Use of imaging prediction model for omission of axillary surgery in early-stage breast cancer patients
Purpose To develop a prediction model incorporating clinicopathological information, US, and MRI to diagnose axillary lymph node (LN) metastasis with acceptable false negative rate (FNR) in patients with early stage, clinically node-negative breast cancers. Methods In this single center retrospectiv...
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| Veröffentlicht in: | Breast cancer research and treatment 2023-06, Vol.199 (3), p.489-499 |
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| creator | Kim, Soo-Yeon Choi, Yunhee Kim, Yeon Soo Ha, Su Min Lee, Su Hyun Han, Wonshik Kim, Hong‑Kyu Cho, Nariya Moon, Woo Kyung Chang, Jung Min |
| description | Purpose
To develop a prediction model incorporating clinicopathological information, US, and MRI to diagnose axillary lymph node (LN) metastasis with acceptable false negative rate (FNR) in patients with early stage, clinically node-negative breast cancers.
Methods
In this single center retrospective study, the inclusion criteria comprised women with clinical T1 or T2 and N0 breast cancers who underwent preoperative US and MRI between January 2017 and July 2018. Patients were temporally divided into the development and validation cohorts. Clinicopathological information, US, and MRI findings were collected. Two prediction models (US model and combined US and MRI model) were created using logistic regression analysis from the development cohort. FNRs of the two models were compared using the McNemar test.
Results
A total of 964 women comprised the development (603 women, 54 ± 11 years) and validation (361 women, 53 ± 10 years) cohorts with 107 (18%) and 77 (21%) axillary LN metastases in each cohort, respectively. The US model consisted of tumor size and morphology of LN on US. The combined US and MRI model consisted of asymmetry of LN number, long diameter of LN, tumor type, and multiplicity of breast cancers on MRI, in addition to tumor size and morphology of LN on US. The combined model showed significantly lower FNR than the US model in both development (5% vs. 32%,
P
|
| doi_str_mv | 10.1007/s10549-023-06952-w |
| format | Article |
| fullrecord | <record><control><sourceid>gale_proqu</sourceid><recordid>TN_cdi_proquest_miscellaneous_2806072557</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A748966518</galeid><sourcerecordid>A748966518</sourcerecordid><originalsourceid>FETCH-LOGICAL-c517t-8d4d5592b8a078e05f62e0ad040bdc464d06f8c80d9b7d4cc1f7c1ee406d22f3</originalsourceid><addsrcrecordid>eNp9klFrFDEQx4Mo9jz9Aj5IQBBftk6ym2T3sRSrQsGX-hyyyWSbsrs5k13qfXtzvWqtiIQwMPn9h8nMn5DXDE4ZgPqQGYimq4DXFchO8Or2CdkwoepKcaaekg0wqSrZgjwhL3K-AYBOQfecnNQlqFqJDfHfMtLoaZjMEOaB7hK6YJcQZzpFhyP1MdE4hZwPqQKaH2EcTdrTvKYBSwwzRZPGfZUXMyDtE5q8UGtmi4nuzBJwXvJL8sybMeOr-7glVxcfr84_V5dfP305P7usrGBqqVrXOCE63rcGVIsgvOQIxkEDvbONbBxI39oWXNcr11jLvLIMsQHpOPf1lrw_lt2l-H3FvOjSucXS8IxxzZqXWYDiosxoS97-hd7ENc2luUIxXq5o4IEazIg6zD4uydhDUX2mmraTUrC2UKf_oMpxOAUbZ_Sh5B8J3v0huEYzLtc5juth7vkxyI-gTTHnhF7vUllV2msG-mACfTSBLibQdybQt0X05v5raz-h-y35tfUC1Ecgl6e5bPHh7_8p-xOecrv3</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2812281540</pqid></control><display><type>article</type><title>Use of imaging prediction model for omission of axillary surgery in early-stage breast cancer patients</title><source>MEDLINE</source><source>SpringerLink Journals</source><creator>Kim, Soo-Yeon ; Choi, Yunhee ; Kim, Yeon Soo ; Ha, Su Min ; Lee, Su Hyun ; Han, Wonshik ; Kim, Hong‑Kyu ; Cho, Nariya ; Moon, Woo Kyung ; Chang, Jung Min</creator><creatorcontrib>Kim, Soo-Yeon ; Choi, Yunhee ; Kim, Yeon Soo ; Ha, Su Min ; Lee, Su Hyun ; Han, Wonshik ; Kim, Hong‑Kyu ; Cho, Nariya ; Moon, Woo Kyung ; Chang, Jung Min</creatorcontrib><description>Purpose
To develop a prediction model incorporating clinicopathological information, US, and MRI to diagnose axillary lymph node (LN) metastasis with acceptable false negative rate (FNR) in patients with early stage, clinically node-negative breast cancers.
Methods
In this single center retrospective study, the inclusion criteria comprised women with clinical T1 or T2 and N0 breast cancers who underwent preoperative US and MRI between January 2017 and July 2018. Patients were temporally divided into the development and validation cohorts. Clinicopathological information, US, and MRI findings were collected. Two prediction models (US model and combined US and MRI model) were created using logistic regression analysis from the development cohort. FNRs of the two models were compared using the McNemar test.
Results
A total of 964 women comprised the development (603 women, 54 ± 11 years) and validation (361 women, 53 ± 10 years) cohorts with 107 (18%) and 77 (21%) axillary LN metastases in each cohort, respectively. The US model consisted of tumor size and morphology of LN on US. The combined US and MRI model consisted of asymmetry of LN number, long diameter of LN, tumor type, and multiplicity of breast cancers on MRI, in addition to tumor size and morphology of LN on US. The combined model showed significantly lower FNR than the US model in both development (5% vs. 32%,
P
< .001) and validation (9% vs. 35%,
P
< .001) cohorts.
Conclusion
Our prediction model combining US and MRI characteristics of index cancer and LN lowered FNR compared to using US alone, and could potentially lead to avoid unnecessary SLNB in early stage, clinically node-negative breast cancers.</description><identifier>ISSN: 0167-6806</identifier><identifier>ISSN: 1573-7217</identifier><identifier>EISSN: 1573-7217</identifier><identifier>DOI: 10.1007/s10549-023-06952-w</identifier><identifier>PMID: 37097375</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Axilla - pathology ; Breast cancer ; Breast Neoplasms - diagnostic imaging ; Breast Neoplasms - surgery ; Cancer ; Cancer research ; Care and treatment ; Clinical Trial ; Female ; Humans ; Lymph nodes ; Lymph Nodes - diagnostic imaging ; Lymph Nodes - pathology ; Lymph Nodes - surgery ; Lymphatic Metastasis - pathology ; Magnetic resonance imaging ; Magnetic Resonance Imaging - methods ; Medical colleges ; Medicine ; Medicine & Public Health ; Metastases ; Metastasis ; Morphology ; Oncology ; Oncology, Experimental ; Patients ; Prediction models ; Retrospective Studies ; Sentinel Lymph Node Biopsy ; Surgery ; Tumors</subject><ispartof>Breast cancer research and treatment, 2023-06, Vol.199 (3), p.489-499</ispartof><rights>The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 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 Springer Science+Business Media, LLC, part of Springer Nature.</rights><rights>COPYRIGHT 2023 Springer</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c517t-8d4d5592b8a078e05f62e0ad040bdc464d06f8c80d9b7d4cc1f7c1ee406d22f3</citedby><cites>FETCH-LOGICAL-c517t-8d4d5592b8a078e05f62e0ad040bdc464d06f8c80d9b7d4cc1f7c1ee406d22f3</cites><orcidid>0000-0001-8931-3772 ; 0000-0002-0171-8060 ; 0000-0003-4425-972X ; 0000-0001-7310-0764 ; 0000-0001-8915-3924 ; 0000-0001-5726-9797 ; 0000-0001-5305-1803 ; 0000-0003-1838-202X ; 0000-0002-1833-0919 ; 0000-0003-4290-2777</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/s10549-023-06952-w$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10549-023-06952-w$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37097375$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Kim, Soo-Yeon</creatorcontrib><creatorcontrib>Choi, Yunhee</creatorcontrib><creatorcontrib>Kim, Yeon Soo</creatorcontrib><creatorcontrib>Ha, Su Min</creatorcontrib><creatorcontrib>Lee, Su Hyun</creatorcontrib><creatorcontrib>Han, Wonshik</creatorcontrib><creatorcontrib>Kim, Hong‑Kyu</creatorcontrib><creatorcontrib>Cho, Nariya</creatorcontrib><creatorcontrib>Moon, Woo Kyung</creatorcontrib><creatorcontrib>Chang, Jung Min</creatorcontrib><title>Use of imaging prediction model for omission of axillary surgery in early-stage breast cancer patients</title><title>Breast cancer research and treatment</title><addtitle>Breast Cancer Res Treat</addtitle><addtitle>Breast Cancer Res Treat</addtitle><description>Purpose
To develop a prediction model incorporating clinicopathological information, US, and MRI to diagnose axillary lymph node (LN) metastasis with acceptable false negative rate (FNR) in patients with early stage, clinically node-negative breast cancers.
Methods
In this single center retrospective study, the inclusion criteria comprised women with clinical T1 or T2 and N0 breast cancers who underwent preoperative US and MRI between January 2017 and July 2018. Patients were temporally divided into the development and validation cohorts. Clinicopathological information, US, and MRI findings were collected. Two prediction models (US model and combined US and MRI model) were created using logistic regression analysis from the development cohort. FNRs of the two models were compared using the McNemar test.
Results
A total of 964 women comprised the development (603 women, 54 ± 11 years) and validation (361 women, 53 ± 10 years) cohorts with 107 (18%) and 77 (21%) axillary LN metastases in each cohort, respectively. The US model consisted of tumor size and morphology of LN on US. The combined US and MRI model consisted of asymmetry of LN number, long diameter of LN, tumor type, and multiplicity of breast cancers on MRI, in addition to tumor size and morphology of LN on US. The combined model showed significantly lower FNR than the US model in both development (5% vs. 32%,
P
< .001) and validation (9% vs. 35%,
P
< .001) cohorts.
Conclusion
Our prediction model combining US and MRI characteristics of index cancer and LN lowered FNR compared to using US alone, and could potentially lead to avoid unnecessary SLNB in early stage, clinically node-negative breast cancers.</description><subject>Axilla - pathology</subject><subject>Breast cancer</subject><subject>Breast Neoplasms - diagnostic imaging</subject><subject>Breast Neoplasms - surgery</subject><subject>Cancer</subject><subject>Cancer research</subject><subject>Care and treatment</subject><subject>Clinical Trial</subject><subject>Female</subject><subject>Humans</subject><subject>Lymph nodes</subject><subject>Lymph Nodes - diagnostic imaging</subject><subject>Lymph Nodes - pathology</subject><subject>Lymph Nodes - surgery</subject><subject>Lymphatic Metastasis - pathology</subject><subject>Magnetic resonance imaging</subject><subject>Magnetic Resonance Imaging - methods</subject><subject>Medical colleges</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Metastases</subject><subject>Metastasis</subject><subject>Morphology</subject><subject>Oncology</subject><subject>Oncology, Experimental</subject><subject>Patients</subject><subject>Prediction models</subject><subject>Retrospective Studies</subject><subject>Sentinel Lymph Node Biopsy</subject><subject>Surgery</subject><subject>Tumors</subject><issn>0167-6806</issn><issn>1573-7217</issn><issn>1573-7217</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>BENPR</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp9klFrFDEQx4Mo9jz9Aj5IQBBftk6ym2T3sRSrQsGX-hyyyWSbsrs5k13qfXtzvWqtiIQwMPn9h8nMn5DXDE4ZgPqQGYimq4DXFchO8Or2CdkwoepKcaaekg0wqSrZgjwhL3K-AYBOQfecnNQlqFqJDfHfMtLoaZjMEOaB7hK6YJcQZzpFhyP1MdE4hZwPqQKaH2EcTdrTvKYBSwwzRZPGfZUXMyDtE5q8UGtmi4nuzBJwXvJL8sybMeOr-7glVxcfr84_V5dfP305P7usrGBqqVrXOCE63rcGVIsgvOQIxkEDvbONbBxI39oWXNcr11jLvLIMsQHpOPf1lrw_lt2l-H3FvOjSucXS8IxxzZqXWYDiosxoS97-hd7ENc2luUIxXq5o4IEazIg6zD4uydhDUX2mmraTUrC2UKf_oMpxOAUbZ_Sh5B8J3v0huEYzLtc5juth7vkxyI-gTTHnhF7vUllV2msG-mACfTSBLibQdybQt0X05v5raz-h-y35tfUC1Ecgl6e5bPHh7_8p-xOecrv3</recordid><startdate>20230601</startdate><enddate>20230601</enddate><creator>Kim, Soo-Yeon</creator><creator>Choi, Yunhee</creator><creator>Kim, Yeon Soo</creator><creator>Ha, Su Min</creator><creator>Lee, Su Hyun</creator><creator>Han, Wonshik</creator><creator>Kim, Hong‑Kyu</creator><creator>Cho, Nariya</creator><creator>Moon, Woo Kyung</creator><creator>Chang, Jung Min</creator><general>Springer US</general><general>Springer</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>7TO</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>H94</scope><scope>K9-</scope><scope>K9.</scope><scope>M0R</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>MBDVC</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-8931-3772</orcidid><orcidid>https://orcid.org/0000-0002-0171-8060</orcidid><orcidid>https://orcid.org/0000-0003-4425-972X</orcidid><orcidid>https://orcid.org/0000-0001-7310-0764</orcidid><orcidid>https://orcid.org/0000-0001-8915-3924</orcidid><orcidid>https://orcid.org/0000-0001-5726-9797</orcidid><orcidid>https://orcid.org/0000-0001-5305-1803</orcidid><orcidid>https://orcid.org/0000-0003-1838-202X</orcidid><orcidid>https://orcid.org/0000-0002-1833-0919</orcidid><orcidid>https://orcid.org/0000-0003-4290-2777</orcidid></search><sort><creationdate>20230601</creationdate><title>Use of imaging prediction model for omission of axillary surgery in early-stage breast cancer patients</title><author>Kim, Soo-Yeon ; Choi, Yunhee ; Kim, Yeon Soo ; Ha, Su Min ; Lee, Su Hyun ; Han, Wonshik ; Kim, Hong‑Kyu ; Cho, Nariya ; Moon, Woo Kyung ; Chang, Jung Min</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c517t-8d4d5592b8a078e05f62e0ad040bdc464d06f8c80d9b7d4cc1f7c1ee406d22f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Axilla - pathology</topic><topic>Breast cancer</topic><topic>Breast Neoplasms - diagnostic imaging</topic><topic>Breast Neoplasms - surgery</topic><topic>Cancer</topic><topic>Cancer research</topic><topic>Care and treatment</topic><topic>Clinical Trial</topic><topic>Female</topic><topic>Humans</topic><topic>Lymph nodes</topic><topic>Lymph Nodes - diagnostic imaging</topic><topic>Lymph Nodes - pathology</topic><topic>Lymph Nodes - surgery</topic><topic>Lymphatic Metastasis - pathology</topic><topic>Magnetic resonance imaging</topic><topic>Magnetic Resonance Imaging - methods</topic><topic>Medical colleges</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Metastases</topic><topic>Metastasis</topic><topic>Morphology</topic><topic>Oncology</topic><topic>Oncology, Experimental</topic><topic>Patients</topic><topic>Prediction models</topic><topic>Retrospective Studies</topic><topic>Sentinel Lymph Node Biopsy</topic><topic>Surgery</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kim, Soo-Yeon</creatorcontrib><creatorcontrib>Choi, Yunhee</creatorcontrib><creatorcontrib>Kim, Yeon Soo</creatorcontrib><creatorcontrib>Ha, Su Min</creatorcontrib><creatorcontrib>Lee, Su Hyun</creatorcontrib><creatorcontrib>Han, Wonshik</creatorcontrib><creatorcontrib>Kim, Hong‑Kyu</creatorcontrib><creatorcontrib>Cho, Nariya</creatorcontrib><creatorcontrib>Moon, Woo Kyung</creatorcontrib><creatorcontrib>Chang, Jung Min</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>Oncogenes and Growth Factors Abstracts</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>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Consumer Health Database (Alumni Edition)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Consumer Health Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Research Library (Corporate)</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 Basic</collection><collection>MEDLINE - Academic</collection><jtitle>Breast cancer research and treatment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kim, Soo-Yeon</au><au>Choi, Yunhee</au><au>Kim, Yeon Soo</au><au>Ha, Su Min</au><au>Lee, Su Hyun</au><au>Han, Wonshik</au><au>Kim, Hong‑Kyu</au><au>Cho, Nariya</au><au>Moon, Woo Kyung</au><au>Chang, Jung Min</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Use of imaging prediction model for omission of axillary surgery in early-stage breast cancer patients</atitle><jtitle>Breast cancer research and treatment</jtitle><stitle>Breast Cancer Res Treat</stitle><addtitle>Breast Cancer Res Treat</addtitle><date>2023-06-01</date><risdate>2023</risdate><volume>199</volume><issue>3</issue><spage>489</spage><epage>499</epage><pages>489-499</pages><issn>0167-6806</issn><issn>1573-7217</issn><eissn>1573-7217</eissn><abstract>Purpose
To develop a prediction model incorporating clinicopathological information, US, and MRI to diagnose axillary lymph node (LN) metastasis with acceptable false negative rate (FNR) in patients with early stage, clinically node-negative breast cancers.
Methods
In this single center retrospective study, the inclusion criteria comprised women with clinical T1 or T2 and N0 breast cancers who underwent preoperative US and MRI between January 2017 and July 2018. Patients were temporally divided into the development and validation cohorts. Clinicopathological information, US, and MRI findings were collected. Two prediction models (US model and combined US and MRI model) were created using logistic regression analysis from the development cohort. FNRs of the two models were compared using the McNemar test.
Results
A total of 964 women comprised the development (603 women, 54 ± 11 years) and validation (361 women, 53 ± 10 years) cohorts with 107 (18%) and 77 (21%) axillary LN metastases in each cohort, respectively. The US model consisted of tumor size and morphology of LN on US. The combined US and MRI model consisted of asymmetry of LN number, long diameter of LN, tumor type, and multiplicity of breast cancers on MRI, in addition to tumor size and morphology of LN on US. The combined model showed significantly lower FNR than the US model in both development (5% vs. 32%,
P
< .001) and validation (9% vs. 35%,
P
< .001) cohorts.
Conclusion
Our prediction model combining US and MRI characteristics of index cancer and LN lowered FNR compared to using US alone, and could potentially lead to avoid unnecessary SLNB in early stage, clinically node-negative breast cancers.</abstract><cop>New York</cop><pub>Springer US</pub><pmid>37097375</pmid><doi>10.1007/s10549-023-06952-w</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0001-8931-3772</orcidid><orcidid>https://orcid.org/0000-0002-0171-8060</orcidid><orcidid>https://orcid.org/0000-0003-4425-972X</orcidid><orcidid>https://orcid.org/0000-0001-7310-0764</orcidid><orcidid>https://orcid.org/0000-0001-8915-3924</orcidid><orcidid>https://orcid.org/0000-0001-5726-9797</orcidid><orcidid>https://orcid.org/0000-0001-5305-1803</orcidid><orcidid>https://orcid.org/0000-0003-1838-202X</orcidid><orcidid>https://orcid.org/0000-0002-1833-0919</orcidid><orcidid>https://orcid.org/0000-0003-4290-2777</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Axilla - pathology Breast cancer Breast Neoplasms - diagnostic imaging Breast Neoplasms - surgery Cancer Cancer research Care and treatment Clinical Trial Female Humans Lymph nodes Lymph Nodes - diagnostic imaging Lymph Nodes - pathology Lymph Nodes - surgery Lymphatic Metastasis - pathology Magnetic resonance imaging Magnetic Resonance Imaging - methods Medical colleges Medicine Medicine & Public Health Metastases Metastasis Morphology Oncology Oncology, Experimental Patients Prediction models Retrospective Studies Sentinel Lymph Node Biopsy Surgery Tumors |
| title | Use of imaging prediction model for omission of axillary surgery in early-stage breast cancer patients |
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