Toward a web-based real-time radiation treatment planning system in a cloud computing environment

Na, Yong Hum; Suh, Tae-Suk; Kapp, Daniel S.; Li, Xing

doi:10.1088/0031-9155/58/18/6525

Cited by 12 publications

(9 citation statements)

References 42 publications

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“…Similarly, Miras et al [ 62 ] used Microsoft Azure with up to 64 virtual machines of different sizes to measure a speedup of up to 37x. A more complex calculation is performed by Na et al [ 63 ] who uses Amazon cloud with up to 100 worker instances for a speedup of 10-14x. The difference in the speedup is caused by the ability to parallelize the algorithms and the overhead for worker management and data communication.…”

Section: Resultsmentioning

confidence: 99%

“…Our literature research revealed 22 theoretical papers that did not describe a specific cloud project but provided more common information on cloud computing in healthcare [ 4 - 6 , 8 , 14 , 23 , 24 , 26 , 49 - 52 , 56 , 79 - 87 ]. 12 articles include descriptions of basic conceptual work for cloud projects, but included no creation of a real system [ 16 , 30 , 35 , 46 , 53 , 54 , 63 , 77 , 88 - 92 ]. If applications are described they are usually in a prototype status [ 1 , 7 , 10 - 13 , 15 , 17 - 22 , 28 , 29 , 31 , 33 , 34 , 39 - 45 , 47 , 48 , 55 , 58 , 59 , 61 - 70 , 72 - 75 , 93 - 101 ].…”

Section: Resultsmentioning

confidence: 99%

“…rapid elasticity, followed by broad network access, resource pooling, on-demand self-service and measured service at least. But only eight publications directly cite NIST’s definition [ 4 , 5 , 18 , 23 , 24 , 40 , 52 , 55 , 63 , 67 , 75 , 81 , 83 , 85 , 100 ]. Thus, in most of the articles presenting cloud applications, details about the real deployment and service models remain unclear.…”

Section: Resultsmentioning

confidence: 99%

“…Most cloud-based services are provided using own, proprietary infrastructure. If commercial services are used, Amazon services are applied most often: Elastic Computing Cloud (EC2) is referred to eleven times [ 13 , 34 , 48 , 61 , 63 , 66 , 72 , 90 , 93 , 97 ] and Amazon’s S3 service (Simple Storage Service) three times [ 25 , 29 , 40 ]. One application [ 65 ] used the Virtual Private Cloud (Amazon VPC) to provide more secure services.…”

Section: Resultsmentioning

confidence: 99%

“…First, in our literature we found that many authors mentioned data privacy and data confidentiality concerns. There is the fear that unauthorized persons might access sensible medical data in a cloud [ 1 , 5 , 13 , 18 , 24 , 25 , 28 , 29 , 31 , 46 , 51 , 52 , 63 , 65 - 67 , 72 , 81 , 83 , 85 , 88 , 94 , 96 ] which might hurt confidentiality of sensitive data about patients, therapies or physicians [ 41 , 103 ].…”

Section: Resultsmentioning

confidence: 99%

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A scoping review of cloud computing in healthcare

Griebel

Prokosch

Köpcke

et al. 2015

BMC Med Inform Decis Mak

190

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BackgroundCloud computing is a recent and fast growing area of development in healthcare. Ubiquitous, on-demand access to virtually endless resources in combination with a pay-per-use model allow for new ways of developing, delivering and using services. Cloud computing is often used in an “OMICS-context”, e.g. for computing in genomics, proteomics and molecular medicine, while other field of application still seem to be underrepresented. Thus, the objective of this scoping review was to identify the current state and hot topics in research on cloud computing in healthcare beyond this traditional domain.MethodsMEDLINE was searched in July 2013 and in December 2014 for publications containing the terms “cloud computing” and “cloud-based”. Each journal and conference article was categorized and summarized independently by two researchers who consolidated their findings.Results102 publications have been analyzed and 6 main topics have been found: telemedicine/teleconsultation, medical imaging, public health and patient self-management, hospital management and information systems, therapy, and secondary use of data. Commonly used features are broad network access for sharing and accessing data and rapid elasticity to dynamically adapt to computing demands. Eight articles favor the pay-for-use characteristics of cloud-based services avoiding upfront investments. Nevertheless, while 22 articles present very general potentials of cloud computing in the medical domain and 66 articles describe conceptual or prototypic projects, only 14 articles report from successful implementations. Further, in many articles cloud computing is seen as an analogy to internet-/web-based data sharing and the characteristics of the particular cloud computing approach are unfortunately not really illustrated.ConclusionsEven though cloud computing in healthcare is of growing interest only few successful implementations yet exist and many papers just use the term “cloud” synonymously for “using virtual machines” or “web-based” with no described benefit of the cloud paradigm. The biggest threat to the adoption in the healthcare domain is caused by involving external cloud partners: many issues of data safety and security are still to be solved. Until then, cloud computing is favored more for singular, individual features such as elasticity, pay-per-use and broad network access, rather than as cloud paradigm on its own.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-015-0145-7) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

A scoping review of cloud computing in healthcare

Griebel

Prokosch

Köpcke

et al. 2015

BMC Med Inform Decis Mak

190

View full text Add to dashboard Cite

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Use of proximal operator graph solver for radiation therapy inverse treatment planning

et al. 2017

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Purpose Most radiation therapy optimization problems can be formulated as an unconstrained problem and solved efficiently by quasi-Newton methods such as the Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm. However, several next generation planning techniques such as total variation regularization based optimization and MV+kV optimization, involve constrained or mixed-norm optimization, and cannot be solved by quasi-Newton methods. Using standard optimization algorithms on such problems often leads to prohibitively long optimization times and large memory requirements. This work investigates the use of a recently developed proximal operator graph solver (POGS) in solving such radiation therapy optimization problems. Methods Radiation therapy inverse treatment planning was formulated as a graph form problem, and the proximal operators of POGS for quadratic optimization were derived. POGS was exploited for the first time to impose hard dose constraints along with soft constraints in the objective function. The solver was applied to several clinical treatment sites (TG119, liver, prostate, and head&neck), and the results were compared to the solutions obtained by other commercial and non-commercial optimizers. Results For inverse planning optimization with nonnegativity box constraints on beamlet intensity, the speed of POGS can compete with that of LBFGSB in some situations. For constrained and mixed-norm optimization, POGS is about one or two orders of magnitude faster than the other solvers while requiring less computer memory. Conclusions POGS was used for solving inverse treatment planning problems involving constrained or mixed-norm formulation on several example sites. This approach was found to improve upon standard solvers in terms of computation speed and memory usage, and is capable of solving traditionally difficult problems, such as total variation regularization based optimization and combined MV+kV optimization.

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