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…
- 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
You may not qualify if…
- 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 it is running
- The Sixth Affiliated Hospital, Sun Yat-sen University — Guangzhou, Guangdong, China (enrolling)
Full record on ClinicalTrials.gov
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