EP23.44: Increasing number of borderline ovarian tumours: do we have an effective tool to diagnose them preoperatively?
Year: 2024
Type: conference-abstract
Abstract: Borderline Ovarian tumours (BOTs) are a rare and heterogenous histological group which can carry significant malignant potential if not treated properly. Effective preoperative diagnosis can be challenging however IOTA ADNEX model is capable of differentiating between subgroups of malignancy. Pattern recognition by expert examiner is thought to be the best diagnostic method. The aim of this study was to check the diagnostic efficacy of the preoperative US algorithm based on the mentioned methods. In a tertiary university hospital's ultrasound specialised outpatient 611 non-selected pre- and postmenopausal patients were scanned by the same experienced, IOTA certified examiner. ADNEX score was calculated in all cases where it was possible (age restriction under 14 years) but the subjective opinion of the examiner was recorded in all cases. According to the subjective assessment 81 cases (13.26%) were selected as a candidate for being BOT. Of those who underwent operation BOT was diagnosed in 18 cases (22.22%): serous BOT in 13 cases (72.22%), mucinous BOT in 4 cases (22.22%), rare type BOT in one case (5.56%). Malignancy was detected in 11 (13.58%) further cases: serous adenocarcinoma in 5 cases (45.45%), mucinous adenocarcinoma in 3 cases (27.27%), endometrioid tumour in 2 cases (18.18%) and granulosa cell tumour in one case (9.09%). Benign lesions were reported in all other cases. There was only one case without any preoperative finding for BOT which turned to be borderline in the histopathological evaluation. The number of papillary projections, the presence of blood flow and the ADNEX score all support the BOT diagnosis. IOTA terms and definitions and IOTA models such ADNEX can be a reliable tool for the selection of high-risk population of patients carrying risk of malignancy. Experienced examiners' pattern recognition can elevate this diagnostic performance for a more sophisticated preoperative algorithm allowing a more powerful discrimination from malignant lesions.
DOI: 10.1002/uog.28980
Language: English
Subfield: Reproductive Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W4402360898