NDLI: Development and Testing of a Machine Learning Model Using <sup>18</sup>F-Fluorodeoxyglucose PET/CT-Derived Metabolic Parameters to Classify Human Papillomavirus Status in Oropharyngeal Squamous Carcinoma.

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Development and Testing of a Machine Learning Model Using ¹⁸F-Fluorodeoxyglucose PET/CT-Derived Metabolic Parameters to Classify Human Papillomavirus Status in Oropharyngeal Squamous Carcinoma.

Content Provider	Europe PMC
Author	Woo, Changsoo Jo, Kwan Hyeong Sohn, Beomseok Park, Kisung Cho, Hojin Kang, Won Jun Kim, Jinna Lee, Seung-Koo
Copyright Year	2023
Abstract	ObjectiveTo develop and test a machine learning model for classifying human papillomavirus (HPV) status of patients with oropharyngeal squamous cell carcinoma (OPSCC) using 18F-fluorodeoxyglucose (18F-FDG) PET-derived parameters in derived parameters and an appropriate combination of machine learning methods in patients with OPSCC.Materials and MethodsThis retrospective study enrolled 126 patients (118 male; mean age, 60 years) with newly diagnosed, pathologically confirmed OPSCC, that underwent 18F-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.ResultsIn 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.ConclusionUsing oversampling and mutual information selection, an ExtraTree-based HPV status classifier was developed by combining metabolic parameters derived from 18F-FDG PET/CT and clinical parameters in OPSCC, which exhibited higher performance than the models using either PET or clinical parameters alone.
ISSN	12296929
Journal	Korean Journal of Radiology
Volume Number	24
PubMed Central reference number	PMC9830147
Issue Number	1
PubMed reference number	36606620
e-ISSN	20058330
DOI	10.3348/kjr.2022.0397
Language	English
Publisher	The Korean Society of Radiology
Publisher Date	2023-01-01
Access Restriction	Open
Rights License	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) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Copyright © 2023 The Korean Society of Radiology
Subject Keyword	Human papillomavirus Machine learning Oropharynx Positron emission tomography Squamous cell carcinoma
Content Type	Text
Resource Type	Article
Subject	Radiology, Nuclear Medicine and Imaging

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Development and Testing of a Machine Learning Model Using 18F-Fluorodeoxyglucose PET/CT-Derived Metabolic Parameters to Classify Human Papillomavirus Status in Oropharyngeal Squamous Carcinoma.

Development and Testing of a Machine Learning Model Using ¹⁸F-Fluorodeoxyglucose PET/CT-Derived Metabolic Parameters to Classify Human Papillomavirus Status in Oropharyngeal Squamous Carcinoma.