Actively Recruiting
Deep Learning Model Predicts Pathological Complete Response of Esophageal Squamous Cell Carcinoma Following Neoadjuvant Immunochemotherapy
Led by Tongji Hospital · Updated on 2025-07-28
300
Participants Needed
1
Research Sites
26 weeks
Total Duration
On this page
AI-Summary
What this Trial Is About
Researchers are evaluating a deep learning model to predict pathological complete response (pCR) in patients with esophageal squamous cell carcinoma (ESCC) who have undergone neoadjuvant immunochemotherapy. This study collects clinical, imaging, and pathological data from past patients to develop and validate a model that may help forecast treatment outcomes and guide personalized therapy decisions. The study is retrospective and multicenter, focusing on patients treated between January 2019 and July 2025. The study involves collecting chest CT images and clinical data from ESCC patients who received neoadjuvant immunochemotherapy followed by surgery. Deep learning techniques will extract quantitative features from these CT images to build the predictive model. The model's accuracy will be evaluated using metrics such as area under the curve (AUC), sensitivity, specificity, positive predictive value, and negative predictive value. An additional analysis called SHapley Additive exPlanations (SHAP) will assess how CT imaging features contribute to the model's predictions. Participants' clinical and imaging data before and after neoadjuvant treatment will be analyzed, focusing on CT scans taken before treatment and after completion but before surgery. The main outcome measure is the rate of pathological complete response assessed at surgery within one month post-treatment. The model's performance will be validated about a year after the study completion. The study duration extends through late 2026, and no direct treatment is given during the study since it is observational.
CONDITIONS
Brief Title
Deep Learning Model Predicts Pathological Complete Response of Esophageal Squamous Cell Carcinoma Following Neoadjuvant Immunochemotherapy
Who Can Participate
Eligibility Criteria
You may qualify if you...
- Pathologically confirmed esophageal squamous cell carcinoma (ESCC).
- Received at least one cycle of neoadjuvant chemotherapy combined with immunotherapy.
- Underwent contrast-enhanced chest CT before starting neoadjuvant treatment.
- Underwent contrast-enhanced chest CT after completing neoadjuvant treatment and before surgery.
You will not qualify if you...
- Diagnosis of other malignancies.
- Received other anti-tumor therapies before or during neoadjuvant chemo-immunotherapy.
- Incomplete clinical data.
- Poor-quality CT imaging.
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
Your Study Journey
Duration - 2 to 4 weeks
Participants are screened for eligibility to participate in the trial.
1 visit (in-person) for eligibility assessment including clinical data and initial CT imaging
Duration - Varies per participant's treatment schedule
Participants undergo neoadjuvant chemotherapy combined with immunotherapy along with pre- and post-treatment contrast-enhanced chest CT scans for imaging feature extraction.
CT scans before starting therapy and after completing neoadjuvant treatment, prior to surgery
Duration - Within 1 month after completing neoadjuvant treatment
Participants undergo surgery following neoadjuvant immunochemotherapy to remove esophageal squamous cell carcinoma.
1 surgical procedure visit
Duration - Up to approximately 1 year after surgery
Collected CT images and clinical data are used to develop and validate a deep learning model to predict pathological complete response.
No participant visits; retrospective data analysis
Trial Site Locations
Total: 1 location
1
Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
Wuhan, Hubei, China, 430030
Actively Recruiting
Research Team
Y
Yangkai Li, MD, PhD
L
Lin Zhou, MSc
How is the study designed?
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
1
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