Seeing beyond the tumor: computed tomography image-based radiomic analysis helps identify ovarian clear cell carcinoma subtype in epithelial ovarian cancer
Objective To develop and validate a model that can preoperatively identify the ovarian clear cell carcinoma (OCCC) subtype in epithelial ovarian cancer (EOC) using CT imaging radiomics and clinical data. Material and methods We retrospectively analyzed data from 282 patients with EOC (training set =...
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| Veröffentlicht in: | Radiologia medica 2023-08, Vol.128 (8), p.900-911 |
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| creator | Ren, Jing Mao, Li Zhao, Jia Li, Xiu-Li Wang, Chen Liu, Xin-Yu Jin, Zheng-Yu He, Yong-Lan Li, Yuan Xue, Hua-Dan |
| description | Objective
To develop and validate a model that can preoperatively identify the ovarian clear cell carcinoma (OCCC) subtype in epithelial ovarian cancer (EOC) using CT imaging radiomics and clinical data.
Material and methods
We retrospectively analyzed data from 282 patients with EOC (training set = 225, testing set = 57) who underwent pre-surgery CT examinations. Patients were categorized into OCCC or other EOC subtypes based on postoperative pathology. Seven clinical characteristics (age, cancer antigen [CA]-125, CA-199, endometriosis, venous thromboembolism, hypercalcemia, stage) were collected. Primary tumors were manually delineated on portal venous-phase images, and 1218 radiomic features were extracted. The F-test-based feature selection method and logistic regression algorithm were used to build the radiomic signature, clinical model, and integrated model. To explore the effects of integrated model-assisted diagnosis, five radiologists independently interpreted images in the testing set and reevaluated cases two weeks later with knowledge of the integrated model’s output. The diagnostic performances of the predictive models, radiologists, and radiologists aided by the integrated model were evaluated.
Results
The integrated model containing the radiomic signature (constructed by four wavelet radiomic features) and three clinical characteristics (CA-125, endometriosis, and hypercalcinemia), showed better diagnostic performance (AUC = 0.863 [0.762–0.964]) than the clinical model (AUC = 0.792 [0.630–0.953],
p
= 0.295) and the radiomic signature alone (AUC = 0.781 [0.636–0.926],
p
= 0.185). The diagnostic sensitivities of the radiologists were significantly improved when using the integrated model (
p
= 0.023–0.041), while the specificities and accuracies were maintained (
p
= 0.074–1.000).
Conclusion
Our integrated model shows great potential to facilitate the early identification of the OCCC subtype in EOC, which may enhance subtype-specific therapy and clinical management. |
| doi_str_mv | 10.1007/s11547-023-01666-x |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2830218622</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2830218622</sourcerecordid><originalsourceid>FETCH-LOGICAL-c326t-8ab1887b24d72b11aae7da155fd7fa1902d2eecc62d695e801a45adcb650e0443</originalsourceid><addsrcrecordid>eNp9kc1u1TAQhS0EoqXwAiyQJTZsAv5JHIcdqviTKrGgXVsTe3Kvq8QOdlI1z8LL4vYWWrHoakbj75yx5hDymrP3nLH2Q-a8qduKCVkxrpSqrp-QY66FqlSn5dMH_RF5kfMlYzXjrHtOjmQrlRZCH5PfPxF92NEetxgcXfZIl3WK6SO1cZrXBcssTnGXYN5v1E-ww6qHXMYJnI-TtxQCjFv2me5xnDP1DsPih43GK0geArUjQqIWx5FaSNaHOAHNa79sM1IfKM6-rB09jPcSCBbTS_JsgDHjq7t6Qi6-fD4__Vad_fj6_fTTWWWlUEuloedat72oXSt6zgGwdcCbZnDtALxjwglEa5VwqmtQMw51A872qmHI6lqekHcH3znFXyvmxUw-3_wXAsY1G6ElE1wrIQr69j_0Mq6pHOCWUjVTsusKJQ6UTTHnhIOZUzld2gxn5iY6c4jOlOjMbXTmuoje3Fmv_YTun-RvVgWQByCXp7DDdL_7Eds_TrWobQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2836406399</pqid></control><display><type>article</type><title>Seeing beyond the tumor: computed tomography image-based radiomic analysis helps identify ovarian clear cell carcinoma subtype in epithelial ovarian cancer</title><source>SpringerNature Journals</source><creator>Ren, Jing ; Mao, Li ; Zhao, Jia ; Li, Xiu-Li ; Wang, Chen ; Liu, Xin-Yu ; Jin, Zheng-Yu ; He, Yong-Lan ; Li, Yuan ; Xue, Hua-Dan</creator><creatorcontrib>Ren, Jing ; Mao, Li ; Zhao, Jia ; Li, Xiu-Li ; Wang, Chen ; Liu, Xin-Yu ; Jin, Zheng-Yu ; He, Yong-Lan ; Li, Yuan ; Xue, Hua-Dan</creatorcontrib><description>Objective
To develop and validate a model that can preoperatively identify the ovarian clear cell carcinoma (OCCC) subtype in epithelial ovarian cancer (EOC) using CT imaging radiomics and clinical data.
Material and methods
We retrospectively analyzed data from 282 patients with EOC (training set = 225, testing set = 57) who underwent pre-surgery CT examinations. Patients were categorized into OCCC or other EOC subtypes based on postoperative pathology. Seven clinical characteristics (age, cancer antigen [CA]-125, CA-199, endometriosis, venous thromboembolism, hypercalcemia, stage) were collected. Primary tumors were manually delineated on portal venous-phase images, and 1218 radiomic features were extracted. The F-test-based feature selection method and logistic regression algorithm were used to build the radiomic signature, clinical model, and integrated model. To explore the effects of integrated model-assisted diagnosis, five radiologists independently interpreted images in the testing set and reevaluated cases two weeks later with knowledge of the integrated model’s output. The diagnostic performances of the predictive models, radiologists, and radiologists aided by the integrated model were evaluated.
Results
The integrated model containing the radiomic signature (constructed by four wavelet radiomic features) and three clinical characteristics (CA-125, endometriosis, and hypercalcinemia), showed better diagnostic performance (AUC = 0.863 [0.762–0.964]) than the clinical model (AUC = 0.792 [0.630–0.953],
p
= 0.295) and the radiomic signature alone (AUC = 0.781 [0.636–0.926],
p
= 0.185). The diagnostic sensitivities of the radiologists were significantly improved when using the integrated model (
p
= 0.023–0.041), while the specificities and accuracies were maintained (
p
= 0.074–1.000).
Conclusion
Our integrated model shows great potential to facilitate the early identification of the OCCC subtype in EOC, which may enhance subtype-specific therapy and clinical management.</description><identifier>ISSN: 1826-6983</identifier><identifier>ISSN: 0033-8362</identifier><identifier>EISSN: 1826-6983</identifier><identifier>DOI: 10.1007/s11547-023-01666-x</identifier><identifier>PMID: 37368228</identifier><language>eng</language><publisher>Milan: Springer Milan</publisher><subject>Abdominal Radiology ; Algorithms ; Antigens ; Cancer ; Computed tomography ; Diagnostic Radiology ; Diagnostic systems ; Endometriosis ; Imaging ; Interventional Radiology ; Medical imaging ; Medicine ; Medicine & Public Health ; Neuroradiology ; Ovarian cancer ; Performance prediction ; Prediction models ; Radiology ; Radiomics ; Tumors ; Ultrasound</subject><ispartof>Radiologia medica, 2023-08, Vol.128 (8), p.900-911</ispartof><rights>Italian Society of Medical Radiology 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. Italian Society of Medical Radiology.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c326t-8ab1887b24d72b11aae7da155fd7fa1902d2eecc62d695e801a45adcb650e0443</cites><orcidid>0000-0003-2567-9710</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/s11547-023-01666-x$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11547-023-01666-x$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>315,781,785,27929,27930,41493,42562,51324</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37368228$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ren, Jing</creatorcontrib><creatorcontrib>Mao, Li</creatorcontrib><creatorcontrib>Zhao, Jia</creatorcontrib><creatorcontrib>Li, Xiu-Li</creatorcontrib><creatorcontrib>Wang, Chen</creatorcontrib><creatorcontrib>Liu, Xin-Yu</creatorcontrib><creatorcontrib>Jin, Zheng-Yu</creatorcontrib><creatorcontrib>He, Yong-Lan</creatorcontrib><creatorcontrib>Li, Yuan</creatorcontrib><creatorcontrib>Xue, Hua-Dan</creatorcontrib><title>Seeing beyond the tumor: computed tomography image-based radiomic analysis helps identify ovarian clear cell carcinoma subtype in epithelial ovarian cancer</title><title>Radiologia medica</title><addtitle>Radiol med</addtitle><addtitle>Radiol Med</addtitle><description>Objective
To develop and validate a model that can preoperatively identify the ovarian clear cell carcinoma (OCCC) subtype in epithelial ovarian cancer (EOC) using CT imaging radiomics and clinical data.
Material and methods
We retrospectively analyzed data from 282 patients with EOC (training set = 225, testing set = 57) who underwent pre-surgery CT examinations. Patients were categorized into OCCC or other EOC subtypes based on postoperative pathology. Seven clinical characteristics (age, cancer antigen [CA]-125, CA-199, endometriosis, venous thromboembolism, hypercalcemia, stage) were collected. Primary tumors were manually delineated on portal venous-phase images, and 1218 radiomic features were extracted. The F-test-based feature selection method and logistic regression algorithm were used to build the radiomic signature, clinical model, and integrated model. To explore the effects of integrated model-assisted diagnosis, five radiologists independently interpreted images in the testing set and reevaluated cases two weeks later with knowledge of the integrated model’s output. The diagnostic performances of the predictive models, radiologists, and radiologists aided by the integrated model were evaluated.
Results
The integrated model containing the radiomic signature (constructed by four wavelet radiomic features) and three clinical characteristics (CA-125, endometriosis, and hypercalcinemia), showed better diagnostic performance (AUC = 0.863 [0.762–0.964]) than the clinical model (AUC = 0.792 [0.630–0.953],
p
= 0.295) and the radiomic signature alone (AUC = 0.781 [0.636–0.926],
p
= 0.185). The diagnostic sensitivities of the radiologists were significantly improved when using the integrated model (
p
= 0.023–0.041), while the specificities and accuracies were maintained (
p
= 0.074–1.000).
Conclusion
Our integrated model shows great potential to facilitate the early identification of the OCCC subtype in EOC, which may enhance subtype-specific therapy and clinical management.</description><subject>Abdominal Radiology</subject><subject>Algorithms</subject><subject>Antigens</subject><subject>Cancer</subject><subject>Computed tomography</subject><subject>Diagnostic Radiology</subject><subject>Diagnostic systems</subject><subject>Endometriosis</subject><subject>Imaging</subject><subject>Interventional Radiology</subject><subject>Medical imaging</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Neuroradiology</subject><subject>Ovarian cancer</subject><subject>Performance prediction</subject><subject>Prediction models</subject><subject>Radiology</subject><subject>Radiomics</subject><subject>Tumors</subject><subject>Ultrasound</subject><issn>1826-6983</issn><issn>0033-8362</issn><issn>1826-6983</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNp9kc1u1TAQhS0EoqXwAiyQJTZsAv5JHIcdqviTKrGgXVsTe3Kvq8QOdlI1z8LL4vYWWrHoakbj75yx5hDymrP3nLH2Q-a8qduKCVkxrpSqrp-QY66FqlSn5dMH_RF5kfMlYzXjrHtOjmQrlRZCH5PfPxF92NEetxgcXfZIl3WK6SO1cZrXBcssTnGXYN5v1E-ww6qHXMYJnI-TtxQCjFv2me5xnDP1DsPih43GK0geArUjQqIWx5FaSNaHOAHNa79sM1IfKM6-rB09jPcSCBbTS_JsgDHjq7t6Qi6-fD4__Vad_fj6_fTTWWWlUEuloedat72oXSt6zgGwdcCbZnDtALxjwglEa5VwqmtQMw51A872qmHI6lqekHcH3znFXyvmxUw-3_wXAsY1G6ElE1wrIQr69j_0Mq6pHOCWUjVTsusKJQ6UTTHnhIOZUzld2gxn5iY6c4jOlOjMbXTmuoje3Fmv_YTun-RvVgWQByCXp7DDdL_7Eds_TrWobQ</recordid><startdate>20230801</startdate><enddate>20230801</enddate><creator>Ren, Jing</creator><creator>Mao, Li</creator><creator>Zhao, Jia</creator><creator>Li, Xiu-Li</creator><creator>Wang, Chen</creator><creator>Liu, Xin-Yu</creator><creator>Jin, Zheng-Yu</creator><creator>He, Yong-Lan</creator><creator>Li, Yuan</creator><creator>Xue, Hua-Dan</creator><general>Springer Milan</general><general>Springer Nature B.V</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-2567-9710</orcidid></search><sort><creationdate>20230801</creationdate><title>Seeing beyond the tumor: computed tomography image-based radiomic analysis helps identify ovarian clear cell carcinoma subtype in epithelial ovarian cancer</title><author>Ren, Jing ; Mao, Li ; Zhao, Jia ; Li, Xiu-Li ; Wang, Chen ; Liu, Xin-Yu ; Jin, Zheng-Yu ; He, Yong-Lan ; Li, Yuan ; Xue, Hua-Dan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c326t-8ab1887b24d72b11aae7da155fd7fa1902d2eecc62d695e801a45adcb650e0443</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Abdominal Radiology</topic><topic>Algorithms</topic><topic>Antigens</topic><topic>Cancer</topic><topic>Computed tomography</topic><topic>Diagnostic Radiology</topic><topic>Diagnostic systems</topic><topic>Endometriosis</topic><topic>Imaging</topic><topic>Interventional Radiology</topic><topic>Medical imaging</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Neuroradiology</topic><topic>Ovarian cancer</topic><topic>Performance prediction</topic><topic>Prediction models</topic><topic>Radiology</topic><topic>Radiomics</topic><topic>Tumors</topic><topic>Ultrasound</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ren, Jing</creatorcontrib><creatorcontrib>Mao, Li</creatorcontrib><creatorcontrib>Zhao, Jia</creatorcontrib><creatorcontrib>Li, Xiu-Li</creatorcontrib><creatorcontrib>Wang, Chen</creatorcontrib><creatorcontrib>Liu, Xin-Yu</creatorcontrib><creatorcontrib>Jin, Zheng-Yu</creatorcontrib><creatorcontrib>He, Yong-Lan</creatorcontrib><creatorcontrib>Li, Yuan</creatorcontrib><creatorcontrib>Xue, Hua-Dan</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Radiologia medica</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ren, Jing</au><au>Mao, Li</au><au>Zhao, Jia</au><au>Li, Xiu-Li</au><au>Wang, Chen</au><au>Liu, Xin-Yu</au><au>Jin, Zheng-Yu</au><au>He, Yong-Lan</au><au>Li, Yuan</au><au>Xue, Hua-Dan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Seeing beyond the tumor: computed tomography image-based radiomic analysis helps identify ovarian clear cell carcinoma subtype in epithelial ovarian cancer</atitle><jtitle>Radiologia medica</jtitle><stitle>Radiol med</stitle><addtitle>Radiol Med</addtitle><date>2023-08-01</date><risdate>2023</risdate><volume>128</volume><issue>8</issue><spage>900</spage><epage>911</epage><pages>900-911</pages><issn>1826-6983</issn><issn>0033-8362</issn><eissn>1826-6983</eissn><abstract>Objective
To develop and validate a model that can preoperatively identify the ovarian clear cell carcinoma (OCCC) subtype in epithelial ovarian cancer (EOC) using CT imaging radiomics and clinical data.
Material and methods
We retrospectively analyzed data from 282 patients with EOC (training set = 225, testing set = 57) who underwent pre-surgery CT examinations. Patients were categorized into OCCC or other EOC subtypes based on postoperative pathology. Seven clinical characteristics (age, cancer antigen [CA]-125, CA-199, endometriosis, venous thromboembolism, hypercalcemia, stage) were collected. Primary tumors were manually delineated on portal venous-phase images, and 1218 radiomic features were extracted. The F-test-based feature selection method and logistic regression algorithm were used to build the radiomic signature, clinical model, and integrated model. To explore the effects of integrated model-assisted diagnosis, five radiologists independently interpreted images in the testing set and reevaluated cases two weeks later with knowledge of the integrated model’s output. The diagnostic performances of the predictive models, radiologists, and radiologists aided by the integrated model were evaluated.
Results
The integrated model containing the radiomic signature (constructed by four wavelet radiomic features) and three clinical characteristics (CA-125, endometriosis, and hypercalcinemia), showed better diagnostic performance (AUC = 0.863 [0.762–0.964]) than the clinical model (AUC = 0.792 [0.630–0.953],
p
= 0.295) and the radiomic signature alone (AUC = 0.781 [0.636–0.926],
p
= 0.185). The diagnostic sensitivities of the radiologists were significantly improved when using the integrated model (
p
= 0.023–0.041), while the specificities and accuracies were maintained (
p
= 0.074–1.000).
Conclusion
Our integrated model shows great potential to facilitate the early identification of the OCCC subtype in EOC, which may enhance subtype-specific therapy and clinical management.</abstract><cop>Milan</cop><pub>Springer Milan</pub><pmid>37368228</pmid><doi>10.1007/s11547-023-01666-x</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0003-2567-9710</orcidid></addata></record> |
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| subjects | Abdominal Radiology Algorithms Antigens Cancer Computed tomography Diagnostic Radiology Diagnostic systems Endometriosis Imaging Interventional Radiology Medical imaging Medicine Medicine & Public Health Neuroradiology Ovarian cancer Performance prediction Prediction models Radiology Radiomics Tumors Ultrasound |
| title | Seeing beyond the tumor: computed tomography image-based radiomic analysis helps identify ovarian clear cell carcinoma subtype in epithelial ovarian cancer |
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