Rathore Saima, Bakas Spyridon, Pati Sarthak, Akbari Hamed, Kalarot Ratheesh, Sridharan Patmaa, Rozycki Martin, Bergman Mark, Tunc Birkan, Verma Ragini, Bilello Michel, Davatzikos Christos
Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
Brainlesion. 2018;10670:133-145. doi: 10.1007/978-3-319-75238-9_12. Epub 2018 Feb 17.
Quantitative research, especially in the field of radio(geno)mics, has helped us understand fundamental mechanisms of neurologic diseases. Such research is integrally based on advanced algorithms to derive extensive radiomic features and integrate them into diagnostic and predictive models. To exploit the benefit of such complex algorithms, their swift translation into clinical practice is required, currently hindered by their complicated nature. brain-CaPTk is a modular platform, with components spanning across image processing, segmentation, feature extraction, and machine learning, that facilitates such translation, enabling quantitative analyses without requiring substantial computational background. Thus, brain-CaPTk can be seamlessly integrated into the typical quantification, analysis and reporting workflow of a radiologist, underscoring its clinical potential. This paper describes currently available components of brain-CaPTk and example results from their application in glioblastoma.
定量研究，尤其是在放射（基因）组学领域，帮助我们理解了神经疾病的基本机制。此类研究完全基于先进算法，以导出广泛的放射组学特征并将其整合到诊断和预测模型中。为了利用这些复杂算法的优势，需要将它们迅速转化为临床实践，而目前它们的复杂性阻碍了这一转化。brain-CaPTk是一个模块化平台，其组件涵盖图像处理、分割、特征提取和机器学习，有助于实现这种转化，无需大量计算背景知识就能进行定量分析。因此，brain-CaPTk可以无缝集成到放射科医生的典型定量、分析和报告工作流程中，凸显了其临床潜力。本文介绍了brain-CaPTk目前可用的组件以及它们在胶质母细胞瘤中的应用示例结果。
