Multimodal Deep Learning for Predicting Treatment Response to Neoadjuvant Chemoimmunotherapy in Esophageal Cancer
This is not medical advice. AI-assisted translation — inaccuracies may occur. Always verify the original and consult your oncologist before taking any steps.
About the trial
This observational study aims to investigate a clinical cohort of patients with locally advanced esophageal cancer undergoing neoadjuvant chemoimmunotherapy. By integrating multimodal clinical data-including demographic characteristics, medical history, imaging studies, pathological findings, and laboratory tests-and employing deep learning algorithms, the study seeks to develop predictive models for the early and accurate assessment of treatment response prior to surgery. Specifically, this study focuses on addressing the following key scientific questions: 1. Can multimodal clinical data be used to construct an accurate model for predicting pathological complete response (pCR) following neoadjuvant therapy? 2. Can deep learning models enable early identification of patients with suboptimal response to neoadjuvant therapy, defined as stable disease (SD) or progressive disease (PD), before surgery?
Original English text from ClinicalTrials.gov
Who can (and can't) join
✓ Qualifies
- •Rak przełyku potwierdzony badaniem wycinka.
- •Pacjenci, którym zalecono leczenie przedoperacyjne (chemioterapia i immunoterapia).
- •Pacjenci, którzy otrzymali takie leczenie.
- •Pacjenci z kompletnymi wynikami badań obrazowych przed i po leczeniu.
✗ Disqualifies
- •Pacjenci, którzy odmówili operacji, mimo że kwalifikowali się do niej.
- •Pacjenci z brakującymi lub złej jakości zdjęciami z tomografii komputerowej.
- •Pacjenci z innymi nowotworami poza rakiem przełyku.
- •Pacjenci z niekompletnymi danymi klinicznymi.
Simplified criteria — AI translation
Trial details
- Last updated (source)
- July 7, 2026
- Sex
- No restrictions
Locations (1)
The Second Xiangya Hospital of Central South University
Changsha, China
Trial contact
Contact information from ClinicalTrials.gov. Contact in English.
Share this trial
Data from ClinicalTrials.gov. AI-assisted translation, last sync: 7/8/2026.
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