Ignite Creation Date:
2026-03-26 @ 3:18 PM
Ignite Modification Date:
2026-03-31 @ 7:39 AM
Study NCT ID:
NCT07454967
Status:
NOT_YET_RECRUITING
Last Update Posted:
2026-03-06
First Post:
2026-02-12
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Development of a Multimodal AI System for GIST Management
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2026-03-25'}, 'conditionBrowseModule': {'meshes': [{'id': 'D046152', 'term': 'Gastrointestinal Stromal Tumors'}], 'ancestors': [{'id': 'D009372', 'term': 'Neoplasms, Connective Tissue'}, {'id': 'D018204', 'term': 'Neoplasms, Connective and Soft Tissue'}, {'id': 'D009370', 'term': 'Neoplasms by Histologic Type'}, {'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D005770', 'term': 'Gastrointestinal Neoplasms'}, {'id': 'D004067', 'term': 'Digestive System Neoplasms'}, {'id': 'D004066', 'term': 'Digestive System Diseases'}, {'id': 'D005767', 'term': 'Gastrointestinal Diseases'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 1000}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2026-02-20', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2026-03', 'completionDateStruct': {'date': '2026-12-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2026-03-05', 'studyFirstSubmitDate': '2026-02-12', 'studyFirstSubmitQcDate': '2026-03-05', 'lastUpdatePostDateStruct': {'date': '2026-03-06', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2026-03-06', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-12-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Diagnostic Accuracy of the AI Model for Distinguishing GIST from Non-GIST Tumors', 'timeFrame': 'Up to 30 days post-surgery', 'description': 'The diagnostic accuracy is calculated as the proportion of correctly classified patients (GIST vs. Non-GIST) by the multimodal AI model, compared to the gold standard postoperative pathological diagnosis.'}], 'secondaryOutcomes': [{'measure': 'Concordance Rate between AI-predicted Risk Grade and Pathological Modified NIH Criteria', 'timeFrame': 'Up to 30 days post-surgery', 'description': 'The proportion of patients whose risk category (Very Low/Low vs. Intermediate/High) predicted by the AI model matches the actual risk grade determined by postoperative pathology according to the modified National Institutes of Health (NIH) criteria. This will be reported as a percentage (0-100%)'}, {'measure': 'Sensitivity and Specificity of the AI Model in Predicting KIT/PDGFRA Gene Mutations', 'timeFrame': 'Up to 30 days post-surgery', 'description': "The AI model's performance in identifying specific mutations (e.g., KIT exon 11, PDGFRA) compared to the results of Next-Generation Sequencing (NGS). Data will be reported as percentages with 95% confidence intervals."}, {'measure': 'Area Under the Receiver Operating Characteristic Curve (AUC) for All Tasks', 'timeFrame': 'Up to 30 days post-surgery', 'description': 'The AUC values will be calculated to evaluate the overall performance of the AI model in diagnosis, risk stratification, and genotype prediction. Sensitivity and Specificity will also be reported.'}]}, 'conditionsModule': {'conditions': ['Gastrointestinal Stromal Tumors', 'Gastric Subepithelial Tumors', 'Gastric Leiomyoma', 'Artificial Intelligence (AI)', 'Multimodal Imaging']}, 'descriptionModule': {'briefSummary': 'Background: Gastrointestinal Stromal Tumors (GISTs) are the most common mesenchymal tumors of the gastrointestinal tract. Accurate pre-operative diagnosis, risk stratification, and genotyping are critical for determining the appropriate surgical approach and targeted therapy (such as Imatinib). However, current methods often rely on invasive postoperative pathology and expensive genetic testing.\n\nStudy Objective: The purpose of this study is to develop and validate a multimodal Artificial Intelligence (AI) model that integrates clinical data, CT radiomics (imaging features), and pathomics (digital pathology features) to improve the precision of GIST management.\n\nStudy Design: This is a prospective, observational study. The researchers will recruit patients with suspected gastric submucosal tumors who are scheduled for surgery or biopsy at The Fourth Hospital of Hebei Medical University.\n\nCore Tasks: The AI model will be trained to perform three specific tasks:\n\nDiagnosis: Distinguish GISTs from other non-GIST mesenchymal tumors (e.g., leiomyomas, schwannomas).\n\nRisk Assessment: Stratify GISTs into risk categories (e.g., Low vs. High risk) to predict malignant potential.\n\nGenotyping: Predict specific gene mutations (e.g., KIT or PDGFRA mutations) to guide immunotherapy or targeted therapy.\n\nMethodology: Patient data (CT scans, pathology slides, and clinical history) will be collected and analyzed by the AI system. The AI\'s predictions will be compared against the "Gold Standard" results derived from postoperative pathological examination and Next-Generation Sequencing (NGS). This study is non-interventional; the AI results will not affect the standard of care received by the patients.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Patients presenting with gastric submucosal tumors (SMTs) who are admitted to the Department of Gastrointestinal Surgery at The Fourth Hospital of Hebei Medical University for surgical or endoscopic treatment. The cohort includes patients with subsequently pathologically confirmed GISTs and other mesenchymal tumors (e.g., leiomyoma, schwannoma).', 'eligibilityCriteria': 'Inclusion Criteria:\n\nAge ≥ 18 years, gender not limited.\n\nClinical diagnosis of gastric submucosal tumor (SMT) or suspected gastrointestinal stromal tumor (GIST) based on gastroscopy or ultrasound.\n\nScheduled for surgical resection or endoscopic biopsy at the study center.\n\nStandard preoperative contrast-enhanced CT scans are available (performed within 2 weeks prior to surgery).\n\nPatients or their legal guardians have signed the informed consent form.\n\nExclusion Criteria:\n\nReceived neoadjuvant therapy (e.g., Imatinib, chemotherapy, or radiotherapy) prior to surgery/biopsy.\n\nPoor quality of CT images (e.g., severe motion artifacts) affecting radiomics analysis.\n\nInsufficient tissue samples for pathological diagnosis or genetic testing.\n\nConfirmed diagnosis of other primary malignancies.\n\nIncomplete clinical data or lost to follow-up immediately after surgery.'}, 'identificationModule': {'nctId': 'NCT07454967', 'briefTitle': 'Development of a Multimodal AI System for GIST Management', 'organization': {'class': 'OTHER', 'fullName': 'Hebei Medical University'}, 'officialTitle': 'Development and Validation of a Multimodal Artificial Intelligence Model Integrating CT Radiomics, Pathomics, and Clinical Features for the Diagnosis, Risk Stratification, and Genotype Prediction of Gastrointestinal Stromal Tumors', 'orgStudyIdInfo': {'id': 'GIST-DX-RISK-GEN-01'}}, 'armsInterventionsModule': {'interventions': [{'name': 'Multimodal AI Analysis System', 'type': 'DIAGNOSTIC_TEST', 'otherNames': ['GIST-RadPath-AI Model'], 'description': "The Multimodal AI System utilizes deep learning algorithms to integrate patient data from three sources: preoperative CT images (Radiomics), digitized pathology slides (Pathomics), and clinical characteristics. The model generates probability scores for: 1) Differential diagnosis of GIST vs. non-GIST, 2) Risk stratification, and 3) Genotype prediction.\n\nNote: This is an observational study. The AI model's analysis is performed in parallel to standard clinical care. The results are blinded to the treating physicians and will NOT influence the surgical plan or medical management of the participants."}]}, 'contactsLocationsModule': {'locations': [{'zip': '236003', 'city': 'Fuyang', 'state': 'Anhui', 'country': 'China', 'contacts': [{'name': 'Yanlong Shi', 'role': 'CONTACT', 'email': 'yan_long_shi@163.com', 'phone': '031186095363'}], 'facility': 'The Fifth Affiliated Hospital of Anhui Medical University', 'geoPoint': {'lat': 32.9, 'lon': 115.81667}}, {'zip': '071030', 'city': 'Baoding', 'state': 'Hebei', 'country': 'China', 'contacts': [{'name': 'Xiaolong Li', 'role': 'CONTACT', 'email': 'hh185496959@126.com', 'phone': '031186095363'}], 'facility': 'Baoding Central Hospital', 'geoPoint': {'lat': 38.87288, 'lon': 115.46246}}, {'zip': '061000', 'city': 'Cangzhou', 'state': 'Hebei', 'country': 'China', 'contacts': [{'name': 'Kaixuan Gao', 'role': 'CONTACT', 'email': '790806885@qq.com', 'phone': '031186095363'}], 'facility': "Cangzhou People's Hospital", 'geoPoint': {'lat': 38.31124, 'lon': 116.85334}}, {'zip': '053099', 'city': 'Hengshui', 'state': 'Hebei', 'country': 'China', 'contacts': [{'name': 'Zhenjiang Guo', 'role': 'CONTACT', 'email': 'guo_zhen_jiang123@163.com', 'phone': '031186095363'}], 'facility': "Hengshui People's Hospital", 'geoPoint': {'lat': 37.73908, 'lon': 115.68348}}, {'zip': '050011', 'city': 'Shijiazhuang', 'state': 'Hebei', 'country': 'China', 'contacts': [{'name': 'Ning Meng', 'role': 'CONTACT', 'email': 'buezasessiany@outlook.com', 'phone': '031186095363'}], 'facility': "Shijiazhuang People's Hospital", 'geoPoint': {'lat': 38.04139, 'lon': 114.47861}}, {'zip': '054000', 'city': 'Xingtai', 'state': 'Hebei', 'country': 'China', 'contacts': [{'name': 'Yongli Chen', 'role': 'CONTACT', 'email': 'chen_yong_li888@163.com', 'phone': '031186095363'}], 'facility': 'The Second Affiliated Hospital of Xingtai Medical College', 'geoPoint': {'lat': 37.06217, 'lon': 114.49272}}, {'zip': '430065', 'city': 'Wuhan', 'state': 'Hubei', 'country': 'China', 'contacts': [{'name': 'Lilong Zhang', 'role': 'CONTACT', 'email': 'hb19843362@163.com', 'phone': '031186095363'}], 'facility': 'Renmin Hospital of Wuhan University', 'geoPoint': {'lat': 30.58333, 'lon': 114.26667}}, {'zip': '421001', 'city': 'Hengyang', 'state': 'Hunan', 'country': 'China', 'contacts': [{'name': 'Hong Long', 'role': 'CONTACT', 'email': 'long_hong123@163.com', 'phone': '031186095363'}], 'facility': 'The First Affiliated Hospital of University of South China', 'geoPoint': {'lat': 26.88946, 'lon': 112.61888}}, {'zip': '210002', 'city': 'Nanjing', 'state': 'Jiangsu', 'country': 'China', 'contacts': [{'name': 'Renjun Gu', 'role': 'CONTACT', 'email': 'hb75296661@163.com', 'phone': '031186095363'}], 'facility': 'Jinling Hospital', 'geoPoint': {'lat': 32.06167, 'lon': 118.77778}}], 'centralContacts': [{'name': 'Qun Zhao, PhD', 'role': 'CONTACT', 'email': 'zhaoqun@hebmu.edu.cn', 'phone': '+8631186095363'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'UNDECIDED'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Qun Zhao', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR_INVESTIGATOR', 'investigatorTitle': 'Professor', 'investigatorFullName': 'Qun Zhao', 'investigatorAffiliation': 'Hebei Medical University'}}}}