Brain tumors are one of the major common causes of cancer-related death, worldwide. Growth prediction of these tumors, particularly gliomas which are the most dominant type, can be quite useful to improve treatment planning, quantify tumor aggressiveness, and estimate patients’ survival time towards precision medicine. Studying tumor growth prediction basically requires multiple time points of single or multimodal medical images of the same patient. Recent models are based on complex mathematical formulations that basically rely on a system of partial differential equations, e.g. reaction diffusion model, to capture the diffusion and proliferation of tumor cells in the surrounding tissue. However, these models usually have small number of parameters that are insufficient to capture different patterns and other characteristics of the tumors. In addition, such models consider tumor growth independently for each subject, not being able to get benefit from possible common growth patterns existed in the whole population under study. In this paper, we propose a novel data-driven method via stacked 3D generative adversarial networks (GANs), named GP-GAN, for growth prediction of glioma. Specifically, we use stacked conditional GANs with a novel objective function that includes both ${\mathit{l}}_1$ and Dice losses. Moreover, we use segmented feature maps to guide the generator for better generated images. Our generator is designed based on a modified 3D U-Net architecture with skip connections to combine hierarchical features and thus have a better generated image. The proposed method is trained and tested on 18 subjects with 3 time points (9 subjects from collaborative hospital and 9 subjects from BRATS 2014 dataset). Results show that our proposed GP-GAN outperforms state-of-the-art methods for glioma growth prediction and attain average Jaccard index and Dice coefficient of 78.97% and 88.26%, respectively.

在世界范围内，脑肿瘤是与癌症相关的死亡的主要原因之一。这些肿瘤的生长预测，尤其是最主要类型的神经胶质瘤，对改善治疗计划，量化肿瘤的侵袭性以及估计患者向精准医学的生存时间非常有用。研究肿瘤生长预测基本上需要同一患者的多个时间点的单模式或多模式医学图像。最近的模型基于复杂的数学公式，这些数学公式基本上依赖于偏微分方程组，例如反应扩散模型，以捕获周围组织中肿瘤细胞的扩散和增殖。然而，这些模型通常具有少量参数，不足以捕获肿瘤的不同模式和其他特征。此外，此类模型针对每个受试者独立考虑肿瘤的生长，无法从整个研究人群中可能存在的常见生长模式中受益。在本文中，我们提出了一种通过堆叠3D生成对抗网络（GANs）命名为GP-GAN的新型数据驱动方法，用于神经胶质瘤的生长预测。具体来说，我们使用具有新目标函数的堆叠条件GAN，其中包括${\mathit{升}}_{\mathrm{1个}}$和骰子损失。此外，我们使用分段特征图来引导生成器以获得更好的生成图像。我们的生成器是基于经过修改的3D U-Net体系结构设计的，具有跳过连接以组合分层功能，从而生成更好的图像。所提出的方法在3个时间点上对18名受试者进行了培训和测试（9名受试者来自协作医院，9名受试者来自BRATS 2014数据集）。结果表明，我们提出的GP-GAN优于最新的神经胶质瘤生长预测方法，平均Jaccard指数和Dice系数分别为78.97％和88.26％。