Prediction of Long-Term Survival Outcome by Lymph Node Ratio in Patients of Parotid Gland Cancer: A Retrospective study

The Role of Neutrophil-to-Lymphocyte Ratio in Predicting Outcomes of Acute Organophosphorus Poisoning: A Comprehensive Review

Organophosphorus poisoning (OPP) poses a significant threat to human health, necessitating accurate prognostic markers for timely intervention and improved outcomes. This review evaluates the potential of the neutrophil-to-lymphocyte ratio (NLR) as a prognostic indicator in acute organophosphorus poisoning (AOPP). A comprehensive analysis of existing literature reveals that elevated NLR values correlate with increased severity of poisoning and adverse clinical outcomes, including mortality and morbidity. NLR assessment offers valuable prognostic information beyond traditional markers, aiding risk stratification and guiding clinical decision-making. Integration of NLR into clinical practice holds promise for optimizing patient care through the early identification of high-risk individuals and tailored therapeutic interventions. Further research is needed to validate the utility of NLR in larger patient cohorts and standardize its incorporation into clinical guidelines. Leveraging NLR as a prognostic tool can enhance risk stratification, optimize treatment strategies, and ultimately improve outcomes in AOPP.

Machine learning-based survival prediction nomogram for postoperative parotid mucoepidermoid carcinoma

Parotid mucoepidermoid carcinoma (P-MEC) is a significant histopathological subtype of salivary gland cancer with inherent heterogeneity and complexity. Existing clinical models inadequately offer personalized treatment options for patients. In response, we assessed the efficacy of four machine learning algorithms vis-à-vis traditional analysis in forecasting the overall survival (OS) of P-MEC patients. Using the SEER database, we analyzed data from 882 postoperative P-MEC patients (stages I-IVA). Single-factor Cox regression and four machine learning techniques (random forest, LASSO, XGBoost, best subset regression) were employed for variable selection. The optimal model was derived via stepwise backward regression, Akaike Information Criterion (AIC), and Area Under the Curve (AUC). Bootstrap resampling facilitated internal validation, while prediction accuracy was gauged through C-index, time-dependent ROC curve, and calibration curve. The model's clinical relevance was ascertained using decision curve analysis (DCA). The study found 3-, 5-, and 10-year OS rates of 0.887, 0.841, and 0.753, respectively. XGBoost, BSR, and LASSO stood out in predictive efficacy, identifying seven key prognostic factors including age, pathological grade, T stage, N stage, radiation therapy, chemotherapy, and marital status. A subsequent nomogram revealed a C-index of 0.8499 (3-year), 0.8557 (5-year), and 0.8375 (10-year) and AUC values of 0.8670, 0.8879, and 0.8767, respectively. The model also highlighted the clinical significance of postoperative radiotherapy across varying risk levels. Our prognostic model, grounded in machine learning, surpasses traditional models in prediction and offer superior visualization of variable importance.