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Radiomics analysis of contrast-enhanced CT predicts lymphovascular invasion and disease outcome in gastric cancer: a preliminary study
To determine whether radiomics features based on contrast-enhanced CT (CECT) can preoperatively predict lymphovascular invasion (LVI) and clinical outcome in gastric cancer (GC) patients. In total, 160 surgically resected patients were retrospectively analyzed, and seven predictive models were const...
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| Published in: | Cancer imaging 2020-04, Vol.20 (1), p.24-24, Article 24 |
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| creator | Chen, Xiaofeng Yang, Zhiqi Yang, Jiada Liao, Yuting Pang, Peipei Fan, Weixiong Chen, Xiangguang |
| description | To determine whether radiomics features based on contrast-enhanced CT (CECT) can preoperatively predict lymphovascular invasion (LVI) and clinical outcome in gastric cancer (GC) patients.
In total, 160 surgically resected patients were retrospectively analyzed, and seven predictive models were constructed. Three radiomics predictive models were built from radiomics features based on arterial (A), venous (V) and combination of two phase (A + V) images. Then, three Radscores (A-Radscore, V-Radscore and A + V-Radscore) were obtained. Another four predictive models were constructed by the three Radscores and clinical risk factors through multivariate logistic regression. A nomogram was developed to predict LVI by incorporating A + V-Radscore and clinical risk factors. Kaplan-Meier curve and log-rank test were utilized to analyze the outcome of LVI.
Radiomics related to tumor size and intratumoral inhomogeneity were the top-ranked LVI predicting features. The related Radscores showed significant differences according to LVI status (P |
| doi_str_mv | 10.1186/s40644-020-00302-5 |
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In total, 160 surgically resected patients were retrospectively analyzed, and seven predictive models were constructed. Three radiomics predictive models were built from radiomics features based on arterial (A), venous (V) and combination of two phase (A + V) images. Then, three Radscores (A-Radscore, V-Radscore and A + V-Radscore) were obtained. Another four predictive models were constructed by the three Radscores and clinical risk factors through multivariate logistic regression. A nomogram was developed to predict LVI by incorporating A + V-Radscore and clinical risk factors. Kaplan-Meier curve and log-rank test were utilized to analyze the outcome of LVI.
Radiomics related to tumor size and intratumoral inhomogeneity were the top-ranked LVI predicting features. The related Radscores showed significant differences according to LVI status (P < 0.01). Univariate logistic analysis identified three clinical features (T stage, N stage and AJCC stage) and three Radscores as LVI predictive factors. The Clinical-Radscore (namely, A + V + C) model that used all these factors showed a higher performance (AUC = 0.856) than the clinical (namely, C, including T stage, N stage and AJCC stage) model (AUC = 0.810) and the A + V-Radscore model (AUC = 0.795) in the train cohort. For patients without LVI and with LVI, the median progression-free survival (PFS) was 11.5 and 8.0 months (P < 0.001),and the median OS was 20.2 and 17.0 months (P = 0.3), respectively. In the Clinical-Radscore-predicted LVI absent and LVI present groups, the median PFS was 11.0 and 8.0 months (P = 0.03), and the median OS was 20.0 and 18.0 months (P = 0.05), respectively. N stage, LVI status and Clinical-Radscore-predicted LVI status were associated with disease-specific recurrence or mortality.
Radiomics features based on CECT may serve as potential markers to successfully predict LVI and PFS, but no evidence was found that these features were related to OS. Considering that it is a single central study, multi-center validation studies will be required in the future to verify its clinical feasibility.</description><identifier>ISSN: 1470-7330</identifier><identifier>ISSN: 1740-5025</identifier><identifier>EISSN: 1470-7330</identifier><identifier>DOI: 10.1186/s40644-020-00302-5</identifier><identifier>PMID: 32248822</identifier><language>eng</language><publisher>England: BioMed Central Ltd</publisher><subject>Analysis ; Antigens ; Artificial intelligence ; Cancer ; Cancer research ; Clinical outcome ; Gastric cancer ; Histopathology ; Inhomogeneity ; Lymphovascular invasion ; Mortality ; Nomograms ; Open source software ; Patient outcomes ; Patients ; Prediction models ; Prognosis ; Radiomics ; Rank tests ; Regression analysis ; Risk analysis ; Risk factors ; Stomach cancer ; Studies ; Surgery</subject><ispartof>Cancer imaging, 2020-04, Vol.20 (1), p.24-24, Article 24</ispartof><rights>COPYRIGHT 2020 BioMed Central Ltd.</rights><rights>2020. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>The Author(s) 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c660t-68bb6b4a36685838f609353c13a495f9829a61b27b7afa43815445aa3e7abb0d3</citedby><cites>FETCH-LOGICAL-c660t-68bb6b4a36685838f609353c13a495f9829a61b27b7afa43815445aa3e7abb0d3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7132895/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2391500095?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,25731,27901,27902,36989,36990,44566,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32248822$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Chen, Xiaofeng</creatorcontrib><creatorcontrib>Yang, Zhiqi</creatorcontrib><creatorcontrib>Yang, Jiada</creatorcontrib><creatorcontrib>Liao, Yuting</creatorcontrib><creatorcontrib>Pang, Peipei</creatorcontrib><creatorcontrib>Fan, Weixiong</creatorcontrib><creatorcontrib>Chen, Xiangguang</creatorcontrib><title>Radiomics analysis of contrast-enhanced CT predicts lymphovascular invasion and disease outcome in gastric cancer: a preliminary study</title><title>Cancer imaging</title><addtitle>Cancer Imaging</addtitle><description>To determine whether radiomics features based on contrast-enhanced CT (CECT) can preoperatively predict lymphovascular invasion (LVI) and clinical outcome in gastric cancer (GC) patients.
In total, 160 surgically resected patients were retrospectively analyzed, and seven predictive models were constructed. Three radiomics predictive models were built from radiomics features based on arterial (A), venous (V) and combination of two phase (A + V) images. Then, three Radscores (A-Radscore, V-Radscore and A + V-Radscore) were obtained. Another four predictive models were constructed by the three Radscores and clinical risk factors through multivariate logistic regression. A nomogram was developed to predict LVI by incorporating A + V-Radscore and clinical risk factors. Kaplan-Meier curve and log-rank test were utilized to analyze the outcome of LVI.
Radiomics related to tumor size and intratumoral inhomogeneity were the top-ranked LVI predicting features. The related Radscores showed significant differences according to LVI status (P < 0.01). Univariate logistic analysis identified three clinical features (T stage, N stage and AJCC stage) and three Radscores as LVI predictive factors. The Clinical-Radscore (namely, A + V + C) model that used all these factors showed a higher performance (AUC = 0.856) than the clinical (namely, C, including T stage, N stage and AJCC stage) model (AUC = 0.810) and the A + V-Radscore model (AUC = 0.795) in the train cohort. For patients without LVI and with LVI, the median progression-free survival (PFS) was 11.5 and 8.0 months (P < 0.001),and the median OS was 20.2 and 17.0 months (P = 0.3), respectively. In the Clinical-Radscore-predicted LVI absent and LVI present groups, the median PFS was 11.0 and 8.0 months (P = 0.03), and the median OS was 20.0 and 18.0 months (P = 0.05), respectively. N stage, LVI status and Clinical-Radscore-predicted LVI status were associated with disease-specific recurrence or mortality.
Radiomics features based on CECT may serve as potential markers to successfully predict LVI and PFS, but no evidence was found that these features were related to OS. Considering that it is a single central study, multi-center validation studies will be required in the future to verify its clinical feasibility.</description><subject>Analysis</subject><subject>Antigens</subject><subject>Artificial intelligence</subject><subject>Cancer</subject><subject>Cancer research</subject><subject>Clinical outcome</subject><subject>Gastric cancer</subject><subject>Histopathology</subject><subject>Inhomogeneity</subject><subject>Lymphovascular invasion</subject><subject>Mortality</subject><subject>Nomograms</subject><subject>Open source software</subject><subject>Patient outcomes</subject><subject>Patients</subject><subject>Prediction models</subject><subject>Prognosis</subject><subject>Radiomics</subject><subject>Rank tests</subject><subject>Regression analysis</subject><subject>Risk analysis</subject><subject>Risk factors</subject><subject>Stomach cancer</subject><subject>Studies</subject><subject>Surgery</subject><issn>1470-7330</issn><issn>1740-5025</issn><issn>1470-7330</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNptkl2L1DAUhoso7rr6B7yQgCDedE2TNB9eCMvgx8KCIOt1OE3SaYa2GZN2Yf6Av9t0ZlxmRHKRkLznOSfnvEXxusLXVSX5h8QwZ6zEBJcYU0zK-klxWTGBS0EpfnpyvihepLTBmCipxPPighLCpCTksvj9A6wPgzcJwQj9LvmEQotMGKcIaSrd2MFonEWre7SNznozJdTvhm0XHiCZuYeI_JiPPoyZYJH1yUFyKMyTCYPLj2idQdEbZBZS_IhgIfV-8CPEHUrTbHcvi2ct9Mm9Ou5Xxc8vn-9X38q7719vVzd3peEcTyWXTcMbBpRzWUsqW44VrampKDBVt0oSBbxqiGgEtMCorGrGagDqBDQNtvSquD1wbYCN3kY_5BJ0AK_3FyGuNcTJm95pQmnTgsVKuJpVFqQUqjWUKyGdyODM-nRgbedmcNa4pWX9GfT8ZfSdXocHLSpKpFoA74-AGH7NLk168Mm4vofRhTnlCmSeL8GYZ-nbf6SbMMc8sEWlqhpjvAceVWvIH_BjG3Jes0D1DSciZ2VCZdX1f1R5WZd9EEbX-nx_FvDuJKBz0E9dCv085ZmncyE5CE0MKUXXPjajwnqxrD5YVmfL6r1l9VL0m9M2Pob89Sj9A8Me5oM</recordid><startdate>20200405</startdate><enddate>20200405</enddate><creator>Chen, Xiaofeng</creator><creator>Yang, Zhiqi</creator><creator>Yang, Jiada</creator><creator>Liao, Yuting</creator><creator>Pang, Peipei</creator><creator>Fan, Weixiong</creator><creator>Chen, Xiangguang</creator><general>BioMed Central Ltd</general><general>BioMed Central</general><general>BMC</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88C</scope><scope>88E</scope><scope>8FE</scope><scope>8FG</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>M0S</scope><scope>M0T</scope><scope>M1P</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20200405</creationdate><title>Radiomics analysis of contrast-enhanced CT predicts lymphovascular invasion and disease outcome in gastric cancer: a preliminary study</title><author>Chen, Xiaofeng ; Yang, Zhiqi ; Yang, Jiada ; Liao, Yuting ; Pang, Peipei ; Fan, Weixiong ; Chen, Xiangguang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c660t-68bb6b4a36685838f609353c13a495f9829a61b27b7afa43815445aa3e7abb0d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Analysis</topic><topic>Antigens</topic><topic>Artificial intelligence</topic><topic>Cancer</topic><topic>Cancer research</topic><topic>Clinical outcome</topic><topic>Gastric cancer</topic><topic>Histopathology</topic><topic>Inhomogeneity</topic><topic>Lymphovascular invasion</topic><topic>Mortality</topic><topic>Nomograms</topic><topic>Open source software</topic><topic>Patient outcomes</topic><topic>Patients</topic><topic>Prediction models</topic><topic>Prognosis</topic><topic>Radiomics</topic><topic>Rank tests</topic><topic>Regression analysis</topic><topic>Risk analysis</topic><topic>Risk factors</topic><topic>Stomach cancer</topic><topic>Studies</topic><topic>Surgery</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Xiaofeng</creatorcontrib><creatorcontrib>Yang, Zhiqi</creatorcontrib><creatorcontrib>Yang, Jiada</creatorcontrib><creatorcontrib>Liao, Yuting</creatorcontrib><creatorcontrib>Pang, Peipei</creatorcontrib><creatorcontrib>Fan, Weixiong</creatorcontrib><creatorcontrib>Chen, Xiangguang</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Healthcare Administration Database (Alumni)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology 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)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Healthcare Administration Database</collection><collection>PML(ProQuest Medical Library)</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content Database</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><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Cancer imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, Xiaofeng</au><au>Yang, Zhiqi</au><au>Yang, Jiada</au><au>Liao, Yuting</au><au>Pang, Peipei</au><au>Fan, Weixiong</au><au>Chen, Xiangguang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Radiomics analysis of contrast-enhanced CT predicts lymphovascular invasion and disease outcome in gastric cancer: a preliminary study</atitle><jtitle>Cancer imaging</jtitle><addtitle>Cancer Imaging</addtitle><date>2020-04-05</date><risdate>2020</risdate><volume>20</volume><issue>1</issue><spage>24</spage><epage>24</epage><pages>24-24</pages><artnum>24</artnum><issn>1470-7330</issn><issn>1740-5025</issn><eissn>1470-7330</eissn><abstract>To determine whether radiomics features based on contrast-enhanced CT (CECT) can preoperatively predict lymphovascular invasion (LVI) and clinical outcome in gastric cancer (GC) patients.
In total, 160 surgically resected patients were retrospectively analyzed, and seven predictive models were constructed. Three radiomics predictive models were built from radiomics features based on arterial (A), venous (V) and combination of two phase (A + V) images. Then, three Radscores (A-Radscore, V-Radscore and A + V-Radscore) were obtained. Another four predictive models were constructed by the three Radscores and clinical risk factors through multivariate logistic regression. A nomogram was developed to predict LVI by incorporating A + V-Radscore and clinical risk factors. Kaplan-Meier curve and log-rank test were utilized to analyze the outcome of LVI.
Radiomics related to tumor size and intratumoral inhomogeneity were the top-ranked LVI predicting features. The related Radscores showed significant differences according to LVI status (P < 0.01). Univariate logistic analysis identified three clinical features (T stage, N stage and AJCC stage) and three Radscores as LVI predictive factors. The Clinical-Radscore (namely, A + V + C) model that used all these factors showed a higher performance (AUC = 0.856) than the clinical (namely, C, including T stage, N stage and AJCC stage) model (AUC = 0.810) and the A + V-Radscore model (AUC = 0.795) in the train cohort. For patients without LVI and with LVI, the median progression-free survival (PFS) was 11.5 and 8.0 months (P < 0.001),and the median OS was 20.2 and 17.0 months (P = 0.3), respectively. In the Clinical-Radscore-predicted LVI absent and LVI present groups, the median PFS was 11.0 and 8.0 months (P = 0.03), and the median OS was 20.0 and 18.0 months (P = 0.05), respectively. N stage, LVI status and Clinical-Radscore-predicted LVI status were associated with disease-specific recurrence or mortality.
Radiomics features based on CECT may serve as potential markers to successfully predict LVI and PFS, but no evidence was found that these features were related to OS. Considering that it is a single central study, multi-center validation studies will be required in the future to verify its clinical feasibility.</abstract><cop>England</cop><pub>BioMed Central Ltd</pub><pmid>32248822</pmid><doi>10.1186/s40644-020-00302-5</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Analysis Antigens Artificial intelligence Cancer Cancer research Clinical outcome Gastric cancer Histopathology Inhomogeneity Lymphovascular invasion Mortality Nomograms Open source software Patient outcomes Patients Prediction models Prognosis Radiomics Rank tests Regression analysis Risk analysis Risk factors Stomach cancer Studies Surgery |
| title | Radiomics analysis of contrast-enhanced CT predicts lymphovascular invasion and disease outcome in gastric cancer: a preliminary study |
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