Ignite Creation Date:
2024-06-16 @ 11:50 AM
Last Modification Date:
2024-10-26 @ 3:31 PM
Study NCT ID:
NCT06447532
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2024-06-07
First Post:
2024-04-26
Brief Title:
Use of Machine Learning Techniques for Serial Assessment of Systemic Inflammatory Markers in Breast Cancer Patients
Sponsor:
Federal University of São Paulo
Organization:
Federal University of São Paulo
Study Overview
Official Title:
Use of Machine Learning Techniques for Serial Assessment of Systemic Inflammatory Markers in Breast Cancer Patients
Status:
ACTIVE_NOT_RECRUITING
Status Verified Date:
2024-06
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
INFLAMMATE
Brief Summary:
Breast cancer is the most common cancer in women globally with 23 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
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
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None