Evidence map›Paper›PMID 41307886›Full record

ArticleInternational journal of computer assisted radiology and surgery2026

Knowledge-based radiation therapy treatment planning decision support system for head and neck cancer utilizing multi-organ constellation matching.

Trent Benedick, Stephanie Zhou, Jorge Solis Galvan, John Asbach, Ryan H B Smith, Anh H Le, Brent Liu

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In one paragraph

Article in International journal of computer assisted radiology and surgery, 2026. 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

7 authors.

Trent BenedickImage Processing and Informatics Laboratory, Department of Biomedical Engineering, University of Southern California, 1042 Downey Way, Los Angeles, CA, 90089, USA. benedick@usc.edu.ORCID http://orcid.org/0009-0002-4998-5659
Stephanie ZhouImage Processing and Informatics Laboratory, Department of Biomedical Engineering, University of Southern California, 1042 Downey Way, Los Angeles, CA, 90089, USA.ORCID http://orcid.org/0000-0002-9394-3723
Jorge Solis GalvanImage Processing and Informatics Laboratory, Department of Biomedical Engineering, University of Southern California, 1042 Downey Way, Los Angeles, CA, 90089, USA.ORCID http://orcid.org/0000-0001-5327-1443
John AsbachRoswell Park Comprehensive Cancer Center, Elm and Carlton Streets, Buffalo, NY, 14203, USA.ORCID http://orcid.org/0000-0002-7300-5967
Ryan H B SmithDepartment of Radiation Oncology, University of Minnesota, 516 Delaware St SE, Minneapolis, MN, 55455, USA.ORCID http://orcid.org/0000-0002-9207-4108
Anh H LeDepartment of Radiation Oncology, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA.ORCID http://orcid.org/0000-0003-1970-8781
Brent LiuImage Processing and Informatics Laboratory, Department of Biomedical Engineering, University of Southern California, 1042 Downey Way, Los Angeles, CA, 90089, USA.ORCID http://orcid.org/0000-0001-6382-366X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeRadiotherapy treats cancers through precise delivery of radiation to target volumes. Radiotherapy treatment plans, prescribing the delivery of therapeutic radiation, are presently created primarily from clinical experience and application of clinical protocols through trial-and-error rather than standardized quantitative methods. We developed an informatics infrastructure and decision support system to assist during treatment plan creation by providing access to applicable retrospective radiotherapy cases.

methodsRadiotherapy treatment planning is based in part on tumor position and spatial relationships to surrounding structural anatomy. Our system data mines retrospective cases to identify cases and treatment plans with similar tumor position and surrounding anatomy (i.e., multi-organ-tumor constellation geometry) for clinicians to use as references during treatment plan creation. The system is based on a database of 390 DICOM RT dosimetry digital radiotherapy datasets with associated extracted quantitative features. Using data mining techniques, overall similarity between cases is calculated with features extracted from tumor volumes and organs at risk (OAR).

resultsWe implemented our radiotherapy treatment planning decision support system with a full-stack infrastructure, including a database of best practice retrospective cases, an algorithmic backend for feature extraction and similarity calculation, and a frontend web application for clinical use. Clinicians can upload current planning cases to the web application whereupon their similarity to knowledge base cases is calculated, and the most similar are presented to clinicians for selection as references during current treatment plan creation.

conclusionsThis radiotherapy treatment planning decision support system, by providing access to geometrically similar retrospective best practice reference cases, presents a novel tool to improve treatment planning. Development of a full system infrastructure increases standardization, facilitates creation of high quality plans, and assists clinicians, particularly during the critical initial beam configuration stage.

Indexed as

Decision Support Systems, ClinicalHead and Neck NeoplasmsKnowledge BasesRadiotherapy Planning, Computer-AssistedAlgorithmsHumansOrgans at RiskRadiotherapy DosageRetrospective StudiesDecision supportImaging informaticsInformatics infrastructureKnowledge-based treatment planningRadiation therapyTreatment planning supportWeb-based application

Identifiers

PMID41307886
PMCPMC13586044

What OpenQuestion holds

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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.