Ignite Creation Date:
2025-12-25 @ 12:59 AM
Ignite Modification Date:
2025-12-25 @ 11:12 PM
Study NCT ID:
NCT07088393
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2025-07-28
First Post:
2025-07-10
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Artificial Intelligence Diagnosis of Different Histopathological Growth Patterns of Colorectal Cancer Liver Metastasis
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'CASE_ONLY'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 437}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'ACTIVE_NOT_RECRUITING', 'startDateStruct': {'date': '2025-07-09', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-07', 'completionDateStruct': {'date': '2025-12-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-07-20', 'studyFirstSubmitDate': '2025-07-10', 'studyFirstSubmitQcDate': '2025-07-20', 'lastUpdatePostDateStruct': {'date': '2025-07-28', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-07-28', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2025-12-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'The accuracy rate of the predictive model for HGP classification', 'timeFrame': 'Half a year', 'description': 'We will build an AI prediction model for HGP prediction and verify the accuracy of the AI-assisted prediction model in classifying HGP.'}], 'secondaryOutcomes': [{'measure': 'The time for the predictive model to perform HGP classification', 'timeFrame': 'Half a year', 'description': 'We will measure the time it takes for the AI-assisted predictive model to classify HGP and compare the difference in interpretation time between the model and pathologists.'}, {'measure': 'Progression-free survival of patients with different HGP classifications', 'timeFrame': 'Through study completion, an average of 1 year', 'description': 'The time from surgery to tumor progression in patients with colorectal cancer liver metastasis of different HGP types'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Liver Metastases of Colorectal Cancer']}, 'descriptionModule': {'briefSummary': 'This study selected cases of colorectal cancer liver metastasis patients who underwent liver metastasis tumor resection, retrieved the pathological HE sections of the metastatic lesions, and constructed a predictive model. AI software was applied to delineate different types of regions, achieving full automation of HGP prediction and constructing a predictive model. Statistical analysis was conducted on the classification of histopathological growth patterns (HGP) of liver metastasis and the survival prognosis of patients, and the differences in prognosis among different HGP classification methods were compared. This provides a new method for judging prognosis and treatment for clinical treatment of colorectal cancer liver metastasis patients.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD', 'ADULT', 'OLDER_ADULT'], 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Patients must meet all inclusion and exclusion criteria. In addition, the patient should be thoroughly informed about the study, including the study visit schedule and required evaluations and all regulatory requirements for informed consent. The written informed consent should be obtained from the patient prior toenrollment. The following criteria apply to all patients enrolled onto the study unless otherwise specified.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Patients with colorectal cancer liver metastases who underwent resection of liver metastases;\n* Confirmed by a pathologist as having liver metastases from colorectal cancer;\n\nExclusion Criteria:\n\n* Cases of colorectal cancer liver metastasis that cannot be classified by histopathology.'}, 'identificationModule': {'nctId': 'NCT07088393', 'briefTitle': 'Artificial Intelligence Diagnosis of Different Histopathological Growth Patterns of Colorectal Cancer Liver Metastasis', 'organization': {'class': 'OTHER', 'fullName': 'Sun Yat-sen University'}, 'officialTitle': 'Artificial Intelligence Diagnosis of Different Histopathological Growth Patterns of Colorectal Cancer Liver Metastasis', 'orgStudyIdInfo': {'id': '2023ZSLYEC-256'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Colorectal cancer liver metastasis cohort', 'description': 'A total of 437 cases of colorectal cancer liver metastasis patients who underwent liver metastasis tumor resection were selected, with a total of 1205 tumor lesions. Pathological HE sections were retrieved and a predictive model was constructed. Among them, 301 cases were in the training set and 106 cases were in the validation set. After constructing the model, it was used to prospectively interpret 30 lesions. The interpretation result of a senior pathologist with a high professional title was taken as the standard to evaluate the accuracy and interpretation time of the model.'}]}, 'contactsLocationsModule': {'locations': [{'zip': '510655', 'city': 'Guangzhou', 'state': 'Guangdong', 'country': 'China', 'facility': 'Sixth Affiliated Hospital, Sun Yat-sen University', 'geoPoint': {'lat': 23.11667, 'lon': 113.25}}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Sun Yat-sen University', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Principal Investigator', 'investigatorFullName': 'Yanhong Deng', 'investigatorAffiliation': 'Sun Yat-sen University'}}}}