MRI-Based Texture Features as Potential Prognostic Biomarkers in Anaplastic Astrocytoma Patients Undergoing Surgical Treatment
Objectives. The purpose of this study was to investigate whether texture features from magnetic resonance imaging (MRI) were associated with the overall survival (OS) of anaplastic astrocytoma (AA) patients undergoing surgical treatment. Methods. A total of 51 qualified patients who were diagnosed w...
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| description | Objectives. The purpose of this study was to investigate whether texture features from magnetic resonance imaging (MRI) were associated with the overall survival (OS) of anaplastic astrocytoma (AA) patients undergoing surgical treatment. Methods. A total of 51 qualified patients who were diagnosed with AA and underwent surgical interventions in our institution were enrolled in this retrospective study. Patients were followed up for at least 30 months or until death. Texture features derived from histogram-based matrix (HISTO) and grey-level co-occurrence matrix (GLCM) were extracted from preoperative contrast-enhanced T1-weighted images. Each texture feature was dichotomized based on its optimal cutoff value calculated by receiver operating characteristics curve analysis. Kaplan–Meier analysis and log rank test were conducted to compare the 30-month OS between the dichotomized subgroups. Multivariate Cox regression analysis was performed to determine independent prognostic factors. Results. Three HISTO-derived features (HISTO-Energy, HISTO-Entropy, and HISTO-Skewness) and five GLCM-derived features (GLCM-Contrast, GLCM-Energy, GLCM-Entropy, GLCM-Homogeneity, and GLCM-Dissimilarity) were found to be significantly correlated with 30-month OS. Moreover, GLCM-Homogeneity (p=0.001, hazard ratio = 6.351) was suggested to be the independent predictor of the patient survival. Conclusion. MRI-based texture features have the potential to be applied as prognostic biomarkers in AA patients undergoing surgical treatment. |
| doi_str_mv | 10.1155/2020/2126768 |
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| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2424880915</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2424880915</sourcerecordid><originalsourceid>FETCH-LOGICAL-c317t-12004c51a797a0da94924786317d36cb77f37acc6a9e5b4a982f89402d9835a33</originalsourceid><addsrcrecordid>eNqFkMtPAjEQxjdGExG9eTZNPOpKX_voEYkoCUaicN4M3S4WYYttN8rFv93iEj16msl8v3l9UXRO8A0hSdKjmOIeJTTN0vwg6oRSEnNGssPfHIvj6MS5JcacM8E60dfj8yi-BadKNFWfvrEKDRXsokPg0MR4VXsNKzSxZlEb57VEt9qswb4p65CuUb-GzQp-hL7z1sitDzKagNeh1aFZXSq7MLpeoJfGLrQMw6Y27FgH-TQ6qmDl1Nk-dqPZ8G46eIjHT_ejQX8cy3C-jwkNB8uEQCYywCUILijP8jSIJUvlPMsqloGUKQiVzDmInFa54JiWImcJMNaNLtu5G2veG-V8sTSNrcPKgnLK8xwLkgTquqWkNc5ZVRUbq8Or24LgYudwsXO42Dsc8KsWf9V1CR_6P_qipVVgVAV_NGGC0px9A5JkhXI</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2424880915</pqid></control><display><type>article</type><title>MRI-Based Texture Features as Potential Prognostic Biomarkers in Anaplastic Astrocytoma Patients Undergoing Surgical Treatment</title><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>PubMed Central Open Access</source><source>PubMed Central</source><source>Alma/SFX Local Collection</source><creator>Zhao, Fumin ; Cheng, Danni ; Cheng, Yangfan ; Chen, Chaoyue ; Zhang, Yang ; Xu, Jianguo</creator><contributor>de Barros, André L. B. ; André L B de Barros</contributor><creatorcontrib>Zhao, Fumin ; Cheng, Danni ; Cheng, Yangfan ; Chen, Chaoyue ; Zhang, Yang ; Xu, Jianguo ; de Barros, André L. B. ; André L B de Barros</creatorcontrib><description>Objectives. The purpose of this study was to investigate whether texture features from magnetic resonance imaging (MRI) were associated with the overall survival (OS) of anaplastic astrocytoma (AA) patients undergoing surgical treatment. Methods. A total of 51 qualified patients who were diagnosed with AA and underwent surgical interventions in our institution were enrolled in this retrospective study. Patients were followed up for at least 30 months or until death. Texture features derived from histogram-based matrix (HISTO) and grey-level co-occurrence matrix (GLCM) were extracted from preoperative contrast-enhanced T1-weighted images. Each texture feature was dichotomized based on its optimal cutoff value calculated by receiver operating characteristics curve analysis. Kaplan–Meier analysis and log rank test were conducted to compare the 30-month OS between the dichotomized subgroups. Multivariate Cox regression analysis was performed to determine independent prognostic factors. Results. Three HISTO-derived features (HISTO-Energy, HISTO-Entropy, and HISTO-Skewness) and five GLCM-derived features (GLCM-Contrast, GLCM-Energy, GLCM-Entropy, GLCM-Homogeneity, and GLCM-Dissimilarity) were found to be significantly correlated with 30-month OS. Moreover, GLCM-Homogeneity (p=0.001, hazard ratio = 6.351) was suggested to be the independent predictor of the patient survival. Conclusion. MRI-based texture features have the potential to be applied as prognostic biomarkers in AA patients undergoing surgical treatment.</description><identifier>ISSN: 1555-4309</identifier><identifier>EISSN: 1555-4317</identifier><identifier>DOI: 10.1155/2020/2126768</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Astrocytoma ; Biomarkers ; Brain cancer ; Chemotherapy ; Confidence intervals ; Dehydrogenases ; Energy ; Entropy ; Feature extraction ; Gender ; Histograms ; Homogeneity ; Image contrast ; Image enhancement ; Magnetic resonance imaging ; Medical prognosis ; Patients ; Radiation therapy ; Rank tests ; Regression analysis ; Researchers ; Studies ; Subgroups ; Surgery ; Surgical outcomes ; Survival ; Texture ; Tumors ; Variance analysis</subject><ispartof>Contrast media and molecular imaging, 2020, Vol.2020 (2020), p.1-7</ispartof><rights>Copyright © 2020 Yang Zhang et al.</rights><rights>Copyright © 2020 Yang Zhang et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. http://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c317t-12004c51a797a0da94924786317d36cb77f37acc6a9e5b4a982f89402d9835a33</cites><orcidid>0000-0001-9651-8186 ; 0000-0001-6796-8356 ; 0000-0001-7536-2549</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,4024,27923,27924,27925</link.rule.ids></links><search><contributor>de Barros, André L. B.</contributor><contributor>André L B de Barros</contributor><creatorcontrib>Zhao, Fumin</creatorcontrib><creatorcontrib>Cheng, Danni</creatorcontrib><creatorcontrib>Cheng, Yangfan</creatorcontrib><creatorcontrib>Chen, Chaoyue</creatorcontrib><creatorcontrib>Zhang, Yang</creatorcontrib><creatorcontrib>Xu, Jianguo</creatorcontrib><title>MRI-Based Texture Features as Potential Prognostic Biomarkers in Anaplastic Astrocytoma Patients Undergoing Surgical Treatment</title><title>Contrast media and molecular imaging</title><description>Objectives. The purpose of this study was to investigate whether texture features from magnetic resonance imaging (MRI) were associated with the overall survival (OS) of anaplastic astrocytoma (AA) patients undergoing surgical treatment. Methods. A total of 51 qualified patients who were diagnosed with AA and underwent surgical interventions in our institution were enrolled in this retrospective study. Patients were followed up for at least 30 months or until death. Texture features derived from histogram-based matrix (HISTO) and grey-level co-occurrence matrix (GLCM) were extracted from preoperative contrast-enhanced T1-weighted images. Each texture feature was dichotomized based on its optimal cutoff value calculated by receiver operating characteristics curve analysis. Kaplan–Meier analysis and log rank test were conducted to compare the 30-month OS between the dichotomized subgroups. Multivariate Cox regression analysis was performed to determine independent prognostic factors. Results. Three HISTO-derived features (HISTO-Energy, HISTO-Entropy, and HISTO-Skewness) and five GLCM-derived features (GLCM-Contrast, GLCM-Energy, GLCM-Entropy, GLCM-Homogeneity, and GLCM-Dissimilarity) were found to be significantly correlated with 30-month OS. Moreover, GLCM-Homogeneity (p=0.001, hazard ratio = 6.351) was suggested to be the independent predictor of the patient survival. Conclusion. MRI-based texture features have the potential to be applied as prognostic biomarkers in AA patients undergoing surgical treatment.</description><subject>Astrocytoma</subject><subject>Biomarkers</subject><subject>Brain cancer</subject><subject>Chemotherapy</subject><subject>Confidence intervals</subject><subject>Dehydrogenases</subject><subject>Energy</subject><subject>Entropy</subject><subject>Feature extraction</subject><subject>Gender</subject><subject>Histograms</subject><subject>Homogeneity</subject><subject>Image contrast</subject><subject>Image enhancement</subject><subject>Magnetic resonance imaging</subject><subject>Medical prognosis</subject><subject>Patients</subject><subject>Radiation therapy</subject><subject>Rank tests</subject><subject>Regression analysis</subject><subject>Researchers</subject><subject>Studies</subject><subject>Subgroups</subject><subject>Surgery</subject><subject>Surgical outcomes</subject><subject>Survival</subject><subject>Texture</subject><subject>Tumors</subject><subject>Variance analysis</subject><issn>1555-4309</issn><issn>1555-4317</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><recordid>eNqFkMtPAjEQxjdGExG9eTZNPOpKX_voEYkoCUaicN4M3S4WYYttN8rFv93iEj16msl8v3l9UXRO8A0hSdKjmOIeJTTN0vwg6oRSEnNGssPfHIvj6MS5JcacM8E60dfj8yi-BadKNFWfvrEKDRXsokPg0MR4VXsNKzSxZlEb57VEt9qswb4p65CuUb-GzQp-hL7z1sitDzKagNeh1aFZXSq7MLpeoJfGLrQMw6Y27FgH-TQ6qmDl1Nk-dqPZ8G46eIjHT_ejQX8cy3C-jwkNB8uEQCYywCUILijP8jSIJUvlPMsqloGUKQiVzDmInFa54JiWImcJMNaNLtu5G2veG-V8sTSNrcPKgnLK8xwLkgTquqWkNc5ZVRUbq8Or24LgYudwsXO42Dsc8KsWf9V1CR_6P_qipVVgVAV_NGGC0px9A5JkhXI</recordid><startdate>2020</startdate><enddate>2020</enddate><creator>Zhao, Fumin</creator><creator>Cheng, Danni</creator><creator>Cheng, Yangfan</creator><creator>Chen, Chaoyue</creator><creator>Zhang, Yang</creator><creator>Xu, Jianguo</creator><general>Hindawi Publishing Corporation</general><general>Hindawi</general><general>Hindawi Limited</general><scope>ADJCN</scope><scope>AHFXO</scope><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QO</scope><scope>7QP</scope><scope>7TK</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FD</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BYOGL</scope><scope>CCPQU</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0001-9651-8186</orcidid><orcidid>https://orcid.org/0000-0001-6796-8356</orcidid><orcidid>https://orcid.org/0000-0001-7536-2549</orcidid></search><sort><creationdate>2020</creationdate><title>MRI-Based Texture Features as Potential Prognostic Biomarkers in Anaplastic Astrocytoma Patients Undergoing Surgical Treatment</title><author>Zhao, Fumin ; Cheng, Danni ; Cheng, Yangfan ; Chen, Chaoyue ; Zhang, Yang ; Xu, Jianguo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c317t-12004c51a797a0da94924786317d36cb77f37acc6a9e5b4a982f89402d9835a33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Astrocytoma</topic><topic>Biomarkers</topic><topic>Brain cancer</topic><topic>Chemotherapy</topic><topic>Confidence intervals</topic><topic>Dehydrogenases</topic><topic>Energy</topic><topic>Entropy</topic><topic>Feature extraction</topic><topic>Gender</topic><topic>Histograms</topic><topic>Homogeneity</topic><topic>Image contrast</topic><topic>Image enhancement</topic><topic>Magnetic resonance imaging</topic><topic>Medical prognosis</topic><topic>Patients</topic><topic>Radiation therapy</topic><topic>Rank tests</topic><topic>Regression analysis</topic><topic>Researchers</topic><topic>Studies</topic><topic>Subgroups</topic><topic>Surgery</topic><topic>Surgical outcomes</topic><topic>Survival</topic><topic>Texture</topic><topic>Tumors</topic><topic>Variance analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhao, Fumin</creatorcontrib><creatorcontrib>Cheng, Danni</creatorcontrib><creatorcontrib>Cheng, Yangfan</creatorcontrib><creatorcontrib>Chen, Chaoyue</creatorcontrib><creatorcontrib>Zhang, Yang</creatorcontrib><creatorcontrib>Xu, Jianguo</creatorcontrib><collection>الدوريات العلمية والإحصائية - e-Marefa Academic and Statistical Periodicals</collection><collection>معرفة - المحتوى العربي الأكاديمي المتكامل - e-Marefa Academic Complete</collection><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>East Europe, Central Europe Database</collection><collection>ProQuest One Community College</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Contrast media and molecular imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhao, Fumin</au><au>Cheng, Danni</au><au>Cheng, Yangfan</au><au>Chen, Chaoyue</au><au>Zhang, Yang</au><au>Xu, Jianguo</au><au>de Barros, André L. B.</au><au>André L B de Barros</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>MRI-Based Texture Features as Potential Prognostic Biomarkers in Anaplastic Astrocytoma Patients Undergoing Surgical Treatment</atitle><jtitle>Contrast media and molecular imaging</jtitle><date>2020</date><risdate>2020</risdate><volume>2020</volume><issue>2020</issue><spage>1</spage><epage>7</epage><pages>1-7</pages><issn>1555-4309</issn><eissn>1555-4317</eissn><abstract>Objectives. The purpose of this study was to investigate whether texture features from magnetic resonance imaging (MRI) were associated with the overall survival (OS) of anaplastic astrocytoma (AA) patients undergoing surgical treatment. Methods. A total of 51 qualified patients who were diagnosed with AA and underwent surgical interventions in our institution were enrolled in this retrospective study. Patients were followed up for at least 30 months or until death. Texture features derived from histogram-based matrix (HISTO) and grey-level co-occurrence matrix (GLCM) were extracted from preoperative contrast-enhanced T1-weighted images. Each texture feature was dichotomized based on its optimal cutoff value calculated by receiver operating characteristics curve analysis. Kaplan–Meier analysis and log rank test were conducted to compare the 30-month OS between the dichotomized subgroups. Multivariate Cox regression analysis was performed to determine independent prognostic factors. Results. Three HISTO-derived features (HISTO-Energy, HISTO-Entropy, and HISTO-Skewness) and five GLCM-derived features (GLCM-Contrast, GLCM-Energy, GLCM-Entropy, GLCM-Homogeneity, and GLCM-Dissimilarity) were found to be significantly correlated with 30-month OS. Moreover, GLCM-Homogeneity (p=0.001, hazard ratio = 6.351) was suggested to be the independent predictor of the patient survival. Conclusion. MRI-based texture features have the potential to be applied as prognostic biomarkers in AA patients undergoing surgical treatment.</abstract><cop>Cairo, Egypt</cop><pub>Hindawi Publishing Corporation</pub><doi>10.1155/2020/2126768</doi><tpages>7</tpages><orcidid>https://orcid.org/0000-0001-9651-8186</orcidid><orcidid>https://orcid.org/0000-0001-6796-8356</orcidid><orcidid>https://orcid.org/0000-0001-7536-2549</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Astrocytoma Biomarkers Brain cancer Chemotherapy Confidence intervals Dehydrogenases Energy Entropy Feature extraction Gender Histograms Homogeneity Image contrast Image enhancement Magnetic resonance imaging Medical prognosis Patients Radiation therapy Rank tests Regression analysis Researchers Studies Subgroups Surgery Surgical outcomes Survival Texture Tumors Variance analysis |
| title | MRI-Based Texture Features as Potential Prognostic Biomarkers in Anaplastic Astrocytoma Patients Undergoing Surgical Treatment |
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