Diagnostic Accuracy of Transvaginal Strain Sonoelastography in Differentiating Between Endometrial Hyperplasia versus Carcinoma Among Postmenopausal Women
Year: 2024
Type: article
Study design: Observational Study
Abstract: Objective: To assess the diagnostic accuracy of transvaginal strain sonelastography in being able to differentiate between endometrial hyperplasia and endometrial carcinoma in postmenopausal women. Study Design: Cross sectional study. Place and Duration of Study: Mayo Hospital, Lahore, Pakistan, from Jul 2019 to May 2021. Methodology: We enrolled 113 patients in our study, after obtaining informed consent. Transvaginal ultrasound was performed by the researcher, and endometrial echo complex thickness was taken, excluding any fluid present in endometrial cavity while strain elastography was also performed. During elastography, repetitive mild compressions and decompressions were given, and measurements were taken at times when bar was green. ELX2/ELX1 readings were calculated and noted on a pre-designed data collection tool. Patients were referred to gynecologist for endometrial sampling after which histopathology reports were obtained, and results were recoded as either benign or malignant. Results: Out of 113 patients, 82 were found to have benign endometrial pathologies with mean ELX2/ELX1 of 0.94±0.65 while 31 patients, with malignant endometrial carcinoma, had mean ELX2/ELX1 of 2.34±1.41. Transvaginal strain sonoelastography was found to have a sensitivity of 90.32%, specificity of 75.61%, positive predictive value of 58.33%, and negative predictive value of 95.35% with an overall diagnostic accuracy of 79.65% in being able to differentiate between benign and malignant endometrial pathologies. Conclusion: Transvaginal strain sonoelastography was found to be a useful investigation for differentiation between endometrial hyperplasia and carcinoma in postmenopausal women and can be used as a complementary investigation to conventional transvaginal ultrasound.
Authors: Abu Bakar Rauf, Mah Jabeen Masood, Awais Ahmed, Ali Mansoor, Aisha Asghar, Sadaf Arooj
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4405964485