This study by Shen et al. examines whether inflammatory biomarkers can be used with machine learning (ML) models to identify depression in pancreatic cancer patients. Researchers assessed 328 pancreatic cancer patients using the Patient Health Questionnaire-9 (PHQ-9) and collected routinely available clinical and inflammatory biomarker data, including C-reactive protein (CRP), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and albumin. Four machine learning models (logistic regression, random forest, support vector machine, and extreme gradient boosting) were trained on 70% of the data and evaluated on the remaining 30% to predict clinically significant depression. Generalized estimating equations (GEE) were also used to analyze the associations between inflammatory markers and depression. The machine learning models showed moderate but consistent predictive performance with AUC values ranging from 0.70 to 0.72. Permutation importance analysis identified CRP, NLR, and albumin as the most significant predictors of depression. GEE analysis confirmed that elevated levels of CRP and NLR were independently associated with a higher likelihood of depression. The findings suggest that inflammatory markers are moderately but significantly correlated with depression in pancreatic cancer patients and that ML can effectively use these markers to predict depression. This also supports the linkage between depression and inflammation.