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

Dominietto, Marco; Pica, Alessia; Safai, Sairos; Lomax, Antony J.; Weber, Damien C.; Capobianco, Enrico (2020). Role of Complex Networks for Integrating Medical Images and Radiomic Features of Intracranial Ependymoma Patients in Response to Proton Radiotherapy. Frontiers in medicine, 6(333), p. 333. Frontiers 10.3389/fmed.2019.00333

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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.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Haematology, Oncology, Infectious Diseases, Laboratory Medicine and Hospital Pharmacy (DOLS) > Clinic of Radiation Oncology

UniBE Contributor:

Weber, Damien Charles

Subjects:

600 Technology > 610 Medicine & health

ISSN:

2296-858X

Publisher:

Frontiers

Language:

English

Submitter:

Beatrice Scheidegger

Date Deposited:

13 Feb 2020 11:11

Last Modified:

16 Feb 2020 02:50

Publisher DOI:

10.3389/fmed.2019.00333

PubMed ID:

32010703

Uncontrolled Keywords:

ependymoma medical imaging network inference precision medicine radiomics therapy response

BORIS DOI:

10.7892/boris.140281

URI:

https://boris.unibe.ch/id/eprint/140281

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