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

Condition(s) studied

Investigational drug(s) / intervention(s)

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

Study 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

Eligibility

Primary outcome measure(s)

• Overall survival — From the date of diagnosis to the date of death, assessed up to 120 months
  Overall survival

Trial sites (13)

Other trials for the same condition