Role of artificial intelligence applied to ultrasound in gynecology oncology: A systematic review

The aim of this paper was to explore the role of artificial intelligence (AI) applied to ultrasound imaging in gynecology oncology. Web of Science, PubMed, and Scopus databases were searched. All studies were imported to RAYYAN QCRI software. The overall quality of the included studies was assessed...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	International journal of cancer Ročník 155; číslo 10; s. 1832 - 1845
Hlavní autoři:	Moro, Francesca, Ciancia, Marianna, Zace, Drieda, Vagni, Marica, Tran, Huong Elena, Giudice, Maria Teresa, Zoccoli, Sofia Gambigliani, Mascilini, Floriana, Ciccarone, Francesca, Boldrini, Luca, D'Antonio, Francesco, Scambia, Giovanni, Testa, Antonia Carla
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Hoboken, USA John Wiley & Sons, Inc 15.11.2024 Wiley Subscription Services, Inc
Témata:	Artificial Intelligence Cervical cancer Diagnosis Endometrial cancer Endometrial Neoplasms - diagnostic imaging Endometrial Neoplasms - pathology Female Genital Neoplasms, Female - diagnostic imaging Genital Neoplasms, Female - pathology Gynecological cancer Gynecology Gynecology - methods Humans Image processing Machine Learning Malignancy Oncology Ovarian cancer Ovarian Neoplasms - diagnostic imaging Ovarian Neoplasms - pathology Radiomics Systematic review tumors Ultrasonic imaging ultrasonography Ultrasonography - methods Ultrasound Uterine cancer Uterine Cervical Neoplasms - diagnostic imaging Uterine Cervical Neoplasms - pathology gynecology tumors machine learning ultrasonography artificial intelligence
ISSN:	0020-7136, 1097-0215, 1097-0215
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	The aim of this paper was to explore the role of artificial intelligence (AI) applied to ultrasound imaging in gynecology oncology. Web of Science, PubMed, and Scopus databases were searched. All studies were imported to RAYYAN QCRI software. The overall quality of the included studies was assessed using QUADAS‐AI tool. Fifty studies were included, of these 37/50 (74.0%) on ovarian masses or ovarian cancer, 5/50 (10.0%) on endometrial cancer, 5/50 (10.0%) on cervical cancer, and 3/50 (6.0%) on other malignancies. Most studies were at high risk of bias for subject selection (i.e., sample size, source, or scanner model were not specified; data were not derived from open‐source datasets; imaging preprocessing was not performed) and index test (AI models was not externally validated) and at low risk of bias for reference standard (i.e., the reference standard correctly classified the target condition) and workflow (i.e., the time between index test and reference standard was reasonable). Most studies presented machine learning models (33/50, 66.0%) for the diagnosis and histopathological correlation of ovarian masses, while others focused on automatic segmentation, reproducibility of radiomics features, improvement of image quality, prediction of therapy resistance, progression‐free survival, and genetic mutation. The current evidence supports the role of AI as a complementary clinical and research tool in diagnosis, patient stratification, and prediction of histopathological correlation in gynecological malignancies. For example, the high performance of AI models to discriminate between benign and malignant ovarian masses or to predict their specific histology can improve the diagnostic accuracy of imaging methods. What's new? Artificial intelligence is increasingly used in advanced medicine and is now being applied to ultrasound imaging in gynecological oncology. However, a deeper understanding of the capabilities and limitations of AI would help improve the management of cancer patients from diagnosis to treatment. The current evidence analyzed in this systematic review of 50 studies supports the role of AI as a complementary research and clinical tool in diagnosis, patient stratification, and prediction of histopathological correlation in gynecological malignancies. AI applications are however still largely lacking for pathologies other than ovarian cancer.
AbstractList	The aim of this paper was to explore the role of artificial intelligence (AI) applied to ultrasound imaging in gynecology oncology. Web of Science, PubMed, and Scopus databases were searched. All studies were imported to RAYYAN QCRI software. The overall quality of the included studies was assessed using QUADAS-AI tool. Fifty studies were included, of these 37/50 (74.0%) on ovarian masses or ovarian cancer, 5/50 (10.0%) on endometrial cancer, 5/50 (10.0%) on cervical cancer, and 3/50 (6.0%) on other malignancies. Most studies were at high risk of bias for subject selection (i.e., sample size, source, or scanner model were not specified; data were not derived from open-source datasets; imaging preprocessing was not performed) and index test (AI models was not externally validated) and at low risk of bias for reference standard (i.e., the reference standard correctly classified the target condition) and workflow (i.e., the time between index test and reference standard was reasonable). Most studies presented machine learning models (33/50, 66.0%) for the diagnosis and histopathological correlation of ovarian masses, while others focused on automatic segmentation, reproducibility of radiomics features, improvement of image quality, prediction of therapy resistance, progression-free survival, and genetic mutation. The current evidence supports the role of AI as a complementary clinical and research tool in diagnosis, patient stratification, and prediction of histopathological correlation in gynecological malignancies. For example, the high performance of AI models to discriminate between benign and malignant ovarian masses or to predict their specific histology can improve the diagnostic accuracy of imaging methods.The aim of this paper was to explore the role of artificial intelligence (AI) applied to ultrasound imaging in gynecology oncology. Web of Science, PubMed, and Scopus databases were searched. All studies were imported to RAYYAN QCRI software. The overall quality of the included studies was assessed using QUADAS-AI tool. Fifty studies were included, of these 37/50 (74.0%) on ovarian masses or ovarian cancer, 5/50 (10.0%) on endometrial cancer, 5/50 (10.0%) on cervical cancer, and 3/50 (6.0%) on other malignancies. Most studies were at high risk of bias for subject selection (i.e., sample size, source, or scanner model were not specified; data were not derived from open-source datasets; imaging preprocessing was not performed) and index test (AI models was not externally validated) and at low risk of bias for reference standard (i.e., the reference standard correctly classified the target condition) and workflow (i.e., the time between index test and reference standard was reasonable). Most studies presented machine learning models (33/50, 66.0%) for the diagnosis and histopathological correlation of ovarian masses, while others focused on automatic segmentation, reproducibility of radiomics features, improvement of image quality, prediction of therapy resistance, progression-free survival, and genetic mutation. The current evidence supports the role of AI as a complementary clinical and research tool in diagnosis, patient stratification, and prediction of histopathological correlation in gynecological malignancies. For example, the high performance of AI models to discriminate between benign and malignant ovarian masses or to predict their specific histology can improve the diagnostic accuracy of imaging methods. The aim of this paper was to explore the role of artificial intelligence (AI) applied to ultrasound imaging in gynecology oncology. Web of Science, PubMed, and Scopus databases were searched. All studies were imported to RAYYAN QCRI software. The overall quality of the included studies was assessed using QUADAS‐AI tool. Fifty studies were included, of these 37/50 (74.0%) on ovarian masses or ovarian cancer, 5/50 (10.0%) on endometrial cancer, 5/50 (10.0%) on cervical cancer, and 3/50 (6.0%) on other malignancies. Most studies were at high risk of bias for subject selection (i.e., sample size, source, or scanner model were not specified; data were not derived from open‐source datasets; imaging preprocessing was not performed) and index test (AI models was not externally validated) and at low risk of bias for reference standard (i.e., the reference standard correctly classified the target condition) and workflow (i.e., the time between index test and reference standard was reasonable). Most studies presented machine learning models (33/50, 66.0%) for the diagnosis and histopathological correlation of ovarian masses, while others focused on automatic segmentation, reproducibility of radiomics features, improvement of image quality, prediction of therapy resistance, progression‐free survival, and genetic mutation. The current evidence supports the role of AI as a complementary clinical and research tool in diagnosis, patient stratification, and prediction of histopathological correlation in gynecological malignancies. For example, the high performance of AI models to discriminate between benign and malignant ovarian masses or to predict their specific histology can improve the diagnostic accuracy of imaging methods. The aim of this paper was to explore the role of artificial intelligence (AI) applied to ultrasound imaging in gynecology oncology. Web of Science, PubMed, and Scopus databases were searched. All studies were imported to RAYYAN QCRI software. The overall quality of the included studies was assessed using QUADAS‐AI tool. Fifty studies were included, of these 37/50 (74.0%) on ovarian masses or ovarian cancer, 5/50 (10.0%) on endometrial cancer, 5/50 (10.0%) on cervical cancer, and 3/50 (6.0%) on other malignancies. Most studies were at high risk of bias for subject selection (i.e., sample size, source, or scanner model were not specified; data were not derived from open‐source datasets; imaging preprocessing was not performed) and index test (AI models was not externally validated) and at low risk of bias for reference standard (i.e., the reference standard correctly classified the target condition) and workflow (i.e., the time between index test and reference standard was reasonable). Most studies presented machine learning models (33/50, 66.0%) for the diagnosis and histopathological correlation of ovarian masses, while others focused on automatic segmentation, reproducibility of radiomics features, improvement of image quality, prediction of therapy resistance, progression‐free survival, and genetic mutation. The current evidence supports the role of AI as a complementary clinical and research tool in diagnosis, patient stratification, and prediction of histopathological correlation in gynecological malignancies. For example, the high performance of AI models to discriminate between benign and malignant ovarian masses or to predict their specific histology can improve the diagnostic accuracy of imaging methods. What's new? Artificial intelligence is increasingly used in advanced medicine and is now being applied to ultrasound imaging in gynecological oncology. However, a deeper understanding of the capabilities and limitations of AI would help improve the management of cancer patients from diagnosis to treatment. The current evidence analyzed in this systematic review of 50 studies supports the role of AI as a complementary research and clinical tool in diagnosis, patient stratification, and prediction of histopathological correlation in gynecological malignancies. AI applications are however still largely lacking for pathologies other than ovarian cancer.
Author	Scambia, Giovanni Boldrini, Luca Tran, Huong Elena Mascilini, Floriana Ciancia, Marianna Zoccoli, Sofia Gambigliani Vagni, Marica Giudice, Maria Teresa D'Antonio, Francesco Moro, Francesca Testa, Antonia Carla Zace, Drieda Ciccarone, Francesca
Author_xml	– sequence: 1 givenname: Francesca orcidid: 0000-0002-5070-7245 surname: Moro fullname: Moro, Francesca email: morofrancy@gmail.com, francesca.moro@policlinicogemelli.it organization: Fondazione Policlinico Universitario Agostino Gemelli, IRCCS – sequence: 2 givenname: Marianna surname: Ciancia fullname: Ciancia, Marianna organization: Università degli studi di Padova – sequence: 3 givenname: Drieda surname: Zace fullname: Zace, Drieda organization: Tor Vergata University – sequence: 4 givenname: Marica surname: Vagni fullname: Vagni, Marica organization: Università Cattolica del Sacro Cuore – sequence: 5 givenname: Huong Elena surname: Tran fullname: Tran, Huong Elena organization: Fondazione Policlinico Universitario Agostino Gemelli, IRCCS – sequence: 6 givenname: Maria Teresa surname: Giudice fullname: Giudice, Maria Teresa organization: Fondazione Policlinico Universitario Agostino Gemelli, IRCCS – sequence: 7 givenname: Sofia Gambigliani surname: Zoccoli fullname: Zoccoli, Sofia Gambigliani organization: Azienda Ospedaliero Universitaria Policlinico – sequence: 8 givenname: Floriana surname: Mascilini fullname: Mascilini, Floriana organization: Fondazione Policlinico Universitario Agostino Gemelli, IRCCS – sequence: 9 givenname: Francesca surname: Ciccarone fullname: Ciccarone, Francesca organization: Fondazione Policlinico Universitario Agostino Gemelli, IRCCS – sequence: 10 givenname: Luca surname: Boldrini fullname: Boldrini, Luca organization: Fondazione Policlinico Universitario Agostino Gemelli, IRCCS – sequence: 11 givenname: Francesco surname: D'Antonio fullname: D'Antonio, Francesco organization: University of Chieti – sequence: 12 givenname: Giovanni orcidid: 0000-0002-9503-9041 surname: Scambia fullname: Scambia, Giovanni organization: Università Cattolica del Sacro Cuore – sequence: 13 givenname: Antonia Carla surname: Testa fullname: Testa, Antonia Carla organization: Università Cattolica del Sacro Cuore
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/38989809$$D View this record in MEDLINE/PubMed
BookMark	eNp1kUFr3DAQhUVJaTZpD_0DRdBLe3AyI0W21VtY0jYlUCi5C1keL1q00tayE_zvq2Y3l9Ciw0jM9x6aeWfsJKZIjL1HuEAAcem37kIq0OIVWyHopgKB6oStSg-qBmV9ys5y3gIgKrh6w05lq8sBvWL2VwrE08DtOPnBO28D93GiEPyGoiNu9_vgqedT4nOYRpvTHPuC8M0SyaWQNgtP8XD5wq95XvJEOzt5x0d68PT4lr0ebMj07ljP2f3Xm_v19-ru57fb9fVd5WTbioocqX4AB1e1rNtOE4HqFYpaY9102LaIrgXRg9XC1g2IzqIU2PfljXUnz9mng-1-TL9nypPZ-ezKHDZSmrOR0LQNthpVQT--QLdpHmP5nJEoNILSqi7UhyM1dzvqzX70Ozsu5nl3Bbg8AG5MOY80GOenMniKZU0-GATzNx1T0jFP6RTF5xeKZ9N_sUf3Rx9o-T9obn-sD4o_yHedaA
CitedBy_id	crossref_primary_10_1055_a_2368_9201 crossref_primary_10_3389_fonc_2025_1646826 crossref_primary_10_1186_s12880_025_01705_1 crossref_primary_10_1002_uog_29171 crossref_primary_10_1111_aogs_15146 crossref_primary_10_1002_uog_29168 crossref_primary_10_1016_j_slast_2025_100301 crossref_primary_10_3389_fonc_2025_1567024 crossref_primary_10_3390_jpm15020076 crossref_primary_10_1016_j_ijgc_2025_102653 crossref_primary_10_3390_cancers17071060 crossref_primary_10_1016_j_tranon_2025_102281 crossref_primary_10_7759_cureus_85884 crossref_primary_10_3390_cancers17111810 crossref_primary_10_3390_diagnostics15060735
Cites_doi	10.1016/j.bpobgyn.2014.04.006 10.1007/s40477-020-00503-5 10.1186/s12880-022-00879-2 10.1016/j.pacs.2022.100420 10.7785/tcrtexpress.2013.600273 10.1007/978-981-19-1111-8_6 10.1109/TRPMS.2019.2899538 10.3389/fonc.2020.614201 10.3390/cancers15041121 10.1136/ijgc-2023-004429 10.1002/uog.3996 10.1016/j.ygyno.2021.04.004 10.3349/ymj.2022.0239 10.1609/aimag.v38i1.2709 10.1007/978-3-030-00764-5_36 10.52054/FVVO.13.2.016 10.1016/j.bpobgyn.2014.04.008 10.1259/bjr/13992649 10.1038/s41591-021-01517-0 10.1002/uog.23530 10.1186/s12911-022-02047-6 10.1007/s00330-010-1750-6 10.1148/radiol.211367 10.1136/ijgc-2020-002230 10.1158/1078-0432.CCR-13-1376 10.3390/diagnostics11050812 10.1007/s11227-020-03421-9 10.1016/j.bpobgyn.2014.04.009 10.1259/bjr.20201375 10.3390/cancers13030573 10.1002/uog.22069 10.1136/bmj.g5920 10.1007/s00330-022-09180-w 10.1109/ICDH57206.2022.00023 10.1007/s40815-018-0456-9 10.1177/0161734621998091 10.1177/15330338221099396 10.1016/j.ygyno.2022.07.024 10.1002/uog.5365 10.3322/caac.21552 10.1002/uog.11177 10.7326/M21-2234 10.1109/ACCESS.2018.2836950 10.1016/j.artmed.2021.102164 10.7326/0003-4819-155-8-201110180-00009 10.1371/journal.pone.0219388 10.1002/uog.26080 10.1002/uog.18909 10.1002/uog.24805 10.1016/j.ejso.2021.06.023 10.1016/j.ajog.2022.05.029 10.3390/cancers14215291 10.3389/fonc.2021.770683 10.1186/s12885-019-5629-x 10.1007/s00404-022-06578-1 10.1016/j.ultrasmedbio.2015.11.014 10.7785/tcrt.2012.500272 10.3389/fgene.2021.753948 10.1007/s10278-012-9553-8 10.1007/978-0-387-45528-0 10.1016/j.eclinm.2022.101662 10.1200/JCO.2005.01.7632 10.1038/s41598-020-73505-2 10.1155/2022/1907322 10.1038/nrclinonc.2017.141 10.1007/s10278-022-00620-z 10.1055/s-0033-1354809 10.1109/ICRCICN.2015.7434224 10.1007/978-981-33-4673-4_22 10.1007/s00330-020-06692-1
ContentType	Journal Article
Copyright	2024 The Author(s). published by John Wiley & Sons Ltd on behalf of UICC. 2024 The Author(s). International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC. 2024. This article is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml	– notice: 2024 The Author(s). published by John Wiley & Sons Ltd on behalf of UICC. – notice: 2024 The Author(s). International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC. – notice: 2024. This article is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID	24P AAYXX CITATION CGR CUY CVF ECM EIF NPM 7T5 7TO 7U9 H94 K9. 7X8
DOI	10.1002/ijc.35092
DatabaseName	Wiley Online Library Open Access CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Immunology Abstracts Oncogenes and Growth Factors Abstracts Virology and AIDS Abstracts AIDS and Cancer Research Abstracts ProQuest Health & Medical Complete (Alumni) MEDLINE - Academic
DatabaseTitle	CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) AIDS and Cancer Research Abstracts ProQuest Health & Medical Complete (Alumni) Immunology Abstracts Virology and AIDS Abstracts Oncogenes and Growth Factors Abstracts MEDLINE - Academic
DatabaseTitleList	MEDLINE - Academic CrossRef MEDLINE AIDS and Cancer Research Abstracts
Database_xml	– sequence: 1 dbid: 24P name: Wiley Online Library Open Access url: https://authorservices.wiley.com/open-science/open-access/browse-journals.html sourceTypes: Publisher – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Medicine
EISSN	1097-0215
EndPage	1845
ExternalDocumentID	38989809 10_1002_ijc_35092 IJC35092
Genre	researchArticle Systematic Review Journal Article
GroupedDBID	--- -~X .3N .GA 05W 0R~ 10A 1L6 1OB 1OC 1ZS 24P 33P 3SF 3WU 4.4 4ZD 50Y 50Z 51W 51X 52M 52N 52O 52P 52R 52S 52T 52U 52V 52W 52X 5GY 5VS 66C 702 7PT 8-0 8-1 8-3 8-4 8-5 8UM 930 A01 A03 AAESR AAEVG AAHHS AAHQN AAIPD AAMNL AANLZ AAONW AAXRX AAYCA AAZKR ABCQN ABCUV ABIJN ABJNI ABLJU ABOCM ABPVW ABQWH ABXGK ACAHQ ACCFJ ACCZN ACFBH ACGFO ACGFS ACGOF ACIWK ACMXC ACPOU ACPRK ACXBN ACXQS ADBBV ADBTR ADEOM ADIZJ ADKYN ADMGS ADOZA ADXAS ADZMN ADZOD AEEZP AEGXH AEIGN AEIMD AENEX AEQDE AEUQT AEUYR AFBPY AFFPM AFGKR AFPWT AFRAH AFWVQ AFZJQ AHBTC AHMBA AIACR AIAGR AITYG AIURR AIWBW AJBDE ALAGY ALMA_UNASSIGNED_HOLDINGS ALUQN ALVPJ AMBMR AMYDB ATUGU AZBYB AZVAB BAFTC BFHJK BHBCM BMXJE BROTX BRXPI BY8 C45 CS3 D-6 D-7 D-E D-F DCZOG DPXWK DR2 DRFUL DRMAN DRSTM DU5 EBS F00 F01 F04 F5P FUBAC G-S G.N GNP GODZA H.X HBH HGLYW HHY HHZ HZ~ IH2 IX1 J0M JPC KBYEO KQQ L7B LATKE LAW LC2 LC3 LEEKS LH4 LITHE LOXES LP6 LP7 LUTES LYRES MEWTI MK4 MRFUL MRMAN MRSTM MSFUL MSMAN MSSTM MXFUL MXMAN MXSTM N04 N05 N9A NF~ NNB O66 O9- OIG OK1 OVD P2P P2W P2X P2Z P4B P4D PQQKQ Q.N Q11 QB0 QRW R.K RIWAO ROL RWI RX1 RYL SUPJJ TEORI UB1 UDS V2E V8K V9Y W2D W8V W99 WBKPD WHWMO WIB WIH WIJ WIK WJL WOHZO WQJ WRC WUP WVDHM WWO WXI WXSBR XG1 XPP XV2 ZZTAW ~IA ~WT AAMMB AAYXX AEFGJ AEYWJ AGHNM AGXDD AGYGG AIDQK AIDYY CITATION EMOBN O8X CGR CUY CVF ECM EIF NPM 7T5 7TO 7U9 H94 K9. 7X8
ID	FETCH-LOGICAL-c3882-ece5df0c046368b9ee05d51269167b18811c802d0a92a6702ba1321dda9216b3
IEDL.DBID	24P
ISICitedReferencesCount	16
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001268819700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0020-7136 1097-0215
IngestDate	Sun Nov 09 10:38:22 EST 2025 Sat Nov 29 14:23:50 EST 2025 Mon Jul 21 06:02:06 EDT 2025 Sat Nov 29 07:39:35 EST 2025 Tue Nov 18 22:55:02 EST 2025 Wed Jan 22 17:14:53 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	10
Keywords	gynecology tumors machine learning ultrasonography artificial intelligence
Language	English
License	Attribution 2024 The Author(s). International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC.
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c3882-ece5df0c046368b9ee05d51269167b18811c802d0a92a6702ba1321dda9216b3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Undefined-1 ObjectType-Feature-3 content type line 23
ORCID	0000-0002-9503-9041 0000-0002-5070-7245
OpenAccessLink	https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fijc.35092
PMID	38989809
PQID	3129105956
PQPubID	105430
PageCount	14
ParticipantIDs	proquest_miscellaneous_3078718915 proquest_journals_3129105956 pubmed_primary_38989809 crossref_citationtrail_10_1002_ijc_35092 crossref_primary_10_1002_ijc_35092 wiley_primary_10_1002_ijc_35092_IJC35092
PublicationCentury	2000
PublicationDate	15 November 2024
PublicationDateYYYYMMDD	2024-11-15
PublicationDate_xml	– month: 11 year: 2024 text: 15 November 2024 day: 15
PublicationDecade	2020
PublicationPlace	Hoboken, USA
PublicationPlace_xml	– name: Hoboken, USA – name: United States – name: Hoboken
PublicationTitle	International journal of cancer
PublicationTitleAlternate	Int J Cancer
PublicationYear	2024
Publisher	John Wiley & Sons, Inc Wiley Subscription Services, Inc
Publisher_xml	– name: John Wiley & Sons, Inc – name: Wiley Subscription Services, Inc
References	2021; 24 2023; 10:184 2021; 27 2013; 26 2023; 33 2021; 66 2021; 28 2019; 14 2019; 19 2021; 161 2022; 21 2014; 28 2008; 31 2020; 10 2022; 22 2023; 15:1121 2012; 11 2005; 23 2011; 155 2022; 28 2022; 166 2013; 19 2023; 64 2018; 6 2007; 29 2010; 20 2021; 31 2021; 77 2017; 38 2019; 69 2022; 21:15330338221118412 2016; 42 2022; 35 2014; 13 2021; 120:102164 2021; 47 2021; 43 2019; 3 2012 2011 2006 2021; 94 2018; 20 2023; 61 2014; 349 2021; 13 2021; 57 2021; 10 2019; 84 2021; 12 2022; 2022 2021; 11 2017; 14 2022 2020; 30 2022; 60 2021 2020 2013; 34 2018 2007; 80 2022; 14 2014; 35 2018; 51 2022; 53 2021; 174 2015 2022; 306 2022; 227 2022; 304 2012; 40 e_1_2_9_31_1 e_1_2_9_52_1 e_1_2_9_50_1 e_1_2_9_73_1 Acharya UR (e_1_2_9_79_1) 2014; 35 e_1_2_9_10_1 e_1_2_9_35_1 e_1_2_9_56_1 e_1_2_9_77_1 e_1_2_9_12_1 e_1_2_9_33_1 e_1_2_9_54_1 Fischerová D (e_1_2_9_6_1) 2019; 84 Acharya UR (e_1_2_9_75_1) 2012 e_1_2_9_14_1 e_1_2_9_16_1 e_1_2_9_37_1 e_1_2_9_58_1 e_1_2_9_18_1 e_1_2_9_20_1 e_1_2_9_62_1 e_1_2_9_22_1 e_1_2_9_45_1 e_1_2_9_68_1 Yi J (e_1_2_9_64_1) 2022; 21 e_1_2_9_24_1 e_1_2_9_43_1 e_1_2_9_66_1 e_1_2_9_8_1 e_1_2_9_81_1 e_1_2_9_4_1 e_1_2_9_60_1 e_1_2_9_2_1 Yin Q (e_1_2_9_53_1) 2022; 2022 Chiappa V (e_1_2_9_41_1) 2021; 28 Fragomeni SM (e_1_2_9_67_1) 2023; 15 Chen L (e_1_2_9_55_1) 2023; 10 e_1_2_9_49_1 e_1_2_9_28_1 e_1_2_9_47_1 e_1_2_9_30_1 e_1_2_9_74_1 e_1_2_9_51_1 e_1_2_9_72_1 e_1_2_9_11_1 e_1_2_9_34_1 e_1_2_9_57_1 e_1_2_9_78_1 e_1_2_9_13_1 e_1_2_9_32_1 e_1_2_9_76_1 Akter L (e_1_2_9_39_1) 2021 e_1_2_9_15_1 e_1_2_9_38_1 Akazawa M (e_1_2_9_26_1) 2021; 120 Ahmadi E (e_1_2_9_70_1) 2011 e_1_2_9_17_1 e_1_2_9_36_1 e_1_2_9_59_1 e_1_2_9_19_1 e_1_2_9_42_1 e_1_2_9_63_1 e_1_2_9_40_1 e_1_2_9_61_1 e_1_2_9_21_1 e_1_2_9_46_1 e_1_2_9_23_1 e_1_2_9_44_1 e_1_2_9_65_1 Hussein IJ (e_1_2_9_71_1) 2021; 66 e_1_2_9_7_1 e_1_2_9_80_1 e_1_2_9_5_1 e_1_2_9_82_1 e_1_2_9_3_1 e_1_2_9_9_1 e_1_2_9_25_1 e_1_2_9_27_1 e_1_2_9_48_1 e_1_2_9_69_1 e_1_2_9_29_1
References_xml	– volume: 2022 start-page: 1 year: 2022 end-page: 9 article-title: Clinical analysis of 137 cases of ovarian tumors in pregnancy publication-title: J Oncol – volume: 94 year: 2021 article-title: Ultrasound evaluation of ovarian masses and assessment of the extension of ovarian malignancy publication-title: Br J Radiol – volume: 28 start-page: 5 year: 2021 article-title: A decision support system based on radiomics and machine learning to predict the risk of malignancy of ovarian masses from transvaginal ultrasonography and serum CA‐125 publication-title: Eur Radiol Exp – volume: 28 start-page: 28 year: 2022 article-title: Ultrasound‐enhanced U‐Net model for quantitative photoacoustic tomography of ovarian lesions publication-title: Photoacoustics – volume: 27 start-page: 1663 year: 2021 end-page: 1665 article-title: A quality assessment tool for artificial intelligence‐centered diagnostic test accuracy studies: QUADAS‐AI publication-title: Nat Med – volume: 155 start-page: 529 year: 2011 end-page: 536 article-title: QUADAS‐2: a revised tool for the quality assessment of diagnostic accuracy studies publication-title: Ann Intern Med – volume: 6 start-page: 35365 year: 2018 end-page: 35381 article-title: Machine learning and deep learning methods for cybersecurity publication-title: IEEE Access – volume: 31 start-page: 12 year: 2021 end-page: 39 article-title: ESGO/ESTRO/ESP guidelines for the management of patients with endometrial carcinoma publication-title: Int J Gynecol Cancer – volume: 77 start-page: 4151 year: 2021 end-page: 4171 article-title: Ultrasound image analysis technology under deep belief networks in evaluation on the effects of diagnosis and chemotherapy of cervical cancer publication-title: J Supercomput – volume: 174 start-page: 1592 year: 2021 end-page: 1599 article-title: QUADAS‐C: a tool for assessing risk of bias in comparative diagnostic accuracy studies publication-title: Ann Intern Med – volume: 120:102164 year: 2021 article-title: Artificial intelligence in gynecologic cancers: current status and future challenges—a systematic review publication-title: Artif Intell Med – volume: 22 start-page: 22 year: 2022 article-title: Ultrasound‐based radiomics for predicting different pathological subtypes of epithelial ovarian cancer before surgery publication-title: BMC Med Imaging – volume: 26 start-page: 544 year: 2013 end-page: 553 article-title: Ovarian tumor characterization and classification using ultrasound—a new online paradigm publication-title: J Digit Imaging – start-page: 100 year: 2022 end-page: 106 – volume: 14 start-page: 5291 year: 2022 article-title: A deep learning model system for diagnosis and management of adnexal masses publication-title: Cancers – volume: 57 start-page: 155 year: 2021 end-page: 163 article-title: Ultrasound image analysis using deep neural networks for discriminating between benign and malignant ovarian tumors: comparison with expert subjective assessment publication-title: Ultrasound Obstet Gynecol – volume: 14 start-page: 749 year: 2017 end-page: 762 article-title: Radiomics: the bridge between medical imaging and personalized medicine publication-title: Nat Rev Clin Oncol – volume: 35 start-page: 237 year: 2014 end-page: 245 article-title: Evolutionary algorithm‐based classifier parameter tuning for automatic ovarian cancer tissue characterization and classification publication-title: Ultraschall Medizin – volume: 47 start-page: 2734 year: 2021 end-page: 2741 article-title: Artificial intelligence‐based radiomics models in endometrial cancer: a systematic review publication-title: Eur J Surg Oncol – volume: 34 start-page: 145 year: 2013 end-page: 150 article-title: Automatic texture‐based analysis in ultrasound imaging of ovarian masses publication-title: Ultraschall Medizin – volume: 19 start-page: 19 year: 2019 article-title: Development and validation of a model that includes two ultrasound parameters and the plasma D‐dimer level for predicting malignancy in adnexal masses: an observational study publication-title: BMC Cancer – volume: 227 start-page: 601.e1 year: 2022 end-page: 601.e20 article-title: Diagnostic performance of ultrasound in assessing the extension of disease in advanced ovarian cancer publication-title: Am J Obstet Gynecol – volume: 40 start-page: 582 year: 2012 end-page: 591 article-title: Clinically oriented three‐step strategy for assessment of adnexal pathology publication-title: Ultrasound Obstet Gynecol – volume: 31 start-page: 681 year: 2008 end-page: 690 article-title: Simple ultrasound‐based rules for the diagnosis of ovarian cancer publication-title: Ultrasound Obstet Gynecol – volume: 11 start-page: 812 year: 2021 article-title: Ultrasonography in the diagnosis of adnexal lesions: the role of texture analysis publication-title: Diagnostics – volume: 306 start-page: 2143 year: 2022 end-page: 2154 article-title: A machine learning approach applied to gynecological ultrasound to predict progression‐free survival in ovarian cancer patients publication-title: Arch Gynecol Obstet – start-page: 364 year: 2011 end-page: 375 – year: 2022 – volume: 304 start-page: 106 year: 2022 end-page: 113 article-title: Deep learning prediction of ovarian malignancy at US compared with O‐RADS and expert assessment publication-title: Radiology – volume: 21 year: 2022 article-title: The effects of automatic segmentations on preoperative lymph node status prediction models with ultrasound radiomics for patients with early stage cervical cancer publication-title: Technol Cancer Res Treat – volume: 43 start-page: 124 year: 2021 end-page: 138 article-title: An evaluation of the effectiveness of image‐based texture features extracted from static B‐mode ultrasound images in distinguishing between benign and malignant ovarian masses publication-title: Ultrason Imaging – volume: 161 start-page: 838 year: 2021 end-page: 844 article-title: Using rADioMIcs and machine learning with ultrasonography for the differential diagnosis of myometRiAL tumors (the ADMIRAL pilot study). Radiomics and differential diagnosis of myometrial tumors publication-title: Gynecol Oncol – start-page: 395 year: 2018 end-page: 406 – volume: 11 year: 2021 article-title: Application of deep convolutional neural networks for discriminating benign, borderline, and malignant serous ovarian tumors from ultrasound images publication-title: Front Oncol – start-page: 4446 year: 2012 end-page: 4449 article-title: Ovarian tumor characterization and classification: a class of GyneScan™ systems publication-title: Annu Int Conf IEEE Eng Med Biol Soc – volume: 38 start-page: 6 year: 2017 end-page: 22 article-title: Building AI applications: yesterday, today, and tomorrow publication-title: AI Mag – volume: 60 start-page: 256 year: 2022 end-page: 268 article-title: Developing and validating ultrasound‐based radiomics models for predicting high‐risk endometrial cancer publication-title: Ultrasound Obstet Gynecol – volume: 57 start-page: 829 year: 2021 end-page: 839 article-title: Doppler‐based predictive model for methotrexate resistance in low‐risk gestational trophoblastic neoplasia with myometrial invasion: prospective study of 147 patients publication-title: Ultrasound Obstet Gynecol – volume: 13 start-page: 529 year: 2014 end-page: 540 article-title: GyneScan: an improved online paradigm for screening of ovarian cancer via tissue characterization publication-title: Technol Cancer Res Treat – volume: 35 start-page: 983 year: 2022 end-page: 992 article-title: The accuracy and radiomics feature effects of multiple U‐Net‐based automatic segmentation models for transvaginal ultrasound images of cervical cancer publication-title: J Digit Imaging – volume: 22 start-page: 298 year: 2022 article-title: Automatic ovarian tumors recognition system based on ensemble convolutional neural network with ultrasound imaging publication-title: BMC Med Inform Decis Mak – start-page: 279 year: 2021 end-page: 289 – volume: 33 start-page: 649 year: 2023 end-page: 666 article-title: ESGO/ESTRO/ESP guidelines for the management of patients with cervical cancer—update 2023 publication-title: Int J Gynecol Cancer – volume: 28 start-page: 697 year: 2014 end-page: 720 article-title: Imaging techniques for the evaluation of ovarian cancer publication-title: Best Pract Res Clin Obstet Gynaecol – volume: 349 year: 2014 article-title: Evaluating the risk of ovarian cancer before surgery using the ADNEX model to differentiate between benign, borderline, early and advanced stage invasive, and secondary metastatic tumours: prospective multicentre diagnostic study publication-title: BMJ – volume: 30 start-page: 4117 year: 2020 end-page: 4124 article-title: Noninvasive prediction of lymph node status for patients with early‐stage cervical cancer based on radiomics features from ultrasound images publication-title: Eur Radiol – volume: 61 start-page: 231 year: 2023 end-page: 242 article-title: Benign descriptors and ADNEX in two‐step strategy to estimate risk of malignancy in ovarian tumors: retrospective validation in IOTA5 multicenter cohort publication-title: Ultrasound Obstet Gynecol – volume: 19 start-page: 5733 year: 2013 end-page: 5739 article-title: REM (risk of endometrial malignancy): a proposal for a new scoring system to evaluate risk of endometrial malignancy publication-title: Clin Cancer Res – volume: 3 start-page: 104 year: 2019 end-page: 108 article-title: Machine (deep) learning methods for image processing and radiomics publication-title: IEEE Trans Radiat Plasma Med Sci – volume: 28 start-page: 769 year: 2014 end-page: 791 article-title: Which imaging technique should we use in the follow up of gynaecological cancer? publication-title: Best Pract Res Clin Obstet Gynaecol – volume: 24 start-page: 429 year: 2021 end-page: 437 article-title: The adoption of radiomics and machine learning improves the diagnostic processes of women with ovarian MAsses (the AROMA pilot study) publication-title: J Ultrasound – volume: 28 start-page: 741 year: 2014 end-page: 768 article-title: Imaging techniques for the evaluation of cervical cancer publication-title: Best Pract Res Clin Obstet Gynaecol – volume: 29 start-page: 496 year: 2007 end-page: 504 article-title: Preoperative diagnosis of ovarian tumors using Bayesian kernel‐based methods publication-title: Ultrasound Obstet Gynecol – volume: 84 start-page: 40 year: 2019 end-page: 48 article-title: The role of ultrasound in primary workup of cervical cancer staging (ESGO, ESTRO, ESP cervical cancer guidelines) publication-title: Ceska Gynekol – volume: 64 start-page: 197 year: 2023 end-page: 203 article-title: Development and validation of a nomogram prediction model for endometrial malignancy in patients with abnormal uterine bleeding publication-title: Yonsei Med J – volume: 66 start-page: 3161 year: 2021 end-page: 3182 article-title: Fully automatic segmentation of gynaecological abnormality using a new Viola–Jones model publication-title: Comput Mater Cont – volume: 80 start-page: 609 year: 2007 end-page: 616 article-title: Texture analysis of perimenopausal and post‐menopausal endometrial tissue in grayscale transvaginal ultrasonography publication-title: Br J Radiol – volume: 69 start-page: 127 year: 2019 end-page: 157 article-title: Artificial intelligence in cancer imaging: clinical challenges and applications publication-title: CA Cancer J Clin – volume: 20 start-page: 1822 year: 2010 end-page: 1830 article-title: A new computer‐aided diagnostic tool for non‐invasive characterisation of malignant ovarian masses: results of a multicentre validation study publication-title: Eur Radiol – volume: 13 start-page: 107 year: 2021 end-page: 130 article-title: ESGO/ISUOG/IOTA/ESGE consensus statement on preoperative diagnosis of ovarian tumours publication-title: Facts Views Vis Obgyn – volume: 20 start-page: 1385 year: 2018 end-page: 1402 article-title: Use of nonlinear features for automated characterization of suspicious ovarian tumors using ultrasound images in fuzzy Forest framework publication-title: Int J Fuzzy Syst – volume: 33 start-page: 2239 year: 2023 end-page: 2247 article-title: Ovarian imaging radiomics quality score assessment: an EuSoMII radiomics auditing group initiative publication-title: Eur Radiol – volume: 42 start-page: 742 year: 2016 end-page: 752 article-title: Ultrasound image discrimination between benign and malignant adnexal masses based on a neural network approach publication-title: Ultrasound Med Biol – volume: 14 year: 2019 article-title: Evaluation of machine learning methods with Fourier transform features for classifying ovarian tumors based on ultrasound images publication-title: PLoS One – volume: 10 year: 2021 article-title: Multiple U‐Net‐based automatic segmentations and radiomics feature stability on ultrasound images for patients with ovarian cancer publication-title: Front Oncologia – volume: 53 year: 2022 article-title: Artificial intelligence performance in image‐based ovarian cancer identification: a systematic review and meta‐analysis publication-title: EClinicalMedicine – volume: 10:184 year: 2023 article-title: Improving the segmentation accuracy of ovarian‐tumor ultrasound images using image Inpainting publication-title: Bioengineering – volume: 166 start-page: 596 year: 2022 end-page: 605 article-title: A systematic review on the use of artificial intelligence in gynecologic imaging—background, state of the art, and future directions publication-title: Gynecol Oncol – year: 2006 – year: 2020 – start-page: 59 year: 2022 end-page: 70 – volume: 13 start-page: 1 year: 2021 end-page: 11 article-title: Computed tomography based radiomics as a predictor of survival in ovarian cancer patients: a systematic review publication-title: Cancers – volume: 51 start-page: 818 year: 2018 end-page: 828 article-title: Ultrasound characteristics of endometrial cancer as defined by international endometrial tumor analysis (IETA) consensus nomenclature: prospective multicenter study publication-title: Ultrasound Obstet Gynecol – volume: 15:1121 year: 2023 article-title: Evaluating the risk of inguinal lymph node metastases before surgery using the morphonode predictive model: a prospective diagnostic study in vulvar cancer patients publication-title: Cancers – volume: 23 start-page: 8794 year: 2005 end-page: 8801 article-title: International ovarian tumor analysis group. Logistic regression model to distinguish between the benign and malignant adnexal mass before surgery: a multicenter study by the international ovarian tumor analysis group publication-title: J Clin Oncol – start-page: 51 year: 2021 end-page: 61 – volume: 10 year: 2020 article-title: Germline BRCA 1‐2 status prediction through ovarian ultrasound images radiogenomics: a hypothesis generating study (PROBE study) publication-title: Sci Rep – volume: 12 year: 2021 article-title: Diagnosis of ovarian neoplasms using nomogram in combination with ultrasound image‐based radiomics signature and clinical factors publication-title: Front Genet – volume: 11 start-page: 543 year: 2012 end-page: 552 article-title: Ovarian tumor characterization using 3D ultrasound publication-title: Technol Cancer Res Treat – start-page: 137 year: 2015 end-page: 140 – volume: 21:15330338221118412 year: 2022 article-title: The influence of different ultrasonic machines on Radiomics models in prediction lymph node metastasis for patients with cervical cancer publication-title: Technol Cancer Res Treat – ident: e_1_2_9_2_1 doi: 10.1016/j.bpobgyn.2014.04.006 – ident: e_1_2_9_42_1 doi: 10.1007/s40477-020-00503-5 – ident: e_1_2_9_52_1 doi: 10.1186/s12880-022-00879-2 – ident: e_1_2_9_54_1 doi: 10.1016/j.pacs.2022.100420 – ident: e_1_2_9_80_1 doi: 10.7785/tcrtexpress.2013.600273 – ident: e_1_2_9_51_1 doi: 10.1007/978-981-19-1111-8_6 – ident: e_1_2_9_23_1 doi: 10.1109/TRPMS.2019.2899538 – ident: e_1_2_9_44_1 doi: 10.3389/fonc.2020.614201 – volume: 15 year: 2023 ident: e_1_2_9_67_1 article-title: Evaluating the risk of inguinal lymph node metastases before surgery using the morphonode predictive model: a prospective diagnostic study in vulvar cancer patients publication-title: Cancers doi: 10.3390/cancers15041121 – ident: e_1_2_9_16_1 doi: 10.1136/ijgc-2023-004429 – volume: 28 start-page: 5 year: 2021 ident: e_1_2_9_41_1 article-title: A decision support system based on radiomics and machine learning to predict the risk of malignancy of ovarian masses from transvaginal ultrasonography and serum CA‐125 publication-title: Eur Radiol Exp – ident: e_1_2_9_68_1 doi: 10.1002/uog.3996 – ident: e_1_2_9_65_1 doi: 10.1016/j.ygyno.2021.04.004 – ident: e_1_2_9_59_1 doi: 10.3349/ymj.2022.0239 – ident: e_1_2_9_20_1 doi: 10.1609/aimag.v38i1.2709 – ident: e_1_2_9_34_1 doi: 10.1007/978-3-030-00764-5_36 – ident: e_1_2_9_14_1 doi: 10.52054/FVVO.13.2.016 – ident: e_1_2_9_3_1 doi: 10.1016/j.bpobgyn.2014.04.008 – ident: e_1_2_9_56_1 doi: 10.1259/bjr/13992649 – ident: e_1_2_9_30_1 doi: 10.1038/s41591-021-01517-0 – ident: e_1_2_9_24_1 – ident: e_1_2_9_43_1 doi: 10.1002/uog.23530 – ident: e_1_2_9_50_1 doi: 10.1186/s12911-022-02047-6 – ident: e_1_2_9_69_1 doi: 10.1007/s00330-010-1750-6 – volume: 84 start-page: 40 year: 2019 ident: e_1_2_9_6_1 article-title: The role of ultrasound in primary workup of cervical cancer staging (ESGO, ESTRO, ESP cervical cancer guidelines) publication-title: Ceska Gynekol – ident: e_1_2_9_49_1 doi: 10.1148/radiol.211367 – ident: e_1_2_9_15_1 doi: 10.1136/ijgc-2020-002230 – ident: e_1_2_9_57_1 doi: 10.1158/1078-0432.CCR-13-1376 – ident: e_1_2_9_46_1 doi: 10.3390/diagnostics11050812 – ident: e_1_2_9_61_1 doi: 10.1007/s11227-020-03421-9 – ident: e_1_2_9_4_1 doi: 10.1016/j.bpobgyn.2014.04.009 – ident: e_1_2_9_7_1 doi: 10.1259/bjr.20201375 – ident: e_1_2_9_19_1 doi: 10.3390/cancers13030573 – ident: e_1_2_9_66_1 doi: 10.1002/uog.22069 – ident: e_1_2_9_12_1 doi: 10.1136/bmj.g5920 – ident: e_1_2_9_29_1 doi: 10.1007/s00330-022-09180-w – ident: e_1_2_9_58_1 doi: 10.1109/ICDH57206.2022.00023 – ident: e_1_2_9_82_1 doi: 10.1007/s40815-018-0456-9 – ident: e_1_2_9_40_1 doi: 10.1177/0161734621998091 – ident: e_1_2_9_63_1 doi: 10.1177/15330338221099396 – ident: e_1_2_9_27_1 doi: 10.1016/j.ygyno.2022.07.024 – ident: e_1_2_9_10_1 doi: 10.1002/uog.5365 – start-page: 51 volume-title: International Conference on Big Data, IoT, and Machine Learning year: 2021 ident: e_1_2_9_39_1 – ident: e_1_2_9_17_1 doi: 10.3322/caac.21552 – ident: e_1_2_9_11_1 doi: 10.1002/uog.11177 – ident: e_1_2_9_32_1 doi: 10.7326/M21-2234 – volume: 10 year: 2023 ident: e_1_2_9_55_1 article-title: Improving the segmentation accuracy of ovarian‐tumor ultrasound images using image Inpainting publication-title: Bioengineering – ident: e_1_2_9_22_1 doi: 10.1109/ACCESS.2018.2836950 – volume: 120 year: 2021 ident: e_1_2_9_26_1 article-title: Artificial intelligence in gynecologic cancers: current status and future challenges—a systematic review publication-title: Artif Intell Med doi: 10.1016/j.artmed.2021.102164 – ident: e_1_2_9_31_1 doi: 10.7326/0003-4819-155-8-201110180-00009 – ident: e_1_2_9_35_1 doi: 10.1371/journal.pone.0219388 – ident: e_1_2_9_13_1 doi: 10.1002/uog.26080 – ident: e_1_2_9_5_1 doi: 10.1002/uog.18909 – ident: e_1_2_9_74_1 doi: 10.1002/uog.24805 – ident: e_1_2_9_18_1 doi: 10.1016/j.ejso.2021.06.023 – start-page: 364 volume-title: HEALTHINF 2011—Proceedings of the International Conference on Health Informatics year: 2011 ident: e_1_2_9_70_1 – volume: 35 start-page: 237 year: 2014 ident: e_1_2_9_79_1 article-title: Evolutionary algorithm‐based classifier parameter tuning for automatic ovarian cancer tissue characterization and classification publication-title: Ultraschall Medizin – ident: e_1_2_9_8_1 doi: 10.1016/j.ajog.2022.05.029 – volume: 66 start-page: 3161 year: 2021 ident: e_1_2_9_71_1 article-title: Fully automatic segmentation of gynaecological abnormality using a new Viola–Jones model publication-title: Comput Mater Cont – ident: e_1_2_9_73_1 doi: 10.3390/cancers14215291 – ident: e_1_2_9_47_1 doi: 10.3389/fonc.2021.770683 – ident: e_1_2_9_36_1 doi: 10.1186/s12885-019-5629-x – start-page: 4446 year: 2012 ident: e_1_2_9_75_1 article-title: Ovarian tumor characterization and classification: a class of GyneScan™ systems publication-title: Annu Int Conf IEEE Eng Med Biol Soc – ident: e_1_2_9_48_1 doi: 10.1007/s00404-022-06578-1 – ident: e_1_2_9_33_1 doi: 10.1016/j.ultrasmedbio.2015.11.014 – ident: e_1_2_9_76_1 doi: 10.7785/tcrt.2012.500272 – ident: e_1_2_9_45_1 doi: 10.3389/fgene.2021.753948 – ident: e_1_2_9_78_1 doi: 10.1007/s10278-012-9553-8 – volume: 21 year: 2022 ident: e_1_2_9_64_1 article-title: The influence of different ultrasonic machines on Radiomics models in prediction lymph node metastasis for patients with cervical cancer publication-title: Technol Cancer Res Treat – ident: e_1_2_9_72_1 – ident: e_1_2_9_21_1 doi: 10.1007/978-0-387-45528-0 – ident: e_1_2_9_28_1 doi: 10.1016/j.eclinm.2022.101662 – ident: e_1_2_9_9_1 doi: 10.1200/JCO.2005.01.7632 – ident: e_1_2_9_37_1 doi: 10.1038/s41598-020-73505-2 – volume: 2022 start-page: 1 year: 2022 ident: e_1_2_9_53_1 article-title: Clinical analysis of 137 cases of ovarian tumors in pregnancy publication-title: J Oncol doi: 10.1155/2022/1907322 – ident: e_1_2_9_25_1 doi: 10.1038/nrclinonc.2017.141 – ident: e_1_2_9_62_1 doi: 10.1007/s10278-022-00620-z – ident: e_1_2_9_77_1 doi: 10.1055/s-0033-1354809 – ident: e_1_2_9_81_1 doi: 10.1109/ICRCICN.2015.7434224 – ident: e_1_2_9_38_1 doi: 10.1007/978-981-33-4673-4_22 – ident: e_1_2_9_60_1 doi: 10.1007/s00330-020-06692-1
SSID	ssj0011504
Score	2.543639
SecondaryResourceType	review_article
Snippet	The aim of this paper was to explore the role of artificial intelligence (AI) applied to ultrasound imaging in gynecology oncology. Web of Science, PubMed, and...
SourceID	proquest pubmed crossref wiley
SourceType	Aggregation Database Index Database Enrichment Source Publisher
StartPage	1832
SubjectTerms	Artificial Intelligence Cervical cancer Diagnosis Endometrial cancer Endometrial Neoplasms - diagnostic imaging Endometrial Neoplasms - pathology Female Genital Neoplasms, Female - diagnostic imaging Genital Neoplasms, Female - pathology Gynecological cancer Gynecology Gynecology - methods Humans Image processing Machine Learning Malignancy Oncology Ovarian cancer Ovarian Neoplasms - diagnostic imaging Ovarian Neoplasms - pathology Radiomics Systematic review tumors Ultrasonic imaging ultrasonography Ultrasonography - methods Ultrasound Uterine cancer Uterine Cervical Neoplasms - diagnostic imaging Uterine Cervical Neoplasms - pathology
Title	Role of artificial intelligence applied to ultrasound in gynecology oncology: A systematic review
URI	https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fijc.35092 https://www.ncbi.nlm.nih.gov/pubmed/38989809 https://www.proquest.com/docview/3129105956 https://www.proquest.com/docview/3078718915
Volume	155
WOSCitedRecordID	wos001268819700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVWIB databaseName: Wiley Online Library Full Collection 2020 customDbUrl: eissn: 1097-0215 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0011504 issn: 0020-7136 databaseCode: DRFUL dateStart: 19960101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3dSxwxEB9Epfhi1VZ7aiVKH3zZmmRvd7P6JNZDRUXElntb8rVy5diV-xD87ztJ9tZKLQi-LFl2loTMTOaXhPkNwLekVEJJoaJSZnnUFTqOFAZCdDyeGkWN0j49-tdldn0t-v38Zg6OZrkwgR-iPXBznuHXa-fgUo0PnklDB7_19xjDHa6_C4zFwtVt4N2b9goBkU5DwUwj3ImlM1ohyg_aX18Go38Q5kvA6iNO7-O7xroCyw3QJMfBMlZhzlZr8OGquUr_BPK2HlpSl8TZTqCRIIO_-DmJDPiUTGoyHU5GcuwKMKEIuX-qrOe6fiJ1FRqH5Jg8c0KTkA_zGe56p3cnZ1FTbyHSsQPaVtvElFR7EjGhcmtpYhAQpAghM8WEYEwLyg2VOZdpRrmSuJdlxuA7S1W8DvNVXdkvQEQitBaZYSZOu7lWkmqNa6_OS2PKlNMO7M_mvdANF7kriTEsAosyL3DGCj9jHdhrRR8CAcdrQtsz5RWND46LGKGMQ49J2oHd9jN6j7sSkZWtpyiDCAmjc86SDmwEpbe9xK6ypqA5Dtbr9v_dF-cXJ76x-XbRLVjiiI9cWiNLtmF-Mprar7CoHyeD8WjHmzI-s77YgYUft72fl38APl_4Hg
linkProvider	Wiley-Blackwell
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3dT9swED9VBQ1egI0NCmXzpj3wkuEkTeIgXhCs4qNUU1VQ3yJ_BRVVCeoHEv895zgNVIA0iTdHOcvW-c73s637HcDvIBVMcCaclEex02LSdwQGQnQ8L1SCKiGL9OibTtTtssEg_leDo3kujOWHqC7cjGcU-7VxcHMhffDMGjq8k398jHe4AS-10IzQvpdOe-3rTvWKgGCnZGGmDh7GwjmzEPUOqs6L8egVyFzErEXQaa9_bLobsFaCTXJsreMz1HT2BT5dlc_pm8B7-UiTPCXGfiyVBBm-4Ogk3GJUMs3JbDQd84kpwoQi5PYx0wXf9SPJM9s4JMfkmRea2JyYr9Bv_-2fnDllzQVH-gZsa6kDlVJZEIkxEWtNA4WgIEQYGQmXMdeVjHqK8tjjYUQ9wfE86yqF324o_G9Qz_JMbwNhAZOSRcpVftiKpeBUStx_ZZwqlYYebcD-XPGJLPnITVmMUWKZlL0ENZYUGmvAr0r03pJwvCXUnK9eUvrhJPERzhgEGYQN-Fn9Rg8yzyI80_kMZRAlYYSO3aABW3bVq1F8U12T0RgnWyzu-8Mn5xcnRWPn_0V_wMpZ_6qTdM67l7uw6iFeMmmObtCE-nQ803uwLB-mw8n4e2nZT_8b-xw
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1ZS8QwEB5kFfHF-1jPKD74Uk3bbZuKL6IuHusiouJbyVVZWVrZQ_DfO2m6VVFB8C2lUxImmZkvHeYbgN0gFUxwJpyUR7HTYNJ3BAZCNDwvVIIqIYvy6IdW1G6zx8f4ZgyORrUwlh-i-uFmLKPw18bA9YtKDz5YQzvPct_HeIcOeLxhmsjUYPz0tnnfqrIICHZKFmbq4GUsHDELUe-g-vhrPPoGMr9i1iLoNGf-t9xZmC7BJjm2p2MOxnQ2D5PXZTp9Afht3tUkT4k5P5ZKgnQ-cXQSbjEqGeRk2B30eN80YUIR8vSW6YLv-o3kmR0ckmPywQtNbE3MItw1z-5Ozp2y54IjfQO2tdSBSqksiMSYiLWmgUJQECKMjITLmOtKRj1FeezxMKKe4HifdZXCZzcU_hLUsjzTK0BYwKRkkXKVHzZiKTiVEv2vjFOl0tCjddgbKT6RJR-5aYvRTSyTspegxpJCY3XYqURfLAnHT0Lro91LSjvsJz7CGYMgg7AO29VrtCCTFuGZzocogygJI3TsBnVYtrtezeKb7pqMxrjYYnN_nz65uDwpBqt_F92CyZvTZtK6aF-twZSHcMlUObrBOtQGvaHegAn5Ouj0e5vlwX4HkZb6lw
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Role+of+artificial+intelligence+applied+to+ultrasound+in+gynecology+oncology%3A+A+systematic+review&rft.jtitle=International+journal+of+cancer&rft.au=Moro%2C+Francesca&rft.au=Ciancia%2C+Marianna&rft.au=Zace%2C+Drieda&rft.au=Vagni%2C+Marica&rft.date=2024-11-15&rft.issn=0020-7136&rft.eissn=1097-0215&rft.volume=155&rft.issue=10&rft.spage=1832&rft.epage=1845&rft_id=info:doi/10.1002%2Fijc.35092&rft.externalDBID=n%2Fa&rft.externalDocID=10_1002_ijc_35092
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0020-7136&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0020-7136&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0020-7136&client=summon