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Challenges and Promises of PET Radiomics

Research output: Contribution to journalLiterature review

Original languageEnglish
JournalInternational Journal of Radiation Oncology Biology Physics
Early online date31 Jan 2018
DOIs
Accepted/In press14 Dec 2017
E-pub ahead of print31 Jan 2018

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King's Authors

Abstract

Radiomics describes the extraction of multiple, otherwise invisible, features from medical images that, with bioinformatic approaches, can be used to provide additional information that may predict underlying tumor biology and behavior. Radiomic signatures can be used alone or with other patient-specific data to improve tumor phenotyping, treatment response prediction and prognosis, non-invasively. The literature describing 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) radiomics, often using texture or heterogeneity parameters, is increasing rapidly. In relation to radiotherapy practice there are early data reporting the use of radiomic approaches to better define tumor volumes and to predict radiation toxicity and treatment response. Whilst at an early stage of development, with many technical challenges remaining and a need for standardization, there is nevertheless promise that PET radiomics will contribute to personalized medicine, particularly with the availability of increased computing power and the development of machine learning approaches for imaging.

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