Ignite Creation Date:
2026-03-26 @ 3:19 PM
Ignite Modification Date:
2026-03-31 @ 1:12 PM
Study NCT ID:
NCT07304934
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-12-26
First Post:
2025-11-19
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
A Single-arm, Prospective, Multi-center Cohort Study Based on Deep Learning-based cfDNA Fragment Omics to Verify the TuFEst Model for the Staging Diagnosis of Breast Cancer Lesions and Lymph Nodes
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2026-03-25'}, 'conditionBrowseModule': {'meshes': [{'id': 'D001943', 'term': 'Breast Neoplasms'}], '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'}]}}, 'protocolSection': {'designModule': {'bioSpec': {'retention': 'SAMPLES_WITH_DNA', 'description': 'Detect tumor-related cfDNA fragments in plasma'}, 'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 269}, 'targetDuration': '1 Year', 'patientRegistry': True}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2025-12-01', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-10', 'completionDateStruct': {'date': '2027-12-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-12-11', 'studyFirstSubmitDate': '2025-11-19', 'studyFirstSubmitQcDate': '2025-12-11', 'lastUpdatePostDateStruct': {'date': '2025-12-26', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-12-26', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2027-12-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Negative predictive value (NPV) of the TuFEst-based classifier for predicting pathologic node-negative status (pN0)', 'timeFrame': 'up to 2 weeks', 'description': 'Validate the accuracy of the TuFEst model in predicting breast cancer lesion and lymph node tumor burden status among patients with primary breast cancer and those undergoing neoadjuvant therapy.'}]}, 'oversightModule': {'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['breast cancer', 'cfDNA', 'TuFEst model'], 'conditions': ['Breast Cancer']}, 'descriptionModule': {'briefSummary': 'Through the research of this project, we expect to achieve the cfDNA fragment omics liquid biopsy technology based on deep learning, verify the accuracy of the TuFEst model in predicting the tumor burden status of breast cancer lesions and lymph nodes in newly diagnosed breast cancer patients and those receiving neoadjuvant therapy, and provide a theoretical basis for large-scale clinical application in the future', 'detailedDescription': '1. Based on the previously established TuFEst model, the cfDNA fragment omics liquid biopsy technology based on deep learning is utilized to predict the tumor burden status of breast cancer lesions and lymph nodes, thereby enhancing the accuracy of early diagnosis of breast cancer: This study will collect and analyze blood samples from breast cancer patients at different stages, and use deep learning-based cfDNA fragment omics liquid biopsy technology to extract tumor-related cfDNA fragments and construct a cfDNA fragment omics feature library. Predictions are made based on the TuFEst model. Then, accuracy matching and evaluation are carried out according to the prediction results and the actual breast cancer lesion and lymph node tumor burden status. Verify the efficacy of the TuFEst model in the staging diagnosis of breast cancer.\n2. To evaluate the sensitivity, specificity, accuracy and repeatability of the TuFEst model to determine its reliability in clinical application: This study will collect a larger number of blood samples from breast cancer patients based on the previous retrospective cohort to assess the performance of the model in a larger sample prospective cohort. This study will also explore the application of this technology in the monitoring of neoadjuvant therapy for breast cancer, specifically evaluating its application in post-treatment staging diagnosis of breast cancer, prediction of treatment effects, and monitoring of tumor recurrence.'}, 'eligibilityModule': {'sex': 'FEMALE', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '70 Years', 'minimumAge': '18 Years', 'genderBased': True, 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': 'Patients who have been treated at the Second Affiliated Hospital of Zhejiang University School of Medicine (Other Centers) from the date of ethical approval until December 31, 2027', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n1. Patients aged 18 to 70;\n2. Direct Surgery Group (Cohort 1) : Radical surgery was performed without neoadjuvant therapy;\n3. Neoadjuvant therapy group (Cohort 2) : The initial diagnosis was invasive breast cancer with confirmed axillary lymph node metastasis, and the patient was willing to undergo radical surgery at the end of treatment;\n4. Plasma from patients during treatment can be obtained;\n5. Be willing to sign the informed consent form. -\n\nExclusion Criteria:\n\n1. Be pregnant or breastfeeding;\n2. Patients whose lesions have been resected;\n3. Suffered from other types of malignant tumors with a clear pathological diagnosis within 5 years prior to enrollment;\n4. Within the past year of enrollment, the patient had other malignant tumors suspected by imaging, but they were not confirmed by pathology;\n5. Suspected distant metastatic lesions on imaging, or potential lymph node lesions that cannot be completely cured by surgery;\n6. Have received any blood product infusion treatment in the past 30 days. -'}, 'identificationModule': {'nctId': 'NCT07304934', 'briefTitle': 'A Single-arm, Prospective, Multi-center Cohort Study Based on Deep Learning-based cfDNA Fragment Omics to Verify the TuFEst Model for the Staging Diagnosis of Breast Cancer Lesions and Lymph Nodes', 'organization': {'class': 'OTHER', 'fullName': 'Second Affiliated Hospital, School of Medicine, Zhejiang University'}, 'officialTitle': 'A Single-arm, Prospective, Multi-center Cohort Study Based on Deep Learning-based cfDNA Fragment Omics to Verify the TuFEst Model for the Staging Diagnosis of Breast Cancer Lesions and Lymph Nodes', 'orgStudyIdInfo': {'id': '2025-1028'}}, 'armsInterventionsModule': {'armGroups': [{'label': '1', 'description': 'Breast cancer patients who have undergone radical surgery and have not received neoadjuvant therapy', 'interventionNames': ['Other: No Intervention: Observational Cohort']}, {'label': '2', 'description': 'Patients with newly diagnosed invasive breast cancer and confirmed axillary lymph node metastasis, who are willing to undergo radical surgery after treatment (Exploratory Analysis Cohort)', 'interventionNames': ['Other: No Intervention: Observational Cohort']}], 'interventions': [{'name': 'No Intervention: Observational Cohort', 'type': 'OTHER', 'description': 'No Intervention: Observational Cohort', 'armGroupLabels': ['1', '2']}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Second Affiliated Hospital, School of Medicine, Zhejiang University', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}