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Determining Clinical Patient Selection Guidelines for Head and Neck Adaptive Radiation Therapy Using Random Forest Modelling and a Novel Simplification Heuristic

PURPOSE: To determine which head and neck adaptive radiotherapy (ART) correction objectives are feasible and to derive efficient ART patient selection guidelines. METHODS: We considered various head and neck ART objectives including independent consideration of dose-sparing of the brainstem/spinal c...

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Autores principales: Weppler, Sarah, Quon, Harvey, Schinkel, Colleen, Ddamba, James, Harjai, Nabhya, Vigal, Clarisse, Beers, Craig A., Van Dyke, Lukas, Smith, Wendy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8216638/
https://www.ncbi.nlm.nih.gov/pubmed/34164338
http://dx.doi.org/10.3389/fonc.2021.650335
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author Weppler, Sarah
Quon, Harvey
Schinkel, Colleen
Ddamba, James
Harjai, Nabhya
Vigal, Clarisse
Beers, Craig A.
Van Dyke, Lukas
Smith, Wendy
author_facet Weppler, Sarah
Quon, Harvey
Schinkel, Colleen
Ddamba, James
Harjai, Nabhya
Vigal, Clarisse
Beers, Craig A.
Van Dyke, Lukas
Smith, Wendy
author_sort Weppler, Sarah
collection PubMed
description PURPOSE: To determine which head and neck adaptive radiotherapy (ART) correction objectives are feasible and to derive efficient ART patient selection guidelines. METHODS: We considered various head and neck ART objectives including independent consideration of dose-sparing of the brainstem/spinal cord, parotid glands, and pharyngeal constrictor, as well as prediction of patient weight loss. Two-hundred head and neck cancer patients were used for model development and an additional 50 for model validation. Patient chart data, pre-treatment images, treatment plans, on-unit patient measurements, and combinations thereof were assessed as potential predictors of each objective. A stepwise approach identified combinations of predictors maximizing the Youden index of random forest (RF) models. A heuristic translated RF results into simple patient selection guidelines which were further refined to balance predictive capability and practical resource costs. Generalizability of the RF models and simplified guidelines to new data was tested using the validation set. RESULTS: Top performing RF models used various categories of predictors, however, final simplified patient selection guidelines only required pre-treatment information for ART predictions, indicating the potential for significant ART process streamlining. The simplified guidelines for each objective predicted which patients would experience increases in dose to: brainstem/spinal cord with sensitivity = 1.0, specificity = 0.66; parotid glands with sensitivity = 0.82, specificity = 0.70; and pharyngeal constrictor with sensitivity = 0.84, specificity = 0.68. Weight loss could be predicted with sensitivity = 0.60 and specificity = 0.55. Furthermore, depending on the ART objective, 28%-58% of patients required replan assessment, less than for previous studies, indicating a step towards more effective patient selection. CONCLUSIONS: The above ART objectives appear to be practically achievable, with patients selected for ART according to simple clinical patient selection guidelines. Explicit ART guidelines are rare in the literature, and our guidelines may aid in balancing the potential clinical gains of ART with high associated resource costs, formalizing ART trials, and ensuring the reproducibility of clinical successes.
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spelling pubmed-82166382021-06-22 Determining Clinical Patient Selection Guidelines for Head and Neck Adaptive Radiation Therapy Using Random Forest Modelling and a Novel Simplification Heuristic Weppler, Sarah Quon, Harvey Schinkel, Colleen Ddamba, James Harjai, Nabhya Vigal, Clarisse Beers, Craig A. Van Dyke, Lukas Smith, Wendy Front Oncol Oncology PURPOSE: To determine which head and neck adaptive radiotherapy (ART) correction objectives are feasible and to derive efficient ART patient selection guidelines. METHODS: We considered various head and neck ART objectives including independent consideration of dose-sparing of the brainstem/spinal cord, parotid glands, and pharyngeal constrictor, as well as prediction of patient weight loss. Two-hundred head and neck cancer patients were used for model development and an additional 50 for model validation. Patient chart data, pre-treatment images, treatment plans, on-unit patient measurements, and combinations thereof were assessed as potential predictors of each objective. A stepwise approach identified combinations of predictors maximizing the Youden index of random forest (RF) models. A heuristic translated RF results into simple patient selection guidelines which were further refined to balance predictive capability and practical resource costs. Generalizability of the RF models and simplified guidelines to new data was tested using the validation set. RESULTS: Top performing RF models used various categories of predictors, however, final simplified patient selection guidelines only required pre-treatment information for ART predictions, indicating the potential for significant ART process streamlining. The simplified guidelines for each objective predicted which patients would experience increases in dose to: brainstem/spinal cord with sensitivity = 1.0, specificity = 0.66; parotid glands with sensitivity = 0.82, specificity = 0.70; and pharyngeal constrictor with sensitivity = 0.84, specificity = 0.68. Weight loss could be predicted with sensitivity = 0.60 and specificity = 0.55. Furthermore, depending on the ART objective, 28%-58% of patients required replan assessment, less than for previous studies, indicating a step towards more effective patient selection. CONCLUSIONS: The above ART objectives appear to be practically achievable, with patients selected for ART according to simple clinical patient selection guidelines. Explicit ART guidelines are rare in the literature, and our guidelines may aid in balancing the potential clinical gains of ART with high associated resource costs, formalizing ART trials, and ensuring the reproducibility of clinical successes. Frontiers Media S.A. 2021-06-07 /pmc/articles/PMC8216638/ /pubmed/34164338 http://dx.doi.org/10.3389/fonc.2021.650335 Text en Copyright © 2021 Weppler, Quon, Schinkel, Ddamba, Harjai, Vigal, Beers, Van Dyke and Smith https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Weppler, Sarah
Quon, Harvey
Schinkel, Colleen
Ddamba, James
Harjai, Nabhya
Vigal, Clarisse
Beers, Craig A.
Van Dyke, Lukas
Smith, Wendy
Determining Clinical Patient Selection Guidelines for Head and Neck Adaptive Radiation Therapy Using Random Forest Modelling and a Novel Simplification Heuristic
title Determining Clinical Patient Selection Guidelines for Head and Neck Adaptive Radiation Therapy Using Random Forest Modelling and a Novel Simplification Heuristic
title_full Determining Clinical Patient Selection Guidelines for Head and Neck Adaptive Radiation Therapy Using Random Forest Modelling and a Novel Simplification Heuristic
title_fullStr Determining Clinical Patient Selection Guidelines for Head and Neck Adaptive Radiation Therapy Using Random Forest Modelling and a Novel Simplification Heuristic
title_full_unstemmed Determining Clinical Patient Selection Guidelines for Head and Neck Adaptive Radiation Therapy Using Random Forest Modelling and a Novel Simplification Heuristic
title_short Determining Clinical Patient Selection Guidelines for Head and Neck Adaptive Radiation Therapy Using Random Forest Modelling and a Novel Simplification Heuristic
title_sort determining clinical patient selection guidelines for head and neck adaptive radiation therapy using random forest modelling and a novel simplification heuristic
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8216638/
https://www.ncbi.nlm.nih.gov/pubmed/34164338
http://dx.doi.org/10.3389/fonc.2021.650335
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