Ignite Creation Date:
2024-06-16 @ 11:51 AM
Last Modification Date:
2024-10-26 @ 3:31 PM
Study NCT ID:
NCT06451393
Status:
RECRUITING
Last Update Posted:
2024-06-11
First Post:
2024-06-04
Brief Title:
Predicting Gastric Cancer Response to Chemo With Multimodal AI Model
Sponsor:
Sixth Affiliated Hospital Sun Yat-sen University
Organization:
Sixth Affiliated Hospital Sun Yat-sen University
Study Overview
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
Status:
RECRUITING
Status Verified Date:
2024-06
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief 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
Detailed Description:
None
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None