Developing a validated nomogram for predicting ovarian metastasis in endometrial cancer patients: a retrospective research
Objective To explore risk factors and develop a prediction model for ovarian metastasis in endometrial cancer (EC), as well as providing provide a reference for clinical ovarian preservation. Methods We conducted a retrospective observational study enrolling 1496 EC patients having received complete...
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| Veröffentlicht in: | Archives of gynecology and obstetrics 2022-03, Vol.305 (3), p.719-729 |
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| creator | Liu, Xiaodie Wu, Yaohai Liu, Peishu Zhang, Xiaolei |
| description | Objective
To explore risk factors and develop a prediction model for ovarian metastasis in endometrial cancer (EC), as well as providing provide a reference for clinical ovarian preservation.
Methods
We conducted a retrospective observational study enrolling 1496 EC patients having received complete staging surgery from Qilu Hospital of Shandong University from 2012 to 2018. These patients were randomly divided into two cohorts: training cohort (
n
= 1046) and validation cohort (
n
= 448). A nomogram prediction model was developed based on univariate, least absolute shrinkage and selection operator (Lasso), and multivariate logistic regression. Then, the nomogram model’s performance was evaluated in discrimination, calibration, and clinical utility three aspects.
Results
Parametrium invasion, lymph node metastasis, and oviduct metastasis were finally contained in the nomogram prediction model. The AUC of the model in the training cohort was 0.85 compared with 0.72 in the validation cohort. It also behaved well in calibration and had good clinical utility. With a threshold probability of 20% ~ 80%, the nomogram increased the net benefit by 0 ~ 13.6 per 100 patients than surgery for all patients upon validation.
Conclusions
We develop a nomogram with good performances for predicting ovarian metastasis in EC patients, which may help clinicians identify candidate patients appropriate for ovarian preservation in premenopausal EC patients. |
| doi_str_mv | 10.1007/s00404-021-06214-4 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2570371365</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2638312641</sourcerecordid><originalsourceid>FETCH-LOGICAL-c375t-23e4cd973343a060a0774e5f9e8e33f57e055ac573791530db162bdf9954406a3</originalsourceid><addsrcrecordid>eNp9kU2LFDEQhoMo7rj6BzxIwIuX1sr3tDdZP2HBi55DJl09ZulO2qRnYP311jirggchkFTqed-keBl7KuClAHCvGoAG3YEUHVgpdKfvsY3QSnbghLjPNtCfzmDdBXvU2g2AkNutfcgulNa9Ua7fsB9v8YhTWVLe88CPYUpDWHHgucxlX8PMx1L5UnFIcT0x5RhqCpnPuIZGKzWeMsc8FLqhzsRjyBFJE9aEeW2vybZSq7QFyeKIVDUMNX57zB6MYWr45G6_ZF_fv_ty9bG7_vzh09Wb6y4qZ9ZOKtRx6J1SWgWwEMA5jWbscYtKjcYhGBOicTSPMAqGnbByN4x9b7QGG9Qle3H2XWr5fsC2-jm1iNMUMpZD89I4UE4oawh9_g96Uw410--8tGqrhLRaECXPVKSpWsXRLzXNod56Af6UjD8n4ykZ_ysZr0n07M76sJtx-CP5HQUB6gw0auU91r9v_8f2J_Dhmc8</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2638312641</pqid></control><display><type>article</type><title>Developing a validated nomogram for predicting ovarian metastasis in endometrial cancer patients: a retrospective research</title><source>MEDLINE</source><source>SpringerNature Journals</source><creator>Liu, Xiaodie ; Wu, Yaohai ; Liu, Peishu ; Zhang, Xiaolei</creator><creatorcontrib>Liu, Xiaodie ; Wu, Yaohai ; Liu, Peishu ; Zhang, Xiaolei</creatorcontrib><description>Objective
To explore risk factors and develop a prediction model for ovarian metastasis in endometrial cancer (EC), as well as providing provide a reference for clinical ovarian preservation.
Methods
We conducted a retrospective observational study enrolling 1496 EC patients having received complete staging surgery from Qilu Hospital of Shandong University from 2012 to 2018. These patients were randomly divided into two cohorts: training cohort (
n
= 1046) and validation cohort (
n
= 448). A nomogram prediction model was developed based on univariate, least absolute shrinkage and selection operator (Lasso), and multivariate logistic regression. Then, the nomogram model’s performance was evaluated in discrimination, calibration, and clinical utility three aspects.
Results
Parametrium invasion, lymph node metastasis, and oviduct metastasis were finally contained in the nomogram prediction model. The AUC of the model in the training cohort was 0.85 compared with 0.72 in the validation cohort. It also behaved well in calibration and had good clinical utility. With a threshold probability of 20% ~ 80%, the nomogram increased the net benefit by 0 ~ 13.6 per 100 patients than surgery for all patients upon validation.
Conclusions
We develop a nomogram with good performances for predicting ovarian metastasis in EC patients, which may help clinicians identify candidate patients appropriate for ovarian preservation in premenopausal EC patients.</description><identifier>ISSN: 0932-0067</identifier><identifier>EISSN: 1432-0711</identifier><identifier>DOI: 10.1007/s00404-021-06214-4</identifier><identifier>PMID: 34495379</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Age ; Endocrinology ; Endometrial cancer ; Endometrial Neoplasms ; Estrogens ; Female ; Gynecologic Oncology ; Gynecology ; Histology ; Hospitals ; Human Genetics ; Humans ; Lymphatic Metastasis ; Lymphatic system ; Medical prognosis ; Medicine ; Medicine & Public Health ; Metastasis ; Mortality ; Nomograms ; Obstetrics ; Obstetrics/Perinatology/Midwifery ; Oophorectomy ; Ovarian Neoplasms ; Ovaries ; Retrospective Studies ; Risk factors ; Statistical analysis ; Surgery ; Urology ; Variables</subject><ispartof>Archives of gynecology and obstetrics, 2022-03, Vol.305 (3), p.719-729</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021</rights><rights>2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.</rights><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-23e4cd973343a060a0774e5f9e8e33f57e055ac573791530db162bdf9954406a3</citedby><cites>FETCH-LOGICAL-c375t-23e4cd973343a060a0774e5f9e8e33f57e055ac573791530db162bdf9954406a3</cites><orcidid>0000-0003-1668-2037</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/s00404-021-06214-4$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00404-021-06214-4$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34495379$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Liu, Xiaodie</creatorcontrib><creatorcontrib>Wu, Yaohai</creatorcontrib><creatorcontrib>Liu, Peishu</creatorcontrib><creatorcontrib>Zhang, Xiaolei</creatorcontrib><title>Developing a validated nomogram for predicting ovarian metastasis in endometrial cancer patients: a retrospective research</title><title>Archives of gynecology and obstetrics</title><addtitle>Arch Gynecol Obstet</addtitle><addtitle>Arch Gynecol Obstet</addtitle><description>Objective
To explore risk factors and develop a prediction model for ovarian metastasis in endometrial cancer (EC), as well as providing provide a reference for clinical ovarian preservation.
Methods
We conducted a retrospective observational study enrolling 1496 EC patients having received complete staging surgery from Qilu Hospital of Shandong University from 2012 to 2018. These patients were randomly divided into two cohorts: training cohort (
n
= 1046) and validation cohort (
n
= 448). A nomogram prediction model was developed based on univariate, least absolute shrinkage and selection operator (Lasso), and multivariate logistic regression. Then, the nomogram model’s performance was evaluated in discrimination, calibration, and clinical utility three aspects.
Results
Parametrium invasion, lymph node metastasis, and oviduct metastasis were finally contained in the nomogram prediction model. The AUC of the model in the training cohort was 0.85 compared with 0.72 in the validation cohort. It also behaved well in calibration and had good clinical utility. With a threshold probability of 20% ~ 80%, the nomogram increased the net benefit by 0 ~ 13.6 per 100 patients than surgery for all patients upon validation.
Conclusions
We develop a nomogram with good performances for predicting ovarian metastasis in EC patients, which may help clinicians identify candidate patients appropriate for ovarian preservation in premenopausal EC patients.</description><subject>Age</subject><subject>Endocrinology</subject><subject>Endometrial cancer</subject><subject>Endometrial Neoplasms</subject><subject>Estrogens</subject><subject>Female</subject><subject>Gynecologic Oncology</subject><subject>Gynecology</subject><subject>Histology</subject><subject>Hospitals</subject><subject>Human Genetics</subject><subject>Humans</subject><subject>Lymphatic Metastasis</subject><subject>Lymphatic system</subject><subject>Medical prognosis</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Metastasis</subject><subject>Mortality</subject><subject>Nomograms</subject><subject>Obstetrics</subject><subject>Obstetrics/Perinatology/Midwifery</subject><subject>Oophorectomy</subject><subject>Ovarian Neoplasms</subject><subject>Ovaries</subject><subject>Retrospective Studies</subject><subject>Risk factors</subject><subject>Statistical analysis</subject><subject>Surgery</subject><subject>Urology</subject><subject>Variables</subject><issn>0932-0067</issn><issn>1432-0711</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><recordid>eNp9kU2LFDEQhoMo7rj6BzxIwIuX1sr3tDdZP2HBi55DJl09ZulO2qRnYP311jirggchkFTqed-keBl7KuClAHCvGoAG3YEUHVgpdKfvsY3QSnbghLjPNtCfzmDdBXvU2g2AkNutfcgulNa9Ua7fsB9v8YhTWVLe88CPYUpDWHHgucxlX8PMx1L5UnFIcT0x5RhqCpnPuIZGKzWeMsc8FLqhzsRjyBFJE9aEeW2vybZSq7QFyeKIVDUMNX57zB6MYWr45G6_ZF_fv_ty9bG7_vzh09Wb6y4qZ9ZOKtRx6J1SWgWwEMA5jWbscYtKjcYhGBOicTSPMAqGnbByN4x9b7QGG9Qle3H2XWr5fsC2-jm1iNMUMpZD89I4UE4oawh9_g96Uw410--8tGqrhLRaECXPVKSpWsXRLzXNod56Af6UjD8n4ykZ_ysZr0n07M76sJtx-CP5HQUB6gw0auU91r9v_8f2J_Dhmc8</recordid><startdate>20220301</startdate><enddate>20220301</enddate><creator>Liu, Xiaodie</creator><creator>Wu, Yaohai</creator><creator>Liu, Peishu</creator><creator>Zhang, Xiaolei</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>7X7</scope><scope>7XB</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-1668-2037</orcidid></search><sort><creationdate>20220301</creationdate><title>Developing a validated nomogram for predicting ovarian metastasis in endometrial cancer patients: a retrospective research</title><author>Liu, Xiaodie ; Wu, Yaohai ; Liu, Peishu ; Zhang, Xiaolei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-23e4cd973343a060a0774e5f9e8e33f57e055ac573791530db162bdf9954406a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Age</topic><topic>Endocrinology</topic><topic>Endometrial cancer</topic><topic>Endometrial Neoplasms</topic><topic>Estrogens</topic><topic>Female</topic><topic>Gynecologic Oncology</topic><topic>Gynecology</topic><topic>Histology</topic><topic>Hospitals</topic><topic>Human Genetics</topic><topic>Humans</topic><topic>Lymphatic Metastasis</topic><topic>Lymphatic system</topic><topic>Medical prognosis</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Metastasis</topic><topic>Mortality</topic><topic>Nomograms</topic><topic>Obstetrics</topic><topic>Obstetrics/Perinatology/Midwifery</topic><topic>Oophorectomy</topic><topic>Ovarian Neoplasms</topic><topic>Ovaries</topic><topic>Retrospective Studies</topic><topic>Risk factors</topic><topic>Statistical analysis</topic><topic>Surgery</topic><topic>Urology</topic><topic>Variables</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Xiaodie</creatorcontrib><creatorcontrib>Wu, Yaohai</creatorcontrib><creatorcontrib>Liu, Peishu</creatorcontrib><creatorcontrib>Zhang, Xiaolei</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>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</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>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>MEDLINE - Academic</collection><jtitle>Archives of gynecology and obstetrics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Xiaodie</au><au>Wu, Yaohai</au><au>Liu, Peishu</au><au>Zhang, Xiaolei</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Developing a validated nomogram for predicting ovarian metastasis in endometrial cancer patients: a retrospective research</atitle><jtitle>Archives of gynecology and obstetrics</jtitle><stitle>Arch Gynecol Obstet</stitle><addtitle>Arch Gynecol Obstet</addtitle><date>2022-03-01</date><risdate>2022</risdate><volume>305</volume><issue>3</issue><spage>719</spage><epage>729</epage><pages>719-729</pages><issn>0932-0067</issn><eissn>1432-0711</eissn><abstract>Objective
To explore risk factors and develop a prediction model for ovarian metastasis in endometrial cancer (EC), as well as providing provide a reference for clinical ovarian preservation.
Methods
We conducted a retrospective observational study enrolling 1496 EC patients having received complete staging surgery from Qilu Hospital of Shandong University from 2012 to 2018. These patients were randomly divided into two cohorts: training cohort (
n
= 1046) and validation cohort (
n
= 448). A nomogram prediction model was developed based on univariate, least absolute shrinkage and selection operator (Lasso), and multivariate logistic regression. Then, the nomogram model’s performance was evaluated in discrimination, calibration, and clinical utility three aspects.
Results
Parametrium invasion, lymph node metastasis, and oviduct metastasis were finally contained in the nomogram prediction model. The AUC of the model in the training cohort was 0.85 compared with 0.72 in the validation cohort. It also behaved well in calibration and had good clinical utility. With a threshold probability of 20% ~ 80%, the nomogram increased the net benefit by 0 ~ 13.6 per 100 patients than surgery for all patients upon validation.
Conclusions
We develop a nomogram with good performances for predicting ovarian metastasis in EC patients, which may help clinicians identify candidate patients appropriate for ovarian preservation in premenopausal EC patients.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>34495379</pmid><doi>10.1007/s00404-021-06214-4</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0003-1668-2037</orcidid></addata></record> |
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| identifier | ISSN: 0932-0067 |
| ispartof | Archives of gynecology and obstetrics, 2022-03, Vol.305 (3), p.719-729 |
| issn | 0932-0067 1432-0711 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_2570371365 |
| source | MEDLINE; SpringerNature Journals |
| subjects | Age Endocrinology Endometrial cancer Endometrial Neoplasms Estrogens Female Gynecologic Oncology Gynecology Histology Hospitals Human Genetics Humans Lymphatic Metastasis Lymphatic system Medical prognosis Medicine Medicine & Public Health Metastasis Mortality Nomograms Obstetrics Obstetrics/Perinatology/Midwifery Oophorectomy Ovarian Neoplasms Ovaries Retrospective Studies Risk factors Statistical analysis Surgery Urology Variables |
| title | Developing a validated nomogram for predicting ovarian metastasis in endometrial cancer patients: a retrospective research |
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