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

March 10, 2025 updated by: Afonso Celso Pinto Nazario, Federal University of São Paulo
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

Study Overview

Status

Enrolling by invitation

Conditions

Intervention / Treatment

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.

Study Type

Observational

Enrollment (Estimated)

4500

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Argentina
      • Buenos Aires, Argentina
        • Pablo Mandó
  • Brazil
    • Minas Gerais
      • Uberaba, Minas Gerais, Brazil
        • Rosekeila Simoes Nomeline
    • Rio Grande do Sul
      • Porto Alegre, Rio Grande do Sul, Brazil
        • Tomas Reinert
    • Sao Paulo
      • Barretos, Sao Paulo, Brazil
        • Idam Oliveira Junior
      • Campinas, Sao Paulo, Brazil
        • César Cabello
      • Ribeirão Preto, Sao Paulo, Brazil
        • Daniel Guimaraes Tiezzi
  • Canada
    • Ontario
      • Toronto, Ontario, Canada
        • Vasily Giannakeas
  • Egypt
      • Cairo, Egypt
        • Salma Elashwah
  • Japan
      • Osaka, Japan
        • Masahiro Takada
      • Tokyo, Japan, 113-8677
        • Masakazu Toi
  • Korea, Republic of
      • Seul, Korea, Republic of
        • Wonshik Han
  • Mexico
      • Mexico City, Mexico
        • Cynthia Mayte Villarreal Garza
  • Spain
      • Madrid, Spain
        • Cristina Saura

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

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.

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Group I: Breast cancer
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.
Procedure: Surgery (Mastectomy or quadrantectomy)
Surgery (mastectomy or quadrantectomy); Neoadjuvant chemotherapy
Other Names:
  • Neoadjuvant chemotherapy

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Overall survival
Time Frame: From the date of diagnosis to the date of death, assessed up to 120 months
Overall survival
From the date of diagnosis to the date of death, assessed up to 120 months

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Disease free survival
Time Frame: 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
From the date of diagnosis to the date of first progression (local recurrence of tumor or distant metastasis), assessed up to 60 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Federal University of São Paulo

Collaborators

Emory University
University of Sao Paulo
University of Campinas, Brazil
Mansoura University
Seoul National University
Barretos Cancer Hospital
Women's College Hospital
Universidade Federal do Triangulo Mineiro
Instituto Nacional de Cancer, Brazil
Hospital Vall d'Hebron
Kyoto University
Instituto de Cardiología y Medicina Vascular Hospital Zambrano-Hellion Tec Salud
Centro de Educación Medica e Investigaciones Clínicas Norberto Quirno
Kansai Medical University

Investigators

  • Principal Investigator: Afonso C Nazario, PhD, University Federal of Sao Paulo

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

August 1, 2024

Primary Completion (Actual)

December 31, 2024

Study Completion (Estimated)

February 1, 2027

Study Registration Dates

First Submitted

April 26, 2024

First Submitted That Met QC Criteria

June 2, 2024

First Posted (Actual)

June 7, 2024

Study Record Updates

Last Update Posted (Actual)

March 25, 2025

Last Update Submitted That Met QC Criteria

March 10, 2025

Last Verified

June 1, 2024

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • University of Sao Paulo

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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