| Literature DB >> 29733087 |
Saima Rathore1, Spyridon Bakas1, Sarthak Pati1, Hamed Akbari1, Ratheesh Kalarot1, Patmaa Sridharan1, Martin Rozycki1, Mark Bergman1, Birkan Tunc1, Ragini Verma1, Michel Bilello1, Christos Davatzikos1.
Abstract
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.Entities:
Keywords: Computational algorithms; Fiber tracking; Glioblastoma; Image analysis; Open-source software; Radiomics Radiogenomics
Year: 2018 PMID: 29733087 PMCID: PMC5934754 DOI: 10.1007/978-3-319-75238-9_12
Source DB: PubMed Journal: Brainlesion