AI Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy
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ClinicalTrials.gov Identifier: NCT06035250 |
Recruitment Status :
Recruiting
First Posted : September 13, 2023
Last Update Posted : September 28, 2023
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Condition or disease | Intervention/treatment |
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Gastric Cancer Image Pathology | Drug: Neoadjuvant Chemotherapy |
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.
Study Type : | Observational |
Estimated Enrollment : | 200 participants |
Observational Model: | Cohort |
Time Perspective: | Prospective |
Official Title: | Deep Learning-Based Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy |
Actual Study Start Date : | September 10, 2023 |
Estimated Primary Completion Date : | August 31, 2024 |
Estimated Study Completion Date : | December 31, 2029 |
Group/Cohort | Intervention/treatment |
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Gastric Cancer Patients Undergoing Neoadjuvant Chemotherapy
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.
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Drug: Neoadjuvant Chemotherapy
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. |
- Area under the receiver operating characteristic curve (AUC) for TRG prediction by the AI model [ Time Frame: two months ]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.
- Accuracy of TRG prediction by the AI model [ Time Frame: two months ]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.
- Progression-Free Survival (PFS) at 3 years [ Time Frame: Three years ]The duration from the date of patient confirmation to the date of tumor progression or death of the patient, whichever occurs first.
- Overall Survival (OS) at 5 years [ Time Frame: Five years ]The duration from the date of patient confirmation to the date of death of the patient.
Biospecimen Retention: Samples With DNA
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Ages Eligible for Study: | 18 Years and older (Adult, Older Adult) |
Sexes Eligible for Study: | All |
Accepts Healthy Volunteers: | No |
Sampling Method: | Non-Probability Sample |
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.
To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.
Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT06035250
Contact: Di Dong, Ph.D. | +86 13811833760 | di.dong@ia.ac.cn |
Study Director: | Yali Zang, Ph.D. | Institute of Automation, Chinese Academy of Sciences |
Responsible Party: | Di Dong, Professor, Institute of Automation, Chinese Academy of Sciences |
ClinicalTrials.gov Identifier: | NCT06035250 |
Other Study ID Numbers: |
CASMI004 |
First Posted: | September 13, 2023 Key Record Dates |
Last Update Posted: | September 28, 2023 |
Last Verified: | September 2023 |
Individual Participant Data (IPD) Sharing Statement: | |
Plan to Share IPD: | Yes |
Plan Description: | Individual participant data (IPD) may be made available to other researchers upon request. Interested researchers should present a reasonable research proposal and a data usage application. All participating units of this study will review and assess the proposal and application to determine whether to share the data. |
Supporting Materials: |
Study Protocol Statistical Analysis Plan (SAP) Analytic Code |
Time Frame: | Data will become available 1 year after study completion and will remain available for a period of 5 years. |
Access Criteria: | Interested researchers should submit a detailed research proposal and a data usage application for review. All participating units of this study will assess the application to determine eligibility for data access. |
URL: | http://www.radiomics.net.cn/ |
Studies a U.S. FDA-regulated Drug Product: | No |
Studies a U.S. FDA-regulated Device Product: | No |
Gastric Cancer Neoadjuvant Chemotherapy Radiomics |
Treatment Outcome Prediction Pathomics Radiopathomics |
Stomach Neoplasms Gastrointestinal Neoplasms Digestive System Neoplasms Neoplasms by Site |
Neoplasms Digestive System Diseases Gastrointestinal Diseases Stomach Diseases |