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-intend 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. Decision curve analysis (DCA) was conducted to estimate clinical usefulness of our nomogram. 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) were 0.766, which were significantly higher than traditional TNM stage. Decision curve analysis further demonstrated that our nomogram had larger net benefit than 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.

Dual-energy CT may predict post-operative recurrence in early-stage glottic laryngeal cancer: a novel nomogram and risk stratification system

“…Patients even with the same tumor stage could have different prognoses. Our study also proved this, consistent with previous results [13,14,31].…”

“…Compared with the application of clinical variables alone, the model added image variables produced a more accurate estimation of recurrence probability, but additional data is needed before it can be used in clinical applications. Currently, the TNM stage [30] was a common tool to evaluate the recurrence risk [13]. However, compared with the constructed nomogram, the predictive efficacy of the TNM stage alone was limited.…”

“…Nomogram, a visual tool of predictive statistical models for individuals, has been used to quantify risk factors for prognostic outcome in laryngeal cancer [6,[12][13][14][15]. But the existing predictive models are all limited to clinical aspects.…”

Number of Positive Lymph Nodes Combined with the Logarithmic Ratio of Positive Lymph Nodes predicts Survival in Patients with Non-Metastatic Larynx Squamous Cell Carcinoma

“…To our knowledge, this is the first new model that combines LODDS and NPLN to predict long-term survival in patients with LSCC. Previous research has focused on the comparison of the lymph node ratio [27]; we creatively incorporated the LODDS and NPLN in a prognosis model, which strengthened the TNM staging and utilized the valuable pathological evidence for surgery. We believe when compared with the other four models, LODDS + NPLN shows the most significant predictive potential, that is, the higher the value of LODDS with NPLN, the lower the value of OS and CSS.…”

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.…”