Ignite Creation Date:
2025-12-24 @ 9:43 PM
Ignite Modification Date:
2025-12-28 @ 10:37 AM
Study NCT ID:
NCT06447532
Status:
ENROLLING_BY_INVITATION
Last Update Posted:
2025-03-12
First Post:
2024-04-26
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Use of Machine Learning Techniques for Serial Assessment of Systemic Inflammatory Markers in Breast Cancer Patients
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24', 'removedCountries': ['Italy']}, 'conditionBrowseModule': {'meshes': [{'id': 'D001943', 'term': 'Breast Neoplasms'}, {'id': 'D007249', 'term': 'Inflammation'}], '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': 'D010335', 'term': 'Pathologic Processes'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}]}, 'interventionBrowseModule': {'meshes': [{'id': 'D013514', 'term': 'Surgical Procedures, Operative'}, {'id': 'D008408', 'term': 'Mastectomy'}, {'id': 'D020360', 'term': 'Neoadjuvant Therapy'}], 'ancestors': [{'id': 'D003131', 'term': 'Combined Modality Therapy'}, {'id': 'D013812', 'term': 'Therapeutics'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 4500}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'ENROLLING_BY_INVITATION', 'startDateStruct': {'date': '2024-08-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-06', 'completionDateStruct': {'date': '2027-02', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-03-10', 'studyFirstSubmitDate': '2024-04-26', 'studyFirstSubmitQcDate': '2024-06-02', 'lastUpdatePostDateStruct': {'date': '2025-03-12', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-06-07', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2024-12-31', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Overall survival', 'timeFrame': 'From the date of diagnosis to the date of death, assessed up to 120 months', 'description': 'Overall survival'}], 'secondaryOutcomes': [{'measure': 'Disease free survival', 'timeFrame': 'From the date of diagnosis to the date of first progression (local recurrence of tumor or distant metastasis), assessed up to 60 months', 'description': 'Disease-free survival'}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['breast cancer', 'machine learning', 'prognosis', 'inflammation'], 'conditions': ['Breast Cancer']}, 'referencesModule': {'references': [{'pmid': '33538338', 'type': 'BACKGROUND', 'citation': 'Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021 May;71(3):209-249. doi: 10.3322/caac.21660. Epub 2021 Feb 4.'}, {'pmid': '37971409', 'type': 'BACKGROUND', 'citation': 'Faria SS, Giannarelli D, Cordeiro de Lima VC, Anwar SL, Casadei C, De Giorgi U, Madonna G, Ascierto PA, Mendoza Lopez RV, Chammas R, Capone M. Development of a Prognostic Model for Early Breast Cancer Integrating Neutrophil to Lymphocyte Ratio and Clinical-Pathological Characteristics. Oncologist. 2024 Apr 4;29(4):e447-e454. doi: 10.1093/oncolo/oyad303.'}, {'pmid': '28286600', 'type': 'BACKGROUND', 'citation': 'Choi E, Bahadori MT, Schuetz A, Stewart WF, Sun J. Doctor AI: Predicting Clinical Events via Recurrent Neural Networks. JMLR Workshop Conf Proc. 2016 Aug;56:301-318. Epub 2016 Dec 10.'}]}, 'descriptionModule': {'briefSummary': 'Breast cancer is the most common cancer in women globally, with 2.3 million new cases diagnosed in 2020. Hormone receptor positive (HR+), human epidermal growth factor receptor 2 negative (HER2-) breast cancer is the most prevalent subtype, comprising 69% of all breast cancers in the USA. Within the tumor immune microenvironment, a higher intensity of myeloid cell infiltration and low levels of lymphocyte infiltration have been associated with worse outcomes. Markers in peripheral blood have emerged as predictive biomarkers that can be easily obtained non-invasively and at low cost. Experiments have confirmed the relative components of these tests (such as the immune cells) directly or indirectly participated in tumour occurrence, development, and immune escape, underscoring the potential use of laboratory tests as tumour biomarkers', 'detailedDescription': "In breast cancer, increased neutrophil levels and decreased lymphocyte levels in peripheral blood are associated with worse overall survival (OS). In HR+, HER2- metastatic breast cancers, low pretreatment NLR and high pretreatment absolute lymphocyte count (ALC) were related with better progression-free survival (PFS) and OS. The development of predictive models, based on machine learning (ML) algorithms it has been used in prognostication and assist in the diagnosis of different types of cancer.\n\nAlthough regular laboratory tests have potential to be breast cancer biomarkers, a single test is yet to provide adequate sensitivity or specificity. Artificial intelligence (AI) could help with integrating data from multiple tests to aid diagnosis. Technical improvements such as data storage capacity, computing power, and better algorithms mean that ML can process clinically meaningful information from laboratory test data. Models' generalisability and stability still need to be confirmed, in view of limitations such as the absence of various pathological types, small cohorts, and lack of external validation. Therefore, a competitive model is also essential to achieve more accurate stratification of patients with breast cancer. The purpose of this retrospective multicentre study is to systematically evaluate the ability of laboratory tests to predict breast cancer, and develop a robust and generalisable model to assist in identifying patients with breast cancer."}, 'eligibilityModule': {'sex': 'FEMALE', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '75 Years', 'minimumAge': '18 Years', 'genderBased': True, 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'All the women involved in our study are patients who are diagnosed breast cancer pathologically and treated with surgery or neoadjuvant chemotherapy from January 1st 2013 to December 31st 2018.', 'genderDescription': 'Women diagnosed with breast cancer.', 'healthyVolunteers': False, 'eligibilityCriteria': "Inclusion Criteria:\n\n* Women patients with age between 18 and 75 years old;\n* Invasive breast carcinoma patients diagnosed by pathology ;\n* Patients diagnosed between 1 January 2013 and 31 December 2018;\n* Have a complete blood count performed before the surgical intervention (mastectomy or conservative breast surgery) or neoadjuvant chemotherapy;\n\nExclusion Criteria:\n\nPresence of hematological disorders;\n\n* Bilateral breast cancer;\n* Male;\n* Karnofsky Performance Status Score \\< 70';\n* Inflammatory breast cancer and in situ carcinoma;\n* Pregnancy or breastfeeding;\n* Evidence of local or distant recurrence."}, 'identificationModule': {'nctId': 'NCT06447532', 'acronym': 'INFLAMMATE', 'briefTitle': 'Use of Machine Learning Techniques for Serial Assessment of Systemic Inflammatory Markers in Breast Cancer Patients', 'organization': {'class': 'OTHER', 'fullName': 'Federal University of São Paulo'}, 'officialTitle': 'Use of Machine Learning Techniques for Serial Assessment of Systemic Inflammatory Markers in Breast Cancer Patients', 'orgStudyIdInfo': {'id': 'University of Sao Paulo'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Group I: Breast cancer', 'description': 'All the participants involved in our study are women who are diagnosed breast cancer and treated with surgery or neoadjuvant chemotherapy from January 1st 2013 to December 31st 2018.', 'interventionNames': ['Procedure: Surgery (Mastectomy or quadrantectomy)']}], 'interventions': [{'name': 'Surgery (Mastectomy or quadrantectomy)', 'type': 'PROCEDURE', 'otherNames': ['Neoadjuvant chemotherapy'], 'description': 'Surgery (mastectomy or quadrantectomy); Neoadjuvant chemotherapy', 'armGroupLabels': ['Group I: Breast cancer']}]}, 'contactsLocationsModule': {'locations': [{'city': 'Buenos Aires', 'state': 'Buenos Aires', 'country': 'Argentina', 'facility': 'Pablo Mandó'}, {'city': 'Uberaba', 'state': 'Minas Gerais', 'country': 'Brazil', 'facility': 'Rosekeila Simoes Nomeline', 'geoPoint': {'lat': -19.74833, 'lon': -47.93194}}, {'city': 'Porto Alegre', 'state': 'Rio Grande do Sul', 'country': 'Brazil', 'facility': 'Tomás Reinert', 'geoPoint': {'lat': -30.03283, 'lon': -51.23019}}, {'city': 'Barretos', 'state': 'São Paulo', 'country': 'Brazil', 'facility': 'Idam Oliveira Junior', 'geoPoint': {'lat': -20.55722, 'lon': -48.56778}}, {'city': 'Campinas', 'state': 'São Paulo', 'country': 'Brazil', 'facility': 'César Cabello', 'geoPoint': {'lat': -22.90556, 'lon': -47.06083}}, {'city': 'Ribeirão Preto', 'state': 'São Paulo', 'country': 'Brazil', 'facility': 'Daniel Guimaraes Tiezzi', 'geoPoint': {'lat': -21.1775, 'lon': -47.81028}}, {'city': 'Toronto', 'state': 'Ontario', 'country': 'Canada', 'facility': 'Vasily Giannakeas', 'geoPoint': {'lat': 43.70643, 'lon': -79.39864}}, {'city': 'Cairo', 'country': 'Egypt', 'facility': 'Salma Elashwah', 'geoPoint': {'lat': 30.06263, 'lon': 31.24967}}, {'city': 'Osaka', 'state': 'Osaka', 'country': 'Japan', 'facility': 'Masahiro Takada', 'geoPoint': {'lat': 34.69379, 'lon': 135.50107}}, {'zip': '113-8677', 'city': 'Tokyo', 'state': 'Tokyo', 'country': 'Japan', 'facility': 'Masakazu Toi', 'geoPoint': {'lat': 35.6895, 'lon': 139.69171}}, {'city': 'Mexico City', 'country': 'Mexico', 'facility': 'Cynthia Mayte Villarreal Garza', 'geoPoint': {'lat': 19.42847, 'lon': -99.12766}}, {'city': 'Seoul', 'country': 'South Korea', 'facility': 'Wonshik Han', 'geoPoint': {'lat': 37.566, 'lon': 126.9784}}, {'city': 'Madrid', 'state': 'Spain', 'country': 'Spain', 'facility': 'Cristina Saura', 'geoPoint': {'lat': 40.4165, 'lon': -3.70256}}], 'overallOfficials': [{'name': 'Afonso C Nazario, PhD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'University Federal of Sao Paulo'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Federal University of São Paulo', 'class': 'OTHER'}, 'collaborators': [{'name': 'Kansai Medical University', 'class': 'OTHER'}, {'name': 'University of Sao Paulo', 'class': 'OTHER'}, {'name': 'Kyoto University', 'class': 'OTHER'}, {'name': 'Barretos Cancer Hospital', 'class': 'OTHER'}, {'name': "Women's College Hospital", 'class': 'OTHER'}, {'name': 'Emory University', 'class': 'OTHER'}, {'name': 'University of Campinas, Brazil', 'class': 'OTHER'}, {'name': 'Centro de Educación Medica e Investigaciones Clínicas Norberto Quirno', 'class': 'OTHER'}, {'name': 'Instituto Nacional de Cancer, Brazil', 'class': 'OTHER_GOV'}, {'name': 'Universidade Federal do Triangulo Mineiro', 'class': 'OTHER'}, {'name': 'Instituto de Cardiología y Medicina Vascular Hospital Zambrano-Hellion Tec Salud', 'class': 'OTHER'}, {'name': "Hospital Vall d'Hebron", 'class': 'OTHER'}, {'name': 'Mansoura University', 'class': 'OTHER'}, {'name': 'Seoul National University', 'class': 'OTHER'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Professor and Coordinator at the Department of Gynecology at EPM/UNIFESP.', 'investigatorFullName': 'Afonso Celso Pinto Nazario', 'investigatorAffiliation': 'Federal University of São Paulo'}}}}