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Multi‐marker quantitative radiomics for mass characterization in dedicated breast CT imaging

PURPOSE: To develop and evaluate the diagnostic performance of an algorithm for multi‐marker radiomic‐based classification of breast masses in dedicated breast computed tomography (bCT) images. METHODS: Over 1000 radiomic descriptors aimed at quantifying mass and border heterogeneity, morphology, an...

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Autores principales: Caballo, Marco, Pangallo, Domenico R., Sanderink, Wendelien, Hernandez, Andrew M., Lyu, Su Hyun, Molinari, Filippo, Boone, John M., Mann, Ritse M., Sechopoulos, Ioannis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7898616/
https://www.ncbi.nlm.nih.gov/pubmed/33232521
http://dx.doi.org/10.1002/mp.14610
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author Caballo, Marco
Pangallo, Domenico R.
Sanderink, Wendelien
Hernandez, Andrew M.
Lyu, Su Hyun
Molinari, Filippo
Boone, John M.
Mann, Ritse M.
Sechopoulos, Ioannis
author_facet Caballo, Marco
Pangallo, Domenico R.
Sanderink, Wendelien
Hernandez, Andrew M.
Lyu, Su Hyun
Molinari, Filippo
Boone, John M.
Mann, Ritse M.
Sechopoulos, Ioannis
author_sort Caballo, Marco
collection PubMed
description PURPOSE: To develop and evaluate the diagnostic performance of an algorithm for multi‐marker radiomic‐based classification of breast masses in dedicated breast computed tomography (bCT) images. METHODS: Over 1000 radiomic descriptors aimed at quantifying mass and border heterogeneity, morphology, and margin sharpness were developed and implemented. These included well‐established texture and shape feature descriptors, which were supplemented with additional approaches for contour irregularity quantification, spicule and lobe detection, characterization of degree of infiltration, and differences in peritumoral compartments. All descriptors were extracted from a training set of 202 bCT masses (133 benign and 69 malignant), and their individual diagnostic performance was investigated in terms of area under the receiver operating characteristics (ROC) curve (AUC) of single‐feature‐based linear discriminant analysis (LDA) classifiers. Subsequently, the most relevant descriptors were selected through a multiple‐step feature selection process (including stability analysis, statistical significance, evaluation of feature interaction, and dimensionality reduction), and used to develop a final LDA radiomic model for classification of benign and malignant masses, which was then tested on an independent test set of 82 cases (45 benign and 37 malignant). RESULTS: The majority of the individual radiomic descriptors showed, on the training set, an AUC value deriving from a linear decision boundary higher than 0.65, with the lower limit of the associated 95% confidence interval (C.I.) not overlapping with random chance (AUC = 0.5). The final LDA radiomic model resulted in a test set AUC of 0.90 (95% C.I. 0.80–0.96). CONCLUSIONS: The proposed multi‐marker radiomic approach achieved high diagnostic accuracy in bCT mass classification, using a radiomic signature based on different feature types. While future studies with larger datasets are needed to further validate these results, quantitative radiomics applied to bCT shows potential to improve the breast cancer diagnosis pipeline.
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spelling pubmed-78986162021-03-03 Multi‐marker quantitative radiomics for mass characterization in dedicated breast CT imaging Caballo, Marco Pangallo, Domenico R. Sanderink, Wendelien Hernandez, Andrew M. Lyu, Su Hyun Molinari, Filippo Boone, John M. Mann, Ritse M. Sechopoulos, Ioannis Med Phys QUANTITATIVE IMAGING AND IMAGE PROCESSING PURPOSE: To develop and evaluate the diagnostic performance of an algorithm for multi‐marker radiomic‐based classification of breast masses in dedicated breast computed tomography (bCT) images. METHODS: Over 1000 radiomic descriptors aimed at quantifying mass and border heterogeneity, morphology, and margin sharpness were developed and implemented. These included well‐established texture and shape feature descriptors, which were supplemented with additional approaches for contour irregularity quantification, spicule and lobe detection, characterization of degree of infiltration, and differences in peritumoral compartments. All descriptors were extracted from a training set of 202 bCT masses (133 benign and 69 malignant), and their individual diagnostic performance was investigated in terms of area under the receiver operating characteristics (ROC) curve (AUC) of single‐feature‐based linear discriminant analysis (LDA) classifiers. Subsequently, the most relevant descriptors were selected through a multiple‐step feature selection process (including stability analysis, statistical significance, evaluation of feature interaction, and dimensionality reduction), and used to develop a final LDA radiomic model for classification of benign and malignant masses, which was then tested on an independent test set of 82 cases (45 benign and 37 malignant). RESULTS: The majority of the individual radiomic descriptors showed, on the training set, an AUC value deriving from a linear decision boundary higher than 0.65, with the lower limit of the associated 95% confidence interval (C.I.) not overlapping with random chance (AUC = 0.5). The final LDA radiomic model resulted in a test set AUC of 0.90 (95% C.I. 0.80–0.96). CONCLUSIONS: The proposed multi‐marker radiomic approach achieved high diagnostic accuracy in bCT mass classification, using a radiomic signature based on different feature types. While future studies with larger datasets are needed to further validate these results, quantitative radiomics applied to bCT shows potential to improve the breast cancer diagnosis pipeline. John Wiley and Sons Inc. 2020-12-10 2021-01 /pmc/articles/PMC7898616/ /pubmed/33232521 http://dx.doi.org/10.1002/mp.14610 Text en © 2020 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle QUANTITATIVE IMAGING AND IMAGE PROCESSING
Caballo, Marco
Pangallo, Domenico R.
Sanderink, Wendelien
Hernandez, Andrew M.
Lyu, Su Hyun
Molinari, Filippo
Boone, John M.
Mann, Ritse M.
Sechopoulos, Ioannis
Multi‐marker quantitative radiomics for mass characterization in dedicated breast CT imaging
title Multi‐marker quantitative radiomics for mass characterization in dedicated breast CT imaging
title_full Multi‐marker quantitative radiomics for mass characterization in dedicated breast CT imaging
title_fullStr Multi‐marker quantitative radiomics for mass characterization in dedicated breast CT imaging
title_full_unstemmed Multi‐marker quantitative radiomics for mass characterization in dedicated breast CT imaging
title_short Multi‐marker quantitative radiomics for mass characterization in dedicated breast CT imaging
title_sort multi‐marker quantitative radiomics for mass characterization in dedicated breast ct imaging
topic QUANTITATIVE IMAGING AND IMAGE PROCESSING
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7898616/
https://www.ncbi.nlm.nih.gov/pubmed/33232521
http://dx.doi.org/10.1002/mp.14610
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