Radiotherapy outcome prediction with medical imaging

Kareem A. Wahid; Kendall J. Kiser; Keith L. Sanders; Christina Setareh Sharafi; Lance A. McCoy; Juan Ventura; Sara Ahmed; Clifton D. Fuller; Lisanne V. van Dijk

doi:10.1016/B978-0-12-822000-9.00008-2

Radiotherapy outcome prediction with medical imaging

Kareem A. Wahid, Kendall J. Kiser, Keith L. Sanders, Christina Setareh Sharafi, Lance A. McCoy, Juan Ventura, Sara Ahmed, Clifton D. Fuller, Lisanne V. van Dijk

Radiation Oncology

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

This chapter offers an in-depth exploration of advancements in imaging biomarker research within the realm of radiotherapy. It emphasizes radiomic models with high-dimensional feature sets tailored for supervised machine learning prediction. Specifically, this chapter reports on quantitative image biomarkers extracted from computed tomography, positron emission tomography, and magnetic resonance imaging modalities to formulate predictive models for clinical outcomes. Part 1 covers pre-treatment image biomarkers and their utility in predicting tumor response and normal tissue toxicity. Part 2 examines the change of image biomarkers during treatment, assessing their connection to dose and eventual outcomes. Part 3 addresses standardization, validation, interpretation, and practical applications of these biomarkers and models.

Original language	English (US)
Title of host publication	Machine Learning and Artificial Intelligence in Radiation Oncology
Subtitle of host publication	A Guide for Clinicians
Publisher	Elsevier
Pages	239-315
Number of pages	77
ISBN (Electronic)	9780128220009
DOIs	https://doi.org/10.1016/B978-0-12-822000-9.00008-2
State	Published - Jan 1 2023

Keywords

Medical imaging
Medical physics
Medical research
Neoplasia
Omics

ASJC Scopus subject areas

General Agricultural and Biological Sciences
General Biochemistry, Genetics and Molecular Biology

Access to Document

10.1016/B978-0-12-822000-9.00008-2

Cite this

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title = "Radiotherapy outcome prediction with medical imaging",

abstract = "This chapter offers an in-depth exploration of advancements in imaging biomarker research within the realm of radiotherapy. It emphasizes radiomic models with high-dimensional feature sets tailored for supervised machine learning prediction. Specifically, this chapter reports on quantitative image biomarkers extracted from computed tomography, positron emission tomography, and magnetic resonance imaging modalities to formulate predictive models for clinical outcomes. Part 1 covers pre-treatment image biomarkers and their utility in predicting tumor response and normal tissue toxicity. Part 2 examines the change of image biomarkers during treatment, assessing their connection to dose and eventual outcomes. Part 3 addresses standardization, validation, interpretation, and practical applications of these biomarkers and models.",

keywords = "Medical imaging, Medical physics, Medical research, Neoplasia, Omics",

author = "Wahid, {Kareem A.} and Kiser, {Kendall J.} and Sanders, {Keith L.} and Sharafi, {Christina Setareh} and McCoy, {Lance A.} and Juan Ventura and Sara Ahmed and Fuller, {Clifton D.} and {van Dijk}, {Lisanne V.}",

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AU - Wahid, Kareem A.

AU - Kiser, Kendall J.

AU - Sanders, Keith L.

AU - Sharafi, Christina Setareh

AU - McCoy, Lance A.

AU - Ventura, Juan

AU - Ahmed, Sara

AU - Fuller, Clifton D.

AU - van Dijk, Lisanne V.

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AB - This chapter offers an in-depth exploration of advancements in imaging biomarker research within the realm of radiotherapy. It emphasizes radiomic models with high-dimensional feature sets tailored for supervised machine learning prediction. Specifically, this chapter reports on quantitative image biomarkers extracted from computed tomography, positron emission tomography, and magnetic resonance imaging modalities to formulate predictive models for clinical outcomes. Part 1 covers pre-treatment image biomarkers and their utility in predicting tumor response and normal tissue toxicity. Part 2 examines the change of image biomarkers during treatment, assessing their connection to dose and eventual outcomes. Part 3 addresses standardization, validation, interpretation, and practical applications of these biomarkers and models.

KW - Medical imaging

KW - Medical physics

KW - Medical research

KW - Neoplasia

KW - Omics

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BT - Machine Learning and Artificial Intelligence in Radiation Oncology

PB - Elsevier

ER -

Radiotherapy outcome prediction with medical imaging

Abstract

Keywords

ASJC Scopus subject areas

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