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

NCT ID: NCT06447532

Last Updated: 2025-03-12

Basic Information

• Recruitment Status: ENROLLING_BY_INVITATION
• Total Enrollment: 4500 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2024-08-01
• Study Completion Date: 2027-02-28

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.

Conditions

Breast Cancer

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Study Groups

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.

Surgery (Mastectomy or quadrantectomy)

Intervention Type: PROCEDURE

Surgery (mastectomy or quadrantectomy); Neoadjuvant chemotherapy

Interventions

Surgery (Mastectomy or quadrantectomy)

Surgery (mastectomy or quadrantectomy); Neoadjuvant chemotherapy

Intervention Type: PROCEDURE

Other Intervention Names

Neoadjuvant chemotherapy

Eligibility Criteria

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.

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 75 Years
• Eligible Sex: FEMALE
• Accepts Healthy Volunteers: No

Sponsors

Kansai Medical University

OTHER

Sponsor Role: collaborator

University of Sao Paulo

OTHER

Sponsor Role: collaborator

Kyoto University

OTHER

Sponsor Role: collaborator

Barretos Cancer Hospital

OTHER

Sponsor Role: collaborator

Women's College Hospital

OTHER

Sponsor Role: collaborator

Emory University

OTHER

Sponsor Role: collaborator

University of Campinas, Brazil

OTHER

Sponsor Role: collaborator

Centro de Educación Medica e Investigaciones Clínicas Norberto Quirno

OTHER

Sponsor Role: collaborator

Instituto Nacional de Cancer, Brazil

OTHER_GOV

Sponsor Role: collaborator

Universidade Federal do Triangulo Mineiro

OTHER

Sponsor Role: collaborator

Instituto de Cardiología y Medicina Vascular Hospital Zambrano-Hellion Tec Salud

OTHER

Sponsor Role: collaborator

Hospital Vall d'Hebron

OTHER

Sponsor Role: collaborator

Mansoura University

OTHER

Sponsor Role: collaborator

Seoul National University

OTHER

Sponsor Role: collaborator

Federal University of São Paulo

OTHER

Sponsor Role: lead

Responsible Party

Afonso Celso Pinto Nazario

Professor and Coordinator at the Department of Gynecology at EPM/UNIFESP.

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Afonso C Nazario, PhD

Role: PRINCIPAL_INVESTIGATOR

University Federal of Sao Paulo

Locations

Pablo Mandó

Buenos Aires, Buenos Aires, Argentina

Rosekeila Simoes Nomeline

Uberaba, Minas Gerais, Brazil

Tomás Reinert

Porto Alegre, Rio Grande do Sul, Brazil

Idam Oliveira Junior

Barretos, São Paulo, Brazil

César Cabello

Campinas, São Paulo, Brazil

Daniel Guimaraes Tiezzi

Ribeirão Preto, São Paulo, Brazil

Vasily Giannakeas

Toronto, Ontario, Canada

Salma Elashwah

Cairo, , Egypt

Masahiro Takada

Osaka, Osaka, Japan

Masakazu Toi

Tokyo, Tokyo, Japan

Cynthia Mayte Villarreal Garza

Mexico City, , Mexico

Wonshik Han

Seoul, , South Korea

Cristina Saura

Madrid, Spain, Spain

Countries

Argentina; Brazil; Canada; Egypt; Japan; Mexico; South Korea; Spain

References

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.

Reference Type: BACKGROUND

PMID: 33538338 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 37971409 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 28286600 (View on PubMed)

Other Identifiers

University of Sao Paulo

Identifier Type: -

Identifier Source: org_study_id