Challenges and Promises of PET Radiomics

Gary John Russell Cook; Gurdip Azad; Katarzyna Magdalena Owczarczyk; Muhammad Musib Siddique; Vicky Joo-Lin Goh

doi:10.1016/j.ijrobp.2017.12.268

Challenges and Promises of PET Radiomics

Gary John Russell Cook, Gurdip Azad, Katarzyna Magdalena Owczarczyk, Muhammad Musib Siddique, Vicky Joo-Lin Goh

Research output: Contribution to journal › Literature review › peer-review

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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.

Original language	English
Journal	International Journal of Radiation Oncology Biology Physics
Early online date	31 Jan 2018
DOIs	https://doi.org/10.1016/j.ijrobp.2017.12.268
Publication status	E-pub ahead of print - 31 Jan 2018

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title = "Challenges and Promises of PET Radiomics",

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.",

author = "Cook, {Gary John Russell} and Gurdip Azad and Owczarczyk, {Katarzyna Magdalena} and Siddique, {Muhammad Musib} and Goh, {Vicky Joo-Lin}",

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T1 - Challenges and Promises of PET Radiomics

AU - Cook, Gary John Russell

AU - Azad, Gurdip

AU - Owczarczyk, Katarzyna Magdalena

AU - Siddique, Muhammad Musib

AU - Goh, Vicky Joo-Lin

PY - 2018/1/31

Y1 - 2018/1/31

N2 - 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.

AB - 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.

U2 - 10.1016/j.ijrobp.2017.12.268

DO - 10.1016/j.ijrobp.2017.12.268

M3 - Literature review

SN - 0360-3016

JO - International Journal of Radiation Oncology Biology Physics

JF - International Journal of Radiation Oncology Biology Physics

ER -

Challenges and Promises of PET Radiomics

Abstract

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