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A Decision Support Tool to Optimize Selection of Head and Neck Cancer Patients for Proton Therapy

SIMPLE SUMMARY: A decision support tool was developed to select head and neck cancer patients for proton therapy. The tool uses delineation data to predict expected toxicity risk reduction with proton therapy and can be used before a treatment plan is created. The positive predictive value of the to...

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Autores principales: Tambas, Makbule, van der Laan, Hans Paul, van der Schaaf, Arjen, Steenbakkers, Roel J. H. M., Langendijk, Johannes Albertus
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8833534/
https://www.ncbi.nlm.nih.gov/pubmed/35158949
http://dx.doi.org/10.3390/cancers14030681
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author Tambas, Makbule
van der Laan, Hans Paul
van der Schaaf, Arjen
Steenbakkers, Roel J. H. M.
Langendijk, Johannes Albertus
author_facet Tambas, Makbule
van der Laan, Hans Paul
van der Schaaf, Arjen
Steenbakkers, Roel J. H. M.
Langendijk, Johannes Albertus
author_sort Tambas, Makbule
collection PubMed
description SIMPLE SUMMARY: A decision support tool was developed to select head and neck cancer patients for proton therapy. The tool uses delineation data to predict expected toxicity risk reduction with proton therapy and can be used before a treatment plan is created. The positive predictive value of the tool is >90%. This tool significantly reduces delays in commencing treatment and avoid redundant photon vs. proton treatment plan comparison. ABSTRACT: Selection of head and neck cancer (HNC) patients for proton therapy (PT) using plan comparison (VMAT vs. IMPT) for each patient is labor-intensive. Our aim was to develop a decision support tool to identify patients with high probability to qualify for PT, at a very early stage (immediately after delineation) to avoid delay in treatment initiation. A total of 151 HNC patients were included, of which 106 (70%) patients qualified for PT. Linear regression models for individual OARs were created to predict the D(mean) to the OARs for VMAT and IMPT plans. The predictors were OAR volume percentages overlapping with target volumes. Then, actual and predicted plan comparison decisions were compared. Actual and predicted OAR D(mean) (VMAT R(2) = 0.953, IMPT R(2) = 0.975) and NTCP values (VMAT R(2) = 0.986, IMPT R(2) = 0.992) were highly correlated. The sensitivity, specificity, PPV and NPV of the decision support tool were 64%, 87%, 92% and 51%, respectively. The expected toxicity reduction with IMPT can be predicted using only the delineation data. The probability of qualifying for PT is >90% when the tool indicates a positive outcome for PT. This tool will contribute significantly to a more effective selection of HNC patients for PT at a much earlier stage, reducing treatment delay.
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spelling pubmed-88335342022-02-12 A Decision Support Tool to Optimize Selection of Head and Neck Cancer Patients for Proton Therapy Tambas, Makbule van der Laan, Hans Paul van der Schaaf, Arjen Steenbakkers, Roel J. H. M. Langendijk, Johannes Albertus Cancers (Basel) Article SIMPLE SUMMARY: A decision support tool was developed to select head and neck cancer patients for proton therapy. The tool uses delineation data to predict expected toxicity risk reduction with proton therapy and can be used before a treatment plan is created. The positive predictive value of the tool is >90%. This tool significantly reduces delays in commencing treatment and avoid redundant photon vs. proton treatment plan comparison. ABSTRACT: Selection of head and neck cancer (HNC) patients for proton therapy (PT) using plan comparison (VMAT vs. IMPT) for each patient is labor-intensive. Our aim was to develop a decision support tool to identify patients with high probability to qualify for PT, at a very early stage (immediately after delineation) to avoid delay in treatment initiation. A total of 151 HNC patients were included, of which 106 (70%) patients qualified for PT. Linear regression models for individual OARs were created to predict the D(mean) to the OARs for VMAT and IMPT plans. The predictors were OAR volume percentages overlapping with target volumes. Then, actual and predicted plan comparison decisions were compared. Actual and predicted OAR D(mean) (VMAT R(2) = 0.953, IMPT R(2) = 0.975) and NTCP values (VMAT R(2) = 0.986, IMPT R(2) = 0.992) were highly correlated. The sensitivity, specificity, PPV and NPV of the decision support tool were 64%, 87%, 92% and 51%, respectively. The expected toxicity reduction with IMPT can be predicted using only the delineation data. The probability of qualifying for PT is >90% when the tool indicates a positive outcome for PT. This tool will contribute significantly to a more effective selection of HNC patients for PT at a much earlier stage, reducing treatment delay. MDPI 2022-01-28 /pmc/articles/PMC8833534/ /pubmed/35158949 http://dx.doi.org/10.3390/cancers14030681 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Tambas, Makbule
van der Laan, Hans Paul
van der Schaaf, Arjen
Steenbakkers, Roel J. H. M.
Langendijk, Johannes Albertus
A Decision Support Tool to Optimize Selection of Head and Neck Cancer Patients for Proton Therapy
title A Decision Support Tool to Optimize Selection of Head and Neck Cancer Patients for Proton Therapy
title_full A Decision Support Tool to Optimize Selection of Head and Neck Cancer Patients for Proton Therapy
title_fullStr A Decision Support Tool to Optimize Selection of Head and Neck Cancer Patients for Proton Therapy
title_full_unstemmed A Decision Support Tool to Optimize Selection of Head and Neck Cancer Patients for Proton Therapy
title_short A Decision Support Tool to Optimize Selection of Head and Neck Cancer Patients for Proton Therapy
title_sort decision support tool to optimize selection of head and neck cancer patients for proton therapy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8833534/
https://www.ncbi.nlm.nih.gov/pubmed/35158949
http://dx.doi.org/10.3390/cancers14030681
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