scholarly journals Clinical significance of pretreatment prognostic nutritional index and lymphocyte-to-monocyte ratio in patients with advanced p16-negative oropharyngeal cancer—a retrospective study

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10465
Author(s):  
Ming-Hsien Tsai ◽  
Tai-Lin Huang ◽  
Hui-Ching Chuang ◽  
Yu-Tsai Lin ◽  
Fu-Min Fang ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Oropharyngeal Cancer ◽  
Cox Regression ◽  
Disease Free Survival ◽  
Prognostic Nutritional Index ◽  
Survival Prediction ◽  
Optimal Cutoff ◽  
Lymphocyte To Monocyte Ratio ◽  
Nutritional Index ◽  
Cutoff Values

Background Systemic inflammation and nutritional status both play roles in the survival of cancer patients. Therefore, it is important to understand the effects of prognostic nutritional index (PNI) and lymphocyte-to-monocyte ratio (LMR) on the survival of patients with advanced p16-negative oropharyngeal cancer. Methods A total of 142 patients diagnosed with advanced p16-negative oropharyngeal cancer between 2008 and 2015 were enrolled in this study. All patients received primary treatment with definite concurrent chemoradiotherapy (CCRT). Optimal cutoff values for PNI and LMR were determined using receiver operating characteristic curves for survival prediction. Survival rates for different level of PNI and LMR were estimated and compared using Kaplan–Meier method and log-rank test to see if there were significant effects on these end points, including 5-year overall survival (OS), disease-specific survival (DSS) and disease-free survival (DFS) rates. The effects of PNI and LMR on survival were assessed using Cox regression model adjusted for other prognostic factors. Results The results showed the optimal cutoff values for PNI and LMR were 50.5 and 4.45, respectively. A high PNI (≧50.5) was significantly improved the 5-year OS. A low LMR (<4.45) was significantly associated with a poor 5-year DFS, DSS, and OS. In multivariate analysis, both PNI and LMR were independent prognosticators for 5-year OS. Conclusions Elevated pretreatment PNI and LMR are both favorable prognosticators in advanced p16-negative oropharyngeal cancer patients undergoing CCRT.

scholarly journals The role of prognostic nutritional index for clinical outcomes of gastric cancer after total gastrectomy

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Zhu Xishan ◽  
Zhao Ye ◽  
Ma Feiyan ◽  
Xuan Liang ◽  
Wu Shikai
Keyword(s):  
Gastric Cancer ◽  
Cancer Patients ◽  
Total Gastrectomy ◽  
Disease Free Survival ◽  
Prognostic Nutritional Index ◽  
Tnm Stage ◽  
Nutritional Index ◽  
Gastric Cancer Patients ◽  
The Relationship ◽  
Univariate Analyses

Abstract The purpose of this article is to evaluate the relationship between the nutrition-based microenvironment and clinicopathological information for gastric cancer patients and to investigate the prognostic value of nutrition index for gastric cancer patients undergoing total gastrectomy. We retrospectively collected clinical information of 245 gastric cancer patients who underwent total gastrectomy in our hospital between January 1st 2005 and December 30th 2015. According to the prognostic nutritional index (PNI) level, they were divided into low PNI (< 43) group and high PNI (≥ 43) group. The relationship between PNI and the disease-free survival (DFS) and overall survival (OS) were analyzed by statistical analysis. Univariate analyses demonstrated that TNM stage (p = 0.025), patients age (p = 0.042), lymph node metastasis (p = 0.028), tumor differentiation (p = 0.037) and a low PNI (p = 0.033) were closely correlated with a poor prognosis. In multivariate analysis, TNM stage (p = 0.027) and a low PNI (p = 0.041) were found to be independently associated with poor survival. Additionally, when age was considered as a stratified factor, univariate analyses demonstrated that low PNI correlated with shorter DFS in non-elderly (< 65) patients (p = 0.022) and shorter DFS (p = 0.036) and OS (p = 0.047) in elderly (≥ 65) patients. The low prognostic nutritional index is an independent risk factor associated with poor gastric cancer survival which represents the nutritional microenvironment. Patients with low pre-operative prognostic nutritional index levels should be observed more closely after surgery to prevent the occurrence of post-operative complications in the near future.

scholarly journals Prediction of Post-operative Survival of Colorectal Cancer Patient By Using the Prognostic Nutritional Index: An Evidence-Based Case Report

2019 ◽  
Vol 2 (2) ◽  
pp. 25
Author(s):  
Nurul Ratna Manikam ◽  
Yosua Y Kristian ◽  
Luana Lidwina ◽  
Ayu Diandra Sari ◽  
Diana Sunardi
Keyword(s):  
Colorectal Cancer ◽  
Overall Survival ◽  
Cancer Patient ◽  
Cancer Patients ◽  
Colorectal Cancer Patient ◽  
Disease Free Survival ◽  
Prognostic Nutritional Index ◽  
Cochrane Library ◽  
Free Survival ◽  
Nutritional Index

Background: Colorectal cancer patients may be treated with several modalities and one of them is surgical treatment. Surgery in cancer patients is risky procedure and may not always resulted in prolonged survival. Therefore, before receiving any recommended treatment, the patient’s prognosis has to be assessed and defined properly. Several methods are available to assess the prognosis of cancer patients; one of them is the prognostic nutritional index (PNI).Objective: This study aimed to predict the survival of a colorectal cancer patient post-operatively by calculating the pre-operative PNI score.Method: Literature searching was done using inclusion and exclusion criteria on two databases, i.e. the PubMed and the Cochrane Library. The outcome was survival (disease-free survival, relapse-free survival, or overall survival).Results: Five articles that address the clinical question were retrieved. All indicated that a patient with low PNI score (<44.5) had a shorter overall survival (HR between 1.92 and 3.98 with all p values were <0.05).Conclusion: Pre-operative PNI score can be used to assess the overall survival of a colorectal cancer patient who underwent surgical resection. Patients with a PNI score > 44.5 had better survival than lower PNI score.

An MRI-based radiomics signature and clinical characteristics for survival prediction in early-stage cervical cancer

2021 ◽  
Author(s):  
Ru-ru Zheng ◽  
Meng-ting Cai ◽  
Li Lan ◽  
Xiao Wan Huang ◽  
Yun Jun Yang ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Cancer Patients ◽  
Clinical Characteristics ◽  
Early Stage ◽  
Disease Free Survival ◽  
Clinicopathological Parameters ◽  
Rank Test ◽  
Survival Prediction ◽  
Early Stage Cervical Cancer ◽  
Radiomics Signature

Objectives: To investigate the prognostic role of Magnetic Resonance Imaging (MRI) based radiomics signature and clinical characteristics for overall survival (OS) and disease-free survival (DFS) in the early-stage cervical cancer. Methods: A total of 207 cervical cancer patients (training cohort: n = 144; validation cohort: n = 63) were enrolled. 792 radiomics features were extracted from T2-weighted (T2W) and diffusion weighted imaging (DWI). 19 clinicopathological parameters were collected from the electronic medical record system. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to select significant features to construct prognostic model for OS and DFS. Kaplan-Meier (KM) analysis and log-rank test were applied to identify the association between the radiomics score (Rad-score) and survival time. Nomogram discrimination and calibration were evaluated as well. Associations between radiomics features and clinical parameters were investigated by heatmaps. Results: A radiomics signature derived from joint T2W and DWI images showed better prognostic performance than that from either T2W or DWI image alone. Higher Rad-score was associated with worse OS (p < 0.05) and DFS (p < 0.05) in the training and validation set. The joint models outperformed both radiomics model and clinicopathological model alone for 3 year OS and DFS estimation. The calibration curves reached an agreement. Heatmap analysis demonstrated significant associations between radiomics features and clinical characteristics. Conclusions: The MRI-based radiomics nomogram showed a good performance on survival prediction for the OS and DFS in the early-stage cervical cancer. The prediction of the prognostic models could be improved by combining with clinical characteristics, suggesting its potential for clinical application. Advances in knowledge: This is the first study to build the radiomics-derived models based on T2W and DWI images for the prediction of survival outcomes on the early stage cervical cancer patients, and further construct a combined risk scoring system incorporating the clinical features.

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