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Predicting Gastric Cancer Response to Chemo With Multimodal AI Model

NCT06451393 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 500

Last updated 2024-06-11

No results posted yet for this study

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

Interventions

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).

Sponsors & Collaborators

  • Sixth Affiliated Hospital, Sun Yat-sen University

    lead OTHER

Principal Investigators

  • Junsheng Peng, MD · The Sixth Affiliated Hospital, Sun Yat-sen University

Eligibility

Min Age
20 Years
Max Age
90 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2013-02-01
Primary Completion
2022-09-30
Completion
2026-12-30

Countries

  • China

Study Locations

More Related Trials

Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06451393 on ClinicalTrials.gov