Literature DB >> 23387737

Using overlap volume histogram and IMRT plan data to guide and automate VMAT planning: a head-and-neck case study.

Binbin Wu1, Dalong Pang, Patricio Simari, Russell Taylor, Giuseppe Sanguineti, Todd McNutt.   

Abstract

PURPOSE: To investigate whether an overlap volume histogram (OVH)-driven planning application using an intensity-modulated radiation therapy (IMRT) database can guide and automate volumetric-modulated arc therapy (VMAT) planning for head-and-neck cancer.
METHODS: Based on comparable head-and-neck dosimetric results between planner-generated VMAT and IMRT plans, an inhouse developed, OVH-driven automated planning application containing a database of prior clinical head-and-neck IMRT plans is built into Pinnacle(3) SmartArc for VMAT planning. Double-arc VMAT plans of four oropharynx, four nasopharynx, and four larynx patients are generated and compared with corresponding clinical IMRT plans.
RESULTS: Each VMAT plan is automatically generated in two optimization rounds, while the average number of optimization rounds in generating a clinical IMRT plan is 43. In VMAT plans, statistical superiority (p < 0.01) in sparing of the cord+4 mm, brainstem, brachial plexus, larynx, and inner ear is observed with a slight degradation in low-dose-level planning target volume (PTV) coverage. On average, D(0.1 cc) to the cord+4 mm, brainstem and brachial plexus is reduced by 3.7, 4.9, and 1.6 Gy, respectively; V(50 Gy) to the larynx is reduced by 5.3%; mean dose to the inner ear is reduced by 4.4 Gy; V(95) of low-dose-level PTV coverage is reduced by 0.3% with p = 0.25.
CONCLUSIONS: IMRT-data-driven VMAT planning offers a potential method for generating VMAT plans that are comparable to IMRT plans in terms of dosimetric quality.

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Year:  2013        PMID: 23387737     DOI: 10.1118/1.4788671

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  27 in total

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8.  A comparison of Monte Carlo dropout and bootstrap aggregation on the performance and uncertainty estimation in radiation therapy dose prediction with deep learning neural networks.

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Review 10.  Machine learning applications in radiation oncology.

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