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从分支过程模型看癌症复发次数。

Cancer recurrence times from a branching process model.

机构信息

School of Mathematics, University of Edinburgh, Edinburgh, United Kingdom.

出版信息

PLoS Comput Biol. 2019 Nov 21;15(11):e1007423. doi: 10.1371/journal.pcbi.1007423. eCollection 2019 Nov.

Abstract

As cancer advances, cells often spread from the primary tumor to other parts of the body and form metastases. This is the main cause of cancer related mortality. Here we investigate a conceptually simple model of metastasis formation where metastatic lesions are initiated at a rate which depends on the size of the primary tumor. The evolution of each metastasis is described as an independent branching process. We assume that the primary tumor is resected at a given size and study the earliest time at which any metastasis reaches a minimal detectable size. The parameters of our model are estimated independently for breast, colorectal, headneck, lung and prostate cancers. We use these estimates to compare predictions from our model with values reported in clinical literature. For some cancer types, we find a remarkably wide range of resection sizes such that metastases are very likely to be present, but none of them are detectable. Our model predicts that only very early resections can prevent recurrence, and that small delays in the time of surgery can significantly increase the recurrence probability.

摘要

随着癌症的发展,细胞常常从原发肿瘤扩散到身体的其他部位并形成转移。这是癌症相关死亡的主要原因。在这里,我们研究了一种概念上简单的转移形成模型,其中转移病灶的形成速率取决于原发肿瘤的大小。每个转移的演变都被描述为一个独立的分支过程。我们假设原发肿瘤在给定大小时被切除,并研究任何转移达到最小可检测大小的最早时间。我们模型的参数是针对乳腺癌、结直肠癌、头颈部癌、肺癌和前列腺癌分别进行独立估计的。我们使用这些估计值将我们的模型预测与临床文献中报告的值进行比较。对于某些癌症类型,我们发现切除的大小范围非常广泛,以至于很可能存在转移,但它们都无法检测到。我们的模型预测,只有非常早期的切除才能防止复发,而且手术时间的微小延迟会显著增加复发的可能性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/51c7/6871767/f710b0502f50/pcbi.1007423.g001.jpg

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