Ultrasound classification of non-mass breast lesions following BI-RADS presents high positive predictive value
To investigate the positive predictive value of ultrasound classification of non-mass breast lesions (NMLs) following breast imaging reporting and data system (BI-RADS), and enhance understanding of NMLs. Fifty-nine women with 59 ultrasound-detected breast NMLs were finally enrolled. The ultrasound...
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| description | To investigate the positive predictive value of ultrasound classification of non-mass breast lesions (NMLs) following breast imaging reporting and data system (BI-RADS), and enhance understanding of NMLs.
Fifty-nine women with 59 ultrasound-detected breast NMLs were finally enrolled. The ultrasound (US) features of breast NMLs were analyzed; the incidence of malignant NMLs was calculated; the malignancy risk stratification of US for breast NMLs was established using BI-RADS.
The incidence of malignant NMLs was 4.59% of all breast carcinoma. Non-ductal hypoechoic area, microcalcifications and posterior shadowing are the main US features of malignant NMLs, and there were significant differences between malignant and benign NMLs for microcalcifications and posterior shadowing. Taking BI-RADS 4B as a cutoff value, the sensitivity, specificity, area under the receiver operating characteristic curve (AUC), positive and negative predictive values, and odds ratio of the BI-RADS category were 82.98%,41.67%,0.62,84.78%,38.46% and 3.48, respectively.
Stratifying the malignancy risk of breast NMLs using the BI-RADS the sensitivity and positive and predictive value are promising, but the likelihood of malignancy of malignant NMLs is underestimated, and that of benign NMLs is overestimated. The solution may be that to separate NMLs from breast masses and use different malignancy risk stratification protocols. |
| doi_str_mv | 10.1371/journal.pone.0278299 |
| format | Article |
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Fifty-nine women with 59 ultrasound-detected breast NMLs were finally enrolled. The ultrasound (US) features of breast NMLs were analyzed; the incidence of malignant NMLs was calculated; the malignancy risk stratification of US for breast NMLs was established using BI-RADS.
The incidence of malignant NMLs was 4.59% of all breast carcinoma. Non-ductal hypoechoic area, microcalcifications and posterior shadowing are the main US features of malignant NMLs, and there were significant differences between malignant and benign NMLs for microcalcifications and posterior shadowing. Taking BI-RADS 4B as a cutoff value, the sensitivity, specificity, area under the receiver operating characteristic curve (AUC), positive and negative predictive values, and odds ratio of the BI-RADS category were 82.98%,41.67%,0.62,84.78%,38.46% and 3.48, respectively.
Stratifying the malignancy risk of breast NMLs using the BI-RADS the sensitivity and positive and predictive value are promising, but the likelihood of malignancy of malignant NMLs is underestimated, and that of benign NMLs is overestimated. The solution may be that to separate NMLs from breast masses and use different malignancy risk stratification protocols.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0278299</identifier><identifier>PMID: 36449518</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Biology and Life Sciences ; Biopsy ; Breast ; Breast - diagnostic imaging ; Breast cancer ; Breast carcinoma ; Calcinosis - diagnostic imaging ; Care and treatment ; Classification ; Clinical medicine ; Diagnosis ; Female ; Humans ; Informatics ; Lesions ; Lymphoma, Follicular ; Malignancy ; Mammography ; Medicine and Health Sciences ; Methods ; Normal distribution ; Patients ; Physicians ; Predictive Value of Tests ; Research and Analysis Methods ; Risk ; Sensitivity ; Ultrasonic imaging ; Ultrasonography ; Ultrasonography, Mammary ; Ultrasound ; Ultrasound imaging ; Variables</subject><ispartof>PloS one, 2022-11, Vol.17 (11), p.e0278299-e0278299</ispartof><rights>Copyright: © 2022 Lin, Wu. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>COPYRIGHT 2022 Public Library of Science</rights><rights>2022 Lin, Wu. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2022 Lin, Wu 2022 Lin, Wu</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-d317738f34c2384d9502ff931aa08ea1e74bd7f149d513751a9763093609f72a3</citedby><cites>FETCH-LOGICAL-c692t-d317738f34c2384d9502ff931aa08ea1e74bd7f149d513751a9763093609f72a3</cites><orcidid>0000-0002-1086-764X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9710769/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9710769/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36449518$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Garzali, Ibrahim Umar</contributor><creatorcontrib>Lin, Mingnan</creatorcontrib><creatorcontrib>Wu, Size</creatorcontrib><title>Ultrasound classification of non-mass breast lesions following BI-RADS presents high positive predictive value</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>To investigate the positive predictive value of ultrasound classification of non-mass breast lesions (NMLs) following breast imaging reporting and data system (BI-RADS), and enhance understanding of NMLs.
Fifty-nine women with 59 ultrasound-detected breast NMLs were finally enrolled. The ultrasound (US) features of breast NMLs were analyzed; the incidence of malignant NMLs was calculated; the malignancy risk stratification of US for breast NMLs was established using BI-RADS.
The incidence of malignant NMLs was 4.59% of all breast carcinoma. Non-ductal hypoechoic area, microcalcifications and posterior shadowing are the main US features of malignant NMLs, and there were significant differences between malignant and benign NMLs for microcalcifications and posterior shadowing. Taking BI-RADS 4B as a cutoff value, the sensitivity, specificity, area under the receiver operating characteristic curve (AUC), positive and negative predictive values, and odds ratio of the BI-RADS category were 82.98%,41.67%,0.62,84.78%,38.46% and 3.48, respectively.
Stratifying the malignancy risk of breast NMLs using the BI-RADS the sensitivity and positive and predictive value are promising, but the likelihood of malignancy of malignant NMLs is underestimated, and that of benign NMLs is overestimated. The solution may be that to separate NMLs from breast masses and use different malignancy risk stratification protocols.</description><subject>Biology and Life Sciences</subject><subject>Biopsy</subject><subject>Breast</subject><subject>Breast - diagnostic imaging</subject><subject>Breast cancer</subject><subject>Breast carcinoma</subject><subject>Calcinosis - diagnostic imaging</subject><subject>Care and treatment</subject><subject>Classification</subject><subject>Clinical medicine</subject><subject>Diagnosis</subject><subject>Female</subject><subject>Humans</subject><subject>Informatics</subject><subject>Lesions</subject><subject>Lymphoma, Follicular</subject><subject>Malignancy</subject><subject>Mammography</subject><subject>Medicine and Health Sciences</subject><subject>Methods</subject><subject>Normal distribution</subject><subject>Patients</subject><subject>Physicians</subject><subject>Predictive Value of Tests</subject><subject>Research and Analysis Methods</subject><subject>Risk</subject><subject>Sensitivity</subject><subject>Ultrasonic imaging</subject><subject>Ultrasonography</subject><subject>Ultrasonography, Mammary</subject><subject>Ultrasound</subject><subject>Ultrasound imaging</subject><subject>Variables</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</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><sourceid>DOA</sourceid><recordid>eNqNk11v0zAUhiMEYmPwDxBEQkJwkWLHSRzfIJXxVWnSpI1xa536o_Xk2p2dFPj3OGs2NWgXyBe2jp_zHvu1T5a9xGiGCcUfrn0fHNjZ1js1QyVtS8YeZceYkbJoSkQeH6yPsmcxXiNUk7ZpnmZHpKkqVuP2OHNXtgsQfe9kLizEaLQR0Bnvcq9z512xScF8GRTELrcqpp2Ya2-t_2XcKv-0KC7mny_zbVBRuS7ma7Na51sfTWd2aghLI26XO7C9ep490WCjejHOJ9nV1y8_Tr8XZ-ffFqfzs0I0rOwKSTClpNWkEiVpK8lqVGrNCAZArQKsaLWUVOOKyTqZUWNgtCGIkQYxTUsgJ9nrve7W-shHqyIvaUVIi1iFE7HYE9LDNd8Gs4Hwh3sw_Dbgw4pD6IywimOqADSSCCiuhKhhSRUpdS2XRIIs26T1cazWLzdKimREADsRne44s-Yrv-OMYkQblgTejQLB3_QqdnxjolDWglO-35-7RnVTDeibf9CHbzdSK0gXME77VFcMonxO04kpbso6UbMHqDSk2hiR_pU2KT5JeD9JSEynfncr6GPki8uL_2fPf07ZtwfsWoHt1tHbfviHcQpWe1AEH2NQ-t5kjPjQFndu8KEt-NgWKe3V4QPdJ931AfkLQIoInA</recordid><startdate>20221130</startdate><enddate>20221130</enddate><creator>Lin, Mingnan</creator><creator>Wu, Size</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</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>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-1086-764X</orcidid></search><sort><creationdate>20221130</creationdate><title>Ultrasound classification of non-mass breast lesions following BI-RADS presents high positive predictive value</title><author>Lin, Mingnan ; Wu, Size</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-d317738f34c2384d9502ff931aa08ea1e74bd7f149d513751a9763093609f72a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Biology and Life Sciences</topic><topic>Biopsy</topic><topic>Breast</topic><topic>Breast - diagnostic imaging</topic><topic>Breast cancer</topic><topic>Breast carcinoma</topic><topic>Calcinosis - diagnostic imaging</topic><topic>Care and treatment</topic><topic>Classification</topic><topic>Clinical medicine</topic><topic>Diagnosis</topic><topic>Female</topic><topic>Humans</topic><topic>Informatics</topic><topic>Lesions</topic><topic>Lymphoma, Follicular</topic><topic>Malignancy</topic><topic>Mammography</topic><topic>Medicine and Health Sciences</topic><topic>Methods</topic><topic>Normal distribution</topic><topic>Patients</topic><topic>Physicians</topic><topic>Predictive Value of Tests</topic><topic>Research and Analysis Methods</topic><topic>Risk</topic><topic>Sensitivity</topic><topic>Ultrasonic imaging</topic><topic>Ultrasonography</topic><topic>Ultrasonography, Mammary</topic><topic>Ultrasound</topic><topic>Ultrasound imaging</topic><topic>Variables</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lin, Mingnan</creatorcontrib><creatorcontrib>Wu, Size</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</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>Public Health Database</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>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection (ProQuest)</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</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>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lin, Mingnan</au><au>Wu, Size</au><au>Garzali, Ibrahim Umar</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Ultrasound classification of non-mass breast lesions following BI-RADS presents high positive predictive value</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2022-11-30</date><risdate>2022</risdate><volume>17</volume><issue>11</issue><spage>e0278299</spage><epage>e0278299</epage><pages>e0278299-e0278299</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>To investigate the positive predictive value of ultrasound classification of non-mass breast lesions (NMLs) following breast imaging reporting and data system (BI-RADS), and enhance understanding of NMLs.
Fifty-nine women with 59 ultrasound-detected breast NMLs were finally enrolled. The ultrasound (US) features of breast NMLs were analyzed; the incidence of malignant NMLs was calculated; the malignancy risk stratification of US for breast NMLs was established using BI-RADS.
The incidence of malignant NMLs was 4.59% of all breast carcinoma. Non-ductal hypoechoic area, microcalcifications and posterior shadowing are the main US features of malignant NMLs, and there were significant differences between malignant and benign NMLs for microcalcifications and posterior shadowing. Taking BI-RADS 4B as a cutoff value, the sensitivity, specificity, area under the receiver operating characteristic curve (AUC), positive and negative predictive values, and odds ratio of the BI-RADS category were 82.98%,41.67%,0.62,84.78%,38.46% and 3.48, respectively.
Stratifying the malignancy risk of breast NMLs using the BI-RADS the sensitivity and positive and predictive value are promising, but the likelihood of malignancy of malignant NMLs is underestimated, and that of benign NMLs is overestimated. The solution may be that to separate NMLs from breast masses and use different malignancy risk stratification protocols.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>36449518</pmid><doi>10.1371/journal.pone.0278299</doi><tpages>e0278299</tpages><orcidid>https://orcid.org/0000-0002-1086-764X</orcidid><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; DOAJ Directory of Open Access Journals; Public Library of Science (PLoS) Journals Open Access; EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Biology and Life Sciences Biopsy Breast Breast - diagnostic imaging Breast cancer Breast carcinoma Calcinosis - diagnostic imaging Care and treatment Classification Clinical medicine Diagnosis Female Humans Informatics Lesions Lymphoma, Follicular Malignancy Mammography Medicine and Health Sciences Methods Normal distribution Patients Physicians Predictive Value of Tests Research and Analysis Methods Risk Sensitivity Ultrasonic imaging Ultrasonography Ultrasonography, Mammary Ultrasound Ultrasound imaging Variables |
| title | Ultrasound classification of non-mass breast lesions following BI-RADS presents high positive predictive value |
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