Deep Learning Model Predicts Pathological Complete Response of Esophageal Squamous Cell Carcinoma Following Neoadjuvant Immunochemotherapy

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Total Enrollment

300 participants

Study Classification

OBSERVATIONAL

Study Start Date

2025-03-01

Study Completion Date

2026-12-01

Brief Summary

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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.

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Detailed Description

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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

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Esophageal Squamous Cell Carcinoma Neoadjuvant Immunochemotherapy Pathological Complete Response Deep Learning

Study Design

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Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Study Groups

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ESCC Patients Undergoing Neoadjuvant Immunochemotherapy and Surgery

Patients with esophageal squamous cell carcinoma treated with neoadjuvant immunochemotherapy followed by surgery.

The high-throughput extraction of large amounts of quantitative image features from medical images

Intervention Type DIAGNOSTIC_TEST

The high-throughput extraction of large amounts of quantitative image features from medical images

Interventions

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The high-throughput extraction of large amounts of quantitative image features from medical images

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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Inclusion Criteria

1. Pathologically confirmed esophageal squamous cell carcinoma (ESCC).
2. Received at least one cycle of neoadjuvant chemotherapy combined with immunotherapy.
3. Underwent contrast-enhanced chest CT before initiation of neoadjuvant treatment.
4. Underwent contrast-enhanced chest CT after completion of neoadjuvant treatment and prior to surgery.

Exclusion Criteria

1. Diagnosis of other malignancies.
2. Received other anti-tumor therapies before or during neoadjuvant chemo-immunotherapy.
3. Incomplete clinical data.
4. Poor-quality CT imaging.

Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No