2013
DOI: 10.1016/j.ijrobp.2012.04.015
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Toward Fully Automated Multicriterial Plan Generation: A Prospective Clinical Study

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Cited by 132 publications
(125 citation statements)
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“…Generally, a single wish list can be used per patient group, which enables fully automatic treatment planning. Automatic plan generation allows for an objective comparison between different planning strategies and was shown to result in superior plan quality compared with manual planning (10,11).…”
Section: Methods and Materials Treatment Planning Systemmentioning
confidence: 99%
“…Generally, a single wish list can be used per patient group, which enables fully automatic treatment planning. Automatic plan generation allows for an objective comparison between different planning strategies and was shown to result in superior plan quality compared with manual planning (10,11).…”
Section: Methods and Materials Treatment Planning Systemmentioning
confidence: 99%
“…The same holds true for labor and computing resources, which can affect the implementation of new treatment planning techniques and treatment planning capacity. Various solutions are being investigated to improve planning consistency (3)(4)(5)(6)(7), including increased automation of planning by using knowledge-based approaches (8)(9)(10)(11)(12)(13). These approaches typically use libraries of existing patient plans to create models that predict the amount of organ-at-risk (OAR) sparing that can be achieved for a new patient, based, for example, on planning target volume (PTV)-OAR distance and overlap (14).…”
Section: Introductionmentioning
confidence: 99%
“…The first technique is to develop a systematic algorithm to automatically adjust the optimization model parameters for the new patient to generate a satisfactory plan that meets some predetermined clinical criteria. [4][5][6][7] The second technique is to employ a library of clinically approved and delivered plans of previously treated patients with similar medical characteristics in order to find a set of parameters for a new patient that produce a clinically desirable plan. [8][9][10] In this work, we propose an algorithm to automatically adjust the optimization model parameters to replicate a reference plan for a new patient's geometry with the reference plan being selected on patient similarity from a library of preciously delivered plans.…”
Section: Introductionmentioning
confidence: 99%