Dataset Information

VERT, a virtual clinical environment, enhances understanding of radiation therapy planning concepts.

ABSTRACT:

Introduction

The ability to understand treatment plan dosimetry and apply this understanding clinically is fundamental to the role of the radiation therapist. This study evaluates whether or not the Virtual Environment for Radiotherapy Training (VERT) contributes to teaching treatment planning concepts to a cohort of first-year radiation therapy students.

Methods

We directly compared a custom-developed VERT teaching module with a standard teaching module with respect to the understanding of treatment planning concepts using a cross-over design. Students self-reported their understanding of specific concepts before and after delivery of the VERT and standard teaching modules and evaluated aspects of VERT as a learning experience. In addition, teaching staff participated in a semi-structured interview discussing the modules from an educational perspective.

Results

Both the standard teaching module and VERT teaching module enhanced conceptual understanding and level of confidence in the student cohort after both teaching periods. The proportion of students reporting a perceived increase in knowledge/confidence was similar for the VERT teaching module for all but two scenarios. We propose that an integrated approach, providing a strong theoretical conceptual framework, followed by VERT to situate this framework in the (simulated) clinical environment combines the best of both teaching approaches.

Conclusion

This study has established for the first time a clear role for a tailored VERT teaching module in teaching RT planning concepts because of its ability to visualise conceptual information within a simulated clinical environment.

SUBMITTER: Leong A

PROVIDER: S-EPMC5986053 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Publications

VERT, a virtual clinical environment, enhances understanding of radiation therapy planning concepts.

Leong Aidan A Herst Patries P Kane Paul P

Journal of medical radiation sciences 20180308 2

<h4>Introduction</h4>The ability to understand treatment plan dosimetry and apply this understanding clinically is fundamental to the role of the radiation therapist. This study evaluates whether or not the Virtual Environment for Radiotherapy Training (VERT) contributes to teaching treatment planning concepts to a cohort of first-year radiation therapy students.<h4>Methods</h4>We directly compared a custom-developed VERT teaching module with a standard teaching module with respect to the unders ...[more]

PMID: 29516649

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Project description:Treatment planning for whole-brain radiation treatment is technically a simple process, but in practice it takes valuable clinical time of repetitive and tedious tasks. This report presents a method that automatically segments the relevant target and normal tissues, and creates a treatment plan in only a few minutes after patient simulation. Segmentation of target and critical structures is performed automatically through morphological operations on the soft tissue and was validated by comparing with manual clinical segmentation using the Dice coefficient and Hausdorff distance. The treatment plan is generated by searching a database of previous cases for patients with similar anatomy. In this search, each database case is ranked in terms of similarity using a customized metric designed for sensitivity by including only geometrical changes that affect the dose distribution. The database case with the best match is automatically modified to replace relevant patient info and isocenter position while maintaining original beam and MLC settings. Fifteen patients with marginally acceptable treatment plans were used to validate the method. In each of these cases the anatomy was accurately segmented, but the beams and MLC settings led to a suboptimal treatment plan by either underdosing the brain or excessively irradiating critical normal tissues. For each case, the anatomy was automatically segmented with the proposed method, and the automated and anual segmentations were then compared. The mean Dice coefficient was 0.97, with a standard deviation of 0.008 for the brain, 0.85 ± 0.009 for the eyes, and 0.67 ± 0.11 for the lens. The mean Euclidian distance was 0.13 ± 0.13 mm for the brain, 0.27± 0.31 for the eye, and 2.34 ± 7.23 for the lens. Each case was then subsequently matched against a database of 70 validated treatment plans and the best matching plan (termed autoplanned), was compared retrospectively with the clinical plans in terms of brain coverage and maximum doses to critical structures. Maximum doses were reduced by a maximum of 8.37 Gy for the left eye (mean 2.08), 11.67 for the right eye (1.90) and, respectively, 25.44 (5.59) for the left lens and 24.40 (4.85) for the right lens. Time to generate the autoplan, including the segmentation, was 3-4min. Automated database- based matching is an alternative to classical treatment planning that improves quality while providing a cost-effective solution to planning through modifying previous validated plans to match a current patient's anatomy.

Project description:To introduce a method to efficiently identify and calculate meaningful tradeoffs between criteria in an interactive IMRT treatment planning procedure. The method provides a systematic approach to developing high-quality radiation therapy treatment plans.Treatment planners consider numerous dosimetric criteria of varying importance that, when optimized simultaneously through multicriteria optimization, yield a Pareto frontier which represents the set of Pareto-optimal treatment plans. However, generating and navigating this frontier is a time-consuming, nontrivial process. A lexicographic ordering (LO) approach to IMRT uses a physician's criteria preferences to partition the treatment planning decisions into a multistage treatment planning model. Because the relative importance of criteria optimized in the different stages may not necessarily constitute a strict prioritization, the authors introduce an interactive process, sensitivity analysis in lexicographic ordering (SALO), to allow the treatment planner control over the relative sequential-stage tradeoffs. By allowing this flexibility within a structured process, SALO implicitly restricts attention to and allows exploration of a subset of the Pareto efficient frontier that the physicians have deemed most important.Improvements to treatment plans over a LO approach were found by implementing the SALO procedure on a brain case and a prostate case. In each stage, a physician assessed the tradeoff between previous stage and current stage criteria. The SALO method provided critical tradeoff information through curves approximating the relationship between criteria, which allowed the physician to determine the most desirable treatment plan.The SALO procedure provides treatment planners with a directed, systematic process to treatment plan selection. By following a physician's prioritization, the treatment planner can avoid wasting effort considering clinically inferior treatment plans. The planner is guided by criteria importance, but given the information necessary to accurately adjust the relative importance at each stage. Through these attributes, the SALO procedure delivers an approach well balanced between efficiency and flexibility.

Project description:BackgroundNetwork concepts are increasingly used in biology and genetics. For example, the clustering coefficient has been used to understand network architecture; the connectivity (also known as degree) has been used to screen for cancer targets; and the topological overlap matrix has been used to define modules and to annotate genes. Dozens of potentially useful network concepts are known from graph theory.ResultsHere we study network concepts in special types of networks, which we refer to as approximately factorizable networks. In these networks, the pairwise connection strength (adjacency) between 2 network nodes can be factored into node specific contributions, named node 'conformity'. The node conformity turns out to be highly related to the connectivity. To provide a formalism for relating network concepts to each other, we define three types of network concepts: fundamental-, conformity-based-, and approximate conformity-based concepts. Fundamental concepts include the standard definitions of connectivity, density, centralization, heterogeneity, clustering coefficient, and topological overlap. The approximate conformity-based analogs of fundamental network concepts have several theoretical advantages. First, they allow one to derive simple relationships between seemingly disparate networks concepts. For example, we derive simple relationships between the clustering coefficient, the heterogeneity, the density, the centralization, and the topological overlap. The second advantage of approximate conformity-based network concepts is that they allow one to show that fundamental network concepts can be approximated by simple functions of the connectivity in module networks.ConclusionUsing protein-protein interaction, gene co-expression, and simulated data, we show that a) many networks comprised of module nodes are approximately factorizable and b) in these types of networks, simple relationships exist between seemingly disparate network concepts. Our results are implemented in freely available R software code, which can be downloaded from the following webpage: http://www.genetics.ucla.edu/labs/horvath/ModuleConformity/ModuleNetworks.

Dataset Information

VERT, a virtual clinical environment, enhances understanding of radiation therapy planning concepts.

Introduction

Methods

Results

Conclusion

Publications

VERT, a virtual clinical environment, enhances understanding of radiation therapy planning concepts.

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