Deep Learning Model Predicts Pathological Complete Response of Esophageal Squamous Cell Carcinoma Following Neoadjuvant Immunochemotherapy
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: The high-throughput extraction of large amounts of quantitative image features from medical images.
- Who it may be relevant to
- Registry conditions: Esophageal Squamous Cell Carcinoma, Neoadjuvant Immunochemotherapy, Pathological Complete Response, Deep Learning. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Unsure about the terms? Read our patient guide →
Overview
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.
Interventions
- 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
Primary outcome measures
- Pathological Complete Response (pCR) Rate [Time frame: Assessed at the time of surgery, within 1 month post-treatment.]
Secondary outcome measures (1)
- Model Performance Metrics (AUC, Accuracy, Sensitivity, Specificity, PPV, NPV) [Time frame: At the time of model validation, approximately one year on average after the completion of the research.]
Eligibility criteria
Inclusion criteria
- Pathologically confirmed esophageal squamous cell carcinoma (ESCC).
- Received at least one cycle of neoadjuvant chemotherapy combined with immunotherapy.
- Underwent contrast-enhanced chest CT before initiation of neoadjuvant treatment.
- Underwent contrast-enhanced chest CT after completion of neoadjuvant treatment and prior to surgery.
Exclusion criteria
- Diagnosis of other malignancies.
- Received other anti-tumor therapies before or during neoadjuvant chemo-immunotherapy.
- Incomplete clinical data.
- Poor-quality CT imaging.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
China · 1 center
- Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology — Wuhan
Identifiers
NCT: NCT07088354 · ESRA-01