Quantitative parameters of CT texture analysis as potential markersfor early prediction of spontaneous intracranial hemorrhage enlargement
To objectively quantify intracranial hematoma (ICH) enlargement by analysing the image texture of head CT scans and to provide objective and quantitative imaging parameters for predicting early hematoma enlargement. We retrospectively studied 108 ICH patients with baseline non-contrast computed tomo...
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creator | Shen, Qijun Shan, Yanna Hu, Zhengyu Chen, Wenhui Yang, Bing Han, Jing Huang, Yanfang Xu, Wen Feng, Zhan |
description | To objectively quantify intracranial hematoma (ICH) enlargement by analysing the image texture of head CT scans and to provide objective and quantitative imaging parameters for predicting early hematoma enlargement.
We retrospectively studied 108 ICH patients with baseline non-contrast computed tomography (NCCT) and 24-h follow-up CT available. Image data were assessed by a chief radiologist and a resident radiologist. Consistency analysis between observers was tested. The patients were divided into training set (75%) and validation set (25%) by stratified sampling. Patients in the training set were dichotomized according to 24-h hematoma expansion ≥ 33%. Using the Laplacian of Gaussian bandpass filter, we chose different anatomical spatial domains ranging from fine texture to coarse texture to obtain a series of derived parameters (mean grayscale intensity, variance, uniformity) in order to quantify and evaluate all data. The parameters were externally validated on validation set.
Significant differences were found between the two groups of patients within variance at V
and in uniformity at U
, U
and U
. The intraclass correlation coefficients for the texture parameters were between 0.67 and 0.99. The area under the ROC curve between the two groups of ICH cases was between 0.77 and 0.92. The accuracy of validation set by CTTA was 0.59-0.85.
NCCT texture analysis can objectively quantify the heterogeneity of ICH and independently predict early hematoma enlargement.
• Heterogeneity is helpful in predicting ICH enlargement. • CTTA could play an important role in predicting early ICH enlargement. • After filtering, fine texture had the best diagnostic performance. • The histogram-based uniformity parameters can independently predict ICH enlargement. • CTTA is more objective, more comprehensive, more independently operable, than previous methods. |
doi_str_mv | 10.1007/s00330-018-5364-8 |
format | Article |
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We retrospectively studied 108 ICH patients with baseline non-contrast computed tomography (NCCT) and 24-h follow-up CT available. Image data were assessed by a chief radiologist and a resident radiologist. Consistency analysis between observers was tested. The patients were divided into training set (75%) and validation set (25%) by stratified sampling. Patients in the training set were dichotomized according to 24-h hematoma expansion ≥ 33%. Using the Laplacian of Gaussian bandpass filter, we chose different anatomical spatial domains ranging from fine texture to coarse texture to obtain a series of derived parameters (mean grayscale intensity, variance, uniformity) in order to quantify and evaluate all data. The parameters were externally validated on validation set.
Significant differences were found between the two groups of patients within variance at V
and in uniformity at U
, U
and U
. The intraclass correlation coefficients for the texture parameters were between 0.67 and 0.99. The area under the ROC curve between the two groups of ICH cases was between 0.77 and 0.92. The accuracy of validation set by CTTA was 0.59-0.85.
NCCT texture analysis can objectively quantify the heterogeneity of ICH and independently predict early hematoma enlargement.
• Heterogeneity is helpful in predicting ICH enlargement. • CTTA could play an important role in predicting early ICH enlargement. • After filtering, fine texture had the best diagnostic performance. • The histogram-based uniformity parameters can independently predict ICH enlargement. • CTTA is more objective, more comprehensive, more independently operable, than previous methods.</description><identifier>ISSN: 0938-7994</identifier><identifier>EISSN: 1432-1084</identifier><identifier>DOI: 10.1007/s00330-018-5364-8</identifier><identifier>PMID: 29713780</identifier><language>eng</language><publisher>Germany: Springer Nature B.V</publisher><subject>Adult ; Aged ; Algorithms ; Bandpass filters ; Computed tomography ; Computed Tomography Angiography - methods ; Correlation coefficients ; Diagnostic systems ; Disease Progression ; Early Diagnosis ; Enlargement ; Expansion ; Female ; Glasgow Coma Scale ; Hematoma ; Hematoma - diagnostic imaging ; Hematoma - pathology ; Hemorrhage ; Heterogeneity ; Hospitals ; Humans ; Image contrast ; Intracranial Hemorrhages - diagnostic imaging ; Intracranial Hemorrhages - pathology ; Laboratories ; Male ; Medical diagnosis ; Medical imaging ; Middle Aged ; Parameters ; Patients ; Predictions ; Radiographic Image Interpretation, Computer-Assisted ; Retrospective Studies ; ROC Curve ; Stroke ; Texture ; Tomography ; Training</subject><ispartof>European radiology, 2018-10, Vol.28 (10), p.4389-4396</ispartof><rights>European Radiology is a copyright of Springer, (2018). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><orcidid>0000-0001-9496-4211</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29713780$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Shen, Qijun</creatorcontrib><creatorcontrib>Shan, Yanna</creatorcontrib><creatorcontrib>Hu, Zhengyu</creatorcontrib><creatorcontrib>Chen, Wenhui</creatorcontrib><creatorcontrib>Yang, Bing</creatorcontrib><creatorcontrib>Han, Jing</creatorcontrib><creatorcontrib>Huang, Yanfang</creatorcontrib><creatorcontrib>Xu, Wen</creatorcontrib><creatorcontrib>Feng, Zhan</creatorcontrib><title>Quantitative parameters of CT texture analysis as potential markersfor early prediction of spontaneous intracranial hemorrhage enlargement</title><title>European radiology</title><addtitle>Eur Radiol</addtitle><description>To objectively quantify intracranial hematoma (ICH) enlargement by analysing the image texture of head CT scans and to provide objective and quantitative imaging parameters for predicting early hematoma enlargement.
We retrospectively studied 108 ICH patients with baseline non-contrast computed tomography (NCCT) and 24-h follow-up CT available. Image data were assessed by a chief radiologist and a resident radiologist. Consistency analysis between observers was tested. The patients were divided into training set (75%) and validation set (25%) by stratified sampling. Patients in the training set were dichotomized according to 24-h hematoma expansion ≥ 33%. Using the Laplacian of Gaussian bandpass filter, we chose different anatomical spatial domains ranging from fine texture to coarse texture to obtain a series of derived parameters (mean grayscale intensity, variance, uniformity) in order to quantify and evaluate all data. The parameters were externally validated on validation set.
Significant differences were found between the two groups of patients within variance at V
and in uniformity at U
, U
and U
. The intraclass correlation coefficients for the texture parameters were between 0.67 and 0.99. The area under the ROC curve between the two groups of ICH cases was between 0.77 and 0.92. The accuracy of validation set by CTTA was 0.59-0.85.
NCCT texture analysis can objectively quantify the heterogeneity of ICH and independently predict early hematoma enlargement.
• Heterogeneity is helpful in predicting ICH enlargement. • CTTA could play an important role in predicting early ICH enlargement. • After filtering, fine texture had the best diagnostic performance. • The histogram-based uniformity parameters can independently predict ICH enlargement. • CTTA is more objective, more comprehensive, more independently operable, than previous methods.</description><subject>Adult</subject><subject>Aged</subject><subject>Algorithms</subject><subject>Bandpass filters</subject><subject>Computed tomography</subject><subject>Computed Tomography Angiography - methods</subject><subject>Correlation coefficients</subject><subject>Diagnostic systems</subject><subject>Disease Progression</subject><subject>Early Diagnosis</subject><subject>Enlargement</subject><subject>Expansion</subject><subject>Female</subject><subject>Glasgow Coma Scale</subject><subject>Hematoma</subject><subject>Hematoma - diagnostic imaging</subject><subject>Hematoma - pathology</subject><subject>Hemorrhage</subject><subject>Heterogeneity</subject><subject>Hospitals</subject><subject>Humans</subject><subject>Image contrast</subject><subject>Intracranial Hemorrhages - diagnostic imaging</subject><subject>Intracranial Hemorrhages - pathology</subject><subject>Laboratories</subject><subject>Male</subject><subject>Medical diagnosis</subject><subject>Medical imaging</subject><subject>Middle Aged</subject><subject>Parameters</subject><subject>Patients</subject><subject>Predictions</subject><subject>Radiographic Image Interpretation, Computer-Assisted</subject><subject>Retrospective Studies</subject><subject>ROC Curve</subject><subject>Stroke</subject><subject>Texture</subject><subject>Tomography</subject><subject>Training</subject><issn>0938-7994</issn><issn>1432-1084</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNpdkMtOwzAQRS0EoqXwAWyQJTZsAn4Rx0tU8ZIqIaSyribupKQkcbAdRH-Br8aFsmE1m3Ov7hxCTjm75Izpq8CYlCxjvMiuZa6yYo-MuZIi46xQ-2TMjCwybYwakaMQ1owxw5U-JCNhNJe6YGPy9TxAF-sIsf5A2oOHFiP6QF1Fp3Ma8TMOHil00GxCHSgE2ruIKQINbcG_JbZyniL4ZkN7j8vaxtp123zoXRehQzcEWnfRg_XQbXOv2DrvX2GFFLsG_Arb1HhMDipoAp7s7oS83N3Opw_Z7On-cXozy3ohTcxKBUwrLJFJYzXmpS54-leUrFwaKFBai1JYJSvIkySb58qgyXkFS0BhtZyQi9_e3rv3AUNctHWw2DS_UxciSZWFFtcqoef_0LUbfHLxQ4mca8231NmOGsoWl4ve18nMZvFnWX4DtzyC1w</recordid><startdate>201810</startdate><enddate>201810</enddate><creator>Shen, Qijun</creator><creator>Shan, Yanna</creator><creator>Hu, Zhengyu</creator><creator>Chen, Wenhui</creator><creator>Yang, Bing</creator><creator>Han, Jing</creator><creator>Huang, Yanfang</creator><creator>Xu, Wen</creator><creator>Feng, Zhan</creator><general>Springer Nature B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>3V.</scope><scope>7QO</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-9496-4211</orcidid></search><sort><creationdate>201810</creationdate><title>Quantitative parameters of CT texture analysis as potential markersfor early prediction of spontaneous intracranial hemorrhage enlargement</title><author>Shen, Qijun ; Shan, Yanna ; Hu, Zhengyu ; Chen, Wenhui ; Yang, Bing ; Han, Jing ; Huang, Yanfang ; Xu, Wen ; Feng, Zhan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p239t-b4a074ebe039c7e6b7811082b0bd9a8e3cce32c43fa6007c6649e961fadae2c73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Algorithms</topic><topic>Bandpass filters</topic><topic>Computed tomography</topic><topic>Computed Tomography Angiography - methods</topic><topic>Correlation coefficients</topic><topic>Diagnostic systems</topic><topic>Disease Progression</topic><topic>Early Diagnosis</topic><topic>Enlargement</topic><topic>Expansion</topic><topic>Female</topic><topic>Glasgow Coma Scale</topic><topic>Hematoma</topic><topic>Hematoma - diagnostic imaging</topic><topic>Hematoma - pathology</topic><topic>Hemorrhage</topic><topic>Heterogeneity</topic><topic>Hospitals</topic><topic>Humans</topic><topic>Image contrast</topic><topic>Intracranial Hemorrhages - diagnostic imaging</topic><topic>Intracranial Hemorrhages - pathology</topic><topic>Laboratories</topic><topic>Male</topic><topic>Medical diagnosis</topic><topic>Medical imaging</topic><topic>Middle Aged</topic><topic>Parameters</topic><topic>Patients</topic><topic>Predictions</topic><topic>Radiographic Image Interpretation, Computer-Assisted</topic><topic>Retrospective Studies</topic><topic>ROC Curve</topic><topic>Stroke</topic><topic>Texture</topic><topic>Tomography</topic><topic>Training</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shen, Qijun</creatorcontrib><creatorcontrib>Shan, Yanna</creatorcontrib><creatorcontrib>Hu, Zhengyu</creatorcontrib><creatorcontrib>Chen, Wenhui</creatorcontrib><creatorcontrib>Yang, Bing</creatorcontrib><creatorcontrib>Han, Jing</creatorcontrib><creatorcontrib>Huang, Yanfang</creatorcontrib><creatorcontrib>Xu, Wen</creatorcontrib><creatorcontrib>Feng, Zhan</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</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>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</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><collection>MEDLINE - Academic</collection><jtitle>European radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shen, Qijun</au><au>Shan, Yanna</au><au>Hu, Zhengyu</au><au>Chen, Wenhui</au><au>Yang, Bing</au><au>Han, Jing</au><au>Huang, Yanfang</au><au>Xu, Wen</au><au>Feng, Zhan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Quantitative parameters of CT texture analysis as potential markersfor early prediction of spontaneous intracranial hemorrhage enlargement</atitle><jtitle>European radiology</jtitle><addtitle>Eur Radiol</addtitle><date>2018-10</date><risdate>2018</risdate><volume>28</volume><issue>10</issue><spage>4389</spage><epage>4396</epage><pages>4389-4396</pages><issn>0938-7994</issn><eissn>1432-1084</eissn><abstract>To objectively quantify intracranial hematoma (ICH) enlargement by analysing the image texture of head CT scans and to provide objective and quantitative imaging parameters for predicting early hematoma enlargement.
We retrospectively studied 108 ICH patients with baseline non-contrast computed tomography (NCCT) and 24-h follow-up CT available. Image data were assessed by a chief radiologist and a resident radiologist. Consistency analysis between observers was tested. The patients were divided into training set (75%) and validation set (25%) by stratified sampling. Patients in the training set were dichotomized according to 24-h hematoma expansion ≥ 33%. Using the Laplacian of Gaussian bandpass filter, we chose different anatomical spatial domains ranging from fine texture to coarse texture to obtain a series of derived parameters (mean grayscale intensity, variance, uniformity) in order to quantify and evaluate all data. The parameters were externally validated on validation set.
Significant differences were found between the two groups of patients within variance at V
and in uniformity at U
, U
and U
. The intraclass correlation coefficients for the texture parameters were between 0.67 and 0.99. The area under the ROC curve between the two groups of ICH cases was between 0.77 and 0.92. The accuracy of validation set by CTTA was 0.59-0.85.
NCCT texture analysis can objectively quantify the heterogeneity of ICH and independently predict early hematoma enlargement.
• Heterogeneity is helpful in predicting ICH enlargement. • CTTA could play an important role in predicting early ICH enlargement. • After filtering, fine texture had the best diagnostic performance. • The histogram-based uniformity parameters can independently predict ICH enlargement. • CTTA is more objective, more comprehensive, more independently operable, than previous methods.</abstract><cop>Germany</cop><pub>Springer Nature B.V</pub><pmid>29713780</pmid><doi>10.1007/s00330-018-5364-8</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0001-9496-4211</orcidid></addata></record> |
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subjects | Adult Aged Algorithms Bandpass filters Computed tomography Computed Tomography Angiography - methods Correlation coefficients Diagnostic systems Disease Progression Early Diagnosis Enlargement Expansion Female Glasgow Coma Scale Hematoma Hematoma - diagnostic imaging Hematoma - pathology Hemorrhage Heterogeneity Hospitals Humans Image contrast Intracranial Hemorrhages - diagnostic imaging Intracranial Hemorrhages - pathology Laboratories Male Medical diagnosis Medical imaging Middle Aged Parameters Patients Predictions Radiographic Image Interpretation, Computer-Assisted Retrospective Studies ROC Curve Stroke Texture Tomography Training |
title | Quantitative parameters of CT texture analysis as potential markersfor early prediction of spontaneous intracranial hemorrhage enlargement |
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