Diagnostic Accuracy of MRI in Delineation of Ultrasonographically Indeterminate Female Adnexal Lesions keeping Histopathology as Gold Standard
Abstract
Objective: This study was performed to check the diagnostic accuracy of MRI in the detection and delineation
of sonologically indeterminate adnexal masses as benign or malignant, keeping histopathology as gold
standard.
Study Design: Cross-sectional study.
Place and Duration of Study: This cross-sectional study was conducted at the Department of Diagnostic
st st Radiology of POF Hospital, Wah Cantt, Pakistan, from 1 December 2018 to 31 May 2019.
Materials and Methods: All female patients (15-80 years) with sonographically diagnosed indeterminate
adnexal masses, both symptomatic and incidental, were included in the study. Patients who were unfit for
surgery and those for whom MRI is contraindicated (cardiac pacemakers, intracranial metal clips,
claustrophobic patients, hypersensitivity to contrast) were excluded. Total of 115 patients meeting the
inclusion criterion underwent an MRI examination. All patients had undergone surgery in the concerned ward,
and a histopathology report was followed. MRI findings were compared with histopathological findings. Data
was entered and analysed via SPSS version 26.
Results: Mean age of the patients was 48.22±10.5. Out of 115 patients, there were 74 (64.3%) cases related to
the uterine mass category, 35 (30.4%) were related to ovarian mass category, and 6 (5.2%) fell in the
Extraovarian/Extrauterine mass category. Out of 115 patients, 74 (64.3%) patients had a mass size <3cm, 35 had
the size of 3-5 cm (5.2%), and 6 had size >5 cm (5.2%). The sensitivity of MRI was calculated to be 77.14%,
specificity was 87.5%, positive predictive value was 72.9 %, and negative predictive value was 89.7 %.
Conclusion: MRI has high sensitivity and specificity in diagnosing adnexal masses and therefore, can serve as a
good choice in the delineation of sonographically indeterminate adnexal masses.
Copyright (c) 2023 Ms Madiha, Farkhanda Jabeen, Nadia Gul, Khalid Mehmood, Salma Umbreen, Farheen Raza
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