Whole body image analysis for diagnosing patients with monoclonal plasma cell disorders

用于诊断单克隆浆细胞疾病患者的全身图像分析

• 批准号: 283653538
• 负责人: Professor Dr. Björn Menze
• 金额: --
• 依托单位: Computational Image Analysis and Radiology Lab
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2016
• 资助国家: 德国
• 起止时间: 2015-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/283653538?language=en
• 关键词: Whole; body; image; analysis; diagnosing

Whole-body computed tomography (CT) and magnetic resonance imaging (MRI) are increasingly used in staging of monoclonal plasma cell disorders and for evaluating treatment response. PET imaging provides additional means for diagnosing multiple myeloma patients, and is also acquired jointly with CT or MRI scans. All these imaging modalities provide localized information about the state of the disease in addition to an estimate of the global tumor burden, and one might expect that evaluating lesions at the local level may be more sensitive to changes than global systemic reactions diagnosed from blood samples. Still, a general difficulty in using whole-body MRI, CT, and PET for diagnosing and staging the disease is the amount of image information to be processed. Data sets are not easily readable even for trained radiologist and to date only basic information is extracted. Moreover, thoroughly reading full 3D volumes is a very tedious and time consuming task that is is prone to oversights and systematic errors when only a subset of the source images are analyzed due to time constraints. This problem exponentiates in clinical studies when hundreds of multimodal whole-body scans have to be evaluated, and general patterns of disease progression have to be abstracted from the data.To this end we will develop computational tools that will enable clinicians to quantify multiple myeloma status and to inter-actively track progression over time in whole-body image data sets. To access the full information of large multimodal whole-body image series, we will solve the problem of spatial correspondence in different modalities and time, to be used for identifying and re-identifying locations in whole-body data, and map lesions. Moreover, we will develop probabilistic and bio-mathematical models whose parameters can serve as new imaging biomarkers, and can be correlated with clinical, molecular, and genetic evidence to confirm and improve the diagnostic value of the available image information. Finally, we will model disease progression over time and across a large cohort of patients, systematically evaluating large data bases available from clinical diagnostics, which will help to better understand the observed disease progression patterns and to validate the tools and models to be developed in this project.

全身计算机断层扫描（CT）和磁共振成像（MRI）越来越多地用于单克隆浆细胞疾病的分期和评估治疗反应。PET成像为诊断多发性骨髓瘤患者提供了额外的手段，并且还与CT或MRI扫描联合获得。所有这些成像方式提供了关于疾病状态的局部信息，以及对整体肿瘤负荷的估计，人们可能会认为，在局部水平评估病变可能比从血液样本诊断的整体全身反应对变化更敏感。尽管如此，使用全身MRI、CT和PET诊断和分期疾病的一般困难是要处理的图像信息量。数据集不容易读取，即使是受过训练的放射科医生和日期只有基本信息提取。此外，彻底阅读完整的3D体积是非常乏味和耗时的任务，当由于时间限制而仅分析源图像的子集时，该任务易于出现疏忽和系统错误。这个问题在临床研究中的指数时，数百个多模态全身扫描必须进行评估，和疾病进展的一般模式必须从data.To为此，我们将开发计算工具，使临床医生能够量化多发性骨髓瘤的状态和互动跟踪随着时间的推移，在全身图像数据集的进展。为了访问大型多模态全身图像系列的全部信息，我们将解决不同模态和时间的空间对应问题，用于识别和重新识别全身数据中的位置，并映射病变。此外，我们将开发概率和生物数学模型，其参数可以作为新的成像生物标志物，并可以与临床，分子和遗传证据相关联，以确认和提高可用图像信息的诊断价值。最后，我们将对一段时间内和大量患者的疾病进展进行建模，系统地评估临床诊断中可用的大型数据库，这将有助于更好地了解观察到的疾病进展模式并验证该项目中开发的工具和模型。