Ignite Creation Date:
2025-12-25 @ 12:33 AM
Ignite Modification Date:
2025-12-25 @ 10:41 PM
Study NCT ID:
NCT06737367
Status:
COMPLETED
Last Update Posted:
2024-12-19
First Post:
2024-12-11
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Integrating Machine Learning for Prognostic Prediction in Stage I NSCLC by CT Images and Pathological Factors
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': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 800}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2023-09-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-12', 'completionDateStruct': {'date': '2024-11-11', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2024-12-16', 'studyFirstSubmitDate': '2024-12-11', 'studyFirstSubmitQcDate': '2024-12-15', 'lastUpdatePostDateStruct': {'date': '2024-12-19', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-12-17', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2024-09-20', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'DFS（Disease-free survival）', 'timeFrame': 'Record from the date of surgery to the date of recurrence or death from any cause, whichever comes first, and assess up to a maximum of 5 years.', 'description': 'DFS was defined as the duration from the date of primary surgery to the first occurrence of recurrence or death from any cause.'}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['NSCLC, ML, DFS'], 'conditions': ['Lung Cancer - Non Small Cell']}, 'descriptionModule': {'briefSummary': 'The investigators retrospectively collected the participants with stage I non-small cell lung cancer (NSCLC) patients resected between January 2010 to December 2020 for training and internal validation. The Clinical data, preoperative clinical information, laboratory results and CT images were collected. The investigators also collected the disease-free survival time. On the Deepwise multi-modal research platform, the images were semi-automatically segmented and expanded outward by 3mm to obtain the peritumor tissue. PyRadiomics was used to extract the radiomic features. LASSOcox and rsf were used to select the features. we developed a machine learning-based integrative prognostic model that utilizes radiomic and pathological variables as input using LOOCV framework. And it was further tested on the internal and external cohorts. Discrimination was assessed by using the C-index and area under the receiver operating characteristic curve (AUC), IBS, DCA.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': 'Jinling Hospital, China', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\npatients with stage I NSCLC (ninth AJCC edition) who underwent curative R0 resections between January 2010 and December 2020 -\n\nExclusion Criteria:\n\n1. absence of enhanced CT\n2. history of lung cancer or synchronous lung cancers\n3. follow-up records ≤3 Months\n4. carcinoma in situ (CIS) or minimally invasive NSCLC\n5. death within 30 days of surgery\n6. no pathological slides or reports'}, 'identificationModule': {'nctId': 'NCT06737367', 'acronym': 'Stage I NSCLC', 'briefTitle': 'Integrating Machine Learning for Prognostic Prediction in Stage I NSCLC by CT Images and Pathological Factors', 'organization': {'class': 'OTHER', 'fullName': 'Jinling Hospital, China'}, 'officialTitle': 'Integrating Machine Learning for Prognostic Prediction in Stage I NSCLC: a Multicenter Analysis', 'orgStudyIdInfo': {'id': '2023DZKY-089-01'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'training set', 'interventionNames': ['Other: CT radiomic analysis']}, {'label': 'external test set', 'interventionNames': ['Other: CT radiomic analysis']}], 'interventions': [{'name': 'CT radiomic analysis', 'type': 'OTHER', 'description': 'Radiomic features of tumor and peritumor tissue', 'armGroupLabels': ['external test set', 'training set']}]}, 'contactsLocationsModule': {'locations': [{'city': 'Nanjing', 'country': 'China', 'facility': 'Jinling Hospital, China', 'geoPoint': {'lat': 32.06167, 'lon': 118.77778}}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Jinling Hospital, China', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Professor', 'investigatorFullName': 'Guangming Lu', 'investigatorAffiliation': 'Jinling Hospital, China'}}}}