Ignite Creation Date:
2025-12-24 @ 5:24 PM
Ignite Modification Date:
2025-12-28 @ 5:02 AM
Study NCT ID:
NCT06035250
Status:
RECRUITING
Last Update Posted:
2023-09-28
First Post:
2023-08-13
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
AI Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D013274', 'term': 'Stomach Neoplasms'}], 'ancestors': [{'id': 'D005770', 'term': 'Gastrointestinal Neoplasms'}, {'id': 'D004067', 'term': 'Digestive System Neoplasms'}, {'id': 'D009371', 'term': 'Neoplasms by Site'}, {'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D004066', 'term': 'Digestive System Diseases'}, {'id': 'D005767', 'term': 'Gastrointestinal Diseases'}, {'id': 'D013272', 'term': 'Stomach Diseases'}]}, 'interventionBrowseModule': {'meshes': [{'id': 'D020360', 'term': 'Neoadjuvant Therapy'}], 'ancestors': [{'id': 'D003131', 'term': 'Combined Modality Therapy'}, {'id': 'D013812', 'term': 'Therapeutics'}]}}, 'protocolSection': {'designModule': {'bioSpec': {'retention': 'SAMPLES_WITH_DNA', 'description': 'The biospecimens consist of gastric tumor biopsy samples, collected from each patient prior to the initiation of neoadjuvant chemotherapy. These specimens undergo HE (Hematoxylin and Eosin) staining for pathology imaging.'}, 'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 200}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2023-09-10', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2023-09', 'completionDateStruct': {'date': '2029-12-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2023-09-26', 'studyFirstSubmitDate': '2023-08-13', 'studyFirstSubmitQcDate': '2023-09-06', 'lastUpdatePostDateStruct': {'date': '2023-09-28', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2023-09-13', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2024-08-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Area under the receiver operating characteristic curve (AUC) for TRG prediction by the AI model', 'timeFrame': 'two months', 'description': '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.'}, {'measure': 'Accuracy of TRG prediction by the AI model', 'timeFrame': 'two months', 'description': '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.'}], 'secondaryOutcomes': [{'measure': 'Progression-Free Survival (PFS) at 3 years', 'timeFrame': 'Three years', 'description': 'The duration from the date of patient confirmation to the date of tumor progression or death of the patient, whichever occurs first.'}, {'measure': 'Overall Survival (OS) at 5 years', 'timeFrame': 'Five years', 'description': 'The duration from the date of patient confirmation to the date of death of the patient.'}]}, 'oversightModule': {'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Gastric Cancer', 'Neoadjuvant Chemotherapy', 'Radiomics', 'Treatment Outcome Prediction', 'Pathomics', 'Radiopathomics'], 'conditions': ['Gastric Cancer', 'Image', 'Pathology']}, 'descriptionModule': {'briefSummary': "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.", 'detailedDescription': "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.\n\nData 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.\n\nClinical Information: This encompasses the participant's gender, age, tumor markers, staging, type, specific treatment plans, pre and post-treatment lab results, etc.\n\nImaging Data: CT imaging data taken within one month prior to the neoadjuvant chemotherapy, with at least the venous phase CT imaging included.\n\nPathology Data: Pathology images from a gastric tumor biopsy stained with Hematoxylin and Eosin (HE) taken within one month prior to treatment.\n\nTRG Grading: Based on the pathology report of the surgical samples using the Ryan TRG grading system.\n\nPrognostic 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."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'The study population comprises gastric cancer patients from various hospitals. Participants are individuals diagnosed with advanced gastric cancer and are currently undergoing neoadjuvant chemotherapy treatments. Selection is based on criteria such as age, specific diagnosis, past treatment history, and the clarity of their medical images and pathology images.', 'healthyVolunteers': False, 'eligibilityCriteria': "Inclusion Criteria:\n\n* Age 18 years or older;\n* Pathologically diagnosed with advanced gastric cancer in accordance with the American AJCC's TNM staging standards;\n* Have not undergone any systematic anti-cancer treatments before neoadjuvant chemotherapy and have not had surgery for local progression or distant metastasis;\n* Received standard neoadjuvant chemotherapy as recommended by the clinical guidelines, and have documented treatment details;\n* CT imaging and biopsy pathology images strictly taken within one month prior to starting neoadjuvant treatment;\n* Patients possess comprehensive preoperative clinical information and post-operative TRG grading.\n\nExclusion Criteria:\n\n* Patients whose CT or pathology images are unclear, making lesion assessment infeasible;\n* Patients diagnosed with other concurrent tumors."}, 'identificationModule': {'nctId': 'NCT06035250', 'briefTitle': 'AI Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy', 'organization': {'class': 'OTHER_GOV', 'fullName': 'Chinese Academy of Sciences'}, 'officialTitle': 'Deep Learning-Based Prediction of Gastric Cancer Response to Neoadjuvant Chemotherapy', 'orgStudyIdInfo': {'id': 'CASMI004'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Gastric Cancer Patients Undergoing Neoadjuvant Chemotherapy', 'description': '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.', 'interventionNames': ['Drug: Neoadjuvant Chemotherapy']}], 'interventions': [{'name': 'Neoadjuvant Chemotherapy', 'type': 'DRUG', 'description': "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.", 'armGroupLabels': ['Gastric Cancer Patients Undergoing Neoadjuvant Chemotherapy']}]}, 'contactsLocationsModule': {'locations': [{'city': 'Beijing', 'status': 'NOT_YET_RECRUITING', 'country': 'China', 'contacts': [{'name': 'Xinming Zhao', 'role': 'CONTACT'}], 'facility': 'Cancer Institute and Hospital, Chinese Academy of Medical Sciences', 'geoPoint': {'lat': 39.9075, 'lon': 116.39723}}, {'city': 'Beijing', 'status': 'NOT_YET_RECRUITING', 'country': 'China', 'contacts': [{'name': 'Zhenyu Jin', 'role': 'CONTACT'}], 'facility': 'Peking Union Medical College Hospital', 'geoPoint': {'lat': 39.9075, 'lon': 116.39723}}, {'city': 'Beijing', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Lei Tang', 'role': 'CONTACT'}], 'facility': 'Peking University Cancer Hospital & Institute', 'geoPoint': {'lat': 39.9075, 'lon': 116.39723}}, {'city': 'Beijing', 'status': 'NOT_YET_RECRUITING', 'country': 'China', 'contacts': [{'name': 'Yi Wang', 'role': 'CONTACT'}], 'facility': "Peking University People's Hospital", 'geoPoint': {'lat': 39.9075, 'lon': 116.39723}}, {'city': 'Changsha', 'status': 'NOT_YET_RECRUITING', 'country': 'China', 'contacts': [{'name': 'Weihua Liao', 'role': 'CONTACT'}], 'facility': 'Xiangya Hospital of Central South University', 'geoPoint': {'lat': 28.19874, 'lon': 112.97087}}, {'city': 'Fuzhou', 'status': 'NOT_YET_RECRUITING', 'country': 'China', 'contacts': [{'name': 'Yangming Li', 'role': 'CONTACT'}], 'facility': 'Fujian Cancer Hospital', 'geoPoint': {'lat': 26.06139, 'lon': 119.30611}}, {'city': 'Fuzhou', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Changming Huang', 'role': 'CONTACT'}], 'facility': 'Fujian Medical University Union Hospital', 'geoPoint': {'lat': 26.06139, 'lon': 119.30611}}, {'city': 'Guangzhou', 'status': 'NOT_YET_RECRUITING', 'country': 'China', 'contacts': [{'name': 'Shuzhong Cui', 'role': 'CONTACT'}], 'facility': 'Affiliated Cancer Hospital & Institute of Guangzhou Medical University', 'geoPoint': {'lat': 23.11667, 'lon': 113.25}}, {'city': 'Guangzhou', 'status': 'NOT_YET_RECRUITING', 'country': 'China', 'contacts': [{'name': 'Shenping Yu', 'role': 'CONTACT'}], 'facility': 'First Affiliated Hospital, Sun Yat-Sen University', 'geoPoint': {'lat': 23.11667, 'lon': 113.25}}, {'city': 'Guangzhou', 'status': 'NOT_YET_RECRUITING', 'country': 'China', 'contacts': [{'name': 'Guoxin Li', 'role': 'CONTACT'}], 'facility': 'Nanfang Hospital of Southern Medical University', 'geoPoint': {'lat': 23.11667, 'lon': 113.25}}, {'city': 'Guangzhou', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Xiaochun Meng', 'role': 'CONTACT'}], 'facility': 'Sixth Affiliated Hospital, Sun Yat-sen University', 'geoPoint': {'lat': 23.11667, 'lon': 113.25}}, {'city': 'Kunming', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Zhenhui Li', 'role': 'CONTACT'}], 'facility': 'Yunnan Cancer Hospital', 'geoPoint': {'lat': 25.03889, 'lon': 102.71833}}, {'city': 'Nanning', 'status': 'NOT_YET_RECRUITING', 'country': 'China', 'contacts': [{'name': 'Guanqiao Jin', 'role': 'CONTACT'}], 'facility': 'Cancer Hospital of Guangxi Medical University', 'geoPoint': {'lat': 22.81667, 'lon': 108.31667}}, {'city': 'Qingdao', 'status': 'NOT_YET_RECRUITING', 'country': 'China', 'contacts': [{'name': 'Hexiang Wang', 'role': 'CONTACT'}], 'facility': 'The Affiliated Hospital of Qingdao University', 'geoPoint': {'lat': 36.06488, 'lon': 120.38042}}, {'city': 'Shanghai', 'status': 'NOT_YET_RECRUITING', 'country': 'China', 'contacts': [{'name': 'Jun Zhang', 'role': 'CONTACT'}], 'facility': 'Ruijin Hospital', 'geoPoint': {'lat': 31.22222, 'lon': 121.45806}}, {'city': 'Shenyang', 'status': 'NOT_YET_RECRUITING', 'country': 'China', 'contacts': [{'name': 'Zhenning Wang', 'role': 'CONTACT'}], 'facility': 'First Hospital of China Medical University', 'geoPoint': {'lat': 41.79222, 'lon': 123.43278}}, {'city': 'Suzhou', 'status': 'NOT_YET_RECRUITING', 'country': 'China', 'contacts': [{'name': 'Jie Bao', 'role': 'CONTACT'}], 'facility': 'The First Affiliated Hospital of Soochow University', 'geoPoint': {'lat': 31.30408, 'lon': 120.59538}}, {'city': 'Tianjin', 'status': 'NOT_YET_RECRUITING', 'country': 'China', 'contacts': [{'name': 'Zhaoxiang Ye', 'role': 'CONTACT'}], 'facility': 'Tianjin Medical University Cancer Institute and Hospital', 'geoPoint': {'lat': 39.14222, 'lon': 117.17667}}, {'city': 'Zhengzhou', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Jing Li', 'role': 'CONTACT'}], 'facility': 'Henan Cancer Hospital', 'geoPoint': {'lat': 34.75778, 'lon': 113.64861}}, {'city': 'Zhengzhou', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Jianbo Gao', 'role': 'CONTACT'}], 'facility': 'The First Affiliated Hospital of Zhengzhou University', 'geoPoint': {'lat': 34.75778, 'lon': 113.64861}}, {'city': 'Zhenjiang', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Xiuhong Shan', 'role': 'CONTACT'}], 'facility': "Zhenjiang First People's Hospital", 'geoPoint': {'lat': 32.21086, 'lon': 119.45508}}, {'city': 'Milan', 'status': 'RECRUITING', 'country': 'Italy', 'contacts': [{'name': 'Francesco De Cobelli', 'role': 'CONTACT'}], 'facility': 'San Raffaele University Hospital, Italy', 'geoPoint': {'lat': 45.46427, 'lon': 9.18951}}], 'centralContacts': [{'name': 'Di Dong, Ph.D.', 'role': 'CONTACT', 'email': 'di.dong@ia.ac.cn', 'phone': '+86 13811833760'}], 'overallOfficials': [{'name': 'Yali Zang, Ph.D.', 'role': 'STUDY_DIRECTOR', 'affiliation': 'Institute of Automation, Chinese Academy of Sciences'}]}, 'ipdSharingStatementModule': {'url': 'http://www.radiomics.net.cn/', 'infoTypes': ['STUDY_PROTOCOL', 'SAP', 'ANALYTIC_CODE'], 'timeFrame': 'Data will become available 1 year after study completion and will remain available for a period of 5 years.', 'ipdSharing': 'YES', '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.', 'accessCriteria': '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.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Chinese Academy of Sciences', 'class': 'OTHER_GOV'}, 'collaborators': [{'name': 'Peking University Cancer Hospital & Institute', 'class': 'OTHER'}, {'name': 'Cancer Institute and Hospital, Chinese Academy of Medical Sciences', 'class': 'OTHER'}, {'name': 'Yunnan Cancer Hospital', 'class': 'OTHER'}, {'name': 'Henan Cancer Hospital', 'class': 'OTHER_GOV'}, {'name': "Zhenjiang First People's Hospital", 'class': 'OTHER'}, {'name': 'First Hospital of China Medical University', 'class': 'OTHER'}, {'name': 'Cancer Hospital of Guangxi Medical University', 'class': 'OTHER'}, {'name': "Peking University People's Hospital", 'class': 'OTHER'}, {'name': 'Tianjin Medical University Cancer Institute and Hospital', 'class': 'OTHER'}, {'name': 'The First Affiliated Hospital of Zhengzhou University', 'class': 'OTHER'}, {'name': 'Nanfang Hospital, Southern Medical University', 'class': 'OTHER'}, {'name': 'The Affiliated Hospital of Qingdao University', 'class': 'OTHER'}, {'name': 'Ruijin Hospital', 'class': 'OTHER'}, {'name': 'Sixth Affiliated Hospital, Sun Yat-sen University', 'class': 'OTHER'}, {'name': 'Peking Union Medical College Hospital', 'class': 'OTHER'}, {'name': 'Xiangya Hospital of Central South University', 'class': 'OTHER'}, {'name': 'Affiliated Cancer Hospital & Institute of Guangzhou Medical University', 'class': 'OTHER'}, {'name': 'The First Affiliated Hospital of Soochow University', 'class': 'OTHER'}, {'name': 'First Affiliated Hospital, Sun Yat-Sen University', 'class': 'OTHER'}, {'name': 'Fujian Medical University Union Hospital', 'class': 'OTHER'}, {'name': 'Fujian Cancer Hospital', 'class': 'OTHER_GOV'}, {'name': 'San Raffaele University Hospital, Italy', 'class': 'OTHER'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Professor', 'investigatorFullName': 'Di Dong', 'investigatorAffiliation': 'Institute of Automation, Chinese Academy of Sciences'}}}}