Thoracic Oncology Program, Earle A Chiles Research Center, Department of Mathematics and Statistics, Portland State University, Portland, OR 97225-7007, United States.
Eur J Radiol. 2011 Dec;80(3):e458-61. doi: 10.1016/j.ejrad.2010.08.006. Epub 2010 Aug 31.
Mathematical modeling furnishes valuable and otherwise unattainable insights, some counterintuitive, into the natural history of lung cancer. We modeled lung cancer growth dynamics to show that: (1) early diagnosis of lethal lung cancer by means of radiographic or CT screening is an unattainable goal. (2) At a given dimension and constant tumor volume doubling time, the rate of diameter increase is an exponential function (base 1.26) of the number of tumor volume doublings, and the rate of increase in volume, a quadratic function of the radius. (3) This methodology delineates the magnitude of diameter increase in small nodules required to discern volume growth. (4) Under the assumption of a high degree of compliance with sequential screens in large-scale trials, mean tumor volume doubling time can be estimated from the prevalence:incidence ratio. For example, in the Mayo Clinic computerized tomography trial, stage IA tumor volume doubling time was 230 days.
数学建模为肺癌的自然史提供了有价值的、否则难以获得的见解，其中一些是违反直觉的。我们对肺癌的生长动态进行了建模，以表明：(1) 通过放射学或 CT 筛查早期诊断致命性肺癌是无法实现的目标。(2) 在给定的尺寸和恒定的肿瘤体积倍增时间下，直径增加的速度是肿瘤体积倍增次数的指数函数（底数为 1.26），而体积的增加速度是半径的二次函数。(3) 该方法描绘了在小结节中需要增加的直径大小，以便辨别体积的增长。(4) 在大规模试验中高度遵守连续筛查的假设下，可以根据患病率与发病率的比值来估计平均肿瘤体积倍增时间。例如，在梅奥诊所的计算机断层扫描试验中，IA 期肿瘤体积倍增时间为 230 天。
