Evidence map›Paper›PMID 41464383›Full record

ArticleHealthcare (Basel, Switzerland)2025

Medical Physics Adaptive Radiotherapy (MPART) Fellowship: Bridging the Training Gap in Online Adaptive Radiotherapy.

Bin Cai, David Parsons, Mu-Han Lin, Dan Nguyen, Andrew R Godley, Arnold Pompos, Kajal Desai, Shahed Badiyan, David Sher, Robert Timmerman and 1 more

Abstract read
In one paragraph

Article in Healthcare (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Bin CaiDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.ORCID 0000-0002-5915-3664
David ParsonsDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.ORCID 0000-0002-4466-0647
Mu-Han LinDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
Dan NguyenDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
Andrew R GodleyDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.ORCID 0000-0002-8896-5231
Arnold PomposDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
Kajal DesaiDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
Shahed BadiyanDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
David SherDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
Robert TimmermanDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.ORCID 0000-0003-2462-1007
Steve JiangDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Online adaptive radiotherapy (ART) is rapidly transforming clinical radiation oncology by enabling adaptation of treatment plans based on patient-specific anatomical and biological changes. However, most medical physics training programs lack structured education in ART. To address this critical gap, the Medical Physics Adaptive Radiotherapy (MPART) Fellowship was established at our center to train post-residency or practicing physicists in advanced adaptive technologies and workflows. The MPART Fellowship is a two-year program that provides immersive, platform-specific training in CBCT-guided (Varian Ethos), MR-guided (Elekta Unity), and PET-guided (RefleXion X1) radiotherapy. Fellows undergo modular clinical rotations, hands-on training, and dedicated research projects. The curriculum incorporates competencies in imaging, contouring, online planning, quality assurance, and team-based decision-making. Evaluation is based on the Accreditation Council for Graduate Medical Education competency domains and includes milestone tracking, mentor reviews, and structured presentations. The fellowship attracted applicants from both domestic and international institutions, reflecting strong demand for formal ART training. Out of 22 applications, two fellows have been successfully recruited into the program since 2024. Fellows actively participate in all phases of adaptive workflows and are expected to function at near-attending levels by the second year of their training. Each fellow also leads at least one translational or operational research project aimed at improving ART delivery. Fellows contribute to clinical coverage and lead developmental projects, resulting in presentations and publications at the national and international levels. The MPART Fellowship addresses a vital educational need by equipping medical physicists with the advanced competencies necessary for implementing and leading ART. This program offers a replicable framework for other institutions seeking to advance precision radiation therapy through structured post-residency training in adaptive radiotherapy. As this fellowship program is still in its early phase of establishment, the primary goal of this paper is to introduce the structure, framework, and implementation model of the program. Comprehensive outcome analyses-such as quantitative assessments, fellow feedback, and longitudinal competency evaluations-will be incorporated in future work as additional cohorts complete training.

Indexed as

adaptive radiotherapymedical physics educationmedical physics fellowship

Identifiers

PMID41464383
PMCPMC12732406

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.