Which model is better in predicting the survival of laryngeal squamous cell carcinoma?: Comparison of the random survival forest based on machine learning algorithms to Cox regression: analyses based on SEER database.

Prediction of postoperative survival for laryngeal carcinoma patients is very important. This study attempts to demonstrate the utilization of the random survival forest (RSF) and Cox regression model to predict overall survival of laryngeal squamous cell carcinoma (LSCC) and compare their performance. A total of 8677 patients diagnosed with LSCC from 2004 to 2015 were obtained from surveillance, epidemiology, and end results database. Multivariate imputation by chained equations was applied to filling the missing data. Lasso regression algorithm was conducted to find potential predictors. RSF and Cox regression were used to develop the survival prediction models. Harrell's concordance index (C-index), area under the curve (AUC), Brier score, and calibration plot were used to evaluate the predictive performance of the 2 models. For 3-year survival prediction, the C-index in training set were 0.74 (0.011) and 0.84 (0.013) for Cox and RSF respectively. For 5-year survival prediction, the C-index in training set were 0.75 (0.022) and 0.80 (0.011) for Cox and RSF respectively. Similar results were found in validation set. The AUC were 0.795 for RSF and 0.715 for Cox in the training set while the AUC were 0.765 for RSF and 0.705 for Cox in the validation set. The prediction error curves for each model based on Brier score showed the RSF model had lower prediction errors both in training group and validation group. What's more, the calibration curve displayed similar results of 2 models both in training set and validation set. The performance of RSF model were better than Cox regression model. The RSF algorithms provide a relatively better alternatives to be of clinical use for estimating the survival probability of LSCC patients.

Rapid analysis of cyclamate in foods and beverages by gas chromatography-electron capture detector (GC-ECD).

A rapid method for determination of sodium cyclamate in foods and beverages was developed. Sodium cyclamate was converted to N,N-dichloridecyclohexylamine by reaction with sodium hypochlorite under acid condition. N,N-dichloridecyclohexylamine was subsequently extracted by n-hexane and determined by gas chromatography. Conditions such as derivatization time, the concentration of sodium hypochlorite and sulphuric acid were optimised. Amino acids, aliphatic amines, and food additives such as preservatives, dyes and sweeteners showed no interference for quantification of cyclamate. The correlation coefficient of calibration curve was 0.9993 in the range of 5.0-250mg/L. The limits of detection (LOD) and limits of quantification (LOQ) were calculated as three or ten times the signal-to-noise ratio (S/N), respectively. The LOD and LOQ for yellow wine and fruit juice were 0.05 and 0.2mg/L, respectively. The LOD and LOQ for cake and preserved fruit were 0.25 and 0.8mg/kg, respectively. The intra-day and inter-day RSD were 0.28% and 1.1% (n=5), respectively. The method was successfully applied for determination of cyclamate in yellow wine, cake, fruit juice and preserved fruit. This method was simple, fast, and sensitive. It was suitable for the determination of cyclamate in foods and beverages for safety and quality control inspections.