Heesterman Berdine L, Bokhorst John-Melle, de Pont Lisa M H, Verbist Berit M, Bayley Jean-Pierre, van der Mey Andel G L, Corssmit Eleonora P M, Hes Frederik J, van Benthem Peter Paul G, Jansen Jeroen C
Department of Otorhinolaryngology, Leiden University Medical Center, Leiden, The Netherlands.
Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.
J Neurol Surg B Skull Base. 2019 Feb;80(1):72-78. doi: 10.1055/s-0038-1667148. Epub 2018 Jul 23.
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. 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 ( ) 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. Equations generating sigmoidal-shaped growth curves (Gompertz, logistic, Spratt and Bertalanffy) provided a good fit (median : 0.996-1.00) and better described the observed data compared with the linear, exponential, and Mendelsohn equations ( < 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. 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.
为了更好地了解头颈部副神经节瘤（HNPGL）的自然病程，并最终区分哪些病例适合早期治疗，哪些病例最好不予治疗，我们研究了77例接受初步观察的HNPGL的生长动力学。利用数字可得的磁共振图像，在三个时间点估计肿瘤体积。随后，使用非线性最小二乘法回归将七个数学模型拟合到观察到的生长数据。用决定系数（ ）和均方根误差评估拟合优度。通过Kruskal-Wallis单向方差分析和随后的事后检验对模型进行比较。此外，还评估了预测的可信度（肿瘤生长开始时的年龄和90岁时的估计体积）。生成S形生长曲线的方程（Gompertz、logistic、Spratt和Bertalanffy）拟合良好（中位数 ：0.996 - 1.00），与线性、指数和Mendelsohn方程相比，能更好地描述观察到的数据（ < 0.001）。尽管S形生长曲线在拟合优度方面没有统计学上的显著差异，但Bertalanffy模型最常预测出符合实际的肿瘤生长开始年龄和90岁时的估计体积。HNPGL的生长最好用肿瘤生长减速规律来描述，其中Bertalanffy模型更为适用。据我们所知，这是该常被忽视的模型首次成功拟合临床获得的生长数据。
