A Review on Application of Deep Learning Algorithms in External Beam Radiotherapy Automated Treatment Planning

Wang, Mingqing; Zhang, Qilin; Lam, Saikit; Cai, Jing; Yang, Renjie

doi:10.3389/fonc.2020.580919

Cited by 86 publications

(81 citation statements)

References 58 publications

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“…A general workflow is shown in Figure 7. Compared to DVH prediction, dose distribution prediction could provide more spatial information of dose (46). Furthermore, dose distribution prediction could…”

Section: Dose Prediction Modelsmentioning

confidence: 99%

Artificial intelligence applications in intensity modulated radiation treatment planning: an overview

Yang¹,

Zhang²,

Ge³

et al. 2021

Quant Imaging Med Surg

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Artificial intelligence (AI) refers to methods that improve and automate challenging human tasks by systematically capturing and applying relevant knowledge in these tasks. Over the past decades, a number of approaches have been developed to address different types and needs of system intelligence ranging from search strategies to knowledge representation and inference to robotic planning. In the context of radiation treatment planning, multiple AI approaches may be adopted to improve the planning quality and efficiency. For example, knowledge representation and inference methods may improve dose prescription by integrating and reasoning about the domain knowledge described in many clinical guidelines and clinical trials reports. In this review, we will focus on the most studied AI approach in intensity modulated radiation therapy (IMRT)/volumetric modulated arc therapy (VMAT)-machine learning (ML) and describe our recent efforts in applying ML to improve the quality, consistency, and efficiency of IMRT/VMAT planning.With the available high-quality data, we can build models to accurately predict critical variables for each step of the planning process and thus automate and improve its outcomes. Specific to the IMRT/VMAT planning process, we can build models for each of the four critical components in the process: DVH, Dose, Fluence, and Human Planner. These models can be divided into two general groups. The first group focuses on encoding prior experience and knowledge through ML and more recently deep learning (DL) from prior clinical plans and using these models to predict the optimal DVH (DVH prediction model), or 3D dose distribution (dose prediction model), or fluence map (fluence map model). The goal of these models is to reduce or remove the trial-and-error process and guarantee consistently high-quality plans. The second group of models focuses on mimicking human planners' decision-making process (planning strategy model) during the iterative adjustments/guidance of the optimization engine. Each critical step of the IMRT/VMAT treatment planning process can be improved and automated by AI methods. As more training data becomes available and more sophisticated models are developed, we can expect that the AI methods in treatment planning will continue to improve accuracy, efficiency, and robustness.

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Section: Dose Prediction Modelsmentioning

confidence: 99%

Artificial intelligence applications in intensity modulated radiation treatment planning: an overview

Yang¹,

Zhang²,

Ge³

et al. 2021

Quant Imaging Med Surg

View full text Add to dashboard Cite

show abstract

“…Advanced AI techniques of decision trees, neural networks in all their flavors, and various AI learning algorithms have been applied to clinical problems in the field ( 10 ). Recent accomplishments include fast, efficient contour autosegmentation that can drastically reduce the tedium and error rate brought about by simple reliance on voxel intensities of radiology images ( 11 ). Deep learning algorithms have been employed to generate optimal dose distributions almost on the fly, with promising potential for adaptive radiotherapy.…”

Section: Physicsmentioning

confidence: 99%

Moving Forward in the Next Decade: Radiation Oncology Sciences for Patient-Centered Cancer Care

Coleman¹,

Buchsbaum

Prasanna

et al. 2021

JNCI Cancer Spectrum

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In a time of rapid advances in science and technology the opportunities for radiation oncology are undergoing transformational change. The linkage between and understanding of the physical dose and induced biological perturbations are opening entirely new areas of application. The ability to define anatomic extent of disease and the elucidation of the biology of metastases has brought a key role for radiation oncology for treating metastatic disease. That radiation can stimulate and suppress subpopulations of the immune response makes radiation a key participant in cancer immunotherapy. Targeted radiopharmaceutical therapy delivers radiation systemically with radionuclides and carrier molecules selected for their physical, chemical and biochemical properties. Radiation oncology usage of “big data” and machine learning/artificial intelligence adds the opportunity to markedly change the workflow for clinical practice while physically targeting and adapting radiation fields in real time. Future precision targeting requires multi-dimensional understanding of the imaging, underlying biology and anatomical relationship among tissues for radiation as spatial and temporal “focused biology”. Other means of energy delivery are available as are agents that can be activated by radiation with increasing ability to target treatments. With broad applicability of radiation in cancer treatment, it is a necessity for effective cancer care, opening a career path for global health serving the medically underserved in geographically isolated populations as a substantial societal contribution addressing health disparities. Understanding of risk and mitigation of radiation injury make it an important discipline for and beyond cancer care including energy policy, space exploration, national security and global partnerships.

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“…Self-exploration mapping is the backbone of architectures such as deep convolutional neural networks (CNN) and recurrent neural networks (RNN), with the latter one empowering the learning of sequential data such as text and speech. Alongside other application sectors, healthcare has reported breakthroughs achieved with deep learning adoption in neuroimaging, genetics, oncology, radiation therapy, and drug discovery, to name a few (Wang et al, 2018 , 2020 ; Boldrini et al, 2019 ; David et al, 2019 ; Serag et al, 2019 ; Tang et al, 2019 ; Zhu et al, 2019 ; Chen et al, 2020 ; Zhang et al, 2020 ).…”

Section: Deep Learning Something New or The New Face Of The Oldmentioning

confidence: 99%

Can Deep Learning Hit a Moving Target? A Scoping Review of Its Role to Study Neurological Disorders in Children

Sargolzaei

2021

Front. Comput. Neurosci.

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Neurological disorders dramatically impact patients of any age population, their families, and societies. Pediatrics are among vulnerable age populations who differently experience the devastating consequences of neurological conditions, such as attention-deficit hyperactivity disorders (ADHD), autism spectrum disorders (ASD), cerebral palsy, concussion, and epilepsy. System-level understanding of these neurological disorders, particularly from the brain networks' dynamic perspective, has led to the significant trend of recent scientific investigations. While a dramatic maturation in the network science application domain is evident, leading to a better understanding of neurological disorders, such rapid utilization for studying pediatric neurological disorders falls behind that of the adult population. Aside from the specific technological needs and constraints in studying neurological disorders in children, the concept of development introduces uncertainty and further complexity topping the existing neurologically driven processes caused by disorders. To unravel these complexities, indebted to the availability of high-dimensional data and computing capabilities, approaches based on machine learning have rapidly emerged a new trend to understand pathways better, accurately diagnose, and better manage the disorders. Deep learning has recently gained an ever-increasing role in the era of health and medical investigations. Thanks to its relatively more minor dependency on feature exploration and engineering, deep learning may overcome the challenges mentioned earlier in studying neurological disorders in children. The current scoping review aims to explore challenges concerning pediatric brain development studies under the constraints of neurological disorders and offer an insight into the potential role of deep learning methodology on such a task with varying and uncertain nature. Along with pinpointing recent advancements, possible research directions are highlighted where deep learning approaches can assist in computationally targeting neurological disorder-related processes and translating them into windows of opportunities for interventions in diagnosis, treatment, and management of neurological disorders in children.

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A Review on Application of Deep Learning Algorithms in External Beam Radiotherapy Automated Treatment Planning

Cited by 86 publications

References 58 publications

Artificial intelligence applications in intensity modulated radiation treatment planning: an overview

Artificial intelligence applications in intensity modulated radiation treatment planning: an overview

Moving Forward in the Next Decade: Radiation Oncology Sciences for Patient-Centered Cancer Care

Can Deep Learning Hit a Moving Target? A Scoping Review of Its Role to Study Neurological Disorders in Children

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