Predictive Values of Preoperative Index Analysis in Patients with Esophageal Squamous Cell Carcinoma

The prognostic nutritional index (PNI) has been widely used to predict survival outcomes of patients with various malignant tumors. The purpose of this study is to assess the prognostic value of PNI and analyze the effects of preoperative clinical indicators in patients with esophageal squamous cell carcinoma (ESCC). The optimal threshold of PNI is determined by ROC curve analysis, and the correlation between PNI and clinical indicators is evaluated by Chi square test and Fisher’s exact test. The results showed that PNI has been significantly correlated with gender, BMI, T stage, Eosinophil count, Erythrocyte count, Hemoglobin concentration, TP, Albumin and Globulin. PNI is positively related to LMR, NLR and PLR by Spearman’s rank correlation coefficient. Univariate Cox regression model declared that PFS has been vitally correlated with T stage, N stage, TNM stage, EC and PT. Further, multivariate Cox regression model analyses showed that PFS has been significantly correlated with N stage and PT. ROC curve analysis expressed that the combination of N stage and EC has better accuracy and validity in predicting the severity of the patient. The present study determines that N stages, EC and PT are valuable factors in predicting patient’s survival outcomes, as well as independent indicators for poor prognosis.

[1]  M. Ni,et al.  Margin diagnosis for endoscopic submucosal dissection of early gastric cancer using multiphoton microscopy , 2020, Surgical Endoscopy.

[2]  J. Wolchok,et al.  Immune-Modified Response Evaluation Criteria In Solid Tumors (imRECIST): Refining Guidelines to Assess the Clinical Benefit of Cancer Immunotherapy. , 2018, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[3]  R. Grimm,et al.  Intravoxel incoherent motion diffusion‐weighted MRI of invasive breast cancer: Correlation with prognostic factors and kinetic features acquired with computer‐aided diagnosis , 2018, Journal of magnetic resonance imaging : JMRI.

[4]  Junwei Sun,et al.  Autonomous memristor chaotic systems of infinite chaotic attractors and circuitry realization , 2018, Nonlinear Dynamics.

[5]  L. Scalfi,et al.  Malnutrition and sarcopenia assessment in patients with chronic obstructive pulmonary disease according to international diagnostic criteria, and evaluation of raw BIA variables. , 2018, Respiratory medicine.

[6]  I. Gill,et al.  Automated Performance Metrics and Machine Learning Algorithms to Measure Surgeon Performance and Anticipate Clinical Outcomes in Robotic Surgery. , 2018, JAMA surgery.

[7]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[8]  Aimin Li,et al.  Occurrence and potential human health risks of semi-volatile organic compounds in drinking water from cities along the Chinese coastland of the Yellow Sea. , 2018, Chemosphere.

[9]  L. Rubinstein,et al.  Characteristics and outcomes of breast cancer patients enrolled in the National Cancer Institute Cancer Therapy Evaluation Program sponsored phase I clinical trials , 2018, Breast Cancer Research and Treatment.

[10]  E. McCaughan,et al.  Development and evaluation of a holistic surgical head and neck cancer post‐treatment follow‐up clinic using touchscreen technology—Feasibility study , 2018, European journal of cancer care.

[11]  A. Ray,et al.  Neutrophil-to-lymphocyte Ratio (NLR) as a predictor for recurrence in patients with stage III melanoma , 2018, Scientific Reports.

[12]  Wei Ma,et al.  Health Impacts Due to Major Climate and Weather Extremes , 2019, Ambient Temperature and Health in China.

[13]  M. McCarter,et al.  Perioperative and Survival Outcomes Following Neoadjuvant FOLFIRINOX versus Gemcitabine Abraxane in Patients with Pancreatic Adenocarcinoma. , 2018, JOP : Journal of the pancreas.

[14]  A. Mahvi,et al.  Health Risk Assessment of Heavy Metals in Vegetables in an Endemic Esophageal Cancer Region in Iran , 2018, Health Scope.

[15]  Hans Knutsson,et al.  Reply to Chen et al.: Parametric methods for cluster inference perform worse for two‐sided t‐tests , 2019, Human brain mapping.

[16]  S. Mohammadi,et al.  Dysregulation of helper T lymphocytes in esophageal squamous cell carcinoma (ESCC) patients is highly associated with aberrant production of miR-21 , 2019, Immunologic Research.

[17]  Z. Pan,et al.  Prognostic value of preoperative prognostic nutritional index and its associations with systemic inflammatory response markers in patients with stage III colon cancer , 2017, Chinese journal of cancer.

[18]  Xiang-hui Zhang,et al.  Patterns of Life Lost to Cancers with High Risk of Death in China , 2019, International journal of environmental research and public health.

[19]  Jun Shen,et al.  Predictive value of pretreatment MRI texture analysis in patients with primary nasopharyngeal carcinoma , 2019, European Radiology.

[20]  Haiyong Wang,et al.  Including positive lymph node count in the AJCC N staging may be a better predictor of the prognosis of NSCLC patients, especially stage III patients: a large population-based study , 2019, International Journal of Clinical Oncology.

[21]  D. Maucort-Boulch,et al.  Assessing long-term survival benefits of immune checkpoint inhibitors using the net survival benefit. , 2019, Journal of the National Cancer Institute.

[22]  Y. Jiao,et al.  High Trophinin-Associated Protein Expression Is an Independent Predictor of Poor Survival in Liver Cancer , 2018, Digestive Diseases and Sciences.

[23]  Arjun Gupta,et al.  Feasibility of Wearable Physical Activity Monitors in Patients With Cancer. , 2018, JCO clinical cancer informatics.

[24]  Q. Song,et al.  Prognostic significance of preoperative lymphocyte-monocyte ratio in patients with resectable esophageal squamous cell carcinoma. , 2015, Asian Pacific journal of cancer prevention : APJCP.

[25]  J. Ryu,et al.  A High Monocyte-to-Lymphocyte Ratio Predicts Poor Prognosis in Patients with Advanced Gallbladder Cancer Receiving Chemotherapy , 2019, Cancer Epidemiology, Biomarkers & Prevention.

[26]  Justin K. Rajendra,et al.  A tail of two sides: Artificially doubled false positive rates in neuroimaging due to the sidedness choice with t-tests , 2018, bioRxiv.