EP23.08: False negatives and true negatives using the modified benign descriptors for the evaluation of adnexal masses

To describe the proportion of false positives and true negatives using the modified benign descriptors (MBD) for the evaluation of adnexal masses. Ambispective observational study based on ultrasound data collected at a University Hospital with a gynecologic oncology unit between 2012 and 2023. Two IOTA-certified level III gynecologists classified adnexal masses using the MBD. The main result was the correct classification of benign masses. Histopathology was used as the diagnostic reference standard. A descriptive analysis of the results was performed. A total of 1,047 adnexal masses were evaluated, of which 656 (62.6%) were included when they met the classification criteria for evaluation based on MBD. 652 (99.3%) of the masses corresponded to benign masses by histopathology (true negatives) and 4 (0.7%) were malignant masses (false negatives). The false negatives corresponded to: one borderline serous tumour and one mucinous adenocarcinoma that presented histological changes due to torsion, one mucinous cystadenoma with foci of low-grade adenocarcinoma atypia, and one borderline tumour that, despite showing benign features, underwent cystectomy due to a history of doubling in size and previous resection of a borderline tumour in the same ovary. In our experience, MBD have the ability to identify a high percentage of benign masses. We emphasise the importance of correlating images with symptoms, signs, and patient history to reduce the probability of confusion.

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Publication Details

Journal
Ultrasound in Obstetrics and Gynecology
Published
2024-09-01
DOI
https://doi.org/10.1002/uog.28944
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

EP23.08: False negatives and true negatives using the modified benign descriptors for the evaluation of adnexal masses

S. Ortiz, A.L. Esquivel, N. Rodríguez, Nidia Raquel Gualdrón Cantor et al.
Ultrasound in Obstetrics and Gynecology
Radiomics and Machine Learning in Medical Imaging
article

EP23.08: False negatives and true negatives using the modified benign descriptors for the evaluation of adnexal masses

S. Ortiz, A.L. Esquivel, N. Rodríguez, Nidia Raquel Gualdrón Cantor, Soto Pérez, N. Rodríguez, F.A. Ardila
article en

Abstract

To describe the proportion of false positives and true negatives using the modified benign descriptors (MBD) for the evaluation of adnexal masses. Ambispective observational study based on ultrasound data collected at a University Hospital with a gynecologic oncology unit between 2012 and 2023. Two IOTA-certified level III gynecologists classified adnexal masses using the MBD. The main result was the correct classification of benign masses. Histopathology was used as the diagnostic reference standard. A descriptive analysis of the results was performed. A total of 1,047 adnexal masses were evaluated, of which 656 (62.6%) were included when they met the classification criteria for evaluation based on MBD. 652 (99.3%) of the masses corresponded to benign masses by histopathology (true negatives) and 4 (0.7%) were malignant masses (false negatives). The false negatives corresponded to: one borderline serous tumour and one mucinous adenocarcinoma that presented histological changes due to torsion, one mucinous cystadenoma with foci of low-grade adenocarcinoma atypia, and one borderline tumour that, despite showing benign features, underwent cystectomy due to a history of doubling in size and previous resection of a borderline tumour in the same ovary. In our experience, MBD have the ability to identify a high percentage of benign masses. We emphasise the importance of correlating images with symptoms, signs, and patient history to reduce the probability of confusion.

Ultrasound in Obstetrics and GynecologyVol. 64(S1)
Universidad de La Sabana (CO), Universidad de Los Andes (CO), Fundación Santa Fe de Bogotá (CO)
Gender equality
Openalex Percentile: Top 75%
Radiomics and Machine Learning in Medical Imaging
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