Piraud Marie, Wennmann Markus, Kintzelé Laurent, Hillengass Jens, Keller Ulrich, Langs Georg, Weber Marc-André, Menze Björn H
Department of Computer Science, Technical University of Munich, Munich, Germany; Center for Translational Cancer Research (Translatum), Klinikum rechts der Isar, Technical University of Munich, Germany.
Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany.
Med Image Anal. 2019 Oct;57:214-225. doi: 10.1016/j.media.2019.07.001. Epub 2019 Jul 4.
The advent of medical imaging and automatic image analysis is bringing the full quantitative assessment of lesions and tumor burden at every clinical examination within reach. This opens avenues for the development and testing of functional disease models, as well as their use in the clinical practice for personalized medicine. In this paper, we introduce a Bayesian statistical framework, based on mixed-effects models, to quantitatively test and learn functional disease models at different scales, on population longitudinal data. We also derive an effective mathematical model for the crossover between initially detected lesions and tumor dissemination, based on the Iwata-Kawasaki-Shigesada model. We finally propose to leverage this descriptive disease progression model into model-aware biomarkers for personalized risk-assessment, taking all available examinations and relevant covariates into account. As a use case, we study Multiple Myeloma, a disseminated plasma cell cancer, in which proper diagnostics is essential, to differentiate frequent precursor state without end-organ damage from the rapidly developing disease requiring therapy. After learning the best biological models for local lesion growth and global tumor burden evolution on clinical data, and computing corresponding population priors, we use individual model parameters as biomarkers, and can study them systematically for correlation with external covariates, such as sex or location of the lesion. On our cohort of 63 patients with smoldering Multiple Myeloma, we show that they perform substantially better than other radiological criteria, to predict progression into symptomatic Multiple Myeloma. Our study paves the way for modeling disease progression patterns for Multiple Myeloma, but also for other metastatic and disseminated tumor growth processes, and for analyzing large longitudinal image data sets acquired in oncological imaging. It shows the unprecedented potential of model-based biomarkers for better and more personalized treatment decisions and deserves being validated on larger cohorts to establish its role in clinical decision making.
医学成像和自动图像分析的出现，使得在每次临床检查时对病变和肿瘤负荷进行全面定量评估成为可能。这为功能疾病模型的开发和测试，以及其在个性化医疗临床实践中的应用开辟了道路。在本文中，我们引入了一个基于混合效应模型的贝叶斯统计框架，用于在不同尺度上对人群纵向数据进行功能疾病模型的定量测试和学习。我们还基于岩田-川崎-重定模型，推导出了一个关于初始检测到的病变与肿瘤播散之间转变的有效数学模型。最后，我们建议将这个描述性疾病进展模型转化为用于个性化风险评估的模型感知生物标志物，同时考虑所有可用检查和相关协变量。作为一个应用案例，我们研究多发性骨髓瘤，一种播散性浆细胞癌，其中正确的诊断至关重要，以区分无终末器官损伤的常见前驱状态与需要治疗的快速发展疾病。在根据临床数据学习了局部病变生长和整体肿瘤负荷演变的最佳生物学模型，并计算了相应的人群先验概率后，我们将个体模型参数用作生物标志物，并可以系统地研究它们与外部协变量（如性别或病变位置）的相关性。在我们63例冒烟型多发性骨髓瘤患者的队列中，我们表明这些生物标志物在预测进展为有症状的多发性骨髓瘤方面，比其他放射学标准表现得要好得多。我们的研究为多发性骨髓瘤以及其他转移性和播散性肿瘤生长过程的疾病进展模式建模，以及分析肿瘤影像学中获取的大型纵向图像数据集铺平了道路。它展示了基于模型的生物标志物在做出更好、更个性化治疗决策方面前所未有的潜力，值得在更大的队列中进行验证，以确立其在临床决策中的作用。
