Ignite Creation Date:
2025-12-24 @ 4:38 PM
Ignite Modification Date:
2025-12-24 @ 4:38 PM
Study NCT ID:
NCT06788366
Status:
RECRUITING
Last Update Posted:
2025-01-23
First Post:
2024-02-29
Is Possible Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
AI-HOPE Lung Cancer: Building a Predictive Tool for Metastatic 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'}]}, 'interventionBrowseModule': {'meshes': [{'id': 'D007167', 'term': 'Immunotherapy'}, {'id': 'D004358', 'term': 'Drug Therapy'}], 'ancestors': [{'id': 'D056747', 'term': 'Immunomodulation'}, {'id': 'D001691', 'term': 'Biological Therapy'}, {'id': 'D013812', 'term': 'Therapeutics'}]}}, 'documentSection': {'largeDocumentModule': {'largeDocs': [{'date': '2024-12-06', 'size': 330651, 'label': 'Study Protocol and Statistical Analysis Plan', 'hasIcf': False, 'hasSap': True, 'filename': 'Prot_SAP_000.pdf', 'typeAbbrev': 'Prot_SAP', 'uploadDate': '2025-01-15T07:05', 'hasProtocol': True}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'CROSS_SECTIONAL', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 2000}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2024-02-12', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-01', 'completionDateStruct': {'date': '2026-12-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-01-20', 'studyFirstSubmitDate': '2024-02-29', 'studyFirstSubmitQcDate': '2025-01-20', 'lastUpdatePostDateStruct': {'date': '2025-01-23', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-01-23', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-12-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Early progressive disease', 'timeFrame': 'From date of enrolment until the date of first documented disease progression or death, whichever comes first, assessed within 8 to 12 weeks from first-line treatment start', 'description': 'Number of patients experiencing progressive disease (PD) as best response to first-line treatment'}, {'measure': 'Lung toxicity', 'timeFrame': 'From date of enrolment until the date of first documented immune-related pneumonitis of G3 or more, assessed up to 96 months', 'description': 'Number of patients experiencing immune-related pneumonitis of G3 or more'}, {'measure': 'Long survivors', 'timeFrame': 'At a 3-year cut-off', 'description': 'Numbero of patients experiencing an overall survival (time from treatment initiation to death) longer than 3 years (1.5x median overall survival from clinical trials)'}]}, 'oversightModule': {'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['NSCLC Stage IV']}, 'descriptionModule': {'briefSummary': 'The goal of our project is building a predictive response algorithm for patients with metastatic lung cancer, exploiting an artificial intelligence platform. It will collect patient information from all areas (clinical, laboratory, radiological, pathological) and analyse them, understanding connections and correlations, both at baseline and at pre-specified timepoints. It would lead to the development of a reliable and constantly evolving predictive score, able to continuously re-weight the importance of each variable as new data come in.\n\nSince the greatest clinical need is identifying non-responders to immunotherapy and chemo-immunotherapy combination (30% of all treated patients), these two populations are defined as the starting cohorts (Cohort A, immunotherapy alone, Cohort B, chemo-immunotherapy combinations).\n\nFor each cohort, three main questions are to be answered:\n\nQ1) Early progressors (defined as progressive disease or death within three months of treatment or at first radiological restaging) Q2) Toxicity (with a special focus on severe toxicities G≥3) Q3) Long survivors (defined as patients reaching an overall survival of at least 1.5x median overall survival in registrative trials)\n\nThe early identification of non-responders, high-risk patients (or on the other hand, long survivors) would help their healthcare planning, providing individualised follow-up strategies or prompting their inclusion in alternative treatments (eg clinical trials).\n\nFor all cohorts, first data entry will be retrospective and second data entry will be prospective (as validation set).'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Patients with NSCLC stage IV treated with at least 1 cycle of first-line treatment', 'healthyVolunteers': False, 'eligibilityCriteria': "Inclusion criteria:\n\n* Patients with histological or cytological diagnosis of NSCLC\n* Stage IV according to investigator's staging procedures (or any locally advanced tumour not feasible for local radical treatment)\n* Treatment with at least 1 cycle of mono-immunotherapy or chemo-immunotherapy (as per clinical practice)\n* Availability of follow-up\n\nExclusion criteria:\n\n* Patients with other thoracic tumours non-NSCLC (i.e. SCLC)\n* Stage other than IV or feasible for radical treatment upfront\n* Treatment within clinical trials (with combination regimens different from the aforementioned combinations)\n* Lost to follow-up"}, 'identificationModule': {'nctId': 'NCT06788366', 'briefTitle': 'AI-HOPE Lung Cancer: Building a Predictive Tool for Metastatic Lung Cancer', 'organization': {'class': 'OTHER', 'fullName': 'IRCCS San Raffaele'}, 'officialTitle': 'AI-HOPE Lung Cancer: Building a Predictive Tool for Metastatic Lung Cancer', 'orgStudyIdInfo': {'id': 'AI-HOPE'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Mono-immunotherapy', 'description': 'Patients with NSCLC stage IV treated with immunotherapy alone as first-line treatment', 'interventionNames': ['Drug: Immunotherapy']}, {'label': 'Chemo-immunotherapy', 'description': 'Patients with NSCLC stage IV treated with chemo-immunotherapy as first-line treatment', 'interventionNames': ['Drug: Immunotherapy', 'Drug: Chemotherapy']}], 'interventions': [{'name': 'Immunotherapy', 'type': 'DRUG', 'description': 'First-line regimen according to clinical practice', 'armGroupLabels': ['Chemo-immunotherapy', 'Mono-immunotherapy']}, {'name': 'Chemotherapy', 'type': 'DRUG', 'description': 'First-line regimen according to clinical practice', 'armGroupLabels': ['Chemo-immunotherapy']}]}, 'contactsLocationsModule': {'locations': [{'zip': '20132', 'city': 'Milan', 'status': 'RECRUITING', 'country': 'Italy', 'contacts': [{'name': 'Francesca Rita Ogliari', 'role': 'CONTACT', 'email': 'oncologia.medica@hsr.it'}, {'name': 'Francesca Rita Ogliari', 'role': 'PRINCIPAL_INVESTIGATOR'}], 'facility': 'Francesca Rita Ogliari', 'geoPoint': {'lat': 45.46427, 'lon': 9.18951}}], 'centralContacts': [{'name': 'Francesca Rita Ogliari, MD', 'role': 'CONTACT', 'email': 'oncologia.medica@hsr.it', 'phone': '0039 02 2643 2643'}, {'name': 'Clinical Trial Center OSR', 'role': 'CONTACT', 'email': 'ctc.trialmanagement@hsr.it'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'IRCCS San Raffaele', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Principal investigator, Medical doctor', 'investigatorFullName': 'Francesca Rita Ogliari', 'investigatorAffiliation': 'IRCCS San Raffaele'}}}}