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RecruitingNCT07088354

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

Sponsor

Tongji Hospital

Enrollment

300 participants

Start Date

Mar 1, 2025

Study Type

OBSERVATIONAL

Conditions

Esophageal Squamous Cell CarcinomaPathological Complete ResponseDeep LearningNeoadjuvant Immunochemotherapy

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.

Eligibility

Min Age: 18 Years

Inclusion Criteria4

  • 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 Criteria4

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

Interventions

DIAGNOSTIC_TESTThe 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

Locations(1)

Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology

Wuhan, Hubei, China

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NCT07088354

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