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Multiphasic CT-Based Radiomics Analysis for the Differentiation of Benign and Malignant Parotid Tumors

OBJECTIVE: This study aims to investigate the value of machine learning models based on clinical-radiological features and multiphasic CT radiomics features in the differentiation of benign parotid tumors (BPTs) and malignant parotid tumors (MPTs). METHODS: This retrospective study included 312 pati...

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Autores principales: Yu, Qiang, Wang, Anran, Gu, Jinming, Li, Quanjiang, Ning, Youquan, Peng, Juan, Lv, Fajin, Zhang, Xiaodi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9280642/
https://www.ncbi.nlm.nih.gov/pubmed/35847942
http://dx.doi.org/10.3389/fonc.2022.913898
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author Yu, Qiang
Wang, Anran
Gu, Jinming
Li, Quanjiang
Ning, Youquan
Peng, Juan
Lv, Fajin
Zhang, Xiaodi
author_facet Yu, Qiang
Wang, Anran
Gu, Jinming
Li, Quanjiang
Ning, Youquan
Peng, Juan
Lv, Fajin
Zhang, Xiaodi
author_sort Yu, Qiang
collection PubMed
description OBJECTIVE: This study aims to investigate the value of machine learning models based on clinical-radiological features and multiphasic CT radiomics features in the differentiation of benign parotid tumors (BPTs) and malignant parotid tumors (MPTs). METHODS: This retrospective study included 312 patients (205 cases of BPTs and 107 cases of MPTs) who underwent multiphasic enhanced CT examinations, which were randomly divided into training (N = 218) and test (N = 94) sets. The radiomics features were extracted from the plain, arterial, and venous phases. The synthetic minority oversampling technique was used to balance minority class samples in the training set. Feature selection methods were done using the least absolute shrinkage and selection operator (LASSO), mutual information (MI), and recursive feature extraction (RFE). Two machine learning classifiers, support vector machine (SVM), and logistic regression (LR), were then combined in pairs with three feature selection methods to build different radiomics models. Meanwhile, the prediction performances of different radiomics models based on single phase (plain, arterial, and venous phase) and multiphase (three-phase combination) were compared to determine which model construction method and phase were more discriminative. In addition, clinical models based on clinical-radiological features and combined models integrating radiomics features and clinical-radiological features were established. The prediction performances of the different models were evaluated by the area under the receiver operating characteristic (ROC) curve (AUC) and the drawing of calibration curves. RESULTS: Among the 24 established radiomics models composed of four different phases, three feature selection methods, and two machine learning classifiers, the LASSO-SVM model based on a three-phase combination had the optimal prediction performance with AUC (0.936 [95% CI = 0.866, 0.976]), sensitivity (0.78), specificity (0.90), and accuracy (0.86) in the test set, and its prediction performance was significantly better than with the clinical model based on LR (AUC = 0.781, p = 0.012). In the test set, the combined model based on LR had a lower AUC than the optimal radiomics model (AUC = 0.933 vs. 0.936), but no statistically significant difference (p = 0.888). CONCLUSION: Multiphasic CT-based radiomics analysis showed a machine learning model based on clinical-radiological features and radiomics features has the potential to provide a valuable tool for discriminating benign from malignant parotid tumors.
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spelling pubmed-92806422022-07-15 Multiphasic CT-Based Radiomics Analysis for the Differentiation of Benign and Malignant Parotid Tumors Yu, Qiang Wang, Anran Gu, Jinming Li, Quanjiang Ning, Youquan Peng, Juan Lv, Fajin Zhang, Xiaodi Front Oncol Oncology OBJECTIVE: This study aims to investigate the value of machine learning models based on clinical-radiological features and multiphasic CT radiomics features in the differentiation of benign parotid tumors (BPTs) and malignant parotid tumors (MPTs). METHODS: This retrospective study included 312 patients (205 cases of BPTs and 107 cases of MPTs) who underwent multiphasic enhanced CT examinations, which were randomly divided into training (N = 218) and test (N = 94) sets. The radiomics features were extracted from the plain, arterial, and venous phases. The synthetic minority oversampling technique was used to balance minority class samples in the training set. Feature selection methods were done using the least absolute shrinkage and selection operator (LASSO), mutual information (MI), and recursive feature extraction (RFE). Two machine learning classifiers, support vector machine (SVM), and logistic regression (LR), were then combined in pairs with three feature selection methods to build different radiomics models. Meanwhile, the prediction performances of different radiomics models based on single phase (plain, arterial, and venous phase) and multiphase (three-phase combination) were compared to determine which model construction method and phase were more discriminative. In addition, clinical models based on clinical-radiological features and combined models integrating radiomics features and clinical-radiological features were established. The prediction performances of the different models were evaluated by the area under the receiver operating characteristic (ROC) curve (AUC) and the drawing of calibration curves. RESULTS: Among the 24 established radiomics models composed of four different phases, three feature selection methods, and two machine learning classifiers, the LASSO-SVM model based on a three-phase combination had the optimal prediction performance with AUC (0.936 [95% CI = 0.866, 0.976]), sensitivity (0.78), specificity (0.90), and accuracy (0.86) in the test set, and its prediction performance was significantly better than with the clinical model based on LR (AUC = 0.781, p = 0.012). In the test set, the combined model based on LR had a lower AUC than the optimal radiomics model (AUC = 0.933 vs. 0.936), but no statistically significant difference (p = 0.888). CONCLUSION: Multiphasic CT-based radiomics analysis showed a machine learning model based on clinical-radiological features and radiomics features has the potential to provide a valuable tool for discriminating benign from malignant parotid tumors. Frontiers Media S.A. 2022-06-30 /pmc/articles/PMC9280642/ /pubmed/35847942 http://dx.doi.org/10.3389/fonc.2022.913898 Text en Copyright © 2022 Yu, Wang, Gu, Li, Ning, Peng, Lv and Zhang 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
Yu, Qiang
Wang, Anran
Gu, Jinming
Li, Quanjiang
Ning, Youquan
Peng, Juan
Lv, Fajin
Zhang, Xiaodi
Multiphasic CT-Based Radiomics Analysis for the Differentiation of Benign and Malignant Parotid Tumors
title Multiphasic CT-Based Radiomics Analysis for the Differentiation of Benign and Malignant Parotid Tumors
title_full Multiphasic CT-Based Radiomics Analysis for the Differentiation of Benign and Malignant Parotid Tumors
title_fullStr Multiphasic CT-Based Radiomics Analysis for the Differentiation of Benign and Malignant Parotid Tumors
title_full_unstemmed Multiphasic CT-Based Radiomics Analysis for the Differentiation of Benign and Malignant Parotid Tumors
title_short Multiphasic CT-Based Radiomics Analysis for the Differentiation of Benign and Malignant Parotid Tumors
title_sort multiphasic ct-based radiomics analysis for the differentiation of benign and malignant parotid tumors
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9280642/
https://www.ncbi.nlm.nih.gov/pubmed/35847942
http://dx.doi.org/10.3389/fonc.2022.913898
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