Role of Complex Networks for Integrating Medical Images and Radiomic Features of Intracranial Ependymoma Patients in Response to Proton Radiotherapy

Frontiers in Medicine

Enrico Capobianco¹  Antony J. Lomax²  Alessia Pica²  Sairos Safai²  Damien C. Weber³  Marco Dominietto³ 

[1] Center for Computational Science, University of Miami, Coral Gables, FL, United States;Center for Proton Therapy, Paul Scherrer Institute, Villigen, Switzerland;Radiation Oncology Department, University Hospital of Bern, Bern, Switzerland;

关键词: ependymoma;    medical imaging;    radiomics;    precision medicine;    therapy response;    network inference;   

DOI  :  10.3389/fmed.2019.00333

来源: DOAJ

【 摘 要 】

Human cancers exhibit phenotypic diversity that medical imaging can precisely and non-invasively detect. Multiple factors underlying innovations and progresses in the medical imaging field exert diagnostic and therapeutic impacts. The emerging field of radiomics has shown unprecedented ability to use imaging information in guiding clinical decisions. To achieve clinical assessment that exploits radiomic knowledge sources, integration between diverse data types is required. A current gap is the accuracy with which radiomics aligns with clinical endpoints. We propose a novel methodological approach that synergizes data volumes (images), tissue-contextualized information breadth, and network-driven resolution depth. Following the Precision Medicine paradigm, disease monitoring and prognostic assessment are tackled at the individual level by examining medical images acquired from two patients affected by intracranial ependymoma (with and without relapse). The challenge of spatially characterizing intratumor heterogeneity is tackled by a network approach that presents two main advantages: (a) Increased detection in the image domain power from high spatial resolution, (b) Superior accuracy in generating hypotheses underlying clinical decisions.

【 授权许可】

Unknown