Intelligent Decision Analytics AB, Sweden.
Karolinska Institutet, Sweden.
Math Biosci. 2022 Nov;353:108897. doi: 10.1016/j.mbs.2022.108897. Epub 2022 Aug 28.
The aim of the current article is to present theory that can help unify continuous growth approaches for modelling breast cancer tumour growth based on human data. We present a framework that has three main features: a general likelihood function to account for patient specific screening regiments; stable disease assumptions describing tumour population dynamics; and mathematical models describing tumour growth, individual variation in tumour growth, a hazard for symptomatic detection, and screening test sensitivity. The framework is able to incorporate any random effects distributions for the tumour growth rate parameter, any hazard functions for symptomatic tumour detection, as well as any monotonously increasing function for the tumour growth model. Based on a sample of 1902 incident breast cancer cases with data on mammography screening, we show how the framework can be used to estimate tumour growth based on different growth functions.
本文旨在提出一种理论，以帮助统一基于人体数据的乳腺癌肿瘤生长的连续增长方法。我们提出了一个具有三个主要特征的框架：一个通用似然函数，用于解释患者特定的筛查方案；描述肿瘤群体动态的稳定疾病假设；以及描述肿瘤生长、肿瘤生长个体差异、症状检测风险和筛查试验敏感性的数学模型。该框架能够结合肿瘤生长率参数的任何随机效应分布、任何症状性肿瘤检测的风险函数，以及任何单调递增的肿瘤生长模型。基于 1902 例乳腺癌病例的样本，以及关于乳房 X 线筛查的数据，我们展示了如何使用该框架根据不同的生长函数来估计肿瘤生长。
