ASO Author Reflections: Could the Application of Machine Learning Enhance the Accuracy of Prognosis Estimation Using Serum Inflammatory Markers in Colorectal Cancer Patients?

Excerpt

Systemic inflammation is a critical factor that influences the prognosis of patients with different types of cancer.¹ Despite various proposed compositions of inflammatory markers, there is currently no established primary value that can be integrated into clinical decision making.² The scarcity of studies comparing the clinical effectiveness of different proposed values, such as neutrophil-lymphocyte ratio (NLR), lymphocyte-monocyte ratio (LMR), platelet-lymphocyte ratio (PLR), lymphocyte-C reactive protein ratio (LCR), and others, contributes to this issue. In a recent study, a comparison of various inflammatory markers was performed but their effectiveness was solely assessed based on the area under the curve (AUC) value, which can be considered a significant limitation.³ Fortunately, the advent of machine learning algorithms has provided new opportunities to create composite inflammatory markers that could greatly improve their prognostic value. Therefore, our study aimed to develop a novel machine learning algorithm that could serve as a substitute for previous serum-based inflammatory markers. …