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Radiomics: The New Frontier in Quantitative Image Modeling in Radiotherapy

Radiomics: The New Frontier in Quantitative Image Modeling in Radiotherapy

Julie Constanzo, Issam El Naqa
Copyright: © 2018 |Pages: 27
ISBN13: 9781522530855|ISBN10: 1522530851|EISBN13: 9781522530862
DOI: 10.4018/978-1-5225-3085-5.ch007
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MLA

Constanzo, Julie, and Issam El Naqa. "Radiomics: The New Frontier in Quantitative Image Modeling in Radiotherapy." Emerging Developments and Practices in Oncology, edited by Issam El Naqa, IGI Global, 2018, pp. 191-217. https://doi.org/10.4018/978-1-5225-3085-5.ch007

APA

Constanzo, J. & El Naqa, I. (2018). Radiomics: The New Frontier in Quantitative Image Modeling in Radiotherapy. In I. El Naqa (Ed.), Emerging Developments and Practices in Oncology (pp. 191-217). IGI Global. https://doi.org/10.4018/978-1-5225-3085-5.ch007

Chicago

Constanzo, Julie, and Issam El Naqa. "Radiomics: The New Frontier in Quantitative Image Modeling in Radiotherapy." In Emerging Developments and Practices in Oncology, edited by Issam El Naqa, 191-217. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-3085-5.ch007

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Abstract

Recent advances in image-guided and adaptive radiotherapy have ushered new requirements for using single and/or multiple-imaging modalities in staging, treatment planning, and predicting response of different cancer types. Quantitative information analysis from multi-imaging modalities, known as ‘radiomics', have generated great promises to unravel hidden knowledge embedded in imaging for mining it and its association with observed clinical endpoints and/or underlying biological processes. In this chapter, we will review recent advances and discuss current challenges for using radiomics in radiotherapy. We will discuss issues related to image acquisition, registration, contouring, feature extraction and fusion, statistical modeling, and combination with other imaging modalities and other ‘omics' for developing robust models of treatment outcomes. We will provide examples based on our experience and others for predicting cancer outcomes in radiotherapy generally and brain cancer specifically, and their application in personalizing treatment planning and clinical decision-making.

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