Esophageal Cancer
Conditions
Keywords
Esophageal Cancer; AI; prediction; prognosis
Brief summary
This AI-driven model leverages multimodal data-such as radiomics, pathomics, genomics, and broader multi-omics profiles-to capture complementary aspects of tumor biology and predict treatment response and prognosis.
Detailed description
Built upon retrospective cohorts for model development and rigorously validated in prospective cohorts, the proposed AI predictive model integrates multimodal data (radiomics, pathomics, genomics, and multi-omics)-each reflecting distinct dimensions of tumor heterogeneity-to enable joint prediction of treatment response and clinical outcomes.
Interventions
None listed
Sponsors
Shu Peng
Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
The First Affiliated Hospital of Henan University of Science and Technology
Henan Provincial People's Hospital
Zhongnan Hospital
Renmin Hospital of Wuhan University
Study design
Observational model
COHORT
Time perspective
OTHER
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
Inclusion criteria
1. Histopathologically diagnosed esophageal cancer 2. Complete baseline clinical data available (including demographic characteristics, ECOG performance score, TNM staging, etc.) 3. No other primary malignant tumors 4. Provision of informed consent 5. Availability of pre-treatment CT imaging
Exclusion criteria
1. Imaging data quality insufficient for analysis 2. Presence of another primary malignant tumor 3. Severe systemic disease
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| overall survival | From enrollment to the end of treatment at 3 years | overall survival rate in 3-years |
Countries
China
Contacts
CONTACTShu Peng, Doctor
Outcome results
None listed