Jove
Visualize
Contact Us

Related Experiment Videos

Data for TROTS - The Radiotherapy Optimisation Test Set.

Sebastiaan Breedveld1, Ben Heijmen1

  • 1Erasmus University Medical Center - Cancer Institute, Department of Radiation Oncology, Rotterdam, The Netherlands.

Data in Brief
|April 19, 2017
PubMed
Summary

The Radiotherapy Optimisation Test Set (TROTS) provides a large dataset for evaluating mathematical solvers in radiation therapy planning. It also enables research into the complex multi-criteria optimization inherent in radiotherapy.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Sparse probabilistic evaluation for treatment planning: a feasibility study in IMPT head and neck patients.

Physics in medicine and biology·2026
Same author

Robust VMAT planning strategy to ensure target coverage and reduce NTCP in head and neck cancer.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)·2026
Same author

Automated Planning for Bias-Free Validation of Discrete Proton Arc Therapy for Oropharyngeal Cancer.

International journal of particle therapy·2025
Same author

Qualitative Blinded Clinical Assessment of Automated and Manual IMPT Plans for Head and Neck Cancer.

International journal of radiation oncology, biology, physics·2025
Same author

Mitigating the impact of FLASH-model uncertainties through personalized FLASH optimization functions for delivery pattern optimization for lung IMPT.

Physics in medicine and biology·2025
Same author

Response to letter to editor "Re-evaluating 'Prospective evaluation of AI-based BiCycle autoplanning for advanced cervical cancer brachytherapy': The silent cumulative CTVIR deficit".

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology·2025
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Area of Science:

  • Medical Physics
  • Computational Science
  • Operations Research

Background:

  • Radiotherapy treatment planning involves complex optimization problems.
  • Evaluating the performance of mathematical solvers is crucial for improving treatment efficacy.
  • Understanding the multi-criteria decision-making aspects of radiotherapy is essential.

Purpose of the Study:

  • To introduce the Radiotherapy Optimisation Test Set (TROTS) dataset.
  • To provide a benchmark for assessing the performance and quality of mathematical optimization solvers.
  • To facilitate research into the multi-criteria optimization and decision-making processes in radiotherapy.

Main Methods:

  • The TROTS dataset comprises 120 problems derived from real radiotherapy treatment planning scenarios.
Keywords:
90C0690C2690C2990C30Large-Scale OptimisationMultiple objective programmingNonlinear optimisationOR in health servicesRadiotherapy

Related Experiment Videos

  • Problems are categorized across 6 distinct treatment protocols/tumour types.
  • Data is stored in HDF5 compatible Matlab files, including numerical data, optimization configurations, and visualization tools.

    • Main Results:

    • The dataset offers a large-scale, dense collection of radiotherapy optimization problems.
    • It includes comprehensive data for performance evaluation and result interpretation.
    • Associated scripts facilitate dataset utilization.

    Conclusions:

    • The TROTS dataset serves as a valuable resource for the radiotherapy research community.
    • It enables rigorous benchmarking of optimization algorithms used in treatment planning.
    • Facilitates advancements in multi-criteria optimization for radiation therapy.