Predicting Gastric Cancer Response to Chemo With Multimodal AI Model

Recruiting now

Conditions studied: Gastric Cancer, Chemotherapy Effect

In brief

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.

Key facts

Study ID
NCT06451393
Run by
Sixth Affiliated Hospital, Sun Yat-sen University
People needed
500
Starts
2013-02-01
Expected to finish
2026-12-30
Last updated by the study team
2024-06-11

Who can join

Age: 20 and older, up to 90. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

You may not qualify if…

Where it is running

Full record on ClinicalTrials.gov

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