Evidence map›Paper›PMID 40584456›Full record

ArticlePhysics and imaging in radiation oncology2025

Cost-effectiveness of personalized policies for implementing organ-at-risk sparing adaptive radiation therapy in head and neck cancer.

Seyedmohammadhossein Hosseinian, Daniel Suarez-Aguirre, Cem Dede, Raul Garcia, Lucas McCullum, Mehdi Hemmati, Aysenur Karagoz, Abdallah S R Mohamed, Stephen Y Lai, Katherine A Hutcheson and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Physics and imaging in radiation oncology, 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. Review
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

15 authors.

Seyedmohammadhossein HosseinianEdward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC, USA.
Daniel Suarez-AguirreDepartment of Computational Applied Mathematics and Operations Research, Rice University, Houston, TX, USA.
Cem DedeDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Raul GarciaDepartment of Computational Applied Mathematics and Operations Research, Rice University, Houston, TX, USA.
Lucas McCullumDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Mehdi HemmatiSchool of Industrial and Systems Engineering, University of Oklahoma, Norman, OK, USA.
Aysenur KaragozDepartment of Computational Applied Mathematics and Operations Research, Rice University, Houston, TX, USA.
Abdallah S R MohamedDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Stephen Y LaiDepartment of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Katherine A HutchesonDepartment of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Amy C MorenoDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Kristy K BrockDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Fatemeh NosratDepartment of Computational Applied Mathematics and Operations Research, Rice University, Houston, TX, USA.
Clifton D FullerDepartment of Computational Applied Mathematics and Operations Research, Rice University, Houston, TX, USA.
Andrew J SchaeferDepartment of Computational Applied Mathematics and Operations Research, Rice University, Houston, TX, USA.

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI DIANE BODURKA · 1985 to 2026
$290.8M
NCI NIH HHS P30 CA016672
6 · The paper itself

Abstract

Background and Purpose: The principle of adaptive radiation therapy (ART) is to adjust radiation plans in response to anatomical changes during treatment. The purpose of this study was to develop a decision-making model for implementation of personalized ART that balances the costs and clinical benefits of radiation plan adaptations in head and neck cancer (HNC). Materials and Methods: Using retrospective imaging data from 52 HNC patients, a Markov decision process (MDP) model was developed to determine optimal timing for plan adaptations based on the difference in normal tissue complication probability (ΔNTCP) between planned and delivered doses to organs-at-risk. To capture the trade-off between the costs and benefits of plan adaptations, the end-treatment ΔNTCPs were converted to Quality Adjusted Life Years (QALYs) and then to equivalent monetary values using a willingness-to-pay per QALY parameter. Results: The optimal policies were derived for 96 combinations of willingness-to-pay per QALY (W) and re-planning cost (RC) and validated using Monte Carlo simulation for two representative scenarios: (1) W = $200,000, RC = $1,000; (2) W = $100,000, RC = $2,000. In scenario (1), the MDP model's policy reduced the probability of excessive toxicity (ΔNTCP ≥ 5 %) to zero (from 0.21 without re-planning) at an average cost of $380 per patient. In scenario (2), it reduced this probability to 0.02 at an average cost of $520 per patient. Conclusions: The MDP model's policies outperformed the current fixed-time (one-size-fits-all) approaches in both clinical and financial outcomes in the simulations.

Indexed as

Cost-effective policiesMarkov decision processesOptimal resource allocationOrgan-at-risk sparingPersonalized adaptive radiation therapy

Identifiers

PMID40584456
PMCPMC12206292

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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.