis of Esophageal Cancer

Multimodal Al for Precision Diagnosis of Esophageal Cancer

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.

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.