Clinical trial NCT06447532: Use of Machine Learning Techniques for Serial Assessment of Systemic Inflammatory Markers in Breast Cancer Patients
Official title: Use of Machine Learning Techniques for Serial Assessment of Systemic Inflammatory Markers in Breast Cancer Patients
Study type: OBSERVATIONAL
Overall status: ENROLLING_BY_INVITATION
Conditions: Breast Cancer
Keywords: breast cancer
machine learning
prognosis
inflammation
Lead sponsor: Federal University of São Paulo
Sponsor class: OTHER
Collaborators: name: Kansai Medical University
name: University of Sao Paulo
name: Kyoto University
name: Barretos Cancer Hospital
name: Women's College Hospital
name: Emory University
name: University of Campinas, Brazil
name: Centro de Educación Medica e Investigaciones Clínicas Norberto Quirno
name: Instituto Nacional de Cancer, Brazil
name: Universidade Federal do Triangulo Mineiro
name: Instituto de Cardiología y Medicina Vascular Hospital Zambrano-Hellion Tec Salud
name: Hospital Vall d'Hebron
name: Mansoura University
name: Seoul National University
First posted: 2024-06-07
Enrollment: 4500
Sex: FEMALE
Minimum age (years): 18
Maximum age (years): 75
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
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.

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.

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.

Primary outcomes:
measure: Overall survival
timeFrame: From the date of diagnosis to the date of death, assessed up to 120 months
Secondary outcomes:
measure: Disease free survival
timeFrame: From the date of diagnosis to the date of first progression (local recurrence of tumor or distant metastasis), assessed up to 60 months
Other outcomes:
Locations:
city: Buenos Aires
country: Argentina
facility: Pablo Mandó
city: Uberaba
country: Brazil
facility: Rosekeila Simoes Nomeline
state: Minas Gerais
city: Porto Alegre
country: Brazil
facility: Tomás Reinert
state: Rio Grande do Sul
city: Barretos
country: Brazil
facility: Idam Oliveira Junior
state: Sao Paulo
city: Campinas
country: Brazil
facility: César Cabello
state: Sao Paulo
city: Ribeirão Preto
country: Brazil
facility: Daniel Guimaraes Tiezzi
state: Sao Paulo
city: Toronto
country: Canada
facility: Vasily Giannakeas
state: Ontario
city: Cairo
country: Egypt
facility: Salma Elashwah
city: Osaka
country: Japan
facility: Masahiro Takada
city: Tokyo
country: Japan
facility: Masakazu Toi
city: Seul
country: Korea, Republic of
facility: Wonshik Han
city: Mexico City
country: Mexico
facility: Cynthia Mayte Villarreal Garza
city: Madrid
country: Spain
facility: Cristina Saura
Study officials:
affiliation: University Federal of Sao Paulo
name: Afonso C Nazario, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.
citation: 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.
citation: 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.