Use of Machine Learning Techniques for Serial Assessment of Systemic Inflammatory Markers in Breast Cancer Patients
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Surgery (Mastectomy or quadrantectomy).
- Who it may be relevant to
- Registry conditions: Breast Cancer. Basic parameters: 18 years — 75 years · Female.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Argentina, Brazil, Canada, Egypt, Japan +3
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Overview
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.
Interventions
- Procedure Surgery (Mastectomy or quadrantectomy)
Surgery (mastectomy or quadrantectomy); Neoadjuvant chemotherapy
Primary outcome measures
- Overall survival [Time frame: From the date of diagnosis to the date of death, assessed up to 120 months]
Secondary outcome measures (1)
- 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]
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.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
Brazil · 5 centers
- Rosekeila Simoes Nomeline — Uberaba
- Tomás Reinert — Porto Alegre
- Idam Oliveira Junior — Barretos
- César Cabello — Campinas
- Daniel Guimaraes Tiezzi — Ribeirão Preto
Japan · 2 centers
- Masahiro Takada — Osaka
- Masakazu Toi — Tokyo
Argentina · 1 center
- Pablo Mandó — Buenos Aires
Canada · 1 center
- Vasily Giannakeas — Toronto
Egypt · 1 center
- Salma Elashwah — Cairo
Mexico · 1 center
- Cynthia Mayte Villarreal Garza — Mexico City
South Korea · 1 center
- Wonshik Han — Seoul
Spain · 1 center
- Cristina Saura — Madrid
Publications
- 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. PMID 33538338
- 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. PMID 37971409
- 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. PMID 28286600
Identifiers
NCT: NCT06447532 · University of Sao Paulo