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Preoperative CT image-based assessment for estimating risk of ovarian torsion in women with ovarian lesions and pelvic pain
Purpose To define and weight the preoperative CT findings for ovarian torsion and to develop an integrated nomogram for estimating the probability of ovarian torsion in women with ovarian lesion and pelvic pain. Methods This retrospective study included 218 women with surgically resected ovarian les...
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| Published in: | Abdominal imaging 2021-03, Vol.46 (3), p.1137-1147 |
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| container_title | Abdominal imaging |
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| creator | Hwang, Jeong Ah Shin, Hyeong Cheol Kim, Seung Soo Heo, Nam Hun Choi, Seo-Youn Lee, Ji Eun Lee, Sunyoung |
| description | Purpose
To define and weight the preoperative CT findings for ovarian torsion and to develop an integrated nomogram for estimating the probability of ovarian torsion in women with ovarian lesion and pelvic pain.
Methods
This retrospective study included 218 women with surgically resected ovarian lesions who underwent preoperative contrast-enhanced CT for pelvic pain from January 2014 to February 2019. Significant imaging findings for torsion were extracted using regression analyses and a regression coefficient-based nomogram was constructed. The diagnostic performance with sensitivity, specificity, and accuracy of the significant imaging findings and the nomogram were assessed.
Results
A total of 255 ovarian lesions (123 lesions with torsion and 132 lesions without torsion) were evaluated. Multivariable regression analysis showed that whirl sign (odds ratio [OR] 11.000;
p
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| doi_str_mv | 10.1007/s00261-020-02761-7 |
| format | article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2443515995</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2498798733</sourcerecordid><originalsourceid>FETCH-LOGICAL-c375t-84540c9f9634209ad0e4d241832512556e2ac7aafd023231f87eb34ddf6592743</originalsourceid><addsrcrecordid>eNp9kV1rFDEUhoNYbKn9A72QgDfejJ58TSaXsvgFBb1ooXchO3NmTZ1NxpzZLeKfN3XrCl4ICTnwPudNTl7GLgW8FgD2DQHIVjQgoW5bK_uEnUnVtg2A6Z4ea317yi6I7gBAtEYIaZ6xUyWdMtK4M_bzS8E8YwlL3CNfXfO4DRts1oFw4IEIibaYFj7mwpGWqi4xbXiJ9I3nked9KDEkvuRCMSceE7_PtYHfx-XrUZ3wQSQe0sBnnPax53OI6Tk7GcNEePF4nrOb9--uVx-bq88fPq3eXjW9smZpOm009G50rdISXBgA9SC16JQ0dRzTogy9DWEcQCqpxNhZXCs9DGNrnLRanbNXB9-55O-7OoXfRupxmkLCvCMvtVZGGOdMRV_-g97lXUn1dZVyna1LqUrJA9WXTFRw9HOpP1N-eAH-IR1_SMfXdPzvdLytTS8erXfrLQ7Hlj9ZVEAdAKpS2mD5e_d_bH8BC-eaRg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2498798733</pqid></control><display><type>article</type><title>Preoperative CT image-based assessment for estimating risk of ovarian torsion in women with ovarian lesions and pelvic pain</title><source>Springer Link</source><creator>Hwang, Jeong Ah ; Shin, Hyeong Cheol ; Kim, Seung Soo ; Heo, Nam Hun ; Choi, Seo-Youn ; Lee, Ji Eun ; Lee, Sunyoung</creator><creatorcontrib>Hwang, Jeong Ah ; Shin, Hyeong Cheol ; Kim, Seung Soo ; Heo, Nam Hun ; Choi, Seo-Youn ; Lee, Ji Eun ; Lee, Sunyoung</creatorcontrib><description>Purpose
To define and weight the preoperative CT findings for ovarian torsion and to develop an integrated nomogram for estimating the probability of ovarian torsion in women with ovarian lesion and pelvic pain.
Methods
This retrospective study included 218 women with surgically resected ovarian lesions who underwent preoperative contrast-enhanced CT for pelvic pain from January 2014 to February 2019. Significant imaging findings for torsion were extracted using regression analyses and a regression coefficient-based nomogram was constructed. The diagnostic performance with sensitivity, specificity, and accuracy of the significant imaging findings and the nomogram were assessed.
Results
A total of 255 ovarian lesions (123 lesions with torsion and 132 lesions without torsion) were evaluated. Multivariable regression analysis showed that whirl sign (odds ratio [OR] 11.000;
p
< 0.001), tubal thickening (OR 4.621;
p
= 0.001), unusual location of ovarian lesion (OR 2.712;
p
= 0.020), and hemorrhagic component within adnexal lesion (OR 2.537;
p
= 0.028) were independent significant parameters predicting ovarian torsion. Tubal thickening showed the highest sensitivity (91.1%) and whirl sign showed the highest specificity (94.7%). When probabilities of ovarian torsion of 0.5 or more in the nomogram were diagnosed as ovarian torsion, sensitivity, specificity, and accuracy of the nomogram were 78.1%, 91.7%, and 85.1%, respectively.
Conclusion
The whirl sign, tubal thickening, unusual location of ovarian lesion, and hemorrhagic component within adnexal lesion, and an integrated nomogram derived from these significant findings can be useful for predicting ovarian torsion.</description><identifier>ISSN: 2366-004X</identifier><identifier>EISSN: 2366-0058</identifier><identifier>DOI: 10.1007/s00261-020-02761-7</identifier><identifier>PMID: 32935259</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Computed tomography ; Estimation ; Gastroenterology ; Hemorrhage ; Hepatology ; Imaging ; Lesions ; Medical imaging ; Medicine ; Medicine & Public Health ; Nomograms ; Ovarian cancer ; Ovaries ; Pain ; Parameter sensitivity ; Pelvis ; Radiology ; Regression analysis ; Regression coefficients ; Sensitivity ; Statistical analysis ; Thickening ; Womens health</subject><ispartof>Abdominal imaging, 2021-03, Vol.46 (3), p.1137-1147</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2020</rights><rights>Springer Science+Business Media, LLC, part of Springer Nature 2020.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-84540c9f9634209ad0e4d241832512556e2ac7aafd023231f87eb34ddf6592743</citedby><cites>FETCH-LOGICAL-c375t-84540c9f9634209ad0e4d241832512556e2ac7aafd023231f87eb34ddf6592743</cites><orcidid>0000-0002-6612-0756 ; 0000-0001-7278-2214 ; 0000-0002-1763-8217 ; 0000-0002-6893-3136 ; 0000-0002-8012-995X ; 0000-0002-4442-4441 ; 0000-0002-2434-8779</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32935259$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Hwang, Jeong Ah</creatorcontrib><creatorcontrib>Shin, Hyeong Cheol</creatorcontrib><creatorcontrib>Kim, Seung Soo</creatorcontrib><creatorcontrib>Heo, Nam Hun</creatorcontrib><creatorcontrib>Choi, Seo-Youn</creatorcontrib><creatorcontrib>Lee, Ji Eun</creatorcontrib><creatorcontrib>Lee, Sunyoung</creatorcontrib><title>Preoperative CT image-based assessment for estimating risk of ovarian torsion in women with ovarian lesions and pelvic pain</title><title>Abdominal imaging</title><addtitle>Abdom Radiol</addtitle><addtitle>Abdom Radiol (NY)</addtitle><description>Purpose
To define and weight the preoperative CT findings for ovarian torsion and to develop an integrated nomogram for estimating the probability of ovarian torsion in women with ovarian lesion and pelvic pain.
Methods
This retrospective study included 218 women with surgically resected ovarian lesions who underwent preoperative contrast-enhanced CT for pelvic pain from January 2014 to February 2019. Significant imaging findings for torsion were extracted using regression analyses and a regression coefficient-based nomogram was constructed. The diagnostic performance with sensitivity, specificity, and accuracy of the significant imaging findings and the nomogram were assessed.
Results
A total of 255 ovarian lesions (123 lesions with torsion and 132 lesions without torsion) were evaluated. Multivariable regression analysis showed that whirl sign (odds ratio [OR] 11.000;
p
< 0.001), tubal thickening (OR 4.621;
p
= 0.001), unusual location of ovarian lesion (OR 2.712;
p
= 0.020), and hemorrhagic component within adnexal lesion (OR 2.537;
p
= 0.028) were independent significant parameters predicting ovarian torsion. Tubal thickening showed the highest sensitivity (91.1%) and whirl sign showed the highest specificity (94.7%). When probabilities of ovarian torsion of 0.5 or more in the nomogram were diagnosed as ovarian torsion, sensitivity, specificity, and accuracy of the nomogram were 78.1%, 91.7%, and 85.1%, respectively.
Conclusion
The whirl sign, tubal thickening, unusual location of ovarian lesion, and hemorrhagic component within adnexal lesion, and an integrated nomogram derived from these significant findings can be useful for predicting ovarian torsion.</description><subject>Computed tomography</subject><subject>Estimation</subject><subject>Gastroenterology</subject><subject>Hemorrhage</subject><subject>Hepatology</subject><subject>Imaging</subject><subject>Lesions</subject><subject>Medical imaging</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Nomograms</subject><subject>Ovarian cancer</subject><subject>Ovaries</subject><subject>Pain</subject><subject>Parameter sensitivity</subject><subject>Pelvis</subject><subject>Radiology</subject><subject>Regression analysis</subject><subject>Regression coefficients</subject><subject>Sensitivity</subject><subject>Statistical analysis</subject><subject>Thickening</subject><subject>Womens health</subject><issn>2366-004X</issn><issn>2366-0058</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp9kV1rFDEUhoNYbKn9A72QgDfejJ58TSaXsvgFBb1ooXchO3NmTZ1NxpzZLeKfN3XrCl4ICTnwPudNTl7GLgW8FgD2DQHIVjQgoW5bK_uEnUnVtg2A6Z4ea317yi6I7gBAtEYIaZ6xUyWdMtK4M_bzS8E8YwlL3CNfXfO4DRts1oFw4IEIibaYFj7mwpGWqi4xbXiJ9I3nked9KDEkvuRCMSceE7_PtYHfx-XrUZ3wQSQe0sBnnPax53OI6Tk7GcNEePF4nrOb9--uVx-bq88fPq3eXjW9smZpOm009G50rdISXBgA9SC16JQ0dRzTogy9DWEcQCqpxNhZXCs9DGNrnLRanbNXB9-55O-7OoXfRupxmkLCvCMvtVZGGOdMRV_-g97lXUn1dZVyna1LqUrJA9WXTFRw9HOpP1N-eAH-IR1_SMfXdPzvdLytTS8erXfrLQ7Hlj9ZVEAdAKpS2mD5e_d_bH8BC-eaRg</recordid><startdate>20210301</startdate><enddate>20210301</enddate><creator>Hwang, Jeong Ah</creator><creator>Shin, Hyeong Cheol</creator><creator>Kim, Seung Soo</creator><creator>Heo, Nam Hun</creator><creator>Choi, Seo-Youn</creator><creator>Lee, Ji Eun</creator><creator>Lee, Sunyoung</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>M7Z</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-6612-0756</orcidid><orcidid>https://orcid.org/0000-0001-7278-2214</orcidid><orcidid>https://orcid.org/0000-0002-1763-8217</orcidid><orcidid>https://orcid.org/0000-0002-6893-3136</orcidid><orcidid>https://orcid.org/0000-0002-8012-995X</orcidid><orcidid>https://orcid.org/0000-0002-4442-4441</orcidid><orcidid>https://orcid.org/0000-0002-2434-8779</orcidid></search><sort><creationdate>20210301</creationdate><title>Preoperative CT image-based assessment for estimating risk of ovarian torsion in women with ovarian lesions and pelvic pain</title><author>Hwang, Jeong Ah ; Shin, Hyeong Cheol ; Kim, Seung Soo ; Heo, Nam Hun ; Choi, Seo-Youn ; Lee, Ji Eun ; Lee, Sunyoung</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-84540c9f9634209ad0e4d241832512556e2ac7aafd023231f87eb34ddf6592743</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Computed tomography</topic><topic>Estimation</topic><topic>Gastroenterology</topic><topic>Hemorrhage</topic><topic>Hepatology</topic><topic>Imaging</topic><topic>Lesions</topic><topic>Medical imaging</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Nomograms</topic><topic>Ovarian cancer</topic><topic>Ovaries</topic><topic>Pain</topic><topic>Parameter sensitivity</topic><topic>Pelvis</topic><topic>Radiology</topic><topic>Regression analysis</topic><topic>Regression coefficients</topic><topic>Sensitivity</topic><topic>Statistical analysis</topic><topic>Thickening</topic><topic>Womens health</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hwang, Jeong Ah</creatorcontrib><creatorcontrib>Shin, Hyeong Cheol</creatorcontrib><creatorcontrib>Kim, Seung Soo</creatorcontrib><creatorcontrib>Heo, Nam Hun</creatorcontrib><creatorcontrib>Choi, Seo-Youn</creatorcontrib><creatorcontrib>Lee, Ji Eun</creatorcontrib><creatorcontrib>Lee, Sunyoung</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Nursing & Allied Health Database</collection><collection>ProQuest_Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</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>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</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>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Biological Sciences</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Biochemistry Abstracts 1</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</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>Abdominal imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hwang, Jeong Ah</au><au>Shin, Hyeong Cheol</au><au>Kim, Seung Soo</au><au>Heo, Nam Hun</au><au>Choi, Seo-Youn</au><au>Lee, Ji Eun</au><au>Lee, Sunyoung</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Preoperative CT image-based assessment for estimating risk of ovarian torsion in women with ovarian lesions and pelvic pain</atitle><jtitle>Abdominal imaging</jtitle><stitle>Abdom Radiol</stitle><addtitle>Abdom Radiol (NY)</addtitle><date>2021-03-01</date><risdate>2021</risdate><volume>46</volume><issue>3</issue><spage>1137</spage><epage>1147</epage><pages>1137-1147</pages><issn>2366-004X</issn><eissn>2366-0058</eissn><abstract>Purpose
To define and weight the preoperative CT findings for ovarian torsion and to develop an integrated nomogram for estimating the probability of ovarian torsion in women with ovarian lesion and pelvic pain.
Methods
This retrospective study included 218 women with surgically resected ovarian lesions who underwent preoperative contrast-enhanced CT for pelvic pain from January 2014 to February 2019. Significant imaging findings for torsion were extracted using regression analyses and a regression coefficient-based nomogram was constructed. The diagnostic performance with sensitivity, specificity, and accuracy of the significant imaging findings and the nomogram were assessed.
Results
A total of 255 ovarian lesions (123 lesions with torsion and 132 lesions without torsion) were evaluated. Multivariable regression analysis showed that whirl sign (odds ratio [OR] 11.000;
p
< 0.001), tubal thickening (OR 4.621;
p
= 0.001), unusual location of ovarian lesion (OR 2.712;
p
= 0.020), and hemorrhagic component within adnexal lesion (OR 2.537;
p
= 0.028) were independent significant parameters predicting ovarian torsion. Tubal thickening showed the highest sensitivity (91.1%) and whirl sign showed the highest specificity (94.7%). When probabilities of ovarian torsion of 0.5 or more in the nomogram were diagnosed as ovarian torsion, sensitivity, specificity, and accuracy of the nomogram were 78.1%, 91.7%, and 85.1%, respectively.
Conclusion
The whirl sign, tubal thickening, unusual location of ovarian lesion, and hemorrhagic component within adnexal lesion, and an integrated nomogram derived from these significant findings can be useful for predicting ovarian torsion.</abstract><cop>New York</cop><pub>Springer US</pub><pmid>32935259</pmid><doi>10.1007/s00261-020-02761-7</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-6612-0756</orcidid><orcidid>https://orcid.org/0000-0001-7278-2214</orcidid><orcidid>https://orcid.org/0000-0002-1763-8217</orcidid><orcidid>https://orcid.org/0000-0002-6893-3136</orcidid><orcidid>https://orcid.org/0000-0002-8012-995X</orcidid><orcidid>https://orcid.org/0000-0002-4442-4441</orcidid><orcidid>https://orcid.org/0000-0002-2434-8779</orcidid></addata></record> |
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| subjects | Computed tomography Estimation Gastroenterology Hemorrhage Hepatology Imaging Lesions Medical imaging Medicine Medicine & Public Health Nomograms Ovarian cancer Ovaries Pain Parameter sensitivity Pelvis Radiology Regression analysis Regression coefficients Sensitivity Statistical analysis Thickening Womens health |
| title | Preoperative CT image-based assessment for estimating risk of ovarian torsion in women with ovarian lesions and pelvic pain |
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