Evidence map›Paper›PMID 42185908›Full record

ArticleRadiation oncology (London, England)2026

A hybrid cluster-then-predict machine learning radiotherapy knowledge-based planning framework for similarity matching using holistic target-OAR constellation geometry.

Trent Benedick, J Daniel Chu, Stephanie Zhou, Raphiporn Duangrat, John Asbach, Ryan H B Smith, Anh H Le, Brent Liu

Abstract read

In one paragraph

Article in Radiation oncology (London, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 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

J Daniel ChuImage Processing and Informatics Laboratory, Department of Biomedical Engineering, University of Southern California, 1042 Downey Way, Los Angeles, CA, 90089, USA.ORCID http://orcid.org/0009-0002-2562-674X

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

Raphiporn DuangratImage Processing and Informatics Laboratory, Department of Biomedical Engineering, University of Southern California, 1042 Downey Way, Los Angeles, CA, 90089, USA.ORCID http://orcid.org/0009-0004-4518-9393

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 treatment planning is currently premised on individual clinical experience and use of many dose based optimization and knowledge-based planning (KBP) models. This study introduces and validates a novel KBP algorithm that matches cases based on a holistic model of the target to Organ at Risk (OAR) constellation geometry to assist during treatment plan creation by identifying retrospective cases with similar target-OAR constellation geometry for use as reference cases to serve as planning templates.

methodsWe developed a multi step algorithm to calculate geometric similarity between radiotherapy cases. The method extracts quantitative geometric features (Overlap Volume Histograms and Spatial Target Signatures) to characterize the spatial relationships between all target volumes and OARs. Component, partial, dissimilarities between corresponding features are calculated with an Earth Mover's Distance (EMD). A final, clinically relevant similarity score is computed using a hybrid cluster-then-predict machine learning approach (a hard-gated Mixture-of-Experts architecture), where cases are first partitioned by spectral clustering, then separate supervised models are trained within each cluster to predict case similarity.

resultsThe algorithm was validated with a knowledge base of 192 retrospective Head and Neck Cancer cases. A leave-one-out cross-validation demonstrated a strong correlation (r = 0.8009) between our KBP model's geometry based similarity score and actual dose similarity. Additionally, a clinical evaluation with an ROC analysis yielded a high area under the curve (AUC) of 0.8797, confirming the model's ability to identify cases the clinicians determined were clinically relevant as reference cases to serve as planning templates.

conclusionsThis study validates our novel KBP algorithm that successfully identifies clinically relevant retrospective cases using a comprehensive geometric model of target-OAR constellations. By providing personalized patient specific, knowledge base best practice reference case data before beam configuration and treatment plan creation, this method provides a useful new tool to increase standardization, improve plan quality, and assist clinicians during initial plan creation.

Indexed as

AlgorithmsHead and Neck NeoplasmsMachine LearningOrgans at RiskRadiotherapy Planning, Computer-AssistedHumansKnowledge BasesRadiotherapy DosageRadiotherapy, Intensity-ModulatedRetrospective StudiesDecision supportKnowledge-based treatment planningMachine learningRadiation therapyTreatment planning support

Identifiers

PMID42185908

DOI10.1186/s13014-026-02843-8

PMCPMC13386776

What OpenQuestion holds

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LicenceCC BY-NC-ND

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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.