Development and validation of nomogram to predict risk of survival in patients with laryngeal squamous cell carcinoma

Abstract

Background: Due to a wide variation of tumor behavior, prediction of survival in laryngeal squamous cell carcinoma (LSCC) patients received curative-intent surgery is an important but formidable challenge. We attempted to establish a nomogram to precisely predict survival probability in LSCC patients. Methods: A total of 369 consecutive LSCC patients underwent curative resection between 2008 and 2012 at Hunan Province Cancer Hospital were included in this study. Subsequently, 369 LSCC patients were assigned to a training set (N=261) and a validation set (N=108) at random. On the basis of multivariable Cox regression analysis results, we developed a nomogram. The predictive accuracy and discriminative ability of the nomogram were confirmed by calibration curve and a concordance index (C-index), and compared with TNM stage system by C-index, receiver operating characteristic (ROC) analysis. Results: Six independent parameters to predict prognosis were age, pack years, N-stage, lymph node ratio (LNR), anemia and albumin, which were all assembled into the nomogram. The calibration curve verified excellent models’ concordance. The C-index of the nomogram was 0.73 (0.68-0.78), and the area under curve (AUC) of nomogram in predicting overall survival (OS) was 0.766, which were significantly higher than traditional TNM stage. Decision curve analysis further demonstrated that our nomogram had a larger net benefit than the TNM stage. Conclusion: A risk prediction nomogram for patients with LSCC, incorporating easily assessable clinicopathologic factors, generates more precise estimations of the survival probability when compared TNM stage alone, but still need additional data before being used in clinical application.

Development and validation of a nomogram to predict plastic bronchitis in children with refractory Mycoplasma pneumoniae pneumonia

“…Variables identified by LASSO regression analysis were applied to establish logistic regression model and presented with a nomogram. Furthermore, these selected continuous predictors (i.e., peak body temperature, N%, PLT, IL-6 and LDH) were categorized into three groups based on tertile after being assessed by restricted cubic splines to evaluate the linear relationship assumptions [ 21 , 22 ]. Additionally, the degree of multicollinearity among variables was evaluated by the variance inflation factor (VIF) in the multivariable logistic regression analysis.…”

Development and validation of a risk prediction model for incident liver cancer

“…Decision curve analysis (DCA), a comprehensive method for the assessment of diagnostic tests and prediction models, was conducted to evaluate the clinical utility of our prediction model ( 21 , 22 , 26 , 27 ). Net benefit, a key measure in DCA, was computed by weighting the true positive rate minus the false positive rate weighted on the risk threshold.…”

A nomogram for predicting cancer-specific survival and overall survival in elderly patients with nonmetastatic renal cell carcinoma

“…The nomogram has been widely used to predict the survival of various cancers. Cui et al (6,7) predicted the recurrence and survival risk of patients with laryngeal squamous cell carcinoma using the nomogram.…”