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Development and Testing of a Machine Learning Model Using (18)F-Fluorodeoxyglucose PET/CT-Derived Metabolic Parameters to Classify Human Papillomavirus Status in Oropharyngeal Squamous Carcinoma
OBJECTIVE: To develop and test a machine learning model for classifying human papillomavirus (HPV) status of patients with oropharyngeal squamous cell carcinoma (OPSCC) using (18)F-fluorodeoxyglucose ((18)F-FDG) PET-derived parameters in derived parameters and an appropriate combination of machine l...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Korean Society of Radiology
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9830147/ https://www.ncbi.nlm.nih.gov/pubmed/36606620 http://dx.doi.org/10.3348/kjr.2022.0397 |
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author | Woo, Changsoo Jo, Kwan Hyeong Sohn, Beomseok Park, Kisung Cho, Hojin Kang, Won Jun Kim, Jinna Lee, Seung-Koo |
author_facet | Woo, Changsoo Jo, Kwan Hyeong Sohn, Beomseok Park, Kisung Cho, Hojin Kang, Won Jun Kim, Jinna Lee, Seung-Koo |
author_sort | Woo, Changsoo |
collection | PubMed |
description | OBJECTIVE: To develop and test a machine learning model for classifying human papillomavirus (HPV) status of patients with oropharyngeal squamous cell carcinoma (OPSCC) using (18)F-fluorodeoxyglucose ((18)F-FDG) PET-derived parameters in derived parameters and an appropriate combination of machine learning methods in patients with OPSCC. MATERIALS AND METHODS: This retrospective study enrolled 126 patients (118 male; mean age, 60 years) with newly diagnosed, pathologically confirmed OPSCC, that underwent (18)F-FDG PET-computed tomography (CT) between January 2012 and February 2020. Patients were randomly assigned to training and internal validation sets in a 7:3 ratio. An external test set of 19 patients (16 male; mean age, 65.3 years) was recruited sequentially from two other tertiary hospitals. Model 1 used only PET parameters, Model 2 used only clinical features, and Model 3 used both PET and clinical parameters. Multiple feature transforms, feature selection, oversampling, and training models are all investigated. The external test set was used to test the three models that performed best in the internal validation set. The values for area under the receiver operating characteristic curve (AUC) were compared between models. RESULTS: In the external test set, ExtraTrees-based Model 3, which uses two PET-derived parameters and three clinical features, with a combination of MinMaxScaler, mutual information selection, and adaptive synthetic sampling approach, showed the best performance (AUC = 0.78; 95% confidence interval, 0.46–1). Model 3 outperformed Model 1 using PET parameters alone (AUC = 0.48, p = 0.047) and Model 2 using clinical parameters alone (AUC = 0.52, p = 0.142) in predicting HPV status. CONCLUSION: Using oversampling and mutual information selection, an ExtraTree-based HPV status classifier was developed by combining metabolic parameters derived from (18)F-FDG PET/CT and clinical parameters in OPSCC, which exhibited higher performance than the models using either PET or clinical parameters alone. |
format | Online Article Text |
id | pubmed-9830147 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | The Korean Society of Radiology |
record_format | MEDLINE/PubMed |
spelling | pubmed-98301472023-01-19 Development and Testing of a Machine Learning Model Using (18)F-Fluorodeoxyglucose PET/CT-Derived Metabolic Parameters to Classify Human Papillomavirus Status in Oropharyngeal Squamous Carcinoma Woo, Changsoo Jo, Kwan Hyeong Sohn, Beomseok Park, Kisung Cho, Hojin Kang, Won Jun Kim, Jinna Lee, Seung-Koo Korean J Radiol Neuroimaging and Head & Neck OBJECTIVE: To develop and test a machine learning model for classifying human papillomavirus (HPV) status of patients with oropharyngeal squamous cell carcinoma (OPSCC) using (18)F-fluorodeoxyglucose ((18)F-FDG) PET-derived parameters in derived parameters and an appropriate combination of machine learning methods in patients with OPSCC. MATERIALS AND METHODS: This retrospective study enrolled 126 patients (118 male; mean age, 60 years) with newly diagnosed, pathologically confirmed OPSCC, that underwent (18)F-FDG PET-computed tomography (CT) between January 2012 and February 2020. Patients were randomly assigned to training and internal validation sets in a 7:3 ratio. An external test set of 19 patients (16 male; mean age, 65.3 years) was recruited sequentially from two other tertiary hospitals. Model 1 used only PET parameters, Model 2 used only clinical features, and Model 3 used both PET and clinical parameters. Multiple feature transforms, feature selection, oversampling, and training models are all investigated. The external test set was used to test the three models that performed best in the internal validation set. The values for area under the receiver operating characteristic curve (AUC) were compared between models. RESULTS: In the external test set, ExtraTrees-based Model 3, which uses two PET-derived parameters and three clinical features, with a combination of MinMaxScaler, mutual information selection, and adaptive synthetic sampling approach, showed the best performance (AUC = 0.78; 95% confidence interval, 0.46–1). Model 3 outperformed Model 1 using PET parameters alone (AUC = 0.48, p = 0.047) and Model 2 using clinical parameters alone (AUC = 0.52, p = 0.142) in predicting HPV status. CONCLUSION: Using oversampling and mutual information selection, an ExtraTree-based HPV status classifier was developed by combining metabolic parameters derived from (18)F-FDG PET/CT and clinical parameters in OPSCC, which exhibited higher performance than the models using either PET or clinical parameters alone. The Korean Society of Radiology 2023-01 2023-01-02 /pmc/articles/PMC9830147/ /pubmed/36606620 http://dx.doi.org/10.3348/kjr.2022.0397 Text en Copyright © 2023 The Korean Society of Radiology https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0 (https://creativecommons.org/licenses/by-nc/4.0/) ) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Neuroimaging and Head & Neck Woo, Changsoo Jo, Kwan Hyeong Sohn, Beomseok Park, Kisung Cho, Hojin Kang, Won Jun Kim, Jinna Lee, Seung-Koo Development and Testing of a Machine Learning Model Using (18)F-Fluorodeoxyglucose PET/CT-Derived Metabolic Parameters to Classify Human Papillomavirus Status in Oropharyngeal Squamous Carcinoma |
title | Development and Testing of a Machine Learning Model Using (18)F-Fluorodeoxyglucose PET/CT-Derived Metabolic Parameters to Classify Human Papillomavirus Status in Oropharyngeal Squamous Carcinoma |
title_full | Development and Testing of a Machine Learning Model Using (18)F-Fluorodeoxyglucose PET/CT-Derived Metabolic Parameters to Classify Human Papillomavirus Status in Oropharyngeal Squamous Carcinoma |
title_fullStr | Development and Testing of a Machine Learning Model Using (18)F-Fluorodeoxyglucose PET/CT-Derived Metabolic Parameters to Classify Human Papillomavirus Status in Oropharyngeal Squamous Carcinoma |
title_full_unstemmed | Development and Testing of a Machine Learning Model Using (18)F-Fluorodeoxyglucose PET/CT-Derived Metabolic Parameters to Classify Human Papillomavirus Status in Oropharyngeal Squamous Carcinoma |
title_short | Development and Testing of a Machine Learning Model Using (18)F-Fluorodeoxyglucose PET/CT-Derived Metabolic Parameters to Classify Human Papillomavirus Status in Oropharyngeal Squamous Carcinoma |
title_sort | development and testing of a machine learning model using (18)f-fluorodeoxyglucose pet/ct-derived metabolic parameters to classify human papillomavirus status in oropharyngeal squamous carcinoma |
topic | Neuroimaging and Head & Neck |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9830147/ https://www.ncbi.nlm.nih.gov/pubmed/36606620 http://dx.doi.org/10.3348/kjr.2022.0397 |
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