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

Breast Cancer Clinical Trial

— INFLAMMATE  

Official title:

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

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT06447532
• Other study ID #: University of Sao Paulo
• Status: Active, not recruiting
• Start date: April 1, 2024
• Est. completion date: December 31, 2024

Study information

• Verified date: June 2024
• Source: Federal University of São Paulo
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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

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.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 4500
• Est. completion date: December 31, 2024
• Est. primary completion date: December 31, 2024
• Accepts healthy volunteers: No
• Gender: Female
• Age group: 18 Years to 75 Years

Eligibility

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.

Related Conditions & MeSH terms

• Breast Cancer
• Breast Neoplasms

Intervention

Procedure:
• Surgery (Mastectomy or quadrantectomy)
Surgery (mastectomy or quadrantectomy); Neoadjuvant chemotherapy

Locations

Country	Name	City	State
Brazil	Idam Oliveira Junior	Barretos	Sao Paulo
Brazil	César Cabello	Campinas	Sao Paulo
Brazil	Tomás Reinert	Porto Alegre	Rio Grande Do Sul
Brazil	Daniel Guimaraes Tiezzi	Ribeirão Preto	Sao Paulo
Brazil	Rosekeila Simoes Nomeline	Uberaba	Minas Gerais
Canada	Vasily Giannakeas	Toronto	Ontario
Italy	Claudio Vernieri	Milan
Japan	Masahiro Takada	Osaka
Japan	Masakazu Toi	Tokyo

Sponsors (10)

Lead Sponsor	Collaborator
Federal University of São Paulo	Barretos Cancer Hospital, Emory University, Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Kansai Medical University, Kyoto University, Santa Casa de Porto Alegre, University of Campinas, Brazil, University of Sao Paulo, Women's College Hospital

Countries where clinical trial is conducted

Brazil,  Canada,  Italy,  Japan, 

References & Publications (3)

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. — View 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. — View 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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Overall survival	Overall survival	From the date of diagnosis to the date of death, assessed up to 120 months
Secondary	Disease free survival	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