Use of Machine Learning Techniques for Serial Assessment of Systemic Inflammatory Markers in Breast Cancer Patients
- Conditions
- Breast Cancer
- Interventions
- Procedure: Surgery (Mastectomy or quadrantectomy)
- Registration Number
- NCT06447532
- Lead Sponsor
- Federal University of São Paulo
- Brief Summary
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
- Detailed Description
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.
Although 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.
Recruitment & Eligibility
- Status
- ENROLLING_BY_INVITATION
- Sex
- Female
- Target Recruitment
- 4500
- Women patients with age between 18 and 75 years old;
- Invasive breast carcinoma patients diagnosed by pathology ;
- Patients diagnosed between 1 January 2013 and 31 December 2018;
- Have a complete blood count performed before the surgical intervention (mastectomy or conservative breast surgery) or neoadjuvant chemotherapy;
Presence of hematological disorders;
- Bilateral breast cancer;
- Male;
- Karnofsky Performance Status Score < 70';
- Inflammatory breast cancer and in situ carcinoma;
- Pregnancy or breastfeeding;
- Evidence of local or distant recurrence.
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Arm && Interventions
Group Intervention Description Group I: Breast cancer Surgery (Mastectomy or quadrantectomy) 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.
- Primary Outcome Measures
Name Time Method Overall survival From the date of diagnosis to the date of death, assessed up to 120 months Overall survival
- Secondary Outcome Measures
Name Time Method Disease free survival From the date of diagnosis to the date of first progression (local recurrence of tumor or distant metastasis), assessed up to 60 months Disease-free survival
Trial Locations
- Locations (13)
Rosekeila Simoes Nomeline
🇧🇷Uberaba, Minas Gerais, Brazil
Tomás Reinert
🇧🇷Porto Alegre, Rio Grande do Sul, Brazil
Cristina Saura
🇪🇸Madrid, Spain
Pablo Mandó
🇦🇷Buenos Aires, Argentina
Wonshik Han
🇰🇷Seul, Korea, Republic of
Cynthia Mayte Villarreal Garza
🇲🇽Mexico City, Mexico
Idam Oliveira Junior
🇧🇷Barretos, Sao Paulo, Brazil
César Cabello
🇧🇷Campinas, Sao Paulo, Brazil
Salma Elashwah
🇪🇬Cairo, Egypt
Masahiro Takada
🇯🇵Osaka, Japan
Masakazu Toi
🇯🇵Tokyo, Japan
Daniel Guimaraes Tiezzi
🇧🇷Ribeirão Preto, Sao Paulo, Brazil
Vasily Giannakeas
🇨🇦Toronto, Ontario, Canada