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.
Highlights
Radiotherapy is received by approximately half of all cancer patients.[1]
We discuss two complementary but distinct strategies for predicting radiotherapy outcomes: radiogenomics and radiomics. Both techniques take advantage of biological variables to augment dosimetric risk factors; radiogenomics entails the integration of biological, dosimetric, and clinical factors whereas we considered radiomics to further include the use of imaging correlates
Radiomics quickly necessitates the use of machine learning (MI) strategies for implementation since it is not evident which textures or features may be relevant to a given outcome
Summary
Radiotherapy is received by approximately half of all cancer patients.[1]. The development of predictive models for determining which patients are likely to benefit from radiotherapy and which are at risk of incurring aberrant toxicities could, provide benefit to a large population. We consider conventional dose–volume-based approaches for outcomes modeling, which sets the stage for discussion of radiogenomic and radiomic modeling approaches and highlight techniques for the augmentation of classical models allowing them to incorporate biological and/or clinical risk factors. Both conventional and deep learning strategies are reviewed in the context of radiomics. Given the emergence of next-generation technologies and large datasets obtained from unique sources, we discuss potential outcome modeling strategies using the integration of heterogeneous and high-dimensional multiomics datasets (panomics).[12] As they are critical for any modeling approach, we conclude by discussing common pitfalls for data-intensive radiomics or panomics and validation methods that can be used to maximize reproducibility and robustness of best-fit models
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