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: The high-throughput extraction of large amounts of quantitative image features from medical images
Study 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
Sex
ALL
Min age
18 Years
Max age
—
Healthy volunteers
No
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.
Primary outcome measure(s)
Pathological Complete Response (pCR) Rate — Assessed at the time of surgery, within 1 month post-treatment. The proportion of patients achieving complete pathological remission after neoadjuvant immunochemotherapy followed by surgery.
Trial sites (1)
Facility
City
Region
Status
Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.
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