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

Обзор

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

Подробное описание

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.

Вмешательства

• Процедура Surgery (Mastectomy or quadrantectomy)
  Surgery (mastectomy or quadrantectomy); Neoadjuvant chemotherapy

Первичные конечные точки

• Overall survival [Срок оценки: From the date of diagnosis to the date of death, assessed up to 120 months]

Вторичные конечные точки (1)

• Disease free survival [Срок оценки: From the date of diagnosis to the date of first progression (local recurrence of tumor or distant metastasis), assessed up to 60 months]

Критерии участия

Критерии включения

• 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;

Критерии исключения

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.

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Модель наблюдения

Когортное

Центры проведения

Бразилия · 5 центров

• Rosekeila Simoes Nomeline — Uberaba
• Tomás Reinert — Porto Alegre
• Idam Oliveira Junior — Barretos
• César Cabello — Campinas
• Daniel Guimaraes Tiezzi — Ribeirão Preto

Япония · 2 центра

• Masahiro Takada — Osaka
• Masakazu Toi — Tokyo

Аргентина · 1 центр

• Pablo Mandó — Buenos Aires

Канада · 1 центр

• Vasily Giannakeas — Toronto

Египет · 1 центр

• Salma Elashwah — Cairo

Мексика · 1 центр

• Cynthia Mayte Villarreal Garza — Mexico City

South Korea · 1 центр

• Wonshik Han — Seoul

Испания · 1 центр

• Cristina Saura — Madrid

Публикации

• 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

Идентификаторы

NCT: NCT06447532 · University of Sao Paulo

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

Открыть это исследование на ClinicalTrials.gov ↗