Predicting how well gastric cancer patients respond to chemotherapy using AI
A Radio-Pathomic Multimodal Machine Learning Model for Predicting Pathological Complete Response to Neoadjuvant Chemotherapy in Advanced Gastric Cancer: A Retrospective Observational Study
This study tests if using AI to combine imaging and tissue data can help predict how well advanced gastric cancer patients will respond to chemotherapy.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 500 (estimated) |
| Ages | 20 Years to 90 Years |
| Sex | All |
| Sponsor | Sixth Affiliated Hospital, Sun Yat-sen University Academic / other |
| Drugs / interventions | chemotherapy |
| Locations | 1 site (Guangzhou, Guangdong) |
| Trial ID | NCT06451393 on ClinicalTrials.gov |
What this trial studies
This study aims to create a multimodal model that combines radiomic and pathomic features to predict the pathological complete response (pCR) in patients with advanced gastric cancer undergoing neoadjuvant chemotherapy. Researchers will collect pre-treatment CT images and pathological slides, extract relevant features, and utilize machine learning algorithms to develop a predictive model. The goal is to enhance prediction accuracy by integrating both radiomic and pathomic data, and the model will be validated with a separate patient cohort.
Who should consider this trial
Good fit: Ideal candidates include patients with histologically confirmed adenocarcinoma of the stomach or esophagogastric junction who are receiving neoadjuvant chemotherapy and radical gastrectomy.
Not a fit: Patients with insufficient data or those whose tumor lesions cannot be distinguished on CT images or pathological slides will not benefit from this study.
Why it matters
Potential benefit: If successful, this approach could lead to more accurate predictions of treatment responses, allowing for personalized treatment plans for gastric cancer patients.
How similar studies have performed: While the integration of radiomic and pathomic features is a novel approach, similar studies have shown promise in improving predictive models for cancer treatment responses.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: * patients with histologically confirmed adenocarcinoma of the stomach or esophagogastric 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 Solid Tumors Version 1.1 Exclusion Criteria: * Patients with indistinguishable tumor lesions on the CT images due to insufficient filling of the stomach during the CT inspection; * patients without indistinguishable tumor cell on the pathological slides due to inadequate sampling; * patients with insufficient data.
Where this trial is running
Guangzhou, Guangdong
- The Sixth Affiliated Hospital, Sun Yat-sen University — Guangzhou, Guangdong, China (Recruiting)
Study contacts
- Principal investigator: Junsheng Peng, MD — The Sixth Affiliated Hospital, Sun Yat-sen University
- Study coordinator: Yonghe Chen, MD
- Email: chenyhe@mail2.sysu.edu.cn
- Phone: +86 135 6038 6150
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.