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

Federal University of São Paulo

Status: Invitation-only

Conditions: Breast Cancer

Treatments: Procedure: Surgery (Mastectomy or quadrantectomy)

Study type: Observational

Funder types: Other

Identifiers

NCT06447532

University of Sao Paulo

Details and patient eligibility

About

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

Full 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.

Enrollment: 4,500 estimated patients

Sex: Female

Ages: 18 to 75 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• 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;

Exclusion criteria

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.

Trial design

4,500 participants in 1 patient group

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.

Treatment:

Procedure: Surgery (Mastectomy or quadrantectomy)