Use of Machine Learning Techniques for Serial Assessment of Systemic Inflammatory Markers in Breast Cancer Patients

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

NCT Number	NCT06447532
Lead Sponsor	Federal University of São Paulo Collaborators: Kansai Medical University, University of Sao Paulo, Kyoto University, Barretos Cancer Hospital, Women's College Hospital, Emory University, University of Campinas, Brazil, Centro de Educación Medica e Investigaciones Clínicas Norberto Quirno, Instituto Nacional de Cancer, Brazil, Universidade Federal do Triangulo Mineiro, Instituto de Cardiología y Medicina Vascular Hospital Zambrano-Hellion Tec Salud, Hospital Vall d'Hebron, Mansoura University, Seoul National University
Conditions	Breast Cancer
Enrollment	4,500 participants
Start Date	2024-08-01
Primary Completion	2024-12-31 (estimated)
Study Completion	2027-02 (estimated)
Updated on ClinicalTrials.gov	2025-03-12