Trials / Recruiting
RecruitingNCT07088354
Deep Learning Model Predicts Pathological Complete Response of Esophageal Squamous Cell Carcinoma Following Neoadjuvant Immunochemotherapy
- Status
- Recruiting
- Phase
- —
- Study type
- Observational
- Enrollment
- 300 (estimated)
- Sponsor
- Tongji Hospital · Academic / Other
- Sex
- All
- Age
- 18 Years
- Healthy volunteers
- Not accepted
Summary
This study aims to develop and validate a deep learning model to predict pathological complete response (pCR) in patients with esophageal squamous cell carcinoma who have undergone neoadjuvant immunochemotherapy. Clinical, imaging, and pathological data from previously treated patients will be collected and analyzed. The model is expected to assist in predicting treatment outcomes and guide personalized therapeutic strategies.
Detailed description
This multicenter retrospective study will collect chest CT images and clinical data from patients with esophageal squamous cell carcinoma (ESCC) who underwent surgery following neoadjuvant immunochemotherapy between January 2019 and July 2025. Deep learning features will be extracted from the CT images to develop a predictive model of pathological complete response (pCR). The model's performance will be evaluated using metrics including the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Additionally, SHapley Additive exPlanations (SHAP) analysis will be employed to quantify the contribution of CT imaging features to the model's predictions. This study aims to improve early identification of responders to neoadjuvant immunochemotherapy and support personalized treatment strategies for ESCC patients.
Conditions
- Esophageal Squamous Cell Carcinoma
- Neoadjuvant Immunochemotherapy
- Pathological Complete Response
- Deep Learning
Interventions
| Type | Name | Description |
|---|---|---|
| DIAGNOSTIC_TEST | The high-throughput extraction of large amounts of quantitative image features from medical images | The high-throughput extraction of large amounts of quantitative image features from medical images |
Timeline
- Start date
- 2025-03-01
- Primary completion
- 2026-06-01
- Completion
- 2026-12-01
- First posted
- 2025-07-28
- Last updated
- 2025-07-28
Locations
1 site across 1 country: China
Source: ClinicalTrials.gov record NCT07088354. Inclusion in this directory is not an endorsement.