Ignite Creation Date:
2025-12-25 @ 12:36 AM
Ignite Modification Date:
2025-12-25 @ 10:45 PM
Study NCT ID:
NCT07063667
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-07-14
First Post:
2025-05-25
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Artificial Intelligence Model-Assisted Accurate Diagnosis of Early-Stage Breast Cancer
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D001943', 'term': 'Breast Neoplasms'}, {'id': 'D009362', 'term': 'Neoplasm Metastasis'}], 'ancestors': [{'id': 'D009371', 'term': 'Neoplasms by Site'}, {'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D001941', 'term': 'Breast Diseases'}, {'id': 'D012871', 'term': 'Skin Diseases'}, {'id': 'D017437', 'term': 'Skin and Connective Tissue Diseases'}, {'id': 'D009385', 'term': 'Neoplastic Processes'}, {'id': 'D010335', 'term': 'Pathologic Processes'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 900}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2025-08-01', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-05', 'completionDateStruct': {'date': '2026-10-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-07-02', 'studyFirstSubmitDate': '2025-05-25', 'studyFirstSubmitQcDate': '2025-07-02', 'lastUpdatePostDateStruct': {'date': '2025-07-14', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-07-14', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2025-12-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'AUC (Area Under the ROC Curve)', 'timeFrame': 'Baseline-AUC1 Perioperative/Periprocedural-AUC2'}]}, 'oversightModule': {'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Breast Cancer, Metastatic', 'Artifical Intelligence']}, 'descriptionModule': {'briefSummary': "Retrospectively collect the clinical data, breast MRI images, breast ultrasound images and reports, laboratory indicators (such as CA199, CA153, CA125, CEA/AFP), pathological diagnosis results, HE staining images, and existing immunohistochemical results (including CD8A, KPT5, GFRA1, PFKP, ER/PR percentage, Her-2 expression, Ki-67 index, etc.) of patients pathologically confirmed with or excluded from breast cancer in our center between January 2019 and December 2024. For biopsy specimens from patients diagnosed with breast cancer and immunohistochemically confirmed as HR+/Her-2+ during the same period, additional immunohistochemical staining for CD8A, KPT5, GFRA1, and PFKP should be performed, with images and results collected.\n\nThe collected basic clinical information, imaging data, pathological findings, and laboratory metrics of patients will serve as candidate inputs. Units of measurement will be standardized, and missing data will be imputed using the multiple imputation by chained equations algorithm. Data harmonization will employ the Box-Cox algorithm, while min-max scaling will be used for standardization. The adaptive synthetic sampling method with a balance ratio of 0.5 will address data imbalance. For the collected patient data, deep learning will be applied to screen features from the images, combined with clinical significance to identify malignant risk factors. A neural network classifier will be trained on the training set data, with independent variables including breast MRI/ultrasound images, CA199, CA153, CA125, AFP/CEA, etc., and dependent variables including breast cancer status and subtype. Pathological biopsy results will be set as the validation standard.\n\nModel tuning will be conducted on the validation set to construct a breast cancer prediction model. It should be noted that as a single-center study, the results have limited generalizability. The further optimization and evaluation plan for the model involves using breast disease screening data from external centers for validation and refinement, evaluating the model's practical impact on clinical decision-making, and continuously tracking and optimizing its performance."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '85 Years', 'minimumAge': '19 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Retrospectively collect the clinical data, breast MRI images, breast ultrasound images and reports, laboratory indicators (such as CA199, CA153, CA125, CEA/AEP), pathological diagnosis results, HE staining images, and existing immunohistochemical results (including CD8A, KPT5, GFRA1, PFKP, ER/PR percentage, Her-2 expression, Ki-67 index, etc.) of patients who were pathologically confirmed with breast cancer or excluded from breast cancer in our center between January 2019 and December 2024.\n\nFor biopsy specimens from patients diagnosed with breast cancer and immunohistochemically confirmed as HR+/Her-2+ between January 2019 and December 2024, additional immunohistochemical staining for CD8A, KPT5, GFRA1, and PFKP should be performed, and the immunohistochemical images and results should be collected.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Patients pathologically diagnosed with breast cancer or excluded from breast cancer\n* Available pathological results of breast masses\n* Involving diagnostic population onl\n\nExclusion Criteria:\n\n* Suffering from mental disorders\n* Presence of non-breast diseases during examination\n* Presence of breast implants\n* Undergoing non-breast surgery or having received radiotherapy/chemotherapy\n* Lactating or pregnant women\n* Missing data'}, 'identificationModule': {'nctId': 'NCT07063667', 'briefTitle': 'Artificial Intelligence Model-Assisted Accurate Diagnosis of Early-Stage Breast Cancer', 'organization': {'class': 'OTHER', 'fullName': 'Daping Hospital and the Research Institute of Surgery of the Third Military Medical University'}, 'officialTitle': 'Artificial Intelligence Model-Assisted Accurate Diagnosis of Early-Stage Breast Cancer', 'orgStudyIdInfo': {'id': 'Ratification NO: 2025（188）'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'training group', 'interventionNames': ['Other: bulid primary AI model']}, {'label': 'verdict group', 'interventionNames': ['Other: verdict model and develop its function']}], 'interventions': [{'name': 'bulid primary AI model', 'type': 'OTHER', 'description': 'For the collected patient data, deep learning is used to perform feature screening on the selected or collected images, and malignant risk factors are determined by combining clinical significance. A neural network classifier is trained on the training set data. Variable selection: independent variables (breast MRI images, breast ultrasound images, indicators such as CA199, CA153, CA125, AFP/CEA, etc.), dependent variables (whether suffering from breast cancer and breast cancer subtypes), and the verification accuracy standard is set as the pathological biopsy result.', 'armGroupLabels': ['training group']}, {'name': 'verdict model and develop its function', 'type': 'OTHER', 'description': 'The accuracy of a breast cancer prediction model is typically evaluated using multiple metrics that assess its performance in different aspects', 'armGroupLabels': ['verdict group']}]}, 'contactsLocationsModule': {'locations': [{'city': 'Chongqing', 'state': 'Chongqing Municipality', 'country': 'China', 'contacts': [{'name': 'Yan Xu', 'role': 'CONTACT', 'email': 'xy931@163.com', 'phone': '8615923100038'}], 'facility': 'Army medical Cnter', 'geoPoint': {'lat': 29.56026, 'lon': 106.55771}}], 'centralContacts': [{'name': 'Xu Yan', 'role': 'CONTACT', 'email': 'xy931@163.com', 'phone': '8615923100038'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Daping Hospital and the Research Institute of Surgery of the Third Military Medical University', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}