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Texture-based classification of different single liver lesion based on SPAIR T2W MRI images

BACKGROUND: To assess the feasibility of texture analysis (TA) based on spectral attenuated inversion-recovery T2 weighted magnetic resonance imaging (SPAIR T2W-MRI) for the classification of hepatic hemangioma (HH), hepatic metastases (HM) and hepatocellular carcinoma (HCC). METHODS: The SPAIR T2W-...

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Autores principales: Li, Zhenjiang, Mao, Yu, Huang, Wei, Li, Hongsheng, Zhu, Jian, Li, Wanhu, Li, Baosheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5508617/
https://www.ncbi.nlm.nih.gov/pubmed/28705145
http://dx.doi.org/10.1186/s12880-017-0212-x
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author Li, Zhenjiang
Mao, Yu
Huang, Wei
Li, Hongsheng
Zhu, Jian
Li, Wanhu
Li, Baosheng
author_facet Li, Zhenjiang
Mao, Yu
Huang, Wei
Li, Hongsheng
Zhu, Jian
Li, Wanhu
Li, Baosheng
author_sort Li, Zhenjiang
collection PubMed
description BACKGROUND: To assess the feasibility of texture analysis (TA) based on spectral attenuated inversion-recovery T2 weighted magnetic resonance imaging (SPAIR T2W-MRI) for the classification of hepatic hemangioma (HH), hepatic metastases (HM) and hepatocellular carcinoma (HCC). METHODS: The SPAIR T2W-MRI data of 162 patients with HH (n=55), HM (n=67) and HCC (n=40) were retrospectively analyzed. We used two independent cohorts for training (n = 112 patients) and validation (n = 50 patients). The TA was performed and textual parameters derived from the gray level co-occurrence matrix (GLCM), gray level gradient co-occurrence matrix (GLGCM), gray-level run-length matrix (GLRLM), Gabor wavelet transform (GWTF), intensity-size-zone matrix (ISZM), and histogram features were calculated. The capacity of each parameter to classify three types of single liver lesions was assessed using the Kruskal-Wallis test. Specificity and sensitivity for each of the studied parameters were derived using ROC curves. Four supervised classification algorithms were trained with the most influential textural features in the classification of tumor types. The test datasets validated the reliability of the models. RESULTS: The texture analyses showed that the HH versus HM, HM versus HCC, and HH versus HCC could be differentiated by 9, 16 and 10 feature parameters, respectively. The model’s misclassification rates were 11.7, 9.6 and 9.7% respectively. No texture feature was able to adequately distinguish among the three types of single liver lesions at the same time. The BP-ANN model had better predictive ability. CONCLUSION: Texture features of SPAIR T2W-MRI can classify the three types of single liver lesions (HH, HM and HCC) and may serve as an adjunct tool for accurate diagnosis of these diseases. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12880-017-0212-x) contains supplementary material, which is available to authorized users.
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spelling pubmed-55086172017-07-17 Texture-based classification of different single liver lesion based on SPAIR T2W MRI images Li, Zhenjiang Mao, Yu Huang, Wei Li, Hongsheng Zhu, Jian Li, Wanhu Li, Baosheng BMC Med Imaging Research Article BACKGROUND: To assess the feasibility of texture analysis (TA) based on spectral attenuated inversion-recovery T2 weighted magnetic resonance imaging (SPAIR T2W-MRI) for the classification of hepatic hemangioma (HH), hepatic metastases (HM) and hepatocellular carcinoma (HCC). METHODS: The SPAIR T2W-MRI data of 162 patients with HH (n=55), HM (n=67) and HCC (n=40) were retrospectively analyzed. We used two independent cohorts for training (n = 112 patients) and validation (n = 50 patients). The TA was performed and textual parameters derived from the gray level co-occurrence matrix (GLCM), gray level gradient co-occurrence matrix (GLGCM), gray-level run-length matrix (GLRLM), Gabor wavelet transform (GWTF), intensity-size-zone matrix (ISZM), and histogram features were calculated. The capacity of each parameter to classify three types of single liver lesions was assessed using the Kruskal-Wallis test. Specificity and sensitivity for each of the studied parameters were derived using ROC curves. Four supervised classification algorithms were trained with the most influential textural features in the classification of tumor types. The test datasets validated the reliability of the models. RESULTS: The texture analyses showed that the HH versus HM, HM versus HCC, and HH versus HCC could be differentiated by 9, 16 and 10 feature parameters, respectively. The model’s misclassification rates were 11.7, 9.6 and 9.7% respectively. No texture feature was able to adequately distinguish among the three types of single liver lesions at the same time. The BP-ANN model had better predictive ability. CONCLUSION: Texture features of SPAIR T2W-MRI can classify the three types of single liver lesions (HH, HM and HCC) and may serve as an adjunct tool for accurate diagnosis of these diseases. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12880-017-0212-x) contains supplementary material, which is available to authorized users. BioMed Central 2017-07-13 /pmc/articles/PMC5508617/ /pubmed/28705145 http://dx.doi.org/10.1186/s12880-017-0212-x Text en © The Author(s). 2017 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. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Li, Zhenjiang
Mao, Yu
Huang, Wei
Li, Hongsheng
Zhu, Jian
Li, Wanhu
Li, Baosheng
Texture-based classification of different single liver lesion based on SPAIR T2W MRI images
title Texture-based classification of different single liver lesion based on SPAIR T2W MRI images
title_full Texture-based classification of different single liver lesion based on SPAIR T2W MRI images
title_fullStr Texture-based classification of different single liver lesion based on SPAIR T2W MRI images
title_full_unstemmed Texture-based classification of different single liver lesion based on SPAIR T2W MRI images
title_short Texture-based classification of different single liver lesion based on SPAIR T2W MRI images
title_sort texture-based classification of different single liver lesion based on spair t2w mri images
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5508617/
https://www.ncbi.nlm.nih.gov/pubmed/28705145
http://dx.doi.org/10.1186/s12880-017-0212-x
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