Learning Tool for Tumors

Overseen By Rachel Jimenez, MD

Age: Any Age

Sex: Any

Trial Phase: Academic

Sponsor: Massachusetts General Hospital

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: 1

20 Participants Needed

Trial Summary

What is the purpose of this trial?

This study involves both the development of a novel learning tool, the Radiation Treatment Plan Evaluation Learning Module, as well as a pilot program to validate this tool as an effective resource to teach treatment plan review and optimization to radiation oncology residents.

Do I need to stop my current medications for this trial?

The trial information does not specify whether you need to stop taking your current medications.

What data supports the effectiveness of the Radiation Treatment Plan Evaluation Learning Tool treatment?

The research shows that using educational tools like TaCTICS can improve the accuracy of radiation therapy plans by helping users better define target areas for treatment, which is crucial for effective cancer treatment. Additionally, decision support systems in radiation oncology are being developed to personalize treatment plans, potentially improving outcomes by predicting how tumors and normal tissues will respond to radiation. ^{1 2 3 4 5}

Is the Learning Tool for Tumors generally safe for humans?

The research on radiation treatment safety shows that most incidents are minor and have little impact on patient treatment. Systems to learn from incidents have reduced the rate of severe incidents, suggesting that the treatment is generally safe when proper safety measures are in place. ^{6 7 8 9 10}

How does the Learning Tool for Tumors treatment differ from other treatments for tumors?

The Learning Tool for Tumors is unique because it focuses on improving the accuracy of radiation therapy planning through educational software, helping medical professionals better define target areas for treatment. This approach enhances precision in delivering radiation doses, potentially improving outcomes by minimizing damage to healthy tissues compared to traditional methods. ^{1 4 11 12 13}

Research Team

Rachel Jimenez, MD

Principal Investigator

Massachusetts General Hospital

Eligibility Criteria

This trial is for radiation oncology resident physicians in their PGY-2, PGY-3, or PGY-4 year. Participants must have finished a breast cancer-specific radiation oncology rotation at least 3 months before joining the study. Special populations are not included.

Inclusion Criteria

I am a radiation oncology resident in my 2nd, 3rd, or 4th year.

I completed a breast cancer radiation rotation over 3 months ago.

Exclusion Criteria

The study will not include any special populations.

Timeline

Screening

Participants are screened for eligibility to participate in the trial

1-2 weeks

1 visit (in-person)

Learning Module

Participants complete an initial survey and pre-module examination, followed by the learning module, and then a post-module survey and examination

6-8 weeks

Multiple sessions (virtual)

Follow-up

Participants are monitored for effectiveness of the learning tool and their perceived competence and confidence

4 weeks

Treatment Details

Interventions

• Radiation Treatment Plan Evaluation Learning Tool

Trial Overview The study is testing a new learning module designed to teach treatment plan review and optimization to residents specializing in radiation oncology. It's both about developing this educational tool and checking if it works well as a teaching resource.

Participant Groups

Group I: Radiation Treatment Plan Evaluation Learning Module Experimental Treatment 1 Intervention

Radiation oncology resident physicians will complete study procedures as below: * Initial survey and pre-module examination. * Learning module. * Post-module survey and examination.

Who Is Running the Clinical Trial?

Massachusetts General Hospital

Lead Sponsor

Trials

3,066

Recruited

13,430,000+

Findings from Research

Over a 27-month study involving four clinical physicists, 871 safety events were recorded during radiation therapy quality assurance, with 4.7% classified as high severity, highlighting the importance of incident learning in improving safety protocols.

The study identified that targeted interventions, such as preplanning chart rounds and increased automation in planning processes, could effectively reduce the occurrence of high-risk errors in radiation therapy clinics.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The Fusion of Incident Learning and Failure Mode and Effects Analysis for Data-Driven Patient Safety Improvements. Paradis, KC., Naheedy, KW., Matuszak, MM., et al.[2021]

An automated trigger tool designed for radiation oncology was developed and validated, analyzing 3159 treatment courses over 3.5 years, and successfully identified additional near-miss events that were not reported through voluntary reporting systems.

The tool showed a modest predictive performance with an area under the curve of 0.652, indicating it can help detect potential high-grade near misses, suggesting that further exploration and refinement of such tools could enhance patient safety in radiation therapy.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A Radiation Oncology-Specific Automated Trigger Indicator Tool for High-Risk, Near-Miss Safety Events. Hartvigson, PE., Gensheimer, MF., Spady, PK., et al.[2020]

References

1. United States pubmed.ncbi.nlm.nih.gov

A pilot prospective feasibility study of organ-at-risk definition using Target Contour Testing/Instructional Computer Software (TaCTICS), a training and evaluation platform for radiotherapy target delineation. [2021]

2. Netherlands pubmed.ncbi.nlm.nih.gov

Decision support systems for personalized and participative radiation oncology. [2022]

3. Ireland pubmed.ncbi.nlm.nih.gov

Knowledge-based multi-institution plan prediction of whole breast irradiation with tangential fields. [2022]

4. United States pubmed.ncbi.nlm.nih.gov

An Innovative Learning Tool for Radiation Therapy Treatment Plan Evaluation: Implementation and Evaluation. [2021]

5. Ireland pubmed.ncbi.nlm.nih.gov

Evaluating inter-campus plan consistency using a knowledge based planning model. [2018]

6. United States pubmed.ncbi.nlm.nih.gov

Sci-Thurs PM: Delivery-05: One year of learning from incidents. [2019]

7. United States pubmed.ncbi.nlm.nih.gov

The Fusion of Incident Learning and Failure Mode and Effects Analysis for Data-Driven Patient Safety Improvements. [2021]

8. Ireland pubmed.ncbi.nlm.nih.gov

Benchmarking machine learning approaches to predict radiation-induced toxicities in lung cancer patients. [2023]

9. United States pubmed.ncbi.nlm.nih.gov

A Radiation Oncology-Specific Automated Trigger Indicator Tool for High-Risk, Near-Miss Safety Events. [2020]

10. Ireland pubmed.ncbi.nlm.nih.gov

Risk analysis in radiation treatment: application of a new taxonomic structure. [2008]

11. United States pubmed.ncbi.nlm.nih.gov

CERR: a computational environment for radiotherapy research. [2022]

12. Netherlands pubmed.ncbi.nlm.nih.gov

Three-dimensional radiation treatment planning. [2016]

13. United States pubmed.ncbi.nlm.nih.gov

The objective evaluation of alternative treatment plans: I. Images of regret. [2019]