Radiomic and radiogenomic modeling for radiotherapy: Strategies, pitfalls, and challenges Review


Authors: Coates, J. T. T.; Pirovano, G.; El Naqa, I.
Review Title: Radiomic and radiogenomic modeling for radiotherapy: Strategies, pitfalls, and challenges
Abstract: The power of predictive modeling for radiotherapy outcomes has historically been limited by an inability to adequately capture patient-specific variabilities; however, next-generation platforms together with imaging technologies and powerful bioinformatic tools have facilitated strategies and provided optimism. Integrating clinical, biological, imaging, and treatment-specific data for more accurate prediction of tumor control probabilities or risk of radiation-induced side effects are high-dimensional problems whose solutions could have widespread benefits to a diverse patient population-we discuss technical approaches toward this objective. Increasing interest in the above is specifically reflected by the emergence of two nascent fields, which are distinct but complementary: radiogenomics, which broadly seeks to integrate biological risk factors together with treatment and diagnostic information to generate individualized patient risk profiles, and radiomics, which further leverages large-scale imaging correlates and extracted features for the same purpose. We review classical analytical and data-driven approaches for outcomes prediction that serve as antecedents to both radiomic and radiogenomic strategies. Discussion then focuses on uses of conventional and deep machine learning in radiomics. We further consider promising strategies for the harmonization of high-dimensional, heterogeneous multiomics datasets (panomics) and techniques for nonparametric validation of best-fit models. Strategies to overcome common pitfalls that are unique to data-intensive radiomics are also discussed. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Keywords: adult; review; validation process; radiotherapy; prediction; risk factor; risk assessment; radiation effects; probability; diagnosis; cancer control; outcomes; predictive modeling; tumor control probability; population statistics; imaging techniques; patient population; imaging technology; machine learning; human; radiogenomics; patient risk; deep learning; radiomics; predictive analytics; multiomics; accurate prediction; bioinformatic tools; data-driven approach; high-dimensional problems
Journal Title: Journal of Medical Imaging
Volume: 8
Issue: 3
ISSN: 2329-4302
Publisher: SPIE  
Date Published: 2021-05-01
Start Page: 031902
Language: English
DOI: 10.1117/1.Jmi.8.3.031902
PROVIDER: scopus
PMCID: PMC7985651
PUBMED: 33768134
DOI/URL:
Notes: Review -- Export Date: 2 August 2021 -- Source: Scopus
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