OC04.01: Ultrasound features of mucinous ovarian tumors

Alcázar, Juan Luis; Pascual, M.; Guerriero, S.; Rams, N.; Juez, L.; Ajossa, Silvia; Graupera, B.; Hereter, L.; Cappai, A.; Pero, M.; Perniciano, M.; Jurado, M.

doi:10.1002/uog.14971

Cited by 2 publications

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“…is also probably due to the fact that they did not report results separately for endocervical‐ and GI‐type MBOTs. To the best of our knowledge, the largest series of mucinous ovarian tumors was reported by Alcázar et al . in a multicenter retrospective study.…”

Section: Discussionmentioning

confidence: 99%

Imaging in gynecological disease (11): clinical and ultrasound features of mucinous ovarian tumors

Moro

Zannoni

Arciuolo

et al. 2017

Ultrasound in Obstet & Gyne

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A multilocular cyst with 2-10 locules is representative of a benign cystadenoma, whereas a multilocular cyst with > 10 locules is indicative of a GI-type borderline tumor. Most invasive tumors of mucinous GI-type contain solid components, the most typical ultrasound appearance being that of a multilocular-solid tumor. Papillary projections are typical features of endocervical-type borderline tumors. Copyright © 2016 ISUOG. Published by John Wiley & Sons Ltd.

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Section: Discussionmentioning

confidence: 99%

Imaging in gynecological disease (11): clinical and ultrasound features of mucinous ovarian tumors

Moro

Zannoni

Arciuolo

et al. 2017

Ultrasound in Obstet & Gyne

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Machine learning-based rapid diagnosis of human borderline ovarian cancer on second-harmonic generation images

Wang

Sun

Jiang

et al. 2021

Biomed. Opt. Express

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Regarding growth pattern and cytological characteristics, borderline ovarian tumors fall between benign and malignant, but they tend to develop malignancy. Currently, it is difficult to accurately diagnose ovarian cancer using common medical imaging methods, and histopathological examination is routinely used to obtain a definitive diagnosis. However, such examination requires experienced pathologists, being labor-intensive, time-consuming, and possibly leading to interobserver bias. By using second-harmonic generation imaging and k-nearest neighbors classifier in conjunction with automated machine learning tree-based pipeline optimization tool, we developed a computer-aided diagnosis method to classify ovarian tissues as being malignant, benign, borderline, and normal, obtaining areas under the receiver operating characteristic curve of 1.00, 0.99, 0.98, and 0.97, respectively. These results suggest that diagnosis based on second-harmonic generation images and machine learning can support the rapid and accurate detection of ovarian cancer in clinical practice.

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OC04.01: Ultrasound features of mucinous ovarian tumors

Abstract: Oral communication abstracts OC04: CHARACTERISING OVARIAN PATHOLOGY OC04.01 Ultrasound features of mucinous ovarian tumors

Cited by 2 publications

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Imaging in gynecological disease (11): clinical and ultrasound features of mucinous ovarian tumors

Imaging in gynecological disease (11): clinical and ultrasound features of mucinous ovarian tumors

Machine learning-based rapid diagnosis of human borderline ovarian cancer on second-harmonic generation images

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