IEEE Trans Med Imaging. 2018 Mar;37(3):724-732. doi: 10.1109/TMI.2017.2779811.
Predicting tumor growth and its response to therapy remains a major challenge in cancer research and strongly relies on tumor growth models. In this paper, we introduce, calibrate, and verify a novel image-driven reaction-diffusion model of avascular tumor growth. The model allows for proliferation, death and spread of tumor cells, and accounts for nutrient distribution and hypoxia. It is constrained by longitudinal time series of dynamic contrast-enhancement-MRI images. Tumor specific parameters are estimated from two early time points and used to predict the spatio-temporal evolution of the tumor volume and cell densities at later time points. We first test our parameter estimation approach on synthetic data from 15 generated tumors. Our in silico study resulted in small volume errors (<5%) and high Dice overlaps (>97%), showing that model parameters can be successfully recovered and used to accurately predict the tumor growth. Encouraged by these results, we apply our model to seven pre-clinical cases of breast carcinoma. We are able to show promising preliminary results, especially for the estimation for early time points. Processes like angiogenesis and apoptosis should be included to further improve predictions for later time points.
预测肿瘤生长及其对治疗的反应仍然是癌症研究中的一个主要挑战，并且强烈依赖于肿瘤生长模型。在本文中，我们引入、校准和验证了一种新的无血管肿瘤生长的图像驱动反应扩散模型。该模型允许肿瘤细胞的增殖、死亡和扩散，并考虑了营养物质的分布和缺氧情况。它受到动态对比增强 MRI 图像的纵向时间序列的限制。从两个早期时间点估计肿瘤特异性参数，并用于预测后期时间点肿瘤体积和细胞密度的时空演化。我们首先在 15 个生成的肿瘤的合成数据上测试了我们的参数估计方法。我们的计算机研究结果导致体积误差较小（<5%）和 Dice 重叠率较高（>97%），表明可以成功地恢复模型参数并用于准确预测肿瘤生长。鉴于这些结果，我们将模型应用于七例乳腺癌的临床前病例。我们能够显示出有希望的初步结果，特别是在早期时间点的估计方面。应该包括血管生成和细胞凋亡等过程，以进一步提高对后期时间点的预测。
