Magnetic Resonance Imaging‐Based Radiomics Nomogram for Prediction of the Histopathological Grade of Soft Tissue Sarcomas: A Two‐Center Study
Background Preoperative prediction of soft tissue sarcoma (STS) grade is important for treatment decisions. Therefore, formulation an STS grade model is strongly needed. Purpose To develop and test an magnetic resonance imaging (MRI)‐based radiomics nomogram for predicting the grade of STS (low‐grad...
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| Veröffentlicht in: | Journal of magnetic resonance imaging 2021-06, Vol.53 (6), p.1683-1696 |
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| creator | Yan, Ruixin Hao, Dapeng Li, Jie Liu, Jihua Hou, Feng Chen, Haisong Duan, Lisha Huang, Chencui Wang, Hexiang Yu, Tengbo |
| description | Background
Preoperative prediction of soft tissue sarcoma (STS) grade is important for treatment decisions. Therefore, formulation an STS grade model is strongly needed.
Purpose
To develop and test an magnetic resonance imaging (MRI)‐based radiomics nomogram for predicting the grade of STS (low‐grade vs. high grade).
Study Type
Retrospective
Population
One hundred and eighty patients with STS confirmed by pathologic results at two independent institutions were enrolled (training set, N = 109; external validation set, N = 71).
Field Strength/Sequence
Unenhanced T1‐weighted (T1WI) and fat‐suppressed T2‐weighted images (FS‐T2WI) were acquired at 1.5 T and 3.0 T.
Assessment
Clinical‐MRI characteristics included age, gender, tumor‐node‐metastasis (TNM) stage, American Joint Committee on Cancer (AJCC) stage, progression‐free survival (PFS), and MRI morphological features (ie, margin). Radiomics feature extraction were performed on T1WI and FS‐T2WI images by minimum redundancy maximum relevance (MRMR) method and least absolute shrinkage and selection operator (LASSO) algorithm. The selected features constructed three radiomics signatures models (RS‐T1， RS‐FST2, and RS‐Combined). Univariate and multivariate logistic regression analysis were applied for screening significant risk factors. Radiomics nomogram was constructed by incorporating the radiomics signature and risk factors.
Statistical Tests
Clinical‐MRI characteristics were performed by a univariate analysis. Model performances (discrimination, calibration, and clinical usefulness) were validated in the external validation set. The RS‐T1 model, RS‐FST2 model, and RS‐Combined model had an area under curves (AUCs) of 0.645, 0.641, and 0.829, respectively, in the external validation set. The radiomics nomogram, incorporating significant risk factors and the RS‐Combined model had AUCs of 0.916 (95%CI, 0.866–0.966, training set) and 0.879 (95%CI, 0.791–0.967, external validation set), and demonstrated good calibration and good clinical utility.
Data Conclusion
The proposed noninvasive MRI‐based radiomics models showed good performance in differentiating low‐grade from high‐grade STSs.
Level of Evidence
3
Technical Efficacy Stage
2 |
| doi_str_mv | 10.1002/jmri.27532 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2491953033</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2491953033</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3932-875e0d586a874af2ba8fdacf128306f6ab4b2338e56d586ec4da118bb966dbb03</originalsourceid><addsrcrecordid>eNp9kc1u1DAQxy0Eol9ceABkiQtCSvFHnE24lRW0i1pAu8vZmtiT1Ksk3tqJqr31Eegr9klIuoUDB04z0vz0mxn9CXnN2SlnTHzYtMGdipmS4hk55EqIRKg8ez72TMmE52x2QI5i3DDGiiJVL8mBlBlLC6UOyf0V1B32ztAlRt9BZ5AuWqhdVz_c_foEES1dgnW-dSbSb771dYCWVj7QHwGtM73zHfUV7a-RXrjY-y30177xtTPQ0PMAFqfxylc9XbsYB6QrCMa3ED_SM7q-9eOeOXY9BrrqB7s7IS8qaCK-eqrH5OeXz-v5RXL5_XwxP7tMjCykSPKZQmbHPyGfpVCJEvLKgqm4yCXLqgzKtBRS5qiyiUKTWuA8L8siy2xZMnlM3u292-BvBoy9bl002DTQoR-iFmnBCyWZlCP69h9044fQjddpoUTGx4XpJHy_p0zwMQas9Da4FsJOc6anoPQUlH4MaoTfPCmHskX7F_2TzAjwPXDrGtz9R6W_Xi0Xe-lv1qqg7w</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2526130640</pqid></control><display><type>article</type><title>Magnetic Resonance Imaging‐Based Radiomics Nomogram for Prediction of the Histopathological Grade of Soft Tissue Sarcomas: A Two‐Center Study</title><source>Wiley Journals</source><creator>Yan, Ruixin ; Hao, Dapeng ; Li, Jie ; Liu, Jihua ; Hou, Feng ; Chen, Haisong ; Duan, Lisha ; Huang, Chencui ; Wang, Hexiang ; Yu, Tengbo</creator><creatorcontrib>Yan, Ruixin ; Hao, Dapeng ; Li, Jie ; Liu, Jihua ; Hou, Feng ; Chen, Haisong ; Duan, Lisha ; Huang, Chencui ; Wang, Hexiang ; Yu, Tengbo</creatorcontrib><description>Background
Preoperative prediction of soft tissue sarcoma (STS) grade is important for treatment decisions. Therefore, formulation an STS grade model is strongly needed.
Purpose
To develop and test an magnetic resonance imaging (MRI)‐based radiomics nomogram for predicting the grade of STS (low‐grade vs. high grade).
Study Type
Retrospective
Population
One hundred and eighty patients with STS confirmed by pathologic results at two independent institutions were enrolled (training set, N = 109; external validation set, N = 71).
Field Strength/Sequence
Unenhanced T1‐weighted (T1WI) and fat‐suppressed T2‐weighted images (FS‐T2WI) were acquired at 1.5 T and 3.0 T.
Assessment
Clinical‐MRI characteristics included age, gender, tumor‐node‐metastasis (TNM) stage, American Joint Committee on Cancer (AJCC) stage, progression‐free survival (PFS), and MRI morphological features (ie, margin). Radiomics feature extraction were performed on T1WI and FS‐T2WI images by minimum redundancy maximum relevance (MRMR) method and least absolute shrinkage and selection operator (LASSO) algorithm. The selected features constructed three radiomics signatures models (RS‐T1， RS‐FST2, and RS‐Combined). Univariate and multivariate logistic regression analysis were applied for screening significant risk factors. Radiomics nomogram was constructed by incorporating the radiomics signature and risk factors.
Statistical Tests
Clinical‐MRI characteristics were performed by a univariate analysis. Model performances (discrimination, calibration, and clinical usefulness) were validated in the external validation set. The RS‐T1 model, RS‐FST2 model, and RS‐Combined model had an area under curves (AUCs) of 0.645, 0.641, and 0.829, respectively, in the external validation set. The radiomics nomogram, incorporating significant risk factors and the RS‐Combined model had AUCs of 0.916 (95%CI, 0.866–0.966, training set) and 0.879 (95%CI, 0.791–0.967, external validation set), and demonstrated good calibration and good clinical utility.
Data Conclusion
The proposed noninvasive MRI‐based radiomics models showed good performance in differentiating low‐grade from high‐grade STSs.
Level of Evidence
3
Technical Efficacy Stage
2</description><identifier>ISSN: 1053-1807</identifier><identifier>EISSN: 1522-2586</identifier><identifier>DOI: 10.1002/jmri.27532</identifier><identifier>PMID: 33604955</identifier><language>eng</language><publisher>Hoboken, USA: John Wiley & Sons, Inc</publisher><subject>Algorithms ; Calibration ; Feature extraction ; Field strength ; Image acquisition ; Magnetic resonance imaging ; Medical imaging ; Metastases ; Nomograms ; Population studies ; Radiomics ; Redundancy ; Regression analysis ; Resonance ; Risk analysis ; Risk factors ; Sarcoma ; Soft tissue sarcoma ; soft tissue sarcomas ; Soft tissues ; Statistical analysis ; Statistical tests ; Training ; tumor grading</subject><ispartof>Journal of magnetic resonance imaging, 2021-06, Vol.53 (6), p.1683-1696</ispartof><rights>2021 International Society for Magnetic Resonance in Medicine</rights><rights>2021 International Society for Magnetic Resonance in Medicine.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3932-875e0d586a874af2ba8fdacf128306f6ab4b2338e56d586ec4da118bb966dbb03</citedby><cites>FETCH-LOGICAL-c3932-875e0d586a874af2ba8fdacf128306f6ab4b2338e56d586ec4da118bb966dbb03</cites><orcidid>0000-0001-7809-9133 ; 0000-0002-4270-7317 ; 0000-0003-1027-055X ; 0000-0001-5633-530X</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.27532$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjmri.27532$$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/33604955$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Yan, Ruixin</creatorcontrib><creatorcontrib>Hao, Dapeng</creatorcontrib><creatorcontrib>Li, Jie</creatorcontrib><creatorcontrib>Liu, Jihua</creatorcontrib><creatorcontrib>Hou, Feng</creatorcontrib><creatorcontrib>Chen, Haisong</creatorcontrib><creatorcontrib>Duan, Lisha</creatorcontrib><creatorcontrib>Huang, Chencui</creatorcontrib><creatorcontrib>Wang, Hexiang</creatorcontrib><creatorcontrib>Yu, Tengbo</creatorcontrib><title>Magnetic Resonance Imaging‐Based Radiomics Nomogram for Prediction of the Histopathological Grade of Soft Tissue Sarcomas: A Two‐Center Study</title><title>Journal of magnetic resonance imaging</title><addtitle>J Magn Reson Imaging</addtitle><description>Background
Preoperative prediction of soft tissue sarcoma (STS) grade is important for treatment decisions. Therefore, formulation an STS grade model is strongly needed.
Purpose
To develop and test an magnetic resonance imaging (MRI)‐based radiomics nomogram for predicting the grade of STS (low‐grade vs. high grade).
Study Type
Retrospective
Population
One hundred and eighty patients with STS confirmed by pathologic results at two independent institutions were enrolled (training set, N = 109; external validation set, N = 71).
Field Strength/Sequence
Unenhanced T1‐weighted (T1WI) and fat‐suppressed T2‐weighted images (FS‐T2WI) were acquired at 1.5 T and 3.0 T.
Assessment
Clinical‐MRI characteristics included age, gender, tumor‐node‐metastasis (TNM) stage, American Joint Committee on Cancer (AJCC) stage, progression‐free survival (PFS), and MRI morphological features (ie, margin). Radiomics feature extraction were performed on T1WI and FS‐T2WI images by minimum redundancy maximum relevance (MRMR) method and least absolute shrinkage and selection operator (LASSO) algorithm. The selected features constructed three radiomics signatures models (RS‐T1， RS‐FST2, and RS‐Combined). Univariate and multivariate logistic regression analysis were applied for screening significant risk factors. Radiomics nomogram was constructed by incorporating the radiomics signature and risk factors.
Statistical Tests
Clinical‐MRI characteristics were performed by a univariate analysis. Model performances (discrimination, calibration, and clinical usefulness) were validated in the external validation set. The RS‐T1 model, RS‐FST2 model, and RS‐Combined model had an area under curves (AUCs) of 0.645, 0.641, and 0.829, respectively, in the external validation set. The radiomics nomogram, incorporating significant risk factors and the RS‐Combined model had AUCs of 0.916 (95%CI, 0.866–0.966, training set) and 0.879 (95%CI, 0.791–0.967, external validation set), and demonstrated good calibration and good clinical utility.
Data Conclusion
The proposed noninvasive MRI‐based radiomics models showed good performance in differentiating low‐grade from high‐grade STSs.
Level of Evidence
3
Technical Efficacy Stage
2</description><subject>Algorithms</subject><subject>Calibration</subject><subject>Feature extraction</subject><subject>Field strength</subject><subject>Image acquisition</subject><subject>Magnetic resonance imaging</subject><subject>Medical imaging</subject><subject>Metastases</subject><subject>Nomograms</subject><subject>Population studies</subject><subject>Radiomics</subject><subject>Redundancy</subject><subject>Regression analysis</subject><subject>Resonance</subject><subject>Risk analysis</subject><subject>Risk factors</subject><subject>Sarcoma</subject><subject>Soft tissue sarcoma</subject><subject>soft tissue sarcomas</subject><subject>Soft tissues</subject><subject>Statistical analysis</subject><subject>Statistical tests</subject><subject>Training</subject><subject>tumor grading</subject><issn>1053-1807</issn><issn>1522-2586</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp9kc1u1DAQxy0Eol9ceABkiQtCSvFHnE24lRW0i1pAu8vZmtiT1Ksk3tqJqr31Eegr9klIuoUDB04z0vz0mxn9CXnN2SlnTHzYtMGdipmS4hk55EqIRKg8ez72TMmE52x2QI5i3DDGiiJVL8mBlBlLC6UOyf0V1B32ztAlRt9BZ5AuWqhdVz_c_foEES1dgnW-dSbSb771dYCWVj7QHwGtM73zHfUV7a-RXrjY-y30177xtTPQ0PMAFqfxylc9XbsYB6QrCMa3ED_SM7q-9eOeOXY9BrrqB7s7IS8qaCK-eqrH5OeXz-v5RXL5_XwxP7tMjCykSPKZQmbHPyGfpVCJEvLKgqm4yCXLqgzKtBRS5qiyiUKTWuA8L8siy2xZMnlM3u292-BvBoy9bl002DTQoR-iFmnBCyWZlCP69h9044fQjddpoUTGx4XpJHy_p0zwMQas9Da4FsJOc6anoPQUlH4MaoTfPCmHskX7F_2TzAjwPXDrGtz9R6W_Xi0Xe-lv1qqg7w</recordid><startdate>202106</startdate><enddate>202106</enddate><creator>Yan, Ruixin</creator><creator>Hao, Dapeng</creator><creator>Li, Jie</creator><creator>Liu, Jihua</creator><creator>Hou, Feng</creator><creator>Chen, Haisong</creator><creator>Duan, Lisha</creator><creator>Huang, Chencui</creator><creator>Wang, Hexiang</creator><creator>Yu, Tengbo</creator><general>John Wiley & Sons, Inc</general><general>Wiley Subscription Services, Inc</general><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/0000-0001-7809-9133</orcidid><orcidid>https://orcid.org/0000-0002-4270-7317</orcidid><orcidid>https://orcid.org/0000-0003-1027-055X</orcidid><orcidid>https://orcid.org/0000-0001-5633-530X</orcidid></search><sort><creationdate>202106</creationdate><title>Magnetic Resonance Imaging‐Based Radiomics Nomogram for Prediction of the Histopathological Grade of Soft Tissue Sarcomas: A Two‐Center Study</title><author>Yan, Ruixin ; Hao, Dapeng ; Li, Jie ; Liu, Jihua ; Hou, Feng ; Chen, Haisong ; Duan, Lisha ; Huang, Chencui ; Wang, Hexiang ; Yu, Tengbo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3932-875e0d586a874af2ba8fdacf128306f6ab4b2338e56d586ec4da118bb966dbb03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Calibration</topic><topic>Feature extraction</topic><topic>Field strength</topic><topic>Image acquisition</topic><topic>Magnetic resonance imaging</topic><topic>Medical imaging</topic><topic>Metastases</topic><topic>Nomograms</topic><topic>Population studies</topic><topic>Radiomics</topic><topic>Redundancy</topic><topic>Regression analysis</topic><topic>Resonance</topic><topic>Risk analysis</topic><topic>Risk factors</topic><topic>Sarcoma</topic><topic>Soft tissue sarcoma</topic><topic>soft tissue sarcomas</topic><topic>Soft tissues</topic><topic>Statistical analysis</topic><topic>Statistical tests</topic><topic>Training</topic><topic>tumor grading</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yan, Ruixin</creatorcontrib><creatorcontrib>Hao, Dapeng</creatorcontrib><creatorcontrib>Li, Jie</creatorcontrib><creatorcontrib>Liu, Jihua</creatorcontrib><creatorcontrib>Hou, Feng</creatorcontrib><creatorcontrib>Chen, Haisong</creatorcontrib><creatorcontrib>Duan, Lisha</creatorcontrib><creatorcontrib>Huang, Chencui</creatorcontrib><creatorcontrib>Wang, Hexiang</creatorcontrib><creatorcontrib>Yu, Tengbo</creatorcontrib><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>Yan, Ruixin</au><au>Hao, Dapeng</au><au>Li, Jie</au><au>Liu, Jihua</au><au>Hou, Feng</au><au>Chen, Haisong</au><au>Duan, Lisha</au><au>Huang, Chencui</au><au>Wang, Hexiang</au><au>Yu, Tengbo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Magnetic Resonance Imaging‐Based Radiomics Nomogram for Prediction of the Histopathological Grade of Soft Tissue Sarcomas: A Two‐Center Study</atitle><jtitle>Journal of magnetic resonance imaging</jtitle><addtitle>J Magn Reson Imaging</addtitle><date>2021-06</date><risdate>2021</risdate><volume>53</volume><issue>6</issue><spage>1683</spage><epage>1696</epage><pages>1683-1696</pages><issn>1053-1807</issn><eissn>1522-2586</eissn><abstract>Background
Preoperative prediction of soft tissue sarcoma (STS) grade is important for treatment decisions. Therefore, formulation an STS grade model is strongly needed.
Purpose
To develop and test an magnetic resonance imaging (MRI)‐based radiomics nomogram for predicting the grade of STS (low‐grade vs. high grade).
Study Type
Retrospective
Population
One hundred and eighty patients with STS confirmed by pathologic results at two independent institutions were enrolled (training set, N = 109; external validation set, N = 71).
Field Strength/Sequence
Unenhanced T1‐weighted (T1WI) and fat‐suppressed T2‐weighted images (FS‐T2WI) were acquired at 1.5 T and 3.0 T.
Assessment
Clinical‐MRI characteristics included age, gender, tumor‐node‐metastasis (TNM) stage, American Joint Committee on Cancer (AJCC) stage, progression‐free survival (PFS), and MRI morphological features (ie, margin). Radiomics feature extraction were performed on T1WI and FS‐T2WI images by minimum redundancy maximum relevance (MRMR) method and least absolute shrinkage and selection operator (LASSO) algorithm. The selected features constructed three radiomics signatures models (RS‐T1， RS‐FST2, and RS‐Combined). Univariate and multivariate logistic regression analysis were applied for screening significant risk factors. Radiomics nomogram was constructed by incorporating the radiomics signature and risk factors.
Statistical Tests
Clinical‐MRI characteristics were performed by a univariate analysis. Model performances (discrimination, calibration, and clinical usefulness) were validated in the external validation set. The RS‐T1 model, RS‐FST2 model, and RS‐Combined model had an area under curves (AUCs) of 0.645, 0.641, and 0.829, respectively, in the external validation set. The radiomics nomogram, incorporating significant risk factors and the RS‐Combined model had AUCs of 0.916 (95%CI, 0.866–0.966, training set) and 0.879 (95%CI, 0.791–0.967, external validation set), and demonstrated good calibration and good clinical utility.
Data Conclusion
The proposed noninvasive MRI‐based radiomics models showed good performance in differentiating low‐grade from high‐grade STSs.
Level of Evidence
3
Technical Efficacy Stage
2</abstract><cop>Hoboken, USA</cop><pub>John Wiley & Sons, Inc</pub><pmid>33604955</pmid><doi>10.1002/jmri.27532</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0001-7809-9133</orcidid><orcidid>https://orcid.org/0000-0002-4270-7317</orcidid><orcidid>https://orcid.org/0000-0003-1027-055X</orcidid><orcidid>https://orcid.org/0000-0001-5633-530X</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | Journal of magnetic resonance imaging, 2021-06, Vol.53 (6), p.1683-1696 |
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| language | eng |
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| source | Wiley Journals |
| subjects | Algorithms Calibration Feature extraction Field strength Image acquisition Magnetic resonance imaging Medical imaging Metastases Nomograms Population studies Radiomics Redundancy Regression analysis Resonance Risk analysis Risk factors Sarcoma Soft tissue sarcoma soft tissue sarcomas Soft tissues Statistical analysis Statistical tests Training tumor grading |
| title | Magnetic Resonance Imaging‐Based Radiomics Nomogram for Prediction of the Histopathological Grade of Soft Tissue Sarcomas: A Two‐Center Study |
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