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Automatic segmentation and classification of breast lesions through identification of informative multiparametric PET/MRI features
BACKGROUND: Multiparametric positron emission tomography/magnetic resonance imaging (mpPET/MRI) shows clinical potential for detection and classification of breast lesions. Yet, the contribution of features for computer-aided segmentation and diagnosis (CAD) need to be better understood. We proposed...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6486931/ https://www.ncbi.nlm.nih.gov/pubmed/31030291 http://dx.doi.org/10.1186/s41747-019-0096-3 |
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author | Vogl, Wolf-Dieter Pinker, Katja Helbich, Thomas H. Bickel, Hubert Grabner, Günther Bogner, Wolfgang Gruber, Stephan Bago-Horvath, Zsuzsanna Dubsky, Peter Langs, Georg |
author_facet | Vogl, Wolf-Dieter Pinker, Katja Helbich, Thomas H. Bickel, Hubert Grabner, Günther Bogner, Wolfgang Gruber, Stephan Bago-Horvath, Zsuzsanna Dubsky, Peter Langs, Georg |
author_sort | Vogl, Wolf-Dieter |
collection | PubMed |
description | BACKGROUND: Multiparametric positron emission tomography/magnetic resonance imaging (mpPET/MRI) shows clinical potential for detection and classification of breast lesions. Yet, the contribution of features for computer-aided segmentation and diagnosis (CAD) need to be better understood. We proposed a data-driven machine learning approach for a CAD system combining dynamic contrast-enhanced (DCE)-MRI, diffusion-weighted imaging (DWI), and (18)F-fluorodeoxyglucose ((18)F-FDG)-PET. METHODS: The CAD incorporated a random forest (RF) classifier combined with mpPET/MRI intensity-based features for lesion segmentation and shape features, kinetic and spatio-temporal texture features, for lesion classification. The CAD pipeline detected and segmented suspicious regions and classified lesions as benign or malignant. The inherent feature selection method of RF and alternatively the minimum-redundancy-maximum-relevance feature ranking method were used. RESULTS: In 34 patients, we report a detection rate of 10/12 (83.3%) and 22/22 (100%) for benign and malignant lesions, respectively, a Dice similarity coefficient of 0.665 for segmentation, and a classification performance with an area under the curve at receiver operating characteristics analysis of 0.978, a sensitivity of 0.946, and a specificity of 0.936. Segmentation but not classification performance of DCE-MRI improved with information from DWI and FDG-PET. Feature ranking revealed that kinetic and spatio-temporal texture features had the highest contribution for lesion classification. (18)F-FDG-PET and morphologic features were less predictive. CONCLUSION: Our CAD enables the assessment of the relevance of mpPET/MRI features on segmentation and classification accuracy. It may aid as a novel computational tool for exploring different modalities/features and their contributions for the detection and classification of breast lesions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s41747-019-0096-3) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6486931 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-64869312019-05-15 Automatic segmentation and classification of breast lesions through identification of informative multiparametric PET/MRI features Vogl, Wolf-Dieter Pinker, Katja Helbich, Thomas H. Bickel, Hubert Grabner, Günther Bogner, Wolfgang Gruber, Stephan Bago-Horvath, Zsuzsanna Dubsky, Peter Langs, Georg Eur Radiol Exp Original Article BACKGROUND: Multiparametric positron emission tomography/magnetic resonance imaging (mpPET/MRI) shows clinical potential for detection and classification of breast lesions. Yet, the contribution of features for computer-aided segmentation and diagnosis (CAD) need to be better understood. We proposed a data-driven machine learning approach for a CAD system combining dynamic contrast-enhanced (DCE)-MRI, diffusion-weighted imaging (DWI), and (18)F-fluorodeoxyglucose ((18)F-FDG)-PET. METHODS: The CAD incorporated a random forest (RF) classifier combined with mpPET/MRI intensity-based features for lesion segmentation and shape features, kinetic and spatio-temporal texture features, for lesion classification. The CAD pipeline detected and segmented suspicious regions and classified lesions as benign or malignant. The inherent feature selection method of RF and alternatively the minimum-redundancy-maximum-relevance feature ranking method were used. RESULTS: In 34 patients, we report a detection rate of 10/12 (83.3%) and 22/22 (100%) for benign and malignant lesions, respectively, a Dice similarity coefficient of 0.665 for segmentation, and a classification performance with an area under the curve at receiver operating characteristics analysis of 0.978, a sensitivity of 0.946, and a specificity of 0.936. Segmentation but not classification performance of DCE-MRI improved with information from DWI and FDG-PET. Feature ranking revealed that kinetic and spatio-temporal texture features had the highest contribution for lesion classification. (18)F-FDG-PET and morphologic features were less predictive. CONCLUSION: Our CAD enables the assessment of the relevance of mpPET/MRI features on segmentation and classification accuracy. It may aid as a novel computational tool for exploring different modalities/features and their contributions for the detection and classification of breast lesions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s41747-019-0096-3) contains supplementary material, which is available to authorized users. Springer International Publishing 2019-04-27 /pmc/articles/PMC6486931/ /pubmed/31030291 http://dx.doi.org/10.1186/s41747-019-0096-3 Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Original Article Vogl, Wolf-Dieter Pinker, Katja Helbich, Thomas H. Bickel, Hubert Grabner, Günther Bogner, Wolfgang Gruber, Stephan Bago-Horvath, Zsuzsanna Dubsky, Peter Langs, Georg Automatic segmentation and classification of breast lesions through identification of informative multiparametric PET/MRI features |
title | Automatic segmentation and classification of breast lesions through identification of informative multiparametric PET/MRI features |
title_full | Automatic segmentation and classification of breast lesions through identification of informative multiparametric PET/MRI features |
title_fullStr | Automatic segmentation and classification of breast lesions through identification of informative multiparametric PET/MRI features |
title_full_unstemmed | Automatic segmentation and classification of breast lesions through identification of informative multiparametric PET/MRI features |
title_short | Automatic segmentation and classification of breast lesions through identification of informative multiparametric PET/MRI features |
title_sort | automatic segmentation and classification of breast lesions through identification of informative multiparametric pet/mri features |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6486931/ https://www.ncbi.nlm.nih.gov/pubmed/31030291 http://dx.doi.org/10.1186/s41747-019-0096-3 |
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