Evaluation and Visualization of Radiogenomic Modeling Frameworks for the Prediction of Normal Tissue Toxicities

Coates, James; Jeyaseelan, A.; Ybarra, Norma; Tao, Jessie; David, Marc; Faria, Sergio; Souhami, Luís; Cury, Fabio; Duclos, Marie; Naqa, Issam El

doi:10.1007/978-3-319-19387-8_127

Cited by 2 publications

(3 citation statements)

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“…77,78 PCA is a well-known approach used widely both within and outside of radiology and so further details can be found in previous works. 62,79 Support-vector machines (SVMs) are the most often encountered kernel variant. 80 An SVM seeks to redefine classification steps into maximization problems through the use of quadratic programming.…”

Section: Data-drivenmentioning

confidence: 99%

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Radiomic and radiogenomic modeling for radiotherapy: strategies, pitfalls, and challenges

2021

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The power of predictive modeling for radiotherapy outcomes has historically been limited by an inability to adequately capture patient-specific variabilities; however, nextgeneration 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 datadriven 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 highdimensional, heterogeneous multiomics datasets (panomics) and techniques for nonparametric validation of best-fit models. Strategies to overcome common pitfalls that are unique to dataintensive 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.

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Section: Data-drivenmentioning

confidence: 99%

“…PCA is a well-known approach used widely both within and outside of radiology and so further details can be found in previous works. 62 , 79 …”

Section: Modeling Strategiesmentioning

confidence: 99%

Radiomic and radiogenomic modeling for radiotherapy: strategies, pitfalls, and challenges

2021

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“…Alternatively, the previously described Kernel-PCA technique can be employed to visualize data by improving separation between clusters. Vector biplots, two- and three-dimensional kPCA plots can indeed be used together in order to provide easy-to-interpret heat maps colorized by estimated patient-specific risk (Figure 6 ) ( 147 ).…”

Section: Model Performance Visualizationmentioning

confidence: 99%

Big Data Analytics for Prostate Radiotherapy

2016

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Radiation therapy is a first-line treatment option for localized prostate cancer and radiation-induced normal tissue damage are often the main limiting factor for modern radiotherapy regimens. Conversely, under-dosing of target volumes in an attempt to spare adjacent healthy tissues limits the likelihood of achieving local, long-term control. Thus, the ability to generate personalized data-driven risk profiles for radiotherapy outcomes would provide valuable prognostic information to help guide both clinicians and patients alike. Big data applied to radiation oncology promises to deliver better understanding of outcomes by harvesting and integrating heterogeneous data types, including patient-specific clinical parameters, treatment-related dose–volume metrics, and biological risk factors. When taken together, such variables make up the basis for a multi-dimensional space (the “RadoncSpace”) in which the presented modeling techniques search in order to identify significant predictors. Herein, we review outcome modeling and big data-mining techniques for both tumor control and radiotherapy-induced normal tissue effects. We apply many of the presented modeling approaches onto a cohort of hypofractionated prostate cancer patients taking into account different data types and a large heterogeneous mix of physical and biological parameters. Cross-validation techniques are also reviewed for the refinement of the proposed framework architecture and checking individual model performance. We conclude by considering advanced modeling techniques that borrow concepts from big data analytics, such as machine learning and artificial intelligence, before discussing the potential future impact of systems radiobiology approaches.

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Evaluation and Visualization of Radiogenomic Modeling Frameworks for the Prediction of Normal Tissue Toxicities

Cited by 2 publications

References 9 publications

Radiomic and radiogenomic modeling for radiotherapy: strategies, pitfalls, and challenges

Radiomic and radiogenomic modeling for radiotherapy: strategies, pitfalls, and challenges

Big Data Analytics for Prostate Radiotherapy

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