Bassukas I D, Maurer-Schultze B
Institut für Medizinische Strahlenkunde der Universität Würzburg, FRG.
Growth Dev Aging. 1988 Autumn;52(3):113-22.
A method for analysing tumor growth curves is presented based on regression analysis of the linear relationship between the logarithm of the tumor size at a certain time and of that at a constant time interval earlier. By measuring the tumor size at constant time intervals the Gompertzian growth curve can be transformed into a straight line. This permits both the calculation of best fit Gompertzian curves and the comparison of different growth curves simply based on linear regression analysis. Since this new method includes exponential growth as a special case, it enables a quantitative discrimination between exponential and Gompertzian growth. Furthermore, this method permits the calculation of best fit Gompertz functions based upon individual measurements of tumor collectives without a common time scale (i.e., spontaneous tumors). As an example, it is demonstrated that the growth curves of the transplantable mouse adenocarcinoma EO 771 do not depend on the number of tumor cells inoculated.
提出了一种基于对特定时间的肿瘤大小对数与早于该时间恒定时间间隔的肿瘤大小对数之间线性关系进行回归分析的肿瘤生长曲线分析方法。通过在恒定时间间隔测量肿瘤大小,Gompertz生长曲线可转化为一条直线。这既允许计算最佳拟合的Gompertz曲线,也允许仅基于线性回归分析对不同生长曲线进行比较。由于这种新方法将指数生长作为一种特殊情况包含在内,它能够对指数生长和Gompertz生长进行定量区分。此外,该方法允许基于肿瘤群体的个体测量(即自发肿瘤)计算最佳拟合的Gompertz函数,而无需共同的时间尺度。例如,已证明可移植小鼠腺癌EO 771的生长曲线不依赖于接种的肿瘤细胞数量。