AI Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy

Study Overview
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Brief Summary
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This study seeks to develop a deep-learning-based intelligent predictive model for the efficacy of neoadjuvant chemotherapy in gastric cancer patients. By utilizing the patients' CT imaging data, biopsy pathology images, and clinical information, the intelligent model will predict the post-neoadjuvant chemotherapy efficacy and prognosis, offering assistance in personalized treatment decisions for gastric cancer patients.

Detailed Description
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This study seeks to develop a deep learning model to predict the outcomes of neoadjuvant chemotherapy in patients with gastric cancer. Leveraging participants' CT scans, biopsy pathology images, and clinical profiles, this model aims to forecast the effectiveness of post-neoadjuvant chemotherapy and the subsequent prognosis, thereby aiding in individualized treatment choices for these participants. Data Collection: The investigators will gather data from 1,800 retrospective cases and 200 prospective cases from multiple hospitals. The retrospective data will be divided into training and testing sets to train and validate the model, respectively. The model's performance will subsequently be evaluated using the prospective dataset. Clinical Information: This encompasses the participant's gender, age, tumor markers, staging, type, specific treatment plans, pre and post-treatment lab results, etc. Imaging Data: CT imaging data taken within one month prior to the neoadjuvant chemotherapy, with at least the venous phase CT imaging included. Pathology Data: Pathology images from a gastric tumor biopsy stained with Hematoxylin and Eosin (HE) taken within one month prior to treatment. TRG Grading: Based on the pathology report of the surgical samples using the Ryan TRG grading system. Prognostic Endpoints: The recorded endpoints are a 3-year progression-free survival (PFS) and a 5-year overall survival (OS). All deaths due to non-disease factors are excluded from the prognosis analysis.

Official Title
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Deep Learning-Based Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy

Conditions
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Gastric Cancer, Image, Pathology

Intervention / Treatment
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* Drug: Neoadjuvant Chemotherapy


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Age 18 years or older; Pathologically diagnosed with advanced gastric cancer in accordance with the American AJCC's TNM staging standards; Have not undergone any systematic anti-cancer treatments before neoadjuvant chemotherapy and have not had surgery for local progression or distant metastasis; Received standard neoadjuvant chemotherapy as recommended by the clinical guidelines, and have documented treatment details; CT imaging and biopsy pathology images strictly taken within one month prior to starting neoadjuvant treatment; Patients possess comprehensive preoperative clinical information and post-operative TRG grading. Exclusion Criteria: Patients whose CT or pathology images are unclear, making lesion assessment infeasible; Patients diagnosed with other concurrent tumors.

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Gastric Cancer Patients Undergoing Neoadjuvant Chemotherapy<br>This group comprises participants diagnosed with advanced gastric cancer. The participants will be treated with standard neoadjuvant chemotherapy regimens recommended by clinical guidelines. Treatment details, including the generic name of the drugs, dosage form, dosage, frequency, and duration, will be recorded according to the specific regimen. | Drug: Neoadjuvant Chemotherapy<br>* Participants in this group are diagnosed with gastric cancer and are scheduled to undergo neoadjuvant chemotherapy as a part of their treatment regimen. The specific chemotherapy drugs, dosages, and schedules will be determined according to established clinical guidelines and the participant's specific condition.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Area under the receiver operating characteristic curve (AUC) for TRG prediction by the AI model | The AUC will be used to evaluate the performance of the AI model in predicting TRG grading of gastric cancer patients after neoadjuvant chemotherapy. An AUC of 1 indicates perfect prediction, while an AUC of 0.5 indicates prediction no better than chance. | two months | 
| Accuracy of TRG prediction by the AI model | Accuracy measures the proportion of true positive and true negative predictions made by the AI model among all predictions. It indicates the capability of the model to correctly classify patients into their respective TRG gradings. | two months | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Progression-Free Survival (PFS) at 3 years | The duration from the date of patient confirmation to the date of tumor progression or death of the patient, whichever occurs first. | Three years | 
| Overall Survival (OS) at 5 years | The duration from the date of patient confirmation to the date of death of the patient. | Five years | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Gastric Cancer, Neoadjuvant Chemotherapy, Radiomics, Treatment Outcome Prediction, Pathomics, Radiopathomics