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Predicting Gastric Cancer Response to Chemo With Multimodal AI Model
Sponsor: Sixth Affiliated Hospital, Sun Yat-sen University
Summary
This study aims to develop a multimodal model combining radiomic and pathomic features to predict pathological complete response (pCR) in advanced gastric cancer patients undergoing neoadjuvant chemotherapy (NAC). The researchers intended to collected pre-intervention CT images and pathological slides from patients, extract radiomic and pathomic features, and build a prediction model using machine learning algorithms. The model will be validated using a separate cohort of patients. This research intend to build a radiomic-pathomic model that can outperform models based on either radiomic or pathomic features alone, aiming to improve the prediction of pCR in gastric cancer.
Official title: A Radio-Pathomic Multimodal Machine Learning Model for Predicting Pathological Complete Response to Neoadjuvant Chemotherapy in Advanced Gastric Cancer: A Retrospective Observational Study
Key Details
Gender
All
Age Range
20 Years - 90 Years
Study Type
OBSERVATIONAL
Enrollment
500
Start Date
2013-02-01
Completion Date
2026-12-30
Last Updated
2024-06-11
Healthy Volunteers
No
Conditions
Interventions
Neoadjuvant chemotherapy with radical tumor resection surgery
All patients were pathologically diagnosed as advanced gastric cancer, all receive neoadjuvant chemotherapy, after the completion of neoadjuvant chemotherapy, all patients receive radical tumor resection surgery (partial gastrectomy or total gastrectomy, as proper).
Locations (1)
The Sixth Affiliated Hospital, Sun Yat-sen University
Guangzhou, Guangdong, China