A Novel Predictive Nomogram including Serum Lipoprotein a Level for Nonsentinel Lymph Node Metastases in Chinese Breast Cancer Patients with Positive Sentinel Lymph Node Metastases
Background. We developed a new nomogram combining serum biomarkers with clinicopathological features to improve the accuracy of prediction of nonsentinel lymph node (SLN) metastases in Chinese breast cancer patients. Methods. We enrolled 209 patients with breast cancer who underwent SLN biopsy and a...
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| Veröffentlicht in: | Disease markers 2021, Vol.2021, p.7879508-10 |
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| creator | Fang, Zeng Wang, Ruizhi Yang, Ciqiu Wang, Dong Chen, Wanna Lin, Bo Gong, Dongsheng Li, Songqi Liang, Jiadong Liang, Xiaoli Zeng, Chunxian Li, Jie Wang, Kun Lv, Weiming |
| description | Background. We developed a new nomogram combining serum biomarkers with clinicopathological features to improve the accuracy of prediction of nonsentinel lymph node (SLN) metastases in Chinese breast cancer patients. Methods. We enrolled 209 patients with breast cancer who underwent SLN biopsy and axillary lymph node dissection. We evaluated the relationships between non-SLN metastases and clinicopathologic features, as well as preoperative routine tests of blood indexes, tumor markers, and serum lipids, including lipoprotein a (Lp(a)). Risk factors for non-SLN metastases were identified by logistic regression analysis. The nomogram was created using the R program to predict the risk of non-SLN metastases in the training set. Receiver operating characteristic (ROC) analysis was applied to assess the predictive value of the nomogram model in the validation set. Results. Lp(a) was significantly associated with non-SLN metastasis status. Compared with the MSKCC model, the predictive ability of our new nomogram that combined Lp(a) level and clinical variables (pathologic tumor size, lymphovascular invasion, multifocality, and positive/negative SLN numbers) was significantly greater (AUC: 0.732, 95% CI: 0.643–0.821) (C-index: 0.703, 95% CI: 0.656–0.791) in the training cohorts and also performed well in the validation cohorts (C-index: 0.773, 95% CI: 0.681–0.865). Moreover, the new nomogram with Lp(a) improved the accuracy (12.10%) of identification of patients with non-SLN metastases (NRI: 0.121; 95% CI: 0.081–0.202; P=0.011). Conclusions. This novel nomogram based on preoperative serum indexes combined with clinicopathologic features facilitates accurate prediction of risk of non-SLN metastases in Chinese patients with breast cancer. |
| doi_str_mv | 10.1155/2021/7879508 |
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| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8629655</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2606654107</sourcerecordid><originalsourceid>FETCH-LOGICAL-c448t-ec90df876572e30787a28736e3359d3b0c55b39ec600069b9c0a7343b39692053</originalsourceid><addsrcrecordid>eNp9kk2LFDEQhoMo7rh68ywBL4K2m046XxdhHdYPaHVg9Rwy6ZqZLN2dNumeZf_X_kDTzrioh4VAyFtPvVSlCqHnJXlblpyfUULLM6mk5kQ9QItSSV4owchDtCBUqoLQipygJyldEVJSXenH6IRVijNB2QLdnuOvYQ8tXkVovBv9HrLQhW20Hfa9a6fG91t8CXHqcO2HMMQwgu-xxTXMeZsQc0KfoB99n9_1TTfsstIA_gKjTflAyk54ucvxBPh9hKzipe0dRLyyo8-pCV_7cYdXIfnfJVzea_cUPdrYNsGz432Kfny4-L78VNTfPn5enteFqyo1FuA0aTZKCi4pMJL_yFIlmQDGuG7YmjjO10yDE4QQodfaEStZxbImNCWcnaJ3B99hWnfQuFxUtK0Zou9svDHBevNvpPc7sw17owTVgs8Gr44GMfycII2m88lB29oewpQMFYRzLVVZZfTlf-hVmGKf25spIXhVEpmpNwfKxZBShM1dMSUx8zqYeR3McR0y_uLvBu7gP_PPwOsDkIfT2Gt_v90vGAK_Ow</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2606654107</pqid></control><display><type>article</type><title>A Novel Predictive Nomogram including Serum Lipoprotein a Level for Nonsentinel Lymph Node Metastases in Chinese Breast Cancer Patients with Positive Sentinel Lymph Node Metastases</title><source>MEDLINE</source><source>Wiley-Blackwell Open Access Titles</source><source>EZB-FREE-00999 freely available EZB journals</source><source>PubMed Central</source><source>Alma/SFX Local Collection</source><creator>Fang, Zeng ; Wang, Ruizhi ; Yang, Ciqiu ; Wang, Dong ; Chen, Wanna ; Lin, Bo ; Gong, Dongsheng ; Li, Songqi ; Liang, Jiadong ; Liang, Xiaoli ; Zeng, Chunxian ; Li, Jie ; Wang, Kun ; Lv, Weiming</creator><contributor>Shao, Yi ; Yi Shao</contributor><creatorcontrib>Fang, Zeng ; Wang, Ruizhi ; Yang, Ciqiu ; Wang, Dong ; Chen, Wanna ; Lin, Bo ; Gong, Dongsheng ; Li, Songqi ; Liang, Jiadong ; Liang, Xiaoli ; Zeng, Chunxian ; Li, Jie ; Wang, Kun ; Lv, Weiming ; Shao, Yi ; Yi Shao</creatorcontrib><description>Background. We developed a new nomogram combining serum biomarkers with clinicopathological features to improve the accuracy of prediction of nonsentinel lymph node (SLN) metastases in Chinese breast cancer patients. Methods. We enrolled 209 patients with breast cancer who underwent SLN biopsy and axillary lymph node dissection. We evaluated the relationships between non-SLN metastases and clinicopathologic features, as well as preoperative routine tests of blood indexes, tumor markers, and serum lipids, including lipoprotein a (Lp(a)). Risk factors for non-SLN metastases were identified by logistic regression analysis. The nomogram was created using the R program to predict the risk of non-SLN metastases in the training set. Receiver operating characteristic (ROC) analysis was applied to assess the predictive value of the nomogram model in the validation set. Results. Lp(a) was significantly associated with non-SLN metastasis status. Compared with the MSKCC model, the predictive ability of our new nomogram that combined Lp(a) level and clinical variables (pathologic tumor size, lymphovascular invasion, multifocality, and positive/negative SLN numbers) was significantly greater (AUC: 0.732, 95% CI: 0.643–0.821) (C-index: 0.703, 95% CI: 0.656–0.791) in the training cohorts and also performed well in the validation cohorts (C-index: 0.773, 95% CI: 0.681–0.865). Moreover, the new nomogram with Lp(a) improved the accuracy (12.10%) of identification of patients with non-SLN metastases (NRI: 0.121; 95% CI: 0.081–0.202; P=0.011). Conclusions. This novel nomogram based on preoperative serum indexes combined with clinicopathologic features facilitates accurate prediction of risk of non-SLN metastases in Chinese patients with breast cancer.</description><identifier>ISSN: 0278-0240</identifier><identifier>EISSN: 1875-8630</identifier><identifier>DOI: 10.1155/2021/7879508</identifier><identifier>PMID: 34853623</identifier><language>eng</language><publisher>United States: Hindawi</publisher><subject>Accuracy ; Antigens ; Apolipoproteins ; Biomarkers ; Biomarkers, Tumor - blood ; Biopsy ; Blood ; Breast cancer ; Breast Neoplasms - blood ; Breast Neoplasms - pathology ; Breast Neoplasms - surgery ; China ; Cholesterol ; Estrogens ; Female ; Follow-Up Studies ; High density lipoprotein ; Humans ; Lipids ; Lipoprotein(a) - blood ; Lipoproteins ; Lymph Node Excision ; Lymph nodes ; Lymph Nodes - metabolism ; Lymph Nodes - pathology ; Lymph Nodes - surgery ; Lymphatic Metastasis - pathology ; Lymphatic system ; Lymphocytes ; Metastases ; Metastasis ; Middle Aged ; Multivariate analysis ; Neutrophils ; Nomograms ; Patients ; Prognosis ; Regression analysis ; Retrospective Studies ; Risk analysis ; Risk factors ; ROC Curve ; Sentinel Lymph Node - metabolism ; Sentinel Lymph Node - pathology ; Sentinel Lymph Node - surgery ; Serum lipids ; Surgery ; Training ; Tumor markers ; Tumors</subject><ispartof>Disease markers, 2021, Vol.2021, p.7879508-10</ispartof><rights>Copyright © 2021 Zeng Fang et al.</rights><rights>Copyright © 2021 Zeng Fang et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><rights>Copyright © 2021 Zeng Fang et al. 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c448t-ec90df876572e30787a28736e3359d3b0c55b39ec600069b9c0a7343b39692053</citedby><cites>FETCH-LOGICAL-c448t-ec90df876572e30787a28736e3359d3b0c55b39ec600069b9c0a7343b39692053</cites><orcidid>0000-0002-3077-8572 ; 0000-0002-7029-3916 ; 0000-0002-7488-1555 ; 0000-0002-9648-7866 ; 0000-0001-6566-0120 ; 0000-0002-1864-8601 ; 0000-0003-2970-1137 ; 0000-0003-1466-9875 ; 0000-0001-9796-3767 ; 0000-0002-9191-0063 ; 0000-0001-9014-7732 ; 0000-0003-3919-9304 ; 0000-0003-1232-3248 ; 0000-0001-9851-7080</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/PMC8629655/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8629655/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,4022,27922,27923,27924,53790,53792</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34853623$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Shao, Yi</contributor><contributor>Yi Shao</contributor><creatorcontrib>Fang, Zeng</creatorcontrib><creatorcontrib>Wang, Ruizhi</creatorcontrib><creatorcontrib>Yang, Ciqiu</creatorcontrib><creatorcontrib>Wang, Dong</creatorcontrib><creatorcontrib>Chen, Wanna</creatorcontrib><creatorcontrib>Lin, Bo</creatorcontrib><creatorcontrib>Gong, Dongsheng</creatorcontrib><creatorcontrib>Li, Songqi</creatorcontrib><creatorcontrib>Liang, Jiadong</creatorcontrib><creatorcontrib>Liang, Xiaoli</creatorcontrib><creatorcontrib>Zeng, Chunxian</creatorcontrib><creatorcontrib>Li, Jie</creatorcontrib><creatorcontrib>Wang, Kun</creatorcontrib><creatorcontrib>Lv, Weiming</creatorcontrib><title>A Novel Predictive Nomogram including Serum Lipoprotein a Level for Nonsentinel Lymph Node Metastases in Chinese Breast Cancer Patients with Positive Sentinel Lymph Node Metastases</title><title>Disease markers</title><addtitle>Dis Markers</addtitle><description>Background. We developed a new nomogram combining serum biomarkers with clinicopathological features to improve the accuracy of prediction of nonsentinel lymph node (SLN) metastases in Chinese breast cancer patients. Methods. We enrolled 209 patients with breast cancer who underwent SLN biopsy and axillary lymph node dissection. We evaluated the relationships between non-SLN metastases and clinicopathologic features, as well as preoperative routine tests of blood indexes, tumor markers, and serum lipids, including lipoprotein a (Lp(a)). Risk factors for non-SLN metastases were identified by logistic regression analysis. The nomogram was created using the R program to predict the risk of non-SLN metastases in the training set. Receiver operating characteristic (ROC) analysis was applied to assess the predictive value of the nomogram model in the validation set. Results. Lp(a) was significantly associated with non-SLN metastasis status. Compared with the MSKCC model, the predictive ability of our new nomogram that combined Lp(a) level and clinical variables (pathologic tumor size, lymphovascular invasion, multifocality, and positive/negative SLN numbers) was significantly greater (AUC: 0.732, 95% CI: 0.643–0.821) (C-index: 0.703, 95% CI: 0.656–0.791) in the training cohorts and also performed well in the validation cohorts (C-index: 0.773, 95% CI: 0.681–0.865). Moreover, the new nomogram with Lp(a) improved the accuracy (12.10%) of identification of patients with non-SLN metastases (NRI: 0.121; 95% CI: 0.081–0.202; P=0.011). Conclusions. This novel nomogram based on preoperative serum indexes combined with clinicopathologic features facilitates accurate prediction of risk of non-SLN metastases in Chinese patients with breast cancer.</description><subject>Accuracy</subject><subject>Antigens</subject><subject>Apolipoproteins</subject><subject>Biomarkers</subject><subject>Biomarkers, Tumor - blood</subject><subject>Biopsy</subject><subject>Blood</subject><subject>Breast cancer</subject><subject>Breast Neoplasms - blood</subject><subject>Breast Neoplasms - pathology</subject><subject>Breast Neoplasms - surgery</subject><subject>China</subject><subject>Cholesterol</subject><subject>Estrogens</subject><subject>Female</subject><subject>Follow-Up Studies</subject><subject>High density lipoprotein</subject><subject>Humans</subject><subject>Lipids</subject><subject>Lipoprotein(a) - blood</subject><subject>Lipoproteins</subject><subject>Lymph Node Excision</subject><subject>Lymph nodes</subject><subject>Lymph Nodes - metabolism</subject><subject>Lymph Nodes - pathology</subject><subject>Lymph Nodes - surgery</subject><subject>Lymphatic Metastasis - pathology</subject><subject>Lymphatic system</subject><subject>Lymphocytes</subject><subject>Metastases</subject><subject>Metastasis</subject><subject>Middle Aged</subject><subject>Multivariate analysis</subject><subject>Neutrophils</subject><subject>Nomograms</subject><subject>Patients</subject><subject>Prognosis</subject><subject>Regression analysis</subject><subject>Retrospective Studies</subject><subject>Risk analysis</subject><subject>Risk factors</subject><subject>ROC Curve</subject><subject>Sentinel Lymph Node - metabolism</subject><subject>Sentinel Lymph Node - pathology</subject><subject>Sentinel Lymph Node - surgery</subject><subject>Serum lipids</subject><subject>Surgery</subject><subject>Training</subject><subject>Tumor markers</subject><subject>Tumors</subject><issn>0278-0240</issn><issn>1875-8630</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>EIF</sourceid><recordid>eNp9kk2LFDEQhoMo7rh68ywBL4K2m046XxdhHdYPaHVg9Rwy6ZqZLN2dNumeZf_X_kDTzrioh4VAyFtPvVSlCqHnJXlblpyfUULLM6mk5kQ9QItSSV4owchDtCBUqoLQipygJyldEVJSXenH6IRVijNB2QLdnuOvYQ8tXkVovBv9HrLQhW20Hfa9a6fG91t8CXHqcO2HMMQwgu-xxTXMeZsQc0KfoB99n9_1TTfsstIA_gKjTflAyk54ucvxBPh9hKzipe0dRLyyo8-pCV_7cYdXIfnfJVzea_cUPdrYNsGz432Kfny4-L78VNTfPn5enteFqyo1FuA0aTZKCi4pMJL_yFIlmQDGuG7YmjjO10yDE4QQodfaEStZxbImNCWcnaJ3B99hWnfQuFxUtK0Zou9svDHBevNvpPc7sw17owTVgs8Gr44GMfycII2m88lB29oewpQMFYRzLVVZZfTlf-hVmGKf25spIXhVEpmpNwfKxZBShM1dMSUx8zqYeR3McR0y_uLvBu7gP_PPwOsDkIfT2Gt_v90vGAK_Ow</recordid><startdate>2021</startdate><enddate>2021</enddate><creator>Fang, Zeng</creator><creator>Wang, Ruizhi</creator><creator>Yang, Ciqiu</creator><creator>Wang, Dong</creator><creator>Chen, Wanna</creator><creator>Lin, Bo</creator><creator>Gong, Dongsheng</creator><creator>Li, Songqi</creator><creator>Liang, Jiadong</creator><creator>Liang, Xiaoli</creator><creator>Zeng, Chunxian</creator><creator>Li, Jie</creator><creator>Wang, Kun</creator><creator>Lv, Weiming</creator><general>Hindawi</general><general>Hindawi Limited</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><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>7QL</scope><scope>7QO</scope><scope>7TK</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-3077-8572</orcidid><orcidid>https://orcid.org/0000-0002-7029-3916</orcidid><orcidid>https://orcid.org/0000-0002-7488-1555</orcidid><orcidid>https://orcid.org/0000-0002-9648-7866</orcidid><orcidid>https://orcid.org/0000-0001-6566-0120</orcidid><orcidid>https://orcid.org/0000-0002-1864-8601</orcidid><orcidid>https://orcid.org/0000-0003-2970-1137</orcidid><orcidid>https://orcid.org/0000-0003-1466-9875</orcidid><orcidid>https://orcid.org/0000-0001-9796-3767</orcidid><orcidid>https://orcid.org/0000-0002-9191-0063</orcidid><orcidid>https://orcid.org/0000-0001-9014-7732</orcidid><orcidid>https://orcid.org/0000-0003-3919-9304</orcidid><orcidid>https://orcid.org/0000-0003-1232-3248</orcidid><orcidid>https://orcid.org/0000-0001-9851-7080</orcidid></search><sort><creationdate>2021</creationdate><title>A Novel Predictive Nomogram including Serum Lipoprotein a Level for Nonsentinel Lymph Node Metastases in Chinese Breast Cancer Patients with Positive Sentinel Lymph Node Metastases</title><author>Fang, Zeng ; Wang, Ruizhi ; Yang, Ciqiu ; Wang, Dong ; Chen, Wanna ; Lin, Bo ; Gong, Dongsheng ; Li, Songqi ; Liang, Jiadong ; Liang, Xiaoli ; Zeng, Chunxian ; Li, Jie ; Wang, Kun ; Lv, Weiming</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c448t-ec90df876572e30787a28736e3359d3b0c55b39ec600069b9c0a7343b39692053</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Accuracy</topic><topic>Antigens</topic><topic>Apolipoproteins</topic><topic>Biomarkers</topic><topic>Biomarkers, Tumor - blood</topic><topic>Biopsy</topic><topic>Blood</topic><topic>Breast cancer</topic><topic>Breast Neoplasms - blood</topic><topic>Breast Neoplasms - pathology</topic><topic>Breast Neoplasms - surgery</topic><topic>China</topic><topic>Cholesterol</topic><topic>Estrogens</topic><topic>Female</topic><topic>Follow-Up Studies</topic><topic>High density lipoprotein</topic><topic>Humans</topic><topic>Lipids</topic><topic>Lipoprotein(a) - blood</topic><topic>Lipoproteins</topic><topic>Lymph Node Excision</topic><topic>Lymph nodes</topic><topic>Lymph Nodes - metabolism</topic><topic>Lymph Nodes - pathology</topic><topic>Lymph Nodes - surgery</topic><topic>Lymphatic Metastasis - pathology</topic><topic>Lymphatic system</topic><topic>Lymphocytes</topic><topic>Metastases</topic><topic>Metastasis</topic><topic>Middle Aged</topic><topic>Multivariate analysis</topic><topic>Neutrophils</topic><topic>Nomograms</topic><topic>Patients</topic><topic>Prognosis</topic><topic>Regression analysis</topic><topic>Retrospective Studies</topic><topic>Risk analysis</topic><topic>Risk factors</topic><topic>ROC Curve</topic><topic>Sentinel Lymph Node - metabolism</topic><topic>Sentinel Lymph Node - pathology</topic><topic>Sentinel Lymph Node - surgery</topic><topic>Serum lipids</topic><topic>Surgery</topic><topic>Training</topic><topic>Tumor markers</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fang, Zeng</creatorcontrib><creatorcontrib>Wang, Ruizhi</creatorcontrib><creatorcontrib>Yang, Ciqiu</creatorcontrib><creatorcontrib>Wang, Dong</creatorcontrib><creatorcontrib>Chen, Wanna</creatorcontrib><creatorcontrib>Lin, Bo</creatorcontrib><creatorcontrib>Gong, Dongsheng</creatorcontrib><creatorcontrib>Li, Songqi</creatorcontrib><creatorcontrib>Liang, Jiadong</creatorcontrib><creatorcontrib>Liang, Xiaoli</creatorcontrib><creatorcontrib>Zeng, Chunxian</creatorcontrib><creatorcontrib>Li, Jie</creatorcontrib><creatorcontrib>Wang, Kun</creatorcontrib><creatorcontrib>Lv, Weiming</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access Journals</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Disease markers</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fang, Zeng</au><au>Wang, Ruizhi</au><au>Yang, Ciqiu</au><au>Wang, Dong</au><au>Chen, Wanna</au><au>Lin, Bo</au><au>Gong, Dongsheng</au><au>Li, Songqi</au><au>Liang, Jiadong</au><au>Liang, Xiaoli</au><au>Zeng, Chunxian</au><au>Li, Jie</au><au>Wang, Kun</au><au>Lv, Weiming</au><au>Shao, Yi</au><au>Yi Shao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Novel Predictive Nomogram including Serum Lipoprotein a Level for Nonsentinel Lymph Node Metastases in Chinese Breast Cancer Patients with Positive Sentinel Lymph Node Metastases</atitle><jtitle>Disease markers</jtitle><addtitle>Dis Markers</addtitle><date>2021</date><risdate>2021</risdate><volume>2021</volume><spage>7879508</spage><epage>10</epage><pages>7879508-10</pages><issn>0278-0240</issn><eissn>1875-8630</eissn><abstract>Background. We developed a new nomogram combining serum biomarkers with clinicopathological features to improve the accuracy of prediction of nonsentinel lymph node (SLN) metastases in Chinese breast cancer patients. Methods. We enrolled 209 patients with breast cancer who underwent SLN biopsy and axillary lymph node dissection. We evaluated the relationships between non-SLN metastases and clinicopathologic features, as well as preoperative routine tests of blood indexes, tumor markers, and serum lipids, including lipoprotein a (Lp(a)). Risk factors for non-SLN metastases were identified by logistic regression analysis. The nomogram was created using the R program to predict the risk of non-SLN metastases in the training set. Receiver operating characteristic (ROC) analysis was applied to assess the predictive value of the nomogram model in the validation set. Results. Lp(a) was significantly associated with non-SLN metastasis status. Compared with the MSKCC model, the predictive ability of our new nomogram that combined Lp(a) level and clinical variables (pathologic tumor size, lymphovascular invasion, multifocality, and positive/negative SLN numbers) was significantly greater (AUC: 0.732, 95% CI: 0.643–0.821) (C-index: 0.703, 95% CI: 0.656–0.791) in the training cohorts and also performed well in the validation cohorts (C-index: 0.773, 95% CI: 0.681–0.865). Moreover, the new nomogram with Lp(a) improved the accuracy (12.10%) of identification of patients with non-SLN metastases (NRI: 0.121; 95% CI: 0.081–0.202; P=0.011). Conclusions. This novel nomogram based on preoperative serum indexes combined with clinicopathologic features facilitates accurate prediction of risk of non-SLN metastases in Chinese patients with breast cancer.</abstract><cop>United States</cop><pub>Hindawi</pub><pmid>34853623</pmid><doi>10.1155/2021/7879508</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-3077-8572</orcidid><orcidid>https://orcid.org/0000-0002-7029-3916</orcidid><orcidid>https://orcid.org/0000-0002-7488-1555</orcidid><orcidid>https://orcid.org/0000-0002-9648-7866</orcidid><orcidid>https://orcid.org/0000-0001-6566-0120</orcidid><orcidid>https://orcid.org/0000-0002-1864-8601</orcidid><orcidid>https://orcid.org/0000-0003-2970-1137</orcidid><orcidid>https://orcid.org/0000-0003-1466-9875</orcidid><orcidid>https://orcid.org/0000-0001-9796-3767</orcidid><orcidid>https://orcid.org/0000-0002-9191-0063</orcidid><orcidid>https://orcid.org/0000-0001-9014-7732</orcidid><orcidid>https://orcid.org/0000-0003-3919-9304</orcidid><orcidid>https://orcid.org/0000-0003-1232-3248</orcidid><orcidid>https://orcid.org/0000-0001-9851-7080</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Accuracy Antigens Apolipoproteins Biomarkers Biomarkers, Tumor - blood Biopsy Blood Breast cancer Breast Neoplasms - blood Breast Neoplasms - pathology Breast Neoplasms - surgery China Cholesterol Estrogens Female Follow-Up Studies High density lipoprotein Humans Lipids Lipoprotein(a) - blood Lipoproteins Lymph Node Excision Lymph nodes Lymph Nodes - metabolism Lymph Nodes - pathology Lymph Nodes - surgery Lymphatic Metastasis - pathology Lymphatic system Lymphocytes Metastases Metastasis Middle Aged Multivariate analysis Neutrophils Nomograms Patients Prognosis Regression analysis Retrospective Studies Risk analysis Risk factors ROC Curve Sentinel Lymph Node - metabolism Sentinel Lymph Node - pathology Sentinel Lymph Node - surgery Serum lipids Surgery Training Tumor markers Tumors |
| title | A Novel Predictive Nomogram including Serum Lipoprotein a Level for Nonsentinel Lymph Node Metastases in Chinese Breast Cancer Patients with Positive Sentinel Lymph Node Metastases |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-11T07%3A11%3A12IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20Novel%20Predictive%20Nomogram%20including%20Serum%20Lipoprotein%20a%20Level%20for%20Nonsentinel%20Lymph%20Node%20Metastases%20in%20Chinese%20Breast%20Cancer%20Patients%20with%20Positive%20Sentinel%20Lymph%20Node%20Metastases&rft.jtitle=Disease%20markers&rft.au=Fang,%20Zeng&rft.date=2021&rft.volume=2021&rft.spage=7879508&rft.epage=10&rft.pages=7879508-10&rft.issn=0278-0240&rft.eissn=1875-8630&rft_id=info:doi/10.1155/2021/7879508&rft_dat=%3Cproquest_pubme%3E2606654107%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2606654107&rft_id=info:pmid/34853623&rfr_iscdi=true |