Background: There is evidence that premenopausal hormones may persist for variable time after menopause. Histological specimens from postmenopausal women support the presence of follicular growth at that age. Residual ovarian function may explain postmenopausal bleeding (PMB), which is not associated with endometrial pathology. Our objective was to evaluate the effect of sonographic diagnosis of simple ovarian cysts on the association between thickened endometrium and endometrial pathology in women with PMB.
Materials And Methods: Data were retrospectively collected from medical records of women who underwent office hysteroscopy for PMB between January 2007 and October 2011. Women with sonographic reports within 3 months of presentation were included. Endometrial thickness and the presence of a simple ovarian cyst (≤5 cm) were documented by reviewing sonographic reports. Diagnosis of endometrial pathology was abstracted according to pathology reports or hysteroscopic impression. Endometria with hyperplasia, cancer, or polyps were considered pathological.
Results: Of 836 women with PMB, 356 had recent transvaginal sonography and were included in the analysis. Pathological endometrium was documented in 129 (36.2%) women, including 29 (8.2%) with endometrial cancer. In women with PMB and no evidence of a simple ovarian cyst, endometrial thickness was an independent predictor of endometrial pathology and endometrial cancer with adjusted OR = 1.13 (95% CI = 1.07-1.19) and 1.16 (95% CI = 1.07-1.25), respectively. In the presence of simple ovarian cysts, the adjusted ORs for endometrial thickness as a predictor of endometrial pathology were 1.06 (95% CI = 0.90-1.25) and 0.84 (95% CI = 0.62-1.14), respectively.
Conclusion: The presence of simple ovarian cysts (≤5 cm) tempers the value of endometrial thickness in predicting endometrial pathology in women with PMB.
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Article Synopsis
- Ovarian cancer has the highest death rate among women's cancers and is primarily screened using ultrasound, but accurate assessment is difficult due to complex images and anatomy.
- Researchers developed an AI system called the Ovarian Multi-Task Attention Network (OvaMTA) to automate the detection, segmentation, and classification of ovarian masses from ultrasound images.
- The AI system was trained with thousands of images, validated with real-world tests, and compared against physician assessments to evaluate its effectiveness in aiding diagnoses of ovarian cancer.
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