Ultrasound radiomics-based nomogram to predict lymphovascular invasion in invasive breast cancer: a multicenter, retrospective study
Objectives To develop and validate an ultrasound (US) radiomics-based nomogram for the preoperative prediction of the lymphovascular invasion (LVI) status in patients with invasive breast cancer (IBC). Materials and methods In this multicentre, retrospective study, 456 consecutive women were enrolle...
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| Veröffentlicht in: | European radiology 2024-01, Vol.34 (1), p.136-148 |
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| creator | Du, Yu Cai, Mengjun Zha, Hailing Chen, Baoding Gu, Jun Zhang, Manqi Liu, Wei Liu, Xinpei Liu, Xiaoan Zong, Min Li, Cuiying |
| description | Objectives
To develop and validate an ultrasound (US) radiomics-based nomogram for the preoperative prediction of the lymphovascular invasion (LVI) status in patients with invasive breast cancer (IBC).
Materials and methods
In this multicentre, retrospective study, 456 consecutive women were enrolled from three institutions. Institutions 1 and 2 were used to train (
n
= 320) and test (
n
= 136), and 130 patients from institution 3 were used for external validation. Radiomics features that reflected tumour information were derived from grey-scale US images. The least absolute shrinkage and selection operator and the maximum relevance minimum redundancy (mRMR) algorithm were used for feature selection and radiomics signature (RS) building. US radiomics-based nomogram was constructed by using multivariable logistic regression analysis. Predictive performance was assessed with the receiving operating characteristic curve, discrimination, and calibration.
Results
The nomogram based on clinico-ultrasonic features (menopausal status, US-reported lymph node status, posterior echo features) and RS yielded an optimal AUC of 0.88 (95% confidence interval [CI], 0.84–0.91), 0.89 (95% CI, 0.84–0.94) and 0.95 (95% CI, 0.92–0.99) in the training, internal and external validation cohort. The nomogram outperformed the clinico-ultrasonic and RS model (
p
|
| doi_str_mv | 10.1007/s00330-023-09995-1 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2844089227</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2844089227</sourcerecordid><originalsourceid>FETCH-LOGICAL-c375t-4e85fe8fcb6ee1d7b24ba4d7964315a12158e0b348fa31324567170fbfacdbe03</originalsourceid><addsrcrecordid>eNp9kU1rFTEUhoMotlb_gAsJuHHR0ZOPucm4k2JVKLix65CPM3XKTDImmcLd-8NNvdcPXLjKC3nOmxMeQp4zeM0A1JsCIAR0wEUHwzD0HXtATpkUvGOg5cO_8gl5UsotAAxMqsfkRKie6Z3Sp-T79VyzLWmLgWYbprRMvnTOFgw0piXdZLvQmuiaMUy-0nm_rF_TnS1-m22mU2xxSrGFY75D6jLaUqm30WN-Sy1dtrlOHmPFfE4z1pzKir7es6VuYf-UPBrtXPDZ8Twj15fvv1x87K4-f_h08e6q823h2knU_Yh69G6HyIJyXDorgxp2UrDeMs56jeCE1KMVTHDZ7xRTMLrR-uAQxBl5dehdc_q2YalmmYrHebYR01YM11KCHjhXDX35D3qbthzbdoYPrJdCa6EbxQ-Ub18qGUez5mmxeW8YmHtH5uDINEfmpyPD2tCLY_XmFgy_R35JaYA4AKVdxRvMf97-T-0PciCflg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2915438838</pqid></control><display><type>article</type><title>Ultrasound radiomics-based nomogram to predict lymphovascular invasion in invasive breast cancer: a multicenter, retrospective study</title><source>MEDLINE</source><source>SpringerLink Journals - AutoHoldings</source><creator>Du, Yu ; Cai, Mengjun ; Zha, Hailing ; Chen, Baoding ; Gu, Jun ; Zhang, Manqi ; Liu, Wei ; Liu, Xinpei ; Liu, Xiaoan ; Zong, Min ; Li, Cuiying</creator><creatorcontrib>Du, Yu ; Cai, Mengjun ; Zha, Hailing ; Chen, Baoding ; Gu, Jun ; Zhang, Manqi ; Liu, Wei ; Liu, Xinpei ; Liu, Xiaoan ; Zong, Min ; Li, Cuiying</creatorcontrib><description>Objectives
To develop and validate an ultrasound (US) radiomics-based nomogram for the preoperative prediction of the lymphovascular invasion (LVI) status in patients with invasive breast cancer (IBC).
Materials and methods
In this multicentre, retrospective study, 456 consecutive women were enrolled from three institutions. Institutions 1 and 2 were used to train (
n
= 320) and test (
n
= 136), and 130 patients from institution 3 were used for external validation. Radiomics features that reflected tumour information were derived from grey-scale US images. The least absolute shrinkage and selection operator and the maximum relevance minimum redundancy (mRMR) algorithm were used for feature selection and radiomics signature (RS) building. US radiomics-based nomogram was constructed by using multivariable logistic regression analysis. Predictive performance was assessed with the receiving operating characteristic curve, discrimination, and calibration.
Results
The nomogram based on clinico-ultrasonic features (menopausal status, US-reported lymph node status, posterior echo features) and RS yielded an optimal AUC of 0.88 (95% confidence interval [CI], 0.84–0.91), 0.89 (95% CI, 0.84–0.94) and 0.95 (95% CI, 0.92–0.99) in the training, internal and external validation cohort. The nomogram outperformed the clinico-ultrasonic and RS model (
p
< 0.05). The nomogram performed favourable discrimination (C-index, 0.88; 95% CI: 0.84–0.91) and was confirmed in the validation (0.88 for internal, 0.95 for external) cohorts. The calibration and decision curve demonstrated the nomogram showed good calibration and was clinically useful.
Conclusions
The radiomics nomogram incorporated in the RS and US and the clinical findings exhibited favourable preoperative individualised prediction of LVI.
Clinical relevance statement
The US radiomics-based nomogram incorporating menopausal status, posterior echo features, US reported-ALN status, and radiomics signature has the potential to predict lymphovascular invasion in patients with invasive breast cancer.
Key Points
•
The clinico-ultrsonic model of menopausal status, posterior echo features, and US-reported ALN status achieved a better predictive efficacy for LVI than either of them alone
.
•
The radiomics nomogram showed optimal prediction in predicting LVI from patients with IBC (ROC, 0.88 and 0.89 in the training and validation sets)
.
•
A nomogram demonstrated favourable performance (area under the receiver operating characteristic curve, 0.95) and well calibration (C-index, 0.95) in an independent validation cohort (n = 130)
.</description><identifier>ISSN: 1432-1084</identifier><identifier>ISSN: 0938-7994</identifier><identifier>EISSN: 1432-1084</identifier><identifier>DOI: 10.1007/s00330-023-09995-1</identifier><identifier>PMID: 37518678</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Algorithms ; Auditory discrimination ; Breast ; Breast cancer ; Breast Neoplasms - diagnostic imaging ; Calibration ; Diagnostic Radiology ; Female ; Humans ; Imaging ; Internal Medicine ; Interventional Radiology ; Invasiveness ; Lymph nodes ; Medicine ; Medicine & Public Health ; Menopause ; Neuroradiology ; Nomograms ; Performance prediction ; Predictions ; Radiology ; Radiomics ; Redundancy ; Regression analysis ; Retrospective Studies ; Statistical analysis ; Training ; Ultrasonic imaging ; Ultrasonography ; Ultrasound</subject><ispartof>European radiology, 2024-01, Vol.34 (1), p.136-148</ispartof><rights>The Author(s), under exclusive licence to European Society of Radiology 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><rights>2023. The Author(s), under exclusive licence to European Society of Radiology.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-4e85fe8fcb6ee1d7b24ba4d7964315a12158e0b348fa31324567170fbfacdbe03</citedby><cites>FETCH-LOGICAL-c375t-4e85fe8fcb6ee1d7b24ba4d7964315a12158e0b348fa31324567170fbfacdbe03</cites><orcidid>0000-0002-8633-791X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00330-023-09995-1$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00330-023-09995-1$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27922,27923,41486,42555,51317</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37518678$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Du, Yu</creatorcontrib><creatorcontrib>Cai, Mengjun</creatorcontrib><creatorcontrib>Zha, Hailing</creatorcontrib><creatorcontrib>Chen, Baoding</creatorcontrib><creatorcontrib>Gu, Jun</creatorcontrib><creatorcontrib>Zhang, Manqi</creatorcontrib><creatorcontrib>Liu, Wei</creatorcontrib><creatorcontrib>Liu, Xinpei</creatorcontrib><creatorcontrib>Liu, Xiaoan</creatorcontrib><creatorcontrib>Zong, Min</creatorcontrib><creatorcontrib>Li, Cuiying</creatorcontrib><title>Ultrasound radiomics-based nomogram to predict lymphovascular invasion in invasive breast cancer: a multicenter, retrospective study</title><title>European radiology</title><addtitle>Eur Radiol</addtitle><addtitle>Eur Radiol</addtitle><description>Objectives
To develop and validate an ultrasound (US) radiomics-based nomogram for the preoperative prediction of the lymphovascular invasion (LVI) status in patients with invasive breast cancer (IBC).
Materials and methods
In this multicentre, retrospective study, 456 consecutive women were enrolled from three institutions. Institutions 1 and 2 were used to train (
n
= 320) and test (
n
= 136), and 130 patients from institution 3 were used for external validation. Radiomics features that reflected tumour information were derived from grey-scale US images. The least absolute shrinkage and selection operator and the maximum relevance minimum redundancy (mRMR) algorithm were used for feature selection and radiomics signature (RS) building. US radiomics-based nomogram was constructed by using multivariable logistic regression analysis. Predictive performance was assessed with the receiving operating characteristic curve, discrimination, and calibration.
Results
The nomogram based on clinico-ultrasonic features (menopausal status, US-reported lymph node status, posterior echo features) and RS yielded an optimal AUC of 0.88 (95% confidence interval [CI], 0.84–0.91), 0.89 (95% CI, 0.84–0.94) and 0.95 (95% CI, 0.92–0.99) in the training, internal and external validation cohort. The nomogram outperformed the clinico-ultrasonic and RS model (
p
< 0.05). The nomogram performed favourable discrimination (C-index, 0.88; 95% CI: 0.84–0.91) and was confirmed in the validation (0.88 for internal, 0.95 for external) cohorts. The calibration and decision curve demonstrated the nomogram showed good calibration and was clinically useful.
Conclusions
The radiomics nomogram incorporated in the RS and US and the clinical findings exhibited favourable preoperative individualised prediction of LVI.
Clinical relevance statement
The US radiomics-based nomogram incorporating menopausal status, posterior echo features, US reported-ALN status, and radiomics signature has the potential to predict lymphovascular invasion in patients with invasive breast cancer.
Key Points
•
The clinico-ultrsonic model of menopausal status, posterior echo features, and US-reported ALN status achieved a better predictive efficacy for LVI than either of them alone
.
•
The radiomics nomogram showed optimal prediction in predicting LVI from patients with IBC (ROC, 0.88 and 0.89 in the training and validation sets)
.
•
A nomogram demonstrated favourable performance (area under the receiver operating characteristic curve, 0.95) and well calibration (C-index, 0.95) in an independent validation cohort (n = 130)
.</description><subject>Algorithms</subject><subject>Auditory discrimination</subject><subject>Breast</subject><subject>Breast cancer</subject><subject>Breast Neoplasms - diagnostic imaging</subject><subject>Calibration</subject><subject>Diagnostic Radiology</subject><subject>Female</subject><subject>Humans</subject><subject>Imaging</subject><subject>Internal Medicine</subject><subject>Interventional Radiology</subject><subject>Invasiveness</subject><subject>Lymph nodes</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Menopause</subject><subject>Neuroradiology</subject><subject>Nomograms</subject><subject>Performance prediction</subject><subject>Predictions</subject><subject>Radiology</subject><subject>Radiomics</subject><subject>Redundancy</subject><subject>Regression analysis</subject><subject>Retrospective Studies</subject><subject>Statistical analysis</subject><subject>Training</subject><subject>Ultrasonic imaging</subject><subject>Ultrasonography</subject><subject>Ultrasound</subject><issn>1432-1084</issn><issn>0938-7994</issn><issn>1432-1084</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</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>eNp9kU1rFTEUhoMotlb_gAsJuHHR0ZOPucm4k2JVKLix65CPM3XKTDImmcLd-8NNvdcPXLjKC3nOmxMeQp4zeM0A1JsCIAR0wEUHwzD0HXtATpkUvGOg5cO_8gl5UsotAAxMqsfkRKie6Z3Sp-T79VyzLWmLgWYbprRMvnTOFgw0piXdZLvQmuiaMUy-0nm_rF_TnS1-m22mU2xxSrGFY75D6jLaUqm30WN-Sy1dtrlOHmPFfE4z1pzKir7es6VuYf-UPBrtXPDZ8Twj15fvv1x87K4-f_h08e6q823h2knU_Yh69G6HyIJyXDorgxp2UrDeMs56jeCE1KMVTHDZ7xRTMLrR-uAQxBl5dehdc_q2YalmmYrHebYR01YM11KCHjhXDX35D3qbthzbdoYPrJdCa6EbxQ-Ub18qGUez5mmxeW8YmHtH5uDINEfmpyPD2tCLY_XmFgy_R35JaYA4AKVdxRvMf97-T-0PciCflg</recordid><startdate>20240101</startdate><enddate>20240101</enddate><creator>Du, Yu</creator><creator>Cai, Mengjun</creator><creator>Zha, Hailing</creator><creator>Chen, Baoding</creator><creator>Gu, Jun</creator><creator>Zhang, Manqi</creator><creator>Liu, Wei</creator><creator>Liu, Xinpei</creator><creator>Liu, Xiaoan</creator><creator>Zong, Min</creator><creator>Li, Cuiying</creator><general>Springer Berlin Heidelberg</general><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>AAYXX</scope><scope>CITATION</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/0000-0002-8633-791X</orcidid></search><sort><creationdate>20240101</creationdate><title>Ultrasound radiomics-based nomogram to predict lymphovascular invasion in invasive breast cancer: a multicenter, retrospective study</title><author>Du, Yu ; Cai, Mengjun ; Zha, Hailing ; Chen, Baoding ; Gu, Jun ; Zhang, Manqi ; Liu, Wei ; Liu, Xinpei ; Liu, Xiaoan ; Zong, Min ; Li, Cuiying</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-4e85fe8fcb6ee1d7b24ba4d7964315a12158e0b348fa31324567170fbfacdbe03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Algorithms</topic><topic>Auditory discrimination</topic><topic>Breast</topic><topic>Breast cancer</topic><topic>Breast Neoplasms - diagnostic imaging</topic><topic>Calibration</topic><topic>Diagnostic Radiology</topic><topic>Female</topic><topic>Humans</topic><topic>Imaging</topic><topic>Internal Medicine</topic><topic>Interventional Radiology</topic><topic>Invasiveness</topic><topic>Lymph nodes</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Menopause</topic><topic>Neuroradiology</topic><topic>Nomograms</topic><topic>Performance prediction</topic><topic>Predictions</topic><topic>Radiology</topic><topic>Radiomics</topic><topic>Redundancy</topic><topic>Regression analysis</topic><topic>Retrospective Studies</topic><topic>Statistical analysis</topic><topic>Training</topic><topic>Ultrasonic imaging</topic><topic>Ultrasonography</topic><topic>Ultrasound</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Du, Yu</creatorcontrib><creatorcontrib>Cai, Mengjun</creatorcontrib><creatorcontrib>Zha, Hailing</creatorcontrib><creatorcontrib>Chen, Baoding</creatorcontrib><creatorcontrib>Gu, Jun</creatorcontrib><creatorcontrib>Zhang, Manqi</creatorcontrib><creatorcontrib>Liu, Wei</creatorcontrib><creatorcontrib>Liu, Xinpei</creatorcontrib><creatorcontrib>Liu, Xiaoan</creatorcontrib><creatorcontrib>Zong, Min</creatorcontrib><creatorcontrib>Li, Cuiying</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</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 (ProQuest)</collection><collection>Natural Science Collection (ProQuest)</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>Du, Yu</au><au>Cai, Mengjun</au><au>Zha, Hailing</au><au>Chen, Baoding</au><au>Gu, Jun</au><au>Zhang, Manqi</au><au>Liu, Wei</au><au>Liu, Xinpei</au><au>Liu, Xiaoan</au><au>Zong, Min</au><au>Li, Cuiying</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Ultrasound radiomics-based nomogram to predict lymphovascular invasion in invasive breast cancer: a multicenter, retrospective study</atitle><jtitle>European radiology</jtitle><stitle>Eur Radiol</stitle><addtitle>Eur Radiol</addtitle><date>2024-01-01</date><risdate>2024</risdate><volume>34</volume><issue>1</issue><spage>136</spage><epage>148</epage><pages>136-148</pages><issn>1432-1084</issn><issn>0938-7994</issn><eissn>1432-1084</eissn><abstract>Objectives
To develop and validate an ultrasound (US) radiomics-based nomogram for the preoperative prediction of the lymphovascular invasion (LVI) status in patients with invasive breast cancer (IBC).
Materials and methods
In this multicentre, retrospective study, 456 consecutive women were enrolled from three institutions. Institutions 1 and 2 were used to train (
n
= 320) and test (
n
= 136), and 130 patients from institution 3 were used for external validation. Radiomics features that reflected tumour information were derived from grey-scale US images. The least absolute shrinkage and selection operator and the maximum relevance minimum redundancy (mRMR) algorithm were used for feature selection and radiomics signature (RS) building. US radiomics-based nomogram was constructed by using multivariable logistic regression analysis. Predictive performance was assessed with the receiving operating characteristic curve, discrimination, and calibration.
Results
The nomogram based on clinico-ultrasonic features (menopausal status, US-reported lymph node status, posterior echo features) and RS yielded an optimal AUC of 0.88 (95% confidence interval [CI], 0.84–0.91), 0.89 (95% CI, 0.84–0.94) and 0.95 (95% CI, 0.92–0.99) in the training, internal and external validation cohort. The nomogram outperformed the clinico-ultrasonic and RS model (
p
< 0.05). The nomogram performed favourable discrimination (C-index, 0.88; 95% CI: 0.84–0.91) and was confirmed in the validation (0.88 for internal, 0.95 for external) cohorts. The calibration and decision curve demonstrated the nomogram showed good calibration and was clinically useful.
Conclusions
The radiomics nomogram incorporated in the RS and US and the clinical findings exhibited favourable preoperative individualised prediction of LVI.
Clinical relevance statement
The US radiomics-based nomogram incorporating menopausal status, posterior echo features, US reported-ALN status, and radiomics signature has the potential to predict lymphovascular invasion in patients with invasive breast cancer.
Key Points
•
The clinico-ultrsonic model of menopausal status, posterior echo features, and US-reported ALN status achieved a better predictive efficacy for LVI than either of them alone
.
•
The radiomics nomogram showed optimal prediction in predicting LVI from patients with IBC (ROC, 0.88 and 0.89 in the training and validation sets)
.
•
A nomogram demonstrated favourable performance (area under the receiver operating characteristic curve, 0.95) and well calibration (C-index, 0.95) in an independent validation cohort (n = 130)
.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>37518678</pmid><doi>10.1007/s00330-023-09995-1</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0002-8633-791X</orcidid></addata></record> |
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| source | MEDLINE; SpringerLink Journals - AutoHoldings |
| subjects | Algorithms Auditory discrimination Breast Breast cancer Breast Neoplasms - diagnostic imaging Calibration Diagnostic Radiology Female Humans Imaging Internal Medicine Interventional Radiology Invasiveness Lymph nodes Medicine Medicine & Public Health Menopause Neuroradiology Nomograms Performance prediction Predictions Radiology Radiomics Redundancy Regression analysis Retrospective Studies Statistical analysis Training Ultrasonic imaging Ultrasonography Ultrasound |
| title | Ultrasound radiomics-based nomogram to predict lymphovascular invasion in invasive breast cancer: a multicenter, retrospective study |
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