Predicting Gastric Cancer Response to Chemo With Multimodal AI Model

Last updated: June 8, 2024
Sponsor: Sixth Affiliated Hospital, Sun Yat-sen University
Overall Status: Active - Recruiting

Phase

N/A

Condition

Gastric Cancer

Digestive System Neoplasms

Stomach Cancer

Treatment

Neoadjuvant chemotherapy with radical tumor resection surgery

Clinical Study ID

NCT06451393
E2021088
  • Ages 20-90
  • All Genders

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.

Eligibility Criteria

Inclusion

Inclusion Criteria:

  • patients with histologically confirmed adenocarcinoma of the stomach oresophagogastric junction who received NAC and radical gastrectomy;

  • patients who underwent abdominal multidetector computed tomography (CT) inspection,gastroscope, and tumor tissue biopsy before any intervention started;

  • Lesions that are assessable according to The Response Evaluation Criteria in SolidTumors Version 1.1

Exclusion

Exclusion Criteria:

  • Patients with indistinguishable tumor lesions on the CT images due to insufficientfilling of the stomach during the CT inspection;

  • patients without indistinguishable tumor cell on the pathological slides due toinadequate sampling;

  • patients with insufficient data.

Study Design

Total Participants: 500
Treatment Group(s): 1
Primary Treatment: Neoadjuvant chemotherapy with radical tumor resection surgery
Phase:
Study Start date:
February 01, 2013
Estimated Completion Date:
December 30, 2026

Connect with a study center

  • The Sixth Affiliated Hospital, Sun Yat-sen University

    Guangzhou, Guangdong 510655
    China

    Active - Recruiting

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