Diagnostic Accuracy of MRI in Evaluation of Uterine Masses in Detection of Uterine Carcinoma, Taking Histopathology as a Gold Standard
Uterine carcinoma is one of the most prevalent gynecological malignancies worldwide, and timely diagnosis is critical for guiding treatment decisions. Magnetic resonance imaging (MRI) is increasingly used for preoperative evaluation; however, its diagnostic accuracy compared to histopathology—the reference standard—remains variable across different settings. Objective: This study aims to evaluate the diagnostic accuracy of MRI in detecting uterine carcinoma, comparing it with histopathological findings as the reference standard. Methods: After obtaining ethical approval from the institutional review board, this cross-sectional study was conducted at the Radiology department of JPMC, Karachi, from January 1, 2023, to June 30, 2023. Through non-probability consecutive sampling, patients aged 35 years and above who underwent both pelvic MRI and subsequent histopathological evaluation (via biopsy or post-surgical specimen analysis) were included. Patients with prior hysterectomy, contraindications to MRI (such as metallic implants or severe claustrophobia), or incomplete histopathology reports were excluded from the study. Results: The diagnostic performance of MRI in detecting uterine carcinoma was as follows: sensitivity was 54.90%, specificity was 63.27%, positive predictive value (PPV) was 51.0%, negative predictive value (NPV) was 60.87%, and the overall diagnostic accuracy was calculated to be 59.0%. Conclusion: MRI provides useful but suboptimal discrimination between benign and malignant uterine masses in routine settings, achieving only moderate accuracy without the use of advanced sequences or specialist interpretation.
Authors
- Tariq Mahmood T.I
- Muhammad Saeed Shahbaz (ORCID: https://orcid.org/0000-0002-4167-4592)
- Haripriya Vittal Rao
- A. M. Akbar (ORCID: https://orcid.org/0009-0002-9953-5576)
- Shaista Shauka
- Maryiam Akber
Publication Details
- Journal
- Biological and Clinical Sciences Research Journal
- Published
- 2025-06-30
- DOI
- https://doi.org/10.54112/bcsrj.v6i6.1939
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00