Benign uterine mass—discrimination from leiomyosarcoma by a preoperative risk score: a multicenter cohort study
Purpose Discrimination of uterine leiomyosarcoma (LMS) and leiomyoma (LM) prior to surgery by basic preoperative characteristics and development of a preoperative leiomyosarcoma score. Methods A predominantly prospective cohort of 826 patients with LM from a clinical institution and an outpatient ce...
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| Veröffentlicht in: | Archives of gynecology and obstetrics 2019-12, Vol.300 (6), p.1719-1727 |
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| creator | Köhler, Günter Vollmer, Marcus Nath, Neetika Hessler, Philipp-Andreas Dennis, Katarina Lehr, Angela Köller, Martina Riechmann, Christine Bralo, Helena Trojnarska, Dominika Lehnhoff, Hanka Krichbaum, Johann Krichbaum, Manfred Evert, Katja Evert, Matthias Zygmunt, Marek Kaderali, Lars |
| description | Purpose
Discrimination of uterine leiomyosarcoma (LMS) and leiomyoma (LM) prior to surgery by basic preoperative characteristics and development of a preoperative leiomyosarcoma score.
Methods
A predominantly prospective cohort of 826 patients with LM from a clinical institution and an outpatient center was included in the study. Further a predominantly retrospective cohort of 293 patients with LMS was included from the counseling database of the German Clinical Center of Excellence for Genital Sarcoma and Mixed Tumors (DKSM, University Medicine Greifswald, Germany). We analyzed and compared anamnestic, epidemiological and clinical findings between both cohorts. Tenfold cross-validated logistic regression and random forest was performed on the 80% training set. The preoperative LMS score (pLMS) was developed based on logistic regression and independently evaluated by analyzing the area under the receiver operating characteristic curve (AUC) with the 20% test set.
Results
In the LMS cohort, 63.1% had initially surgery for presumed LM and only 39.6% of endometrial biopsies revealed LMS. Key features for LMS discrimination were found to be bleeding symptoms: intermenstrual bleeding [RR
c
= 2.71, CI = (1.90–3.49),
p
|
| doi_str_mv | 10.1007/s00404-019-05344-0 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2311647660</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2311647660</sourcerecordid><originalsourceid>FETCH-LOGICAL-c375t-9aa685417dafd78fd9b9df877a0f3730bfbc3e55ae6577b519269f28586d18703</originalsourceid><addsrcrecordid>eNp9kc9O3DAQh62qVVloX4BDZamXXgLjOLGd3sqKP5WQeoGz5SRjaprYi50g7a0P0SfkSXC6UCQOPXmk-eZnez5CDhkcMQB5nAAqqApgTQE1r3L1hqxYxcsCJGNvyQqapQYh98h-SrcArFRKvCd7nAkpQakVuTtB7248nSeMziMdTUoPv__0LnXRjc6byQVPbQwjHdCFcRuSiV0YDW231NBNxLDBmKl7pNGlXzR1IeLX3BrnYXId-hxMu_AzxImmae63H8g7a4aEH5_OA3J9dnq1viguf5x_X3-7LDou66lojBGqrpjsje2lsn3TNr1VUhqwXHJobdtxrGuDopayrVlTisaWqlaiZ0oCPyBfdrmbGO5mTJMe86dwGIzHMCddcsZEJYVY0M-v0NswR59ft1CSKV42KlPljupiSCmi1Zu8IhO3moFehOidEJ2F6L9C9BL96Sl6bkfs_408G8gA3wEpt_wNxpe7_xP7CFttmG8</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2317183298</pqid></control><display><type>article</type><title>Benign uterine mass—discrimination from leiomyosarcoma by a preoperative risk score: a multicenter cohort study</title><source>MEDLINE</source><source>SpringerNature Journals</source><creator>Köhler, Günter ; Vollmer, Marcus ; Nath, Neetika ; Hessler, Philipp-Andreas ; Dennis, Katarina ; Lehr, Angela ; Köller, Martina ; Riechmann, Christine ; Bralo, Helena ; Trojnarska, Dominika ; Lehnhoff, Hanka ; Krichbaum, Johann ; Krichbaum, Manfred ; Evert, Katja ; Evert, Matthias ; Zygmunt, Marek ; Kaderali, Lars</creator><creatorcontrib>Köhler, Günter ; Vollmer, Marcus ; Nath, Neetika ; Hessler, Philipp-Andreas ; Dennis, Katarina ; Lehr, Angela ; Köller, Martina ; Riechmann, Christine ; Bralo, Helena ; Trojnarska, Dominika ; Lehnhoff, Hanka ; Krichbaum, Johann ; Krichbaum, Manfred ; Evert, Katja ; Evert, Matthias ; Zygmunt, Marek ; Kaderali, Lars</creatorcontrib><description><![CDATA[Purpose
Discrimination of uterine leiomyosarcoma (LMS) and leiomyoma (LM) prior to surgery by basic preoperative characteristics and development of a preoperative leiomyosarcoma score.
Methods
A predominantly prospective cohort of 826 patients with LM from a clinical institution and an outpatient center was included in the study. Further a predominantly retrospective cohort of 293 patients with LMS was included from the counseling database of the German Clinical Center of Excellence for Genital Sarcoma and Mixed Tumors (DKSM, University Medicine Greifswald, Germany). We analyzed and compared anamnestic, epidemiological and clinical findings between both cohorts. Tenfold cross-validated logistic regression and random forest was performed on the 80% training set. The preoperative LMS score (pLMS) was developed based on logistic regression and independently evaluated by analyzing the area under the receiver operating characteristic curve (AUC) with the 20% test set.
Results
In the LMS cohort, 63.1% had initially surgery for presumed LM and only 39.6% of endometrial biopsies revealed LMS. Key features for LMS discrimination were found to be bleeding symptoms: intermenstrual bleeding [RR
c
= 2.71, CI = (1.90–3.49),
p
< 0.001], hypermenorrhea [RR
c
= 0.28, CI = (0.15–0.50),
p
< 0.001], dysmenorrhea [RR
c
= 0.22, CI = (0.10–0.51),
p
< 0.001], postmenstrual bleeding [RR
c
= 2.08, CI = (1.30–2.75),
p
< 0.001], suspicious sonography [RR
c
= 1.21, CI = (1.19–1.22),
p
< 0.001] and the tumor diameter (each centimeter difference:
β
= 0.24, SD = 0.04,
p
< 0.001). pLMS achieved a mean cross-validated AUC of 0.969 (SD = 0.019) in the training set and an AUC of 0.968 in the test set.
Conclusions
The presented score is based on basic clinical characteristics and allows the prediction of LMS prior to a planned surgery of a uterine mass. In case pLMS is between − 3 and + 1, we suggest subsequent diagnostics, such as endometrial biopsy, color Doppler sonography, LDH measurement, MRI and transcervical biopsy.]]></description><identifier>ISSN: 0932-0067</identifier><identifier>EISSN: 1432-0711</identifier><identifier>DOI: 10.1007/s00404-019-05344-0</identifier><identifier>PMID: 31677088</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Adult ; Biopsy ; Cohort analysis ; Endocrinology ; Endometrium - pathology ; Female ; Fibroids ; Gynecologic Oncology ; Gynecology ; Human Genetics ; Humans ; Leiomyosarcoma - pathology ; Leiomyosarcoma - surgery ; Logistic Models ; Medicine ; Medicine & Public Health ; Middle Aged ; Obstetrics/Perinatology/Midwifery ; Prospective Studies ; Retrospective Studies ; Surgery ; Ultrasonic imaging ; Uterine Neoplasms - pathology ; Uterine Neoplasms - surgery</subject><ispartof>Archives of gynecology and obstetrics, 2019-12, Vol.300 (6), p.1719-1727</ispartof><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2019</rights><rights>Archives of Gynecology and Obstetrics is a copyright of Springer, (2019). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-9aa685417dafd78fd9b9df877a0f3730bfbc3e55ae6577b519269f28586d18703</citedby><cites>FETCH-LOGICAL-c375t-9aa685417dafd78fd9b9df877a0f3730bfbc3e55ae6577b519269f28586d18703</cites><orcidid>0000-0002-4593-2026</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/s00404-019-05344-0$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00404-019-05344-0$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>315,781,785,27928,27929,41492,42561,51323</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31677088$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Köhler, Günter</creatorcontrib><creatorcontrib>Vollmer, Marcus</creatorcontrib><creatorcontrib>Nath, Neetika</creatorcontrib><creatorcontrib>Hessler, Philipp-Andreas</creatorcontrib><creatorcontrib>Dennis, Katarina</creatorcontrib><creatorcontrib>Lehr, Angela</creatorcontrib><creatorcontrib>Köller, Martina</creatorcontrib><creatorcontrib>Riechmann, Christine</creatorcontrib><creatorcontrib>Bralo, Helena</creatorcontrib><creatorcontrib>Trojnarska, Dominika</creatorcontrib><creatorcontrib>Lehnhoff, Hanka</creatorcontrib><creatorcontrib>Krichbaum, Johann</creatorcontrib><creatorcontrib>Krichbaum, Manfred</creatorcontrib><creatorcontrib>Evert, Katja</creatorcontrib><creatorcontrib>Evert, Matthias</creatorcontrib><creatorcontrib>Zygmunt, Marek</creatorcontrib><creatorcontrib>Kaderali, Lars</creatorcontrib><title>Benign uterine mass—discrimination from leiomyosarcoma by a preoperative risk score: a multicenter cohort study</title><title>Archives of gynecology and obstetrics</title><addtitle>Arch Gynecol Obstet</addtitle><addtitle>Arch Gynecol Obstet</addtitle><description><![CDATA[Purpose
Discrimination of uterine leiomyosarcoma (LMS) and leiomyoma (LM) prior to surgery by basic preoperative characteristics and development of a preoperative leiomyosarcoma score.
Methods
A predominantly prospective cohort of 826 patients with LM from a clinical institution and an outpatient center was included in the study. Further a predominantly retrospective cohort of 293 patients with LMS was included from the counseling database of the German Clinical Center of Excellence for Genital Sarcoma and Mixed Tumors (DKSM, University Medicine Greifswald, Germany). We analyzed and compared anamnestic, epidemiological and clinical findings between both cohorts. Tenfold cross-validated logistic regression and random forest was performed on the 80% training set. The preoperative LMS score (pLMS) was developed based on logistic regression and independently evaluated by analyzing the area under the receiver operating characteristic curve (AUC) with the 20% test set.
Results
In the LMS cohort, 63.1% had initially surgery for presumed LM and only 39.6% of endometrial biopsies revealed LMS. Key features for LMS discrimination were found to be bleeding symptoms: intermenstrual bleeding [RR
c
= 2.71, CI = (1.90–3.49),
p
< 0.001], hypermenorrhea [RR
c
= 0.28, CI = (0.15–0.50),
p
< 0.001], dysmenorrhea [RR
c
= 0.22, CI = (0.10–0.51),
p
< 0.001], postmenstrual bleeding [RR
c
= 2.08, CI = (1.30–2.75),
p
< 0.001], suspicious sonography [RR
c
= 1.21, CI = (1.19–1.22),
p
< 0.001] and the tumor diameter (each centimeter difference:
β
= 0.24, SD = 0.04,
p
< 0.001). pLMS achieved a mean cross-validated AUC of 0.969 (SD = 0.019) in the training set and an AUC of 0.968 in the test set.
Conclusions
The presented score is based on basic clinical characteristics and allows the prediction of LMS prior to a planned surgery of a uterine mass. In case pLMS is between − 3 and + 1, we suggest subsequent diagnostics, such as endometrial biopsy, color Doppler sonography, LDH measurement, MRI and transcervical biopsy.]]></description><subject>Adult</subject><subject>Biopsy</subject><subject>Cohort analysis</subject><subject>Endocrinology</subject><subject>Endometrium - pathology</subject><subject>Female</subject><subject>Fibroids</subject><subject>Gynecologic Oncology</subject><subject>Gynecology</subject><subject>Human Genetics</subject><subject>Humans</subject><subject>Leiomyosarcoma - pathology</subject><subject>Leiomyosarcoma - surgery</subject><subject>Logistic Models</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Middle Aged</subject><subject>Obstetrics/Perinatology/Midwifery</subject><subject>Prospective Studies</subject><subject>Retrospective Studies</subject><subject>Surgery</subject><subject>Ultrasonic imaging</subject><subject>Uterine Neoplasms - pathology</subject><subject>Uterine Neoplasms - surgery</subject><issn>0932-0067</issn><issn>1432-0711</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><recordid>eNp9kc9O3DAQh62qVVloX4BDZamXXgLjOLGd3sqKP5WQeoGz5SRjaprYi50g7a0P0SfkSXC6UCQOPXmk-eZnez5CDhkcMQB5nAAqqApgTQE1r3L1hqxYxcsCJGNvyQqapQYh98h-SrcArFRKvCd7nAkpQakVuTtB7248nSeMziMdTUoPv__0LnXRjc6byQVPbQwjHdCFcRuSiV0YDW231NBNxLDBmKl7pNGlXzR1IeLX3BrnYXId-hxMu_AzxImmae63H8g7a4aEH5_OA3J9dnq1viguf5x_X3-7LDou66lojBGqrpjsje2lsn3TNr1VUhqwXHJobdtxrGuDopayrVlTisaWqlaiZ0oCPyBfdrmbGO5mTJMe86dwGIzHMCddcsZEJYVY0M-v0NswR59ft1CSKV42KlPljupiSCmi1Zu8IhO3moFehOidEJ2F6L9C9BL96Sl6bkfs_408G8gA3wEpt_wNxpe7_xP7CFttmG8</recordid><startdate>20191201</startdate><enddate>20191201</enddate><creator>Köhler, Günter</creator><creator>Vollmer, Marcus</creator><creator>Nath, Neetika</creator><creator>Hessler, Philipp-Andreas</creator><creator>Dennis, Katarina</creator><creator>Lehr, Angela</creator><creator>Köller, Martina</creator><creator>Riechmann, Christine</creator><creator>Bralo, Helena</creator><creator>Trojnarska, Dominika</creator><creator>Lehnhoff, Hanka</creator><creator>Krichbaum, Johann</creator><creator>Krichbaum, Manfred</creator><creator>Evert, Katja</creator><creator>Evert, Matthias</creator><creator>Zygmunt, Marek</creator><creator>Kaderali, Lars</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>PRINS</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-4593-2026</orcidid></search><sort><creationdate>20191201</creationdate><title>Benign uterine mass—discrimination from leiomyosarcoma by a preoperative risk score: a multicenter cohort study</title><author>Köhler, Günter ; Vollmer, Marcus ; Nath, Neetika ; Hessler, Philipp-Andreas ; Dennis, Katarina ; Lehr, Angela ; Köller, Martina ; Riechmann, Christine ; Bralo, Helena ; Trojnarska, Dominika ; Lehnhoff, Hanka ; Krichbaum, Johann ; Krichbaum, Manfred ; Evert, Katja ; Evert, Matthias ; Zygmunt, Marek ; Kaderali, Lars</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-9aa685417dafd78fd9b9df877a0f3730bfbc3e55ae6577b519269f28586d18703</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Adult</topic><topic>Biopsy</topic><topic>Cohort analysis</topic><topic>Endocrinology</topic><topic>Endometrium - pathology</topic><topic>Female</topic><topic>Fibroids</topic><topic>Gynecologic Oncology</topic><topic>Gynecology</topic><topic>Human Genetics</topic><topic>Humans</topic><topic>Leiomyosarcoma - pathology</topic><topic>Leiomyosarcoma - surgery</topic><topic>Logistic Models</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Middle Aged</topic><topic>Obstetrics/Perinatology/Midwifery</topic><topic>Prospective Studies</topic><topic>Retrospective Studies</topic><topic>Surgery</topic><topic>Ultrasonic imaging</topic><topic>Uterine Neoplasms - pathology</topic><topic>Uterine Neoplasms - surgery</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Köhler, Günter</creatorcontrib><creatorcontrib>Vollmer, Marcus</creatorcontrib><creatorcontrib>Nath, Neetika</creatorcontrib><creatorcontrib>Hessler, Philipp-Andreas</creatorcontrib><creatorcontrib>Dennis, Katarina</creatorcontrib><creatorcontrib>Lehr, Angela</creatorcontrib><creatorcontrib>Köller, Martina</creatorcontrib><creatorcontrib>Riechmann, Christine</creatorcontrib><creatorcontrib>Bralo, Helena</creatorcontrib><creatorcontrib>Trojnarska, Dominika</creatorcontrib><creatorcontrib>Lehnhoff, Hanka</creatorcontrib><creatorcontrib>Krichbaum, Johann</creatorcontrib><creatorcontrib>Krichbaum, Manfred</creatorcontrib><creatorcontrib>Evert, Katja</creatorcontrib><creatorcontrib>Evert, Matthias</creatorcontrib><creatorcontrib>Zygmunt, Marek</creatorcontrib><creatorcontrib>Kaderali, Lars</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>ProQuest Central China</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>Köhler, Günter</au><au>Vollmer, Marcus</au><au>Nath, Neetika</au><au>Hessler, Philipp-Andreas</au><au>Dennis, Katarina</au><au>Lehr, Angela</au><au>Köller, Martina</au><au>Riechmann, Christine</au><au>Bralo, Helena</au><au>Trojnarska, Dominika</au><au>Lehnhoff, Hanka</au><au>Krichbaum, Johann</au><au>Krichbaum, Manfred</au><au>Evert, Katja</au><au>Evert, Matthias</au><au>Zygmunt, Marek</au><au>Kaderali, Lars</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Benign uterine mass—discrimination from leiomyosarcoma by a preoperative risk score: a multicenter cohort study</atitle><jtitle>Archives of gynecology and obstetrics</jtitle><stitle>Arch Gynecol Obstet</stitle><addtitle>Arch Gynecol Obstet</addtitle><date>2019-12-01</date><risdate>2019</risdate><volume>300</volume><issue>6</issue><spage>1719</spage><epage>1727</epage><pages>1719-1727</pages><issn>0932-0067</issn><eissn>1432-0711</eissn><abstract><![CDATA[Purpose
Discrimination of uterine leiomyosarcoma (LMS) and leiomyoma (LM) prior to surgery by basic preoperative characteristics and development of a preoperative leiomyosarcoma score.
Methods
A predominantly prospective cohort of 826 patients with LM from a clinical institution and an outpatient center was included in the study. Further a predominantly retrospective cohort of 293 patients with LMS was included from the counseling database of the German Clinical Center of Excellence for Genital Sarcoma and Mixed Tumors (DKSM, University Medicine Greifswald, Germany). We analyzed and compared anamnestic, epidemiological and clinical findings between both cohorts. Tenfold cross-validated logistic regression and random forest was performed on the 80% training set. The preoperative LMS score (pLMS) was developed based on logistic regression and independently evaluated by analyzing the area under the receiver operating characteristic curve (AUC) with the 20% test set.
Results
In the LMS cohort, 63.1% had initially surgery for presumed LM and only 39.6% of endometrial biopsies revealed LMS. Key features for LMS discrimination were found to be bleeding symptoms: intermenstrual bleeding [RR
c
= 2.71, CI = (1.90–3.49),
p
< 0.001], hypermenorrhea [RR
c
= 0.28, CI = (0.15–0.50),
p
< 0.001], dysmenorrhea [RR
c
= 0.22, CI = (0.10–0.51),
p
< 0.001], postmenstrual bleeding [RR
c
= 2.08, CI = (1.30–2.75),
p
< 0.001], suspicious sonography [RR
c
= 1.21, CI = (1.19–1.22),
p
< 0.001] and the tumor diameter (each centimeter difference:
β
= 0.24, SD = 0.04,
p
< 0.001). pLMS achieved a mean cross-validated AUC of 0.969 (SD = 0.019) in the training set and an AUC of 0.968 in the test set.
Conclusions
The presented score is based on basic clinical characteristics and allows the prediction of LMS prior to a planned surgery of a uterine mass. In case pLMS is between − 3 and + 1, we suggest subsequent diagnostics, such as endometrial biopsy, color Doppler sonography, LDH measurement, MRI and transcervical biopsy.]]></abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>31677088</pmid><doi>10.1007/s00404-019-05344-0</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-4593-2026</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0932-0067 |
| ispartof | Archives of gynecology and obstetrics, 2019-12, Vol.300 (6), p.1719-1727 |
| issn | 0932-0067 1432-0711 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_2311647660 |
| source | MEDLINE; SpringerNature Journals |
| subjects | Adult Biopsy Cohort analysis Endocrinology Endometrium - pathology Female Fibroids Gynecologic Oncology Gynecology Human Genetics Humans Leiomyosarcoma - pathology Leiomyosarcoma - surgery Logistic Models Medicine Medicine & Public Health Middle Aged Obstetrics/Perinatology/Midwifery Prospective Studies Retrospective Studies Surgery Ultrasonic imaging Uterine Neoplasms - pathology Uterine Neoplasms - surgery |
| title | Benign uterine mass—discrimination from leiomyosarcoma by a preoperative risk score: a multicenter cohort study |
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