Ignite Creation Date:
2025-12-25 @ 4:32 AM
Ignite Modification Date:
2026-03-07 @ 9:26 PM
Study NCT ID:
NCT06684418
Status:
RECRUITING
Last Update Posted:
2025-01-20
First Post:
2024-11-11
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Artificial Intelligence-based Model for the Prediction of Occult Lymph Node Metastasis and Improvement of Clinical Decision-making in Non-small Cell Lung Cancer
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D002289', 'term': 'Carcinoma, Non-Small-Cell Lung'}], 'ancestors': [{'id': 'D002283', 'term': 'Carcinoma, Bronchogenic'}, {'id': 'D001984', 'term': 'Bronchial Neoplasms'}, {'id': 'D008175', 'term': 'Lung Neoplasms'}, {'id': 'D012142', 'term': 'Respiratory Tract Neoplasms'}, {'id': 'D013899', 'term': 'Thoracic Neoplasms'}, {'id': 'D009371', 'term': 'Neoplasms by Site'}, {'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D008171', 'term': 'Lung Diseases'}, {'id': 'D012140', 'term': 'Respiratory Tract Diseases'}]}}, 'protocolSection': {'designModule': {'bioSpec': {'retention': 'SAMPLES_WITH_DNA', 'description': '50 patients will be selected to analyze the histopathology of the lesions and explore the relevant characteristics. RNA sequencing and multicolor fluorescence staining will be performed to explore differential genes and enriched signaling pathways. The tumor immune microenvironment will also be analyzed.'}, 'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 6000}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2024-12-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-01', 'completionDateStruct': {'date': '2026-06-30', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-01-17', 'studyFirstSubmitDate': '2024-11-11', 'studyFirstSubmitQcDate': '2024-11-11', 'lastUpdatePostDateStruct': {'date': '2025-01-20', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-11-12', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2025-12-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Recurrence-free survival (RFS)', 'timeFrame': '1 year', 'description': 'The time from surgical treatment or SBRT to disease recurrence or death. Patients who were still not progressing at the time of analysis will have the date of their last contact as the cutoff date.'}], 'secondaryOutcomes': [{'measure': 'Overall Survival (OS)', 'timeFrame': '1 year', 'description': 'The time from the surgery or SBRT until death from any cause. Patients who are still alive at the time of analysis will have their last contact date used as the cutoff date.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['NSCLC (Non-small Cell Lung Cancer)', 'Artificial Intelligence (AI)', 'Lymphnode Metastasis']}, 'descriptionModule': {'briefSummary': 'This nationwide, multicenter observational study aims to develop and validate a multimodal artificial intelligence (AI) model for detecting occult lymph node metastasis in early-stage non-small cell lung cancer (NSCLC) patients. Despite advances in lymph node staging, 12.9%-39.3% of occult nodal metastasis cases remain undetected preoperatively, affecting treatment decisions. This study will use deep learning to extract imaging features of occult metastasis and combine them with clinical data to build an AI model for risk prediction. This study will provide insights into the feasibility of AI-driven detection of occult metastasis, supporting clinical decision-making and potentially revealing underlying biological mechanisms of lymph node metastasis in NSCLC.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': 'Early-stage NSCLC receiving curative treatment (surgery or SBRT).', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Pathologically confirmed non-small cell lung cancer;\n* Clinical stage I (AJCC, 8th edition, 2017);\n* Age≥18 years old;\n* KPS score≥70;\n* Patients who have undergone primary NSCLC radical surgery or SBRT treatment;\n* Complete systemic lesion imaging assessment before primary NSCLC radical surgery or SBRT treatment (Note: Tumor size ≥ 3 cm or centrally located tumor requires PET/CT and/or invasive mediastinal staging);\n* Patients willing to cooperate with the follow-up after primary NSCLC radical surgery;\n* informed consent of the patient.\n\nExclusion Criteria:\n\n* Poor quality of computed tomography imaging;\n* Baseline imaging shows pure ground-glass nodules (GGO);\n* Uncontrolled epilepsy, central nervous system disease, or history of mental disorders, judged by the researcher to potentially interfere with the signing of the informed consent form or affect patient compliance.;\n* Loss to follow-up.'}, 'identificationModule': {'nctId': 'NCT06684418', 'briefTitle': 'Artificial Intelligence-based Model for the Prediction of Occult Lymph Node Metastasis and Improvement of Clinical Decision-making in Non-small Cell Lung Cancer', 'organization': {'class': 'OTHER', 'fullName': 'Fudan University'}, 'officialTitle': 'Artificial Intelligence-based Model for the Prediction of Occult Lymph Node Metastasis and Improvement of Clinical Decision-making in Non-small Cell Lung Cancer: A Multicenter, Prospective, Observational Study', 'orgStudyIdInfo': {'id': 'OLNM-AI'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Retrospective Cohort', 'description': 'Enrolling about 5,000 early-stage NSCLC patients from January 2018 to June 2024 across 25 centers in China, data including chest CT scans and clinicopathological parameters will be used to train and validate the AI model. Patients will be divided into "high-risk" and "low-risk" groups based on the model\'s risk score, and clinical benefits of treatments like lymph node dissection, adjuvant therapy, and SBRT will be analyzed.', 'interventionNames': ['Diagnostic Test: chest enhanced CT']}, {'label': 'Prospective Cohort', 'description': "Enrolling 1,000 patients from November 2024 to October 2025, this cohort will prospectively validate the AI model's performance and explore the biological basis of metastasis by analyzing pathological tissues, RNA sequencing, and tumor immune microenvironment characteristics.", 'interventionNames': ['Diagnostic Test: chest enhanced CT']}], 'interventions': [{'name': 'chest enhanced CT', 'type': 'DIAGNOSTIC_TEST', 'description': "This is an observational study and patients will receive routine clinical treatment according to the corresponding guidelines. We will collect the enrolled patient's chest enhanced CT and clinicopathological parameters.", 'armGroupLabels': ['Prospective Cohort', 'Retrospective Cohort']}]}, 'contactsLocationsModule': {'locations': [{'city': 'Shanghai', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Zhengfei Zhu, PhD', 'role': 'CONTACT', 'email': 'fuscczzf@163.com', 'phone': '18017312901'}], 'facility': 'Fudan university Shanghai Cancer Center', 'geoPoint': {'lat': 31.22222, 'lon': 121.45806}}], 'centralContacts': [{'name': 'Zhengfei Zhu, PhD', 'role': 'CONTACT', 'email': 'fuscczzf@163.com', 'phone': '+86-18017312901'}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Fudan University', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Professor', 'investigatorFullName': 'Zhengfei Zhu', 'investigatorAffiliation': 'Fudan University'}}}}