Preoperative Diagnosis of Dual‐Phenotype Hepatocellular Carcinoma Using Enhanced MRI Radiomics Models
Background Dual‐phenotype hepatocellular carcinoma (DPHCC) is highly aggressive and difficult to distinguish from hepatocellular carcinoma (HCC). Purpose To develop and validate clinical and radiomics models based on contrast‐enhanced MRI for the preoperative diagnosis of DPHCC. Study type Retrospec...
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| Veröffentlicht in: | Journal of magnetic resonance imaging 2023-04, Vol.57 (4), p.1185-1196 |
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| creator | Wu, Qian Yu, Yi‐xing Zhang, Tao Zhu, Wen‐jing Fan, Yan‐fen Wang, Xi‐ming Hu, Chun‐hong |
| description | Background
Dual‐phenotype hepatocellular carcinoma (DPHCC) is highly aggressive and difficult to distinguish from hepatocellular carcinoma (HCC).
Purpose
To develop and validate clinical and radiomics models based on contrast‐enhanced MRI for the preoperative diagnosis of DPHCC.
Study type
Retrospective.
Population
A total of 87 patients with DPHCC and 92 patients with non‐DPHCC randomly divided into a training cohort (n = 125: 64 non‐DPHCC; 61 DPHCC) and a validation cohort (n = 54: 28 non‐DPHCC; 26 DPHCC).
Field Strength/Sequence
A 3.0 T; dynamic contrast‐enhanced MRI with time‐resolved T1‐weighted imaging sequence.
Assessment
In the clinical model, the maximum tumor diameter and hepatitis B virus (HBV) were independent risk factors of DPHCC. In the radiomics model, a total of 1781 radiomics features were extracted from tumor volumes of interest (VOIs) in the arterial phase (AP) and portal venous phase (PP) images. For feature reduction and selection, Pearson correlation coefficient (PCC) and recursive feature elimination (RFE) were used. Clinical, AP, PP, and combined radiomics models were established using machine learning algorithms (support vector machine [SVM], logistic regression [LR], and logistic regression‐least absolute shrinkage and selection operator [LR‐LASSO]) and their discriminatory efficacy assessed and compared.
Statistical Tests
The independent sample t test, Mann–Whitney U test, Chi‐square test, regression analysis, receiver operating characteristic curve (ROC) analysis, Pearson correlation analysis, the Delong test. A P value |
| doi_str_mv | 10.1002/jmri.28391 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2720929563</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2720929563</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3571-b3c737eec94ce6f20f445eeb2e97b09c0c9a301a466c1bdd0af362413b037fda3</originalsourceid><addsrcrecordid>eNp90L1u2zAUhmGiaFA7aZdeQECgSxBA6SEpUuJYOP9w0CBoZoGijmwakqiQVgtvuYRcY64kcpxm6NCJHB58OHgJ-crghAHw76s2uBOeC80-kCmTnCdc5urj-AcpEpZDNiH7Ma4AQOtUfiIToZgGJdWULG4D-h6DWbvfSE-dWXQ-ukh9TU8H0zw_Pt0usfPrTY_0Enuz9habZmhMoDMTrOt8a-h9dN2CnnVL01ms6M3dFb0zlfOts5He-Aqb-Jns1aaJ-OXtPSD352e_ZpfJ_OfF1ezHPLFCZiwphc1Ehmh1alHVHOo0lYglR52VoC1YbQQwkyplWVlVYGqheMpECSKrKyMOyNFutw_-YcC4LloXtyebDv0QC55x0FxLJUb67R-68kPoxutGlUsOIhdsVMc7ZYOPMWBd9MG1JmwKBsU2f7HNX7zmH_Hh2-RQtli907-9R8B24I9rcPOfqeJ6rLgbfQGmZZE8</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2785203831</pqid></control><display><type>article</type><title>Preoperative Diagnosis of Dual‐Phenotype Hepatocellular Carcinoma Using Enhanced MRI Radiomics Models</title><source>MEDLINE</source><source>Access via Wiley Online Library</source><creator>Wu, Qian ; Yu, Yi‐xing ; Zhang, Tao ; Zhu, Wen‐jing ; Fan, Yan‐fen ; Wang, Xi‐ming ; Hu, Chun‐hong</creator><creatorcontrib>Wu, Qian ; Yu, Yi‐xing ; Zhang, Tao ; Zhu, Wen‐jing ; Fan, Yan‐fen ; Wang, Xi‐ming ; Hu, Chun‐hong</creatorcontrib><description>Background
Dual‐phenotype hepatocellular carcinoma (DPHCC) is highly aggressive and difficult to distinguish from hepatocellular carcinoma (HCC).
Purpose
To develop and validate clinical and radiomics models based on contrast‐enhanced MRI for the preoperative diagnosis of DPHCC.
Study type
Retrospective.
Population
A total of 87 patients with DPHCC and 92 patients with non‐DPHCC randomly divided into a training cohort (n = 125: 64 non‐DPHCC; 61 DPHCC) and a validation cohort (n = 54: 28 non‐DPHCC; 26 DPHCC).
Field Strength/Sequence
A 3.0 T; dynamic contrast‐enhanced MRI with time‐resolved T1‐weighted imaging sequence.
Assessment
In the clinical model, the maximum tumor diameter and hepatitis B virus (HBV) were independent risk factors of DPHCC. In the radiomics model, a total of 1781 radiomics features were extracted from tumor volumes of interest (VOIs) in the arterial phase (AP) and portal venous phase (PP) images. For feature reduction and selection, Pearson correlation coefficient (PCC) and recursive feature elimination (RFE) were used. Clinical, AP, PP, and combined radiomics models were established using machine learning algorithms (support vector machine [SVM], logistic regression [LR], and logistic regression‐least absolute shrinkage and selection operator [LR‐LASSO]) and their discriminatory efficacy assessed and compared.
Statistical Tests
The independent sample t test, Mann–Whitney U test, Chi‐square test, regression analysis, receiver operating characteristic curve (ROC) analysis, Pearson correlation analysis, the Delong test. A P value < 0.05 was considered statistically significant.
Results
In the validation cohort, the combined radiomics model (area under the curve [AUC] = 0.908, 95% confidence interval [CI]: 0.831–0.985) showed the highest diagnostic performance. The AUCs of the PP (AUC = 0.879, 95% CI: 0.779–0.979) and combined radiomics models were significantly higher than that of clinical model (AUC = 0.685, 95% CI: 0.526–0.844). There were no significant differences in AUC between AP or PP radiomics model and combined radiomics model (P = 0.286, 0.180 and 0.543).
Conclusion
MRI radiomics models may be useful for discriminating DPHCC from non‐DPHCC before surgery.
Evidence Level
4
Technical Efficacy
Stage 2</description><identifier>ISSN: 1053-1807</identifier><identifier>EISSN: 1522-2586</identifier><identifier>DOI: 10.1002/jmri.28391</identifier><identifier>PMID: 36190656</identifier><language>eng</language><publisher>Hoboken, USA: John Wiley & Sons, Inc</publisher><subject>Algorithms ; Carcinoma, Hepatocellular - pathology ; Confidence intervals ; Correlation analysis ; Correlation coefficient ; Correlation coefficients ; Diagnosis ; Dual‐phenotype hepatocellular carcinoma ; Effectiveness ; Feature extraction ; Field strength ; Hepatitis B ; Hepatocellular carcinoma ; Humans ; Liver cancer ; Liver Neoplasms - pathology ; Machine learning ; Magnetic resonance imaging ; Magnetic Resonance Imaging - methods ; Patients ; Phenotype ; Phenotypes ; Population studies ; preoperative diagnosis ; Radiomics ; Regression analysis ; Retrospective Studies ; Risk factors ; Statistical analysis ; Statistical tests ; Support vector machines ; Tumors ; Viruses</subject><ispartof>Journal of magnetic resonance imaging, 2023-04, Vol.57 (4), p.1185-1196</ispartof><rights>2022 International Society for Magnetic Resonance in Medicine.</rights><rights>2023 International Society for Magnetic Resonance in Medicine</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3571-b3c737eec94ce6f20f445eeb2e97b09c0c9a301a466c1bdd0af362413b037fda3</citedby><cites>FETCH-LOGICAL-c3571-b3c737eec94ce6f20f445eeb2e97b09c0c9a301a466c1bdd0af362413b037fda3</cites><orcidid>0000-0002-6343-758X ; 0000-0002-0675-3383</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fjmri.28391$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjmri.28391$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,45574,45575</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36190656$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wu, Qian</creatorcontrib><creatorcontrib>Yu, Yi‐xing</creatorcontrib><creatorcontrib>Zhang, Tao</creatorcontrib><creatorcontrib>Zhu, Wen‐jing</creatorcontrib><creatorcontrib>Fan, Yan‐fen</creatorcontrib><creatorcontrib>Wang, Xi‐ming</creatorcontrib><creatorcontrib>Hu, Chun‐hong</creatorcontrib><title>Preoperative Diagnosis of Dual‐Phenotype Hepatocellular Carcinoma Using Enhanced MRI Radiomics Models</title><title>Journal of magnetic resonance imaging</title><addtitle>J Magn Reson Imaging</addtitle><description>Background
Dual‐phenotype hepatocellular carcinoma (DPHCC) is highly aggressive and difficult to distinguish from hepatocellular carcinoma (HCC).
Purpose
To develop and validate clinical and radiomics models based on contrast‐enhanced MRI for the preoperative diagnosis of DPHCC.
Study type
Retrospective.
Population
A total of 87 patients with DPHCC and 92 patients with non‐DPHCC randomly divided into a training cohort (n = 125: 64 non‐DPHCC; 61 DPHCC) and a validation cohort (n = 54: 28 non‐DPHCC; 26 DPHCC).
Field Strength/Sequence
A 3.0 T; dynamic contrast‐enhanced MRI with time‐resolved T1‐weighted imaging sequence.
Assessment
In the clinical model, the maximum tumor diameter and hepatitis B virus (HBV) were independent risk factors of DPHCC. In the radiomics model, a total of 1781 radiomics features were extracted from tumor volumes of interest (VOIs) in the arterial phase (AP) and portal venous phase (PP) images. For feature reduction and selection, Pearson correlation coefficient (PCC) and recursive feature elimination (RFE) were used. Clinical, AP, PP, and combined radiomics models were established using machine learning algorithms (support vector machine [SVM], logistic regression [LR], and logistic regression‐least absolute shrinkage and selection operator [LR‐LASSO]) and their discriminatory efficacy assessed and compared.
Statistical Tests
The independent sample t test, Mann–Whitney U test, Chi‐square test, regression analysis, receiver operating characteristic curve (ROC) analysis, Pearson correlation analysis, the Delong test. A P value < 0.05 was considered statistically significant.
Results
In the validation cohort, the combined radiomics model (area under the curve [AUC] = 0.908, 95% confidence interval [CI]: 0.831–0.985) showed the highest diagnostic performance. The AUCs of the PP (AUC = 0.879, 95% CI: 0.779–0.979) and combined radiomics models were significantly higher than that of clinical model (AUC = 0.685, 95% CI: 0.526–0.844). There were no significant differences in AUC between AP or PP radiomics model and combined radiomics model (P = 0.286, 0.180 and 0.543).
Conclusion
MRI radiomics models may be useful for discriminating DPHCC from non‐DPHCC before surgery.
Evidence Level
4
Technical Efficacy
Stage 2</description><subject>Algorithms</subject><subject>Carcinoma, Hepatocellular - pathology</subject><subject>Confidence intervals</subject><subject>Correlation analysis</subject><subject>Correlation coefficient</subject><subject>Correlation coefficients</subject><subject>Diagnosis</subject><subject>Dual‐phenotype hepatocellular carcinoma</subject><subject>Effectiveness</subject><subject>Feature extraction</subject><subject>Field strength</subject><subject>Hepatitis B</subject><subject>Hepatocellular carcinoma</subject><subject>Humans</subject><subject>Liver cancer</subject><subject>Liver Neoplasms - pathology</subject><subject>Machine learning</subject><subject>Magnetic resonance imaging</subject><subject>Magnetic Resonance Imaging - methods</subject><subject>Patients</subject><subject>Phenotype</subject><subject>Phenotypes</subject><subject>Population studies</subject><subject>preoperative diagnosis</subject><subject>Radiomics</subject><subject>Regression analysis</subject><subject>Retrospective Studies</subject><subject>Risk factors</subject><subject>Statistical analysis</subject><subject>Statistical tests</subject><subject>Support vector machines</subject><subject>Tumors</subject><subject>Viruses</subject><issn>1053-1807</issn><issn>1522-2586</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp90L1u2zAUhmGiaFA7aZdeQECgSxBA6SEpUuJYOP9w0CBoZoGijmwakqiQVgtvuYRcY64kcpxm6NCJHB58OHgJ-crghAHw76s2uBOeC80-kCmTnCdc5urj-AcpEpZDNiH7Ma4AQOtUfiIToZgGJdWULG4D-h6DWbvfSE-dWXQ-ukh9TU8H0zw_Pt0usfPrTY_0Enuz9habZmhMoDMTrOt8a-h9dN2CnnVL01ms6M3dFb0zlfOts5He-Aqb-Jns1aaJ-OXtPSD352e_ZpfJ_OfF1ezHPLFCZiwphc1Ehmh1alHVHOo0lYglR52VoC1YbQQwkyplWVlVYGqheMpECSKrKyMOyNFutw_-YcC4LloXtyebDv0QC55x0FxLJUb67R-68kPoxutGlUsOIhdsVMc7ZYOPMWBd9MG1JmwKBsU2f7HNX7zmH_Hh2-RQtli907-9R8B24I9rcPOfqeJ6rLgbfQGmZZE8</recordid><startdate>202304</startdate><enddate>202304</enddate><creator>Wu, Qian</creator><creator>Yu, Yi‐xing</creator><creator>Zhang, Tao</creator><creator>Zhu, Wen‐jing</creator><creator>Fan, Yan‐fen</creator><creator>Wang, Xi‐ming</creator><creator>Hu, Chun‐hong</creator><general>John Wiley & Sons, Inc</general><general>Wiley Subscription Services, Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>7TK</scope><scope>8FD</scope><scope>FR3</scope><scope>K9.</scope><scope>P64</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-6343-758X</orcidid><orcidid>https://orcid.org/0000-0002-0675-3383</orcidid></search><sort><creationdate>202304</creationdate><title>Preoperative Diagnosis of Dual‐Phenotype Hepatocellular Carcinoma Using Enhanced MRI Radiomics Models</title><author>Wu, Qian ; Yu, Yi‐xing ; Zhang, Tao ; Zhu, Wen‐jing ; Fan, Yan‐fen ; Wang, Xi‐ming ; Hu, Chun‐hong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3571-b3c737eec94ce6f20f445eeb2e97b09c0c9a301a466c1bdd0af362413b037fda3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Carcinoma, Hepatocellular - pathology</topic><topic>Confidence intervals</topic><topic>Correlation analysis</topic><topic>Correlation coefficient</topic><topic>Correlation coefficients</topic><topic>Diagnosis</topic><topic>Dual‐phenotype hepatocellular carcinoma</topic><topic>Effectiveness</topic><topic>Feature extraction</topic><topic>Field strength</topic><topic>Hepatitis B</topic><topic>Hepatocellular carcinoma</topic><topic>Humans</topic><topic>Liver cancer</topic><topic>Liver Neoplasms - pathology</topic><topic>Machine learning</topic><topic>Magnetic resonance imaging</topic><topic>Magnetic Resonance Imaging - methods</topic><topic>Patients</topic><topic>Phenotype</topic><topic>Phenotypes</topic><topic>Population studies</topic><topic>preoperative diagnosis</topic><topic>Radiomics</topic><topic>Regression analysis</topic><topic>Retrospective Studies</topic><topic>Risk factors</topic><topic>Statistical analysis</topic><topic>Statistical tests</topic><topic>Support vector machines</topic><topic>Tumors</topic><topic>Viruses</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wu, Qian</creatorcontrib><creatorcontrib>Yu, Yi‐xing</creatorcontrib><creatorcontrib>Zhang, Tao</creatorcontrib><creatorcontrib>Zhu, Wen‐jing</creatorcontrib><creatorcontrib>Fan, Yan‐fen</creatorcontrib><creatorcontrib>Wang, Xi‐ming</creatorcontrib><creatorcontrib>Hu, Chun‐hong</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of magnetic resonance imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wu, Qian</au><au>Yu, Yi‐xing</au><au>Zhang, Tao</au><au>Zhu, Wen‐jing</au><au>Fan, Yan‐fen</au><au>Wang, Xi‐ming</au><au>Hu, Chun‐hong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Preoperative Diagnosis of Dual‐Phenotype Hepatocellular Carcinoma Using Enhanced MRI Radiomics Models</atitle><jtitle>Journal of magnetic resonance imaging</jtitle><addtitle>J Magn Reson Imaging</addtitle><date>2023-04</date><risdate>2023</risdate><volume>57</volume><issue>4</issue><spage>1185</spage><epage>1196</epage><pages>1185-1196</pages><issn>1053-1807</issn><eissn>1522-2586</eissn><abstract>Background
Dual‐phenotype hepatocellular carcinoma (DPHCC) is highly aggressive and difficult to distinguish from hepatocellular carcinoma (HCC).
Purpose
To develop and validate clinical and radiomics models based on contrast‐enhanced MRI for the preoperative diagnosis of DPHCC.
Study type
Retrospective.
Population
A total of 87 patients with DPHCC and 92 patients with non‐DPHCC randomly divided into a training cohort (n = 125: 64 non‐DPHCC; 61 DPHCC) and a validation cohort (n = 54: 28 non‐DPHCC; 26 DPHCC).
Field Strength/Sequence
A 3.0 T; dynamic contrast‐enhanced MRI with time‐resolved T1‐weighted imaging sequence.
Assessment
In the clinical model, the maximum tumor diameter and hepatitis B virus (HBV) were independent risk factors of DPHCC. In the radiomics model, a total of 1781 radiomics features were extracted from tumor volumes of interest (VOIs) in the arterial phase (AP) and portal venous phase (PP) images. For feature reduction and selection, Pearson correlation coefficient (PCC) and recursive feature elimination (RFE) were used. Clinical, AP, PP, and combined radiomics models were established using machine learning algorithms (support vector machine [SVM], logistic regression [LR], and logistic regression‐least absolute shrinkage and selection operator [LR‐LASSO]) and their discriminatory efficacy assessed and compared.
Statistical Tests
The independent sample t test, Mann–Whitney U test, Chi‐square test, regression analysis, receiver operating characteristic curve (ROC) analysis, Pearson correlation analysis, the Delong test. A P value < 0.05 was considered statistically significant.
Results
In the validation cohort, the combined radiomics model (area under the curve [AUC] = 0.908, 95% confidence interval [CI]: 0.831–0.985) showed the highest diagnostic performance. The AUCs of the PP (AUC = 0.879, 95% CI: 0.779–0.979) and combined radiomics models were significantly higher than that of clinical model (AUC = 0.685, 95% CI: 0.526–0.844). There were no significant differences in AUC between AP or PP radiomics model and combined radiomics model (P = 0.286, 0.180 and 0.543).
Conclusion
MRI radiomics models may be useful for discriminating DPHCC from non‐DPHCC before surgery.
Evidence Level
4
Technical Efficacy
Stage 2</abstract><cop>Hoboken, USA</cop><pub>John Wiley & Sons, Inc</pub><pmid>36190656</pmid><doi>10.1002/jmri.28391</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-6343-758X</orcidid><orcidid>https://orcid.org/0000-0002-0675-3383</orcidid></addata></record> |
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| subjects | Algorithms Carcinoma, Hepatocellular - pathology Confidence intervals Correlation analysis Correlation coefficient Correlation coefficients Diagnosis Dual‐phenotype hepatocellular carcinoma Effectiveness Feature extraction Field strength Hepatitis B Hepatocellular carcinoma Humans Liver cancer Liver Neoplasms - pathology Machine learning Magnetic resonance imaging Magnetic Resonance Imaging - methods Patients Phenotype Phenotypes Population studies preoperative diagnosis Radiomics Regression analysis Retrospective Studies Risk factors Statistical analysis Statistical tests Support vector machines Tumors Viruses |
| title | Preoperative Diagnosis of Dual‐Phenotype Hepatocellular Carcinoma Using Enhanced MRI Radiomics Models |
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