Evolving treatment plan quality criteria from institution‐specific experience

Ruan, Dan; Shao, Weber; DeMarco, John J.; Tenn, Stephen; King, Chris; Low, Daniel A.; Kupelian, Patrick A.; Steinberg, Michael L.

doi:10.1118/1.4704497

Cited by 17 publications

(28 citation statements)

References 11 publications

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“…Most of the researchers and all commercial KBP algorithms use predictive dose volume technique . The issue with the predictive dose volume method is the bandwidth of the predicted DVH is too wide at the mid portion of the curve, sometimes as high as 20% error in the dose and/or volume.…”

Section: Discussionmentioning

confidence: 99%

“…Optimal treatment plans can be generated by grossly reducing any subjectivity introduced by the treatment planner. Recently, knowledge‐based planning (KBP) helped the treatment planner standardize the treatment plans for different sites like brain, esophagus, prostate, head and neck, spine SBRT, and pancreas …”

Section: Introductionmentioning

confidence: 99%

“…Knowledge‐based planning was developed at Duke University and Washington University and is presently commercially available with the Varian planning system, known as RapidPlan (Eclipse, Varian medical System, Polo Alto, CA) . Similar atlas‐based techniques are available from Elekta (Erasmus‐iCycle), and from Philips Pinnacle 3 planning system as ideal dose distribution estimation (PlanIQ) using template‐based optimization for Auto Planning (AP) …”

Section: Introductionmentioning

confidence: 99%

“…The predicted DVH technique is the one described by many authors and used in all commercially available planning systems. It uses database DVH averaging approach . The assumption is that any DVH can be represented as a combination of the average DVH over a population plus a sum of weighted principal components and a residual.…”

Section: Introductionmentioning

confidence: 99%

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Standardization of volumetric modulated arc therapy‐based frameless stereotactic technique using a multidimensional ensemble‐aided knowledge‐based planning

et al. 2019

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Purpose Aim of this article is to describe a new knowledge‐based planning (KBP) methodology using volumetric modulated arc therapy (VMAT) for stereotactic radiosurgery (SRS) and radiotherapy (SRT) assisted by an ensemble mapping technique for use in a Monte Carlo planning system. Methods Libraries of 121 stereotactic patients were assembled on the basis of eight different parameters (a) tumor laterality, (b) whether planning target volume (PTV) dose coverage challenged by the presence of the organ at risk (OAR), (c) prescription dose and number of fractions, (d) number of PTVs, (e) tumor volume, (f) shortest distance between OAR and PTV (edge to edge distance, or EED), (g) center to center distance between OARs and PTV (CCD), and (h) lateral dimension of external contour (brain). For new patients, the most appropriate library plan was selected on the basis of the above categorization. A KBP plan was created based on this selected library plan with all parameters unchanged keeping the isocenter at the center of PTV. Using the same beam configuration, another independent treatment plan (IP) was generated by an experienced dosimetrist for comparison. IP and KBP were compared for 76 new patients. Results Of 197 patients (121 library and 76 new), 103 (52.3%) were placed in the OAR‐challenged category and 94 were placed in the OAR unchallenged category. The ensemble mapping technique shows that, for an OAR‐challenged patient, picking up the library plan is appropriate. IP was marginally better than KBP in PTV coverage and dose conformity (PCI). Library plans, IP, and KBP offer a mean PCI of 0.77 ± 0.2, 0.79 ± 0.2, and 0.78 ± 0.4, mean PTV‐V99% of 97.3 ± 22.0%, 98.9 ± 14.1%, and 98.2 ± 13.2%, and mean MU of 2403.8 ± 2403.8, 2344.0 ± 2423.6, and 2473.6 ± 2296.8, respectively. Statistically significant differences were observed in the planning time between the IP and KBP plans for both OAR‐challenged (P < 0.001) and ‐unchallenged (P < 0.002) categories. Comparison of optimization and dose calculation time showed a much lower average planning time of 111.0 ± 84.1 min for KBP as against 248.2 ± 96.6 min for IP. Conclusion Validation results for KBP plans indicate the multidimensional ensemble mapping mechanism can accurately pick up the most appropriate library plan. KBP plans, although slightly inferior in their dosimetric quality, fulfill all the required clinical conditions and dose constraints. KBP plans save considerable planning time and are nearly independent of the skill and knowledge of the treatment planner. KBP works well with a Monte Carlo planning system like Monaco.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Standardization of volumetric modulated arc therapy‐based frameless stereotactic technique using a multidimensional ensemble‐aided knowledge‐based planning

et al. 2019

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“…Additional tools provide increased requirements, for example safer or faster treatment [1]. In order to identify the features requested, we generated a list based on all features available from a larger radiation oncology vendors.…”

Section: Methodsmentioning

confidence: 99%

Making the right software choice for clinically used equipment in radiation oncology

et al. 2014

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The customer of a new system for clinical use in radiation oncology must consider many options in order to find the optimal combination of software tools. Many commercial systems are available and each system has a large number of technical features. However an appraisal of the technical capabilities, especially the options for clinical implementations, is hardly assessable at first view.The intention of this article was to generate an assessment of the necessary functionalities for high precision radiotherapy and their integration in ROKIS (Radiation oncology clinic information system) for future customers, especially with regard to clinical applicability. Therefore we analysed the clinically required software functionalities and divided them into three categories: minimal, enhanced and optimal requirements for high conformal radiation treatment.

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Radiation Therapy Quality Assurance Tasks and Tools: The Many Roles of Machine Learning

2019

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The recent explosion in machine learning efforts in the quality assurance (QA) space has produced a variety of proofs-of-concept many with promising results. Expected outcomes of model implementation include improvements in planning time, plan quality, advanced dosimetric QA, predictive machine maintenance, increased safety checks, and developments key for new QA paradigms driven by adaptive planning. In this article, we outline several areas of research and discuss some of the unique challenges each area presents.

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Evolving treatment plan quality criteria from institution‐specific experience

Cited by 17 publications

References 11 publications

Standardization of volumetric modulated arc therapy‐based frameless stereotactic technique using a multidimensional ensemble‐aided knowledge‐based planning

Standardization of volumetric modulated arc therapy‐based frameless stereotactic technique using a multidimensional ensemble‐aided knowledge‐based planning

Making the right software choice for clinically used equipment in radiation oncology

Radiation Therapy Quality Assurance Tasks and Tools: The Many Roles of Machine Learning

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