Radiogenomics and radiotherapy response modeling

Naqa, Issam El; Kerns, Sarah L.; Coates, James; Luo, Yi; Speers, Corey; West, Catharine M L; Rosenstein, Barry S.; Haken, Randall K. Ten

doi:10.1088/1361-6560/aa7c55

Cited by 46 publications

(58 citation statements)

References 182 publications

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“…Supervised learning 30 requires a labeled dataset, as it is intended to learn the relationship between input variables and outputs (labels). For instance, prediction of radiotherapy outcomes (e.g., tumor control or normal tissue toxicity) 31 is a supervised learning task. First, one collects relevant patient information (e.g., dosimetric information and clinical variables) together with the treatment outcomes (labels).…”

Section: What Are Machine and Deep Learning?mentioning

confidence: 99%

Introduction to machine and deep learning for medical physicists

et al. 2020

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Recent years have witnessed tremendous growth in the application of machine learning (ML) and deep learning (DL) techniques in medical physics. Embracing the current big data era, medical physicists equipped with these state‐of‐the‐art tools should be able to solve pressing problems in modern radiation oncology. Here, a review of the basic aspects involved in ML/DL model building, including data processing, model training, and validation for medical physics applications is presented and discussed. Machine learning can be categorized based on the underlying task into supervised learning, unsupervised learning, or reinforcement learning; each of these categories has its own input/output dataset characteristics and aims to solve different classes of problems in medical physics ranging from automation of processes to predictive analytics. It is recognized that data size requirements may vary depending on the specific medical physics application and the nature of the algorithms applied. Data processing, which is a crucial step for model stability and precision, should be performed before training the model. Deep learning as a subset of ML is able to learn multilevel representations from raw input data, eliminating the necessity for hand crafted features in classical ML. It can be thought of as an extension of the classical linear models but with multilayer (deep) structures and nonlinear activation functions. The logic of going “deeper" is related to learning complex data structures and its realization has been aided by recent advancements in parallel computing architectures and the development of more robust optimization methods for efficient training of these algorithms. Model validation is an essential part of ML/DL model building. Without it, the model being developed cannot be easily trusted to generalize to unseen data. Whenever applying ML/DL, one should keep in mind, according to Amara’s law, that humans may tend to overestimate the ability of a technology in the short term and underestimate its capability in the long term. To establish ML/DL role into standard clinical workflow, models considering balance between accuracy and interpretability should be developed. Machine learning/DL algorithms have potential in numerous radiation oncology applications, including automatizing mundane procedures, improving efficiency and safety of auto‐contouring, treatment planning, quality assurance, motion management, and outcome predictions. Medical physicists have been at the frontiers of technology translation into medicine and they ought to be prepared to embrace the inevitable role of ML/DL in the practice of radiation oncology and lead its clinical implementation.

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Section: What Are Machine and Deep Learning?mentioning

confidence: 99%

Introduction to machine and deep learning for medical physicists

et al. 2020

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“…A disadvantage is that continuous variables are typically discretized as many BN learning algorithms cannot efficiently handle continuous variables and common software packages do not support continuous variables . In radiogenomics, BN nodes represent genes or proteins while the links between them represent probabilistic similarities or interactions . Due to these advantages, BNs have been used to incorporate both clinical and genomic variables to predict local control and/or toxicity.…”

Section: Radiogenomic Modeling: Mechanistic Data‐driven and Machine mentioning

confidence: 99%

Genomics models in radiotherapy: From mechanistic to machine learning

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Machine learning (ML) provides a broad framework for addressing high‐dimensional prediction problems in classification and regression. While ML is often applied for imaging problems in medical physics, there are many efforts to apply these principles to biological data toward questions of radiation biology. Here, we provide a review of radiogenomics modeling frameworks and efforts toward genomically guided radiotherapy. We first discuss medical oncology efforts to develop precision biomarkers. We next discuss similar efforts to create clinical assays for normal tissue or tumor radiosensitivity. We then discuss modeling frameworks for radiosensitivity and the evolution of ML to create predictive models for radiogenomics.

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“…Furthermore, this also allows for taking into account tumour heterogeneity based on quantitative, functional imaging data, exceeding the purely morphological characteristics and potentially allowing for earlier evaluation of tumour response and local control failure already at the beginning of radiotherapy and in the meantime (Table 3; [91,94]). Independent of general outcome analysis, the incorporation of radiomics into treatment planning may further improve the analysis and prediction of normal tissue tox- [95,96]. There are already promising data with regard to toxicity after radiotherapy of head and neck and lung cancers [97,98].…”

Section: Target Volume Definition: Automatic Segmentation Of Target Vmentioning

confidence: 99%

Radiomics for liver tumours

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Current research, especially in oncology, increasingly focuses on the integration of quantitative, multiparametric and functional imaging data. In this fast-growing field of research, radiomics may allow for a more sophisticated analysis of imaging data, far beyond the qualitative evaluation of visible tissue changes. Through use of quantitative imaging data, more tailored and tumour-specific diagnostic work-up and individualized treatment concepts may be applied for oncologic patients in the future. This is of special importance in cross-sectional disciplines such as radiology and radiation oncology, with already high and still further increasing use of imaging data in daily clinical practice. Liver targets are generally treated with stereotactic body radiotherapy (SBRT), allowing for local dose escalation while preserving surrounding normal tissue. With the introduction of online target surveillance with implanted markers, 3D-ultrasound on conventional linacs and hybrid magnetic resonance imaging (MRI)-linear accelerators, individualized adaptive radiotherapy is heading towards realization. The use of big data such as radiomics and the integration of artificial intelligence techniques have the potential to further improve image-based treatment planning and structured follow-up, with outcome/toxicity prediction and immediate detection of (oligo)progression. The scope of current research in this innovative field is to identify and critically discuss possible application forms of radiomics, which is why this review tries to summarize current knowledge about interdisciplinary integration of radiomics in oncologic patients, with a focus on investigations of radiotherapy in patients with liver cancer or oligometastases including multiparametric, quantitative data into (radio)-oncologic workflow from disease diagnosis, treatment planning, delivery and patient follow-up.

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Radiogenomics and radiotherapy response modeling

Cited by 46 publications

References 182 publications

Introduction to machine and deep learning for medical physicists

Introduction to machine and deep learning for medical physicists

Genomics models in radiotherapy: From mechanistic to machine learning

Radiomics for liver tumours

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