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Evaluation of an automated knowledge based treatment planning system for head and neck

Overview of attention for article published in Radiation Oncology, November 2015
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Title
Evaluation of an automated knowledge based treatment planning system for head and neck
Published in
Radiation Oncology, November 2015
DOI 10.1186/s13014-015-0533-2
Pubmed ID
Authors

Jerome Krayenbuehl, Ian Norton, Gabriela Studer, Matthias Guckenberger

Abstract

This study evaluated an automated inverse treatment planning algorithm, Pinnacle Auto-Planning (AP), and compared automatically generated plans with historical plans in a large cohort of head and neck cancer patients. Fifty consecutive patients treated with volumetric modulated arc therapy (Eclipse, Varian Medical System, Palo Alto, CA) for head and neck were re-planned with AP version 9.10. Only one single cycle of plan optimization using one single template was allowed for AP. The dose to the planning target volumes (PTV's; 3-4 dose levels), the organs at risk (OAR's) and the effective working time for planning was evaluated. Additionally, two experienced radiation oncologists blind-reviewed and ranked 10 plans. Dose coverage and dose homogeneity of the PTV were significantly improved with AP, however manually optimized plans showed significantly improved dose conformity. The mean dose to the parotid glands, oral mucosa, swallowing muscles, dorsal neck tissue and maximal dose to the spinal cord were significantly reduced with AP. In 64 % of the plans, the mean dose to any OAR (spinal cord excluded) was reduced by >20 % with AP in comparison to the manually optimized plans. In 12 % of the plans, the manually optimized plans showed reduced doses by >20 % in at least one OAR. The experienced radiation oncologists preferred the AP plan and the clinical plan in 80 and 20 % of the cases, respectively. The average effective working time was 3.8 min ± 1.1 min in comparison to 48.5 min ± 6.0 min using AP compared to the manually optimized plans, respectively. The evaluated automated planning algorithm achieved highly consistent and significantly improved treatment plans with potentially clinically relevant OAR sparing by >20 % in 64 % of the cases. The effective working time was substantially reduced with Auto-Planning.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 113 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Finland 1 <1%
United Kingdom 1 <1%
Spain 1 <1%
Unknown 110 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 19%
Researcher 21 19%
Student > Master 13 12%
Other 7 6%
Student > Postgraduate 7 6%
Other 19 17%
Unknown 25 22%
Readers by discipline Count As %
Medicine and Dentistry 31 27%
Physics and Astronomy 26 23%
Engineering 5 4%
Nursing and Health Professions 4 4%
Computer Science 3 3%
Other 12 11%
Unknown 32 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 November 2015.
All research outputs
#20,295,501
of 22,832,057 outputs
Outputs from Radiation Oncology
#1,678
of 2,057 outputs
Outputs of similar age
#236,863
of 282,783 outputs
Outputs of similar age from Radiation Oncology
#37
of 53 outputs
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So far Altmetric has tracked 2,057 research outputs from this source. They receive a mean Attention Score of 2.7. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 53 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.