Decision Support System for Breast Cancer Detection Using Biomarker Indicators

Vergis, Spiridon; Bezas, Konstantinos; Exarchos, Themis P.

doi:10.1007/978-3-030-78775-2_3

Cited by 2 publications

(1 citation statement)

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“…Moreover, we employed the GBM algorithm to identify the most predictive variables, and we used the SHAP method to interpret the outputs. Previous studies have discussed the use of different machine learning algorithms such as random forest, GBM, and the extreme boosting machine to find predictive variables in patients with BC [ 44 , 45 ]. However, there is no consensus about which algorithm should be used because the accuracy of each model mainly depends on the dataset.…”

Section: Discussionmentioning

confidence: 99%

Gradient Boosting Machine Identified Predictive Variables for Breast Cancer Patients Pre- and Post-Radiotherapy: Preliminary Results of an 8-Year Follow-Up Study

et al. 2022

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Radiotherapy (RT) is part of the standard treatment of breast cancer (BC) because of its effects on relapse reduction and survival. However, response to treatment is highly variable, and some patients may develop disease progression (DP), a second primary cancer, or may succumb to the disease. Antioxidant systems and inflammatory processes are associated with the onset and development of BC and play a role in resistance to treatment. Here, we report our investigation into the clinical evolution of BC patients, and the impact of RT on the circulating levels of the antioxidant enzyme paraoxonase-1 (PON1), cytokines, and other standard biochemical and hematological variables. Gradient Boosting Machine (GBM) algorithm was used to identify predictive variables. This was a retrospective study in 237 patients with BC. Blood samples were obtained pre- and post-RT, with samples of healthy women used as control subjects. Results showed that 24 patients had DP eight years post-RT, and eight patients developed a second primary tumor. The algorithm identified interleukin-4 and total lymphocyte counts as the most relevant indices discriminating between BC patients and control subjects, while neutrophils, total leukocytes, eosinophils, very low-density lipoprotein cholesterol, and PON1 activity were potential predictors of fatal outcome.

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Section: Discussionmentioning

confidence: 99%