EP23.20: Correlation between ultrasonographic findings and histopathological diagnoses of ovarian cysts

Ovarian cysts are a common gynecological condition. Currently, endovaginal ultrasound combined with Doppler imaging is the first-line diagnostic tool, characterising 80 to 90% of ovarian masses effectively. The aim of this study is to compare the ultrasonographic features of ovarian cysts with their final histopathological diagnoses. This retrospective study evaluated 56 ultrasonographic images of ovarian cystic masses that met inclusion criteria, collected between January 2018 and April 2021 at the CHU Habib Bougatfa Bizerte. Pelvic ultrasound with Doppler confirmed the organic nature of the cyst in 96.42% of cases. It also predicted the histological type of the ovarian tumour in 53% of cases, particularly for serous, mucinous, endometrioid epithelial tumours, and mature teratoma germ cell tumours. Ultrasound detected signs suggestive of malignancy in 80% of cases. Several scoring systems are utilised to simplify and standardise the interpretation of various ultrasonographic signs. In the presence of any organic ovarian cyst, signs of malignancy should be diligently sought to avoid overlooking potential cancer. The risk of missing a malignant lesion is non-negligible, necessitating laparoscopy with biopsy if malignancy is suspected. Ultrasound effectively confirmed the ovarian origin of tumours, distinguished functional cysts from organic ones, and identified signs indicative of malignancy among the latter. The correlation between ultrasound findings and histopathology largely depends on the experience of the ultrasonographer.

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Publication Details

Journal
Ultrasound in Obstetrics and Gynecology
Published
2024-09-01
DOI
https://doi.org/10.1002/uog.28956
Primary Topic
Ovarian cancer diagnosis and treatment
Type
article
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article

EP23.20: Correlation between ultrasonographic findings and histopathological diagnoses of ovarian cysts

Med Ramy Letifi
Ultrasound in Obstetrics and Gynecology
Ovarian cancer diagnosis and treatment
article

EP23.20: Correlation between ultrasonographic findings and histopathological diagnoses of ovarian cysts

Med Ramy Letifi
article en

Abstract

Ovarian cysts are a common gynecological condition. Currently, endovaginal ultrasound combined with Doppler imaging is the first-line diagnostic tool, characterising 80 to 90% of ovarian masses effectively. The aim of this study is to compare the ultrasonographic features of ovarian cysts with their final histopathological diagnoses. This retrospective study evaluated 56 ultrasonographic images of ovarian cystic masses that met inclusion criteria, collected between January 2018 and April 2021 at the CHU Habib Bougatfa Bizerte. Pelvic ultrasound with Doppler confirmed the organic nature of the cyst in 96.42% of cases. It also predicted the histological type of the ovarian tumour in 53% of cases, particularly for serous, mucinous, endometrioid epithelial tumours, and mature teratoma germ cell tumours. Ultrasound detected signs suggestive of malignancy in 80% of cases. Several scoring systems are utilised to simplify and standardise the interpretation of various ultrasonographic signs. In the presence of any organic ovarian cyst, signs of malignancy should be diligently sought to avoid overlooking potential cancer. The risk of missing a malignant lesion is non-negligible, necessitating laparoscopy with biopsy if malignancy is suspected. Ultrasound effectively confirmed the ovarian origin of tumours, distinguished functional cysts from organic ones, and identified signs indicative of malignancy among the latter. The correlation between ultrasound findings and histopathology largely depends on the experience of the ultrasonographer.

Ultrasound in Obstetrics and GynecologyVol. 64(S1)
University of Sousse (TN)
Zero hunger
Openalex Percentile: Top 78%
Ovarian cancer diagnosis and treatment
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EP23.20: Correlation between ultrasonographic findings and histopathological diagnoses of ovarian cysts — Med Ramy Letifi · Ultrasound in Obstetrics and Gynecology (2024) | TGRS Research Map | TGRS