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Beyond Endometrial Thickness: Evaluating Endometrial Volume as a Diagnostic Tool in Middle-aged Women with Abnormal
Keerthana Sugananthan1, R Sajeetha Kumari1, P Shunmathi1
1Department of Obstetrics and Gynaecology, SRM Medical College Hospital and Research Centre, SRM Institute of Science and Technology, Chengalpattu, Tamil Nadu, India.
Background:
Abnormal uterine bleeding (AUB) is a prevalent gynecological complaint with diverse underlying etiologies, ranging from benign endometrial changes to malignancy. Early identification of malignant pathology is critical for effective management. This study aims to evaluate the histopathological spectrum of endometrial abnormalities in women with AUB and assess the diagnostic utility of endometrial thickness (ET) and endometrial volume (EV) in predicting endometrial carcinoma.
Methods:
This retrospective study included women presenting with AUB who underwent transvaginal ultrasound and endometrial sampling. Histopathological findings were categorized into benign, polypoidal, hyperplastic, and malignant groups. Receiver operating characteristic (ROC) curve analysis was used to determine optimal ET and EV cutoff values for predicting endometrial malignancy.
Results:
Disordered proliferative endometrium was the most common histological finding (38.0%), followed by endometrial hyperplasia (14.0%) and endometrial carcinoma (8.7%). Malignancy was significantly associated with age >50 years (46.2%, P = 0.002), higher body mass index (mean 35.6, P = 0.024), and hypertension (HTN) (84.6%, P < 0.001). ROC analysis showed that an EV cutoff of 15 ml had superior diagnostic accuracy (area under the curve [AUC] =0.96, sensitivity = 90.2%, specificity = 95.4%) compared to an ET cutoff of 12 mm (AUC = 0.90, sensitivity = 78.0%, specificity = 96.3%).
Conclusion:
Age, obesity, and HTN are significant risk factors for endometrial carcinoma in patients with AUB. EV demonstrates superior diagnostic performance compared to ET and holds promise as a valuable, noninvasive marker in the evaluation of endometrial malignancy. Incorporating EV into routine diagnostic algorithms improves early cancer detection and reduces the need for invasive procedures.
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