Introduction:Carotid body tumors (CBTs) are slow-growing benign tumors. Therefore, surgical resection is considered in case of tumor growth. The timing of surgery is of the utmost importance as the... Show moreIntroduction:Carotid body tumors (CBTs) are slow-growing benign tumors. Therefore, surgical resection is considered in case of tumor growth. The timing of surgery is of the utmost importance as the risk of iatrogenic surgical complications increases when resecting larger tumors, whereas on the other hand, resections for asymptomatic small CBT should be prevented. The primary aim of this study was to identify which tumor size or dimension is most accurate to predict nerve injury in patients undergoing resection of a CBT.Material and methods:This retrospective cohort study included patients who underwent surgical resection of CBT at the university hospital in South-Holland. Baseline patient characteristics and tumor measurements were retrieved from the medical records. The authors assessed how the different methods of measuring the size of the tumor were interrelated using Pearson correlation. Logistic regression was used to assess which variables were independently associated with nerve injury, including age at surgery, Shamblin classification, and those dimensions that captured different aspects of tumor size (rather than measuring the same as shown by high correlations) as possible independent variables.Results:In 125 patients, 143 CBTs were resected whereof in 35 cases cranial nerve injury occurred, (transient in 16 cases and permanent in 19 cases). The risks for nerve injury increased with larger tumor size and the Shamblin classification. Logistic regression analysis showed that the anterior-posterior (AP) diameter significantly increased the odds of a nerve injury, a doubling for every 1 cm increase in AP diameter [odds ratio (95% CI) 2.12 (1.29-3.48), P=0.003].Conclusion:This study shows that measured tumor size in the AP plane is a strong predictor for postoperative nerve injury of a CBT resection. This predictor can be used in the daily clinic to give insight in operative risks. More research is needed in order to select the most appropriate time window for CBT resection. Show less
Background To improve our understanding of the natural course of head and neck paragangliomas (HNPGL) and ultimately differentiate between cases that benefit from early treatment and those that are... Show moreBackground To improve our understanding of the natural course of head and neck paragangliomas (HNPGL) and ultimately differentiate between cases that benefit from early treatment and those that are best left untreated, we studied the growth dynamics of 77 HNPGL managed with primary observation.Methods Using digitally available magnetic resonance images, tumor volume was estimated at three time points. Subsequently, nonlinear least squares regression was used to fit seven mathematical models to the observed growth data. Goodness of fit was assessed with the coefficient of determination ( R-2 ) and root-mean-squared error. The models were compared with Kruskal-Wallis one-way analysis of variance and subsequent post-hoc tests. In addition, the credibility of predictions (age at onset of neoplastic growth and estimated volume at age 90) was evaluated.Results Equations generating sigmoidal-shaped growth curves (Gompertz, logistic, Spratt and Bertalanffy) provided a good fit (median R-2 : 0.996-1.00) and better described the observed data compared with the linear, exponential, and Mendelsohn equations ( p <0.001). Although there was no statistically significant difference between the sigmoidal-shaped growth curves regarding the goodness of fit, a realistic age at onset and estimated volume at age 90 were most often predicted by the Bertalanffy model.Conclusions Growth of HNPGL is best described by decelerating tumor growth laws, with a preference for the Bertalanffy model. To the best of our knowledge, this is the first time that this often-neglected model has been successfully fitted to clinically obtained growth data. Show less
