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Reliability of IOTA score and ADNEX model in the screening of ovarian malignancy in postmenopausal women.
Journal of Gynecology Obstetrics and Human Reproduction 2018 April 29
BACKGROUND: The IOTA (International Ovarian Tumor Analysis) group has developed the ADNEX (Assessment of Different NEoplasias in the adneXa) model to predict the risk that an ovarian mass is benign, borderline or malignant. This study aimed to test reliability of these risks prediction models to improve the performance of pelvic ultrasound and discriminate between benign and malignant cysts.
MATERIAL AND METHODS: Postmenopausal women with an adnexal mass (including ovarian, para-ovarian and tubal) and who underwent a standardized ultrasound examination before surgery were included. Prospectively and retrospectively collected data and ultrasound appearances of the tumors were described using the terms and definitions of the IOTA group and tested in accordance with the ADNEX model and were compared to the final histological diagnosis.
RESULTS: Of the 107 menopausal patients recruited between 2011 and 2016, 14 were excluded (incomplete inclusion criteria). Thus, 93 patients constituted a cohort in whom 89 had benign cysts (83 ovarian and 6 tubal or para-ovarian cysts), 1 had border line tumor and 3 had invasive ovarian cancers (1 at first stage, 1 at advanced stage and 1 metastatic tumor in the ovary). The overall prevalence of malignancy was 4.3%. Every benign ovarian cyst was classified as probably benign by IOTA score which showed also a high specificity with the totality of probably malignant lesion proved malignant by histological exam. The limit of this score was the important rate of not classified or undetermined cysts. However, the malignancy risks calculated by ADNEX model allowed identifying the totality of malignancy. Thus, the combination of the two methods of analysis showed a sensitivity and specificity rates of respectively 100% and 98%. Evaluation of malignancy risks by these 2 tests highlighted a negative predictive value of 100% (there was no case of false negative) and a positive predictive value of 80%.
DISCUSSION AND CONCLUSION: On the basis of our findings, the IOTA classification and the ADNEX multimodal algorithm used as risks prediction models can improve the performance of pelvic ultrasound and discriminate between benign and malignant cysts in postmenopausal women, especially for undetermined lesions.
MATERIAL AND METHODS: Postmenopausal women with an adnexal mass (including ovarian, para-ovarian and tubal) and who underwent a standardized ultrasound examination before surgery were included. Prospectively and retrospectively collected data and ultrasound appearances of the tumors were described using the terms and definitions of the IOTA group and tested in accordance with the ADNEX model and were compared to the final histological diagnosis.
RESULTS: Of the 107 menopausal patients recruited between 2011 and 2016, 14 were excluded (incomplete inclusion criteria). Thus, 93 patients constituted a cohort in whom 89 had benign cysts (83 ovarian and 6 tubal or para-ovarian cysts), 1 had border line tumor and 3 had invasive ovarian cancers (1 at first stage, 1 at advanced stage and 1 metastatic tumor in the ovary). The overall prevalence of malignancy was 4.3%. Every benign ovarian cyst was classified as probably benign by IOTA score which showed also a high specificity with the totality of probably malignant lesion proved malignant by histological exam. The limit of this score was the important rate of not classified or undetermined cysts. However, the malignancy risks calculated by ADNEX model allowed identifying the totality of malignancy. Thus, the combination of the two methods of analysis showed a sensitivity and specificity rates of respectively 100% and 98%. Evaluation of malignancy risks by these 2 tests highlighted a negative predictive value of 100% (there was no case of false negative) and a positive predictive value of 80%.
DISCUSSION AND CONCLUSION: On the basis of our findings, the IOTA classification and the ADNEX multimodal algorithm used as risks prediction models can improve the performance of pelvic ultrasound and discriminate between benign and malignant cysts in postmenopausal women, especially for undetermined lesions.
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