Systemic inflammatory response was shown to play an important role in development and progression of many cancer types and different inflammation-based indices were used for determining prognosis. We aimed to investigate the prognostic effects of neutrophil to lymphocyte ratio (NLR) and prognostic nutritional index (PNI) in patients with non-small cell lung cancer (NSCLC). NSCLC patients diagnosed in our institution were retrospectively reviewed. Demographic and clinicopathologic characteristics were recorded. NLR and PNI was calculated before the application of any treatment. A total of 138 patients were included in the study. Patients were divided into two groups according to NLR (<3.24 or ≥3.24) and PNI (<49.5 or ≥49.5). While median overall survival was 37.0 (95% CI 17.5-56.5) months in the group with low NLR, it was calculated as 10.0 (95%CI 5.0-15.0) months in the group with high NLR (p<0.0001). While median overall survival was 7.0 (95%CI 3.5-10.5) months in the group with low PNI, it was calculated as 33.0 (95% CI 15.5-50.4) months in the group with high PNI (p<0.0001). Stage, NLR and PNI levels were evaluated as independent risk factors for overall survival for all patients in multivariate analysis (p<0.0001, p=0.04 and p<0.001, respectively). NLR (≥3.24) and PNI (<49.5) at diagnosis is an independent marker of poor outcome in patients with NSCLC. NLR and PNI is an easily measured, reproducible prognostic tests that could be considered in NSCLC patients.

Background and Objectives: In this study, we evaluated the impact of seven immune indexes on treatment response and survival outcomes in advanced non-small-cell lung cancer (NSCLC) patients receiving second-line and subsequent nivolumab treatment under real-life conditions. Materials and Methods: The pan-immune inflammation value (PIV), systemic immune inflammation value (SII), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), derived neutrophil-to-lymphocyte ratio (d-NLR), and prognostic nutritional index (PNI) were calculated. All immune indexes were classified as low and high based on cut-off values. Kaplan–Meier and Cox hazard models were used for survival analysis. Results: The median follow-up was 22.0 months (6.0–96.0). The median overall survival (OS) was 30.0 months and the median progression-free survival (PFS) was 7.0 months. In the univariate analysis, comorbidity (p = 0.03) and nivolumab use for more than eight cycles (p < 0.0001) were associated with an increase in PFS, while smoking history (p < 0.005) and d-NLR (p < 0.05) were more effective regarding OS. Patients who received more than eight cycles of nivolumab had longer median PFS (4 vs. 19 months, p < 0.001) and OS (23 vs. 43 months, p < 0.001). We found longer median OS in the PLR (45.7 vs. 75.4 months; p = 0.05), PIV (53.0 vs. 66.4 months; p = 0.19), SII (50.0 vs. 71.9 vs. months, p = 0.19), and NLR (49.9 vs. 74.55 months, p = 0.10) indexes in nivolumab long-term users (high vs. low groups, respectively). In short-term users of nivolumab, only d-NLR median OS (high vs. low, 19 vs. 75.2 months, p = 0.07) was different. Complete and partial response rates to nivolumab treatment were higher in the PNI-high group (p = 0.04). Conclusions: In these real-life data, we determined that the PLR, PIV, SII, and NLR indexes were effective in the prognosis of patients who received PD1 inhibitor nivolumab for a long time, and the d-NLR index was effective in those who developed progression in a short time. We found that the PNI was effective in patients who responded well to ICI treatment.

Aim of the study : Data are available indicating that red blood cell distribution width (RDW) is higher in cancer patients compared to healthy individuals or benign events. In our study, we aimed to investigate the influence of different RDW levels on survival in lung cancer patients....

Introduction: In recent years, machine learning (ML) methods have gained significant popularity among medical researchers interested in cancer. We aimed to test different (ML) models to predict both overall survival and survival at specific time points in patients with non-metastatic colorectal cancer (CRC). Methods: The clinicopathological and treatment data of non-metastatic CRC patients with more than 10 years of follow-up at a single center were retrospectively reviewed. 1, 2, 3, 5, and 10-year survival rates for all patients and stages I-III were statistically calculated using the Kaplan-Meier method. Five distinct machine-learning algorithms were employed to develop predictive models for patient survival at five designated time points. Results: A total of 498 patients were included in the study. The decision tree model had the highest area under the curve (AUC) for 1-year survival prediction (0.89). The ensemble model had the highest AUC for predicting 2-year, 3-year, and 5-year survival predictions (0.86, 0.92, and 0.89, respectively), while the support vector machine model had the highest AUC (0.84) for predicting 10-year survival. When considering the stages separately and assessing survival for the designated time intervals, the accuracy of all five models was found to be similar, ranging around 70% or higher. Conclusion: ML models can predict short- and long-term survival in patients with CRC, both for the overall patient population and when stratified by stage.