NCT06451393

Predicting Gastric Cancer Response to Chemo With Multimodal AI Model

A Radio-Pathomic Multimodal Machine Learning Model for Predicting Pathological Complete Response to Neoadjuvant Chemotherapy in Advanced Gastric Cancer: A Retrospective Observational Study

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 500 (estimated)
• Sponsor: Sixth Affiliated Hospital, Sun Yat-sen University · Academic / Other
• Sex: All
• Age: 20 Years – 90 Years
• Healthy volunteers: Not accepted

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.

Conditions

• Gastric Cancer
• Chemotherapy Effect

Interventions

Type	Name	Description
DRUG	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).

Timeline

• Start date: 2013-02-01
• Primary completion: 2022-09-30
• Completion: 2026-12-30
• First posted: 2024-06-11
• Last updated: 2024-06-11

Locations

1 site across 1 country: China