Multi-institutional Randomized Trial Testing the Utility of an Interactive Three-dimensional Contouring Atlas Among Radiation Oncology Residents

Gillespie, Erin F.; Panjwani, Neil; Golden, Daniel W.; Gunther, Jillian R.; Chapman, Tobias R.; Brower, Jeffrey V.; Kosztyla, Robert; Larson, Grant; Neppala, Pushpa; Moiseenko, Vitali; Bykowski, Julie; Sanghvi, Parag; Murphy, James D.

doi:10.1016/j.ijrobp.2016.11.050

Cited by 33 publications

(21 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Make your data publically available as already encouraged by QUANTEC almost ten years ago [51] (formatting and standardization guidelines for such efforts were recently published by the Medical Physics editorial team [52]). In addition to investigating the potential effects of other covariates than dose for the development of a certain symptom, nomenclature and contouring standardization of OARs is also important [53, 54]. Of the included studies, there are examples when OARs were either defined by anatomical landmarks or with respect to distances above/below tumor volumes (Table S1).…”

Section: Discussionmentioning

confidence: 99%

A Systematic Post-QUANTEC Review of Tolerance Doses for Late Toxicity After Prostate Cancer Radiation Therapy

Olsson

Jackson

Deasy

et al. 2018

International Journal of Radiation Oncology*Biology*Physics

View full text Add to dashboard Cite

Updated dose/volume tolerances post-QUANTEC were found for 17 GI/GU/SD symptoms. A DVH curve described the joint relationship between dose/volume tolerances across five GI symptoms after EBRT alone. Restricting treatments for EBRT alone using the lower boundaries of this curve are likely to limit overall GI toxicity, but this should be explored prospectively. Dose/volume tolerances for GU and SD toxicity after EBRT alone, and after BT±EBRT were scarce and support further research including data sharing initiatives to untangle the dose/volume relationships for these symptoms.

show abstract

Section: Discussionmentioning

confidence: 99%

A Systematic Post-QUANTEC Review of Tolerance Doses for Late Toxicity After Prostate Cancer Radiation Therapy

Olsson

Jackson

Deasy

et al. 2018

International Journal of Radiation Oncology*Biology*Physics

View full text Add to dashboard Cite

show abstract

“…Davids et al used the SUS first to evaluate an e-learning resource for electrolyte and acid-base disorders [41] and again in 2014 to evaluate the effect of improving usability [42]. The SUS was also used by Gorrindo et al [43], Diehl et al [44], and Gillespie in 2017 [45].…”

Section: Resultsmentioning

confidence: 99%

How We Evaluate Postgraduate Medical E-Learning: Systematic Review

Leeuw¹,

Soet²,

Horst³

et al. 2019

JMIR Med Educ

View full text Add to dashboard Cite

Background Electronic learning (e-learning) in postgraduate medical education has seen a rapid evolution; however, we tend to evaluate it only on its primary outcome or learning aim, whereas its effectiveness also depends on its instructional design. We believe it is important to have an overview of all the methods currently used to evaluate e-learning design so that the preferred method may be identified and the next steps needed to continue to evaluate postgraduate medical e-learning may be outlined. Objective This study aimed to identify and compare the outcomes and methods used to evaluate postgraduate medical e-learning. Methods We performed a systematic literature review using the Web of Science, PubMed, Education Resources Information Center, and Cumulative Index of Nursing and Allied Health Literature databases. Studies that used postgraduates as participants and evaluated any form of e-learning were included. Studies without any evaluation outcome (eg, just a description of e-learning) were excluded. Results The initial search identified 5973 articles, of which we used 418 for our analysis. The types of studies were trials, prospective cohorts, case reports, and reviews. The primary outcomes of the included studies were knowledge, skills, and attitude. A total of 12 instruments were used to evaluate a specific primary outcome, such as laparoscopic skills or stress related to training. The secondary outcomes mainly evaluated satisfaction, motivation, efficiency, and usefulness. We found 13 e-learning design methods across 19 studies (4% 19/418). The methods evaluated usability, motivational characteristics, and the use of learning styles or were based on instructional design theories, such as Gagne’s instructional design, the Heidelberg inventory, Kern’s curriculum development steps, and a scale based on the cognitive load theory. Finally, 2 instruments attempted to evaluate several aspects of a design, based on the experience of creating e-learning. Conclusions Evaluating the effect of e-learning design is complicated. Given the diversity of e-learning methods, there are many ways to carry out such an evaluation, and probably, many ways to do so correctly. However, the current literature shows us that we have yet to reach any form of consensus about which indicators to evaluate. There is a great need for an evaluation tool that is properly constructed, validated, and tested. This could be a more homogeneous way to compare the effects of e-learning and for the authors of e-learning to continue to improve their product.

show abstract

“…In a randomized trial, eContour improved nasopharynx contours and anatomy knowledge compared with traditional resources. 48 A next phase of eContour may involve enabling user-generated contours and segmentations for technology development and research, with a prototype initially pilot tested at the American College of Radiation Oncology Annual Meeting in 2017. 49 Researchers are invited to use the platform to collect contours from large numbers of users from diverse practice locations, though must provide funding to support website programming and content administration.…”

Section: Ap3: Create Publicly Accessible Resourcesmentioning

confidence: 99%

National Cancer Institute Workshop on Artificial Intelligence in Radiation Oncology: Training the Next Generation

Kang

Thompson

Aneja

et al. 2021

Practical Radiation Oncology

View full text Add to dashboard Cite

Artificial intelligence (AI) is about to touch every aspect of radiation therapy, from consultation to treatment planning, quality assurance, therapy delivery, and outcomes modeling. There is an urgent need to train radiation oncologists and medical physicists in data science to help shepherd AI solutions into clinical practice. Poorly trained personnel may do more harm than good when attempting to apply rapidly developing and complex technologies. As the amount of AI research expands in our field, the radiation oncology community needs to discuss how to educate future generations in this area. Methods and Materials: The National Cancer Institute (NCI) Workshop on AI in Radiation Oncology (Shady Grove, MD, April 4-5, 2019) was the first of 2 data science workshops in radiation oncology hosted by the NCI in 2019. During this workshop, the Training and Education Working Group was formed by volunteers among the invited attendees. Its members represent radiation oncology, medical physics, radiology, computer science, industry, and the NCI. Results: In this perspective article written by members of the Training and Education Working Group, we provide and discuss action points relevant for future trainees interested in radiation oncology AI: (1) creating AI awareness and responsible conduct; (2) implementing a practical didactic curriculum; (3) creating a publicly available database of training resources; and (4) accelerating learning and funding opportunities. Conclusion: Together, these action points can facilitate the translation of AI into clinical practice.

show abstract

Multi-institutional Randomized Trial Testing the Utility of an Interactive Three-dimensional Contouring Atlas Among Radiation Oncology Residents

Cited by 33 publications

References 31 publications

A Systematic Post-QUANTEC Review of Tolerance Doses for Late Toxicity After Prostate Cancer Radiation Therapy

A Systematic Post-QUANTEC Review of Tolerance Doses for Late Toxicity After Prostate Cancer Radiation Therapy

How We Evaluate Postgraduate Medical E-Learning: Systematic Review

National Cancer Institute Workshop on Artificial Intelligence in Radiation Oncology: Training the Next Generation

Contact Info

Product

Resources

About