Diagnostic Accuracy of Magnetic Resonance Imaging in diagnosing Urinary Bladder Cancer, Taking Histopathology as Gold Standard
Objective: To determine the diagnostic accuracy of Magnetic resonance imaging in diagnosing and staging urinary bladder cancer, taking histopathology as gold standard. Study Design: Descriptive, Cross-sectional study. Place and Duration of Study: The study was done in Department of Diagnostic Radiology, Combined Military Hospital, Multan, Pakistan from Jan to Dec 2020. Methodology: A total of 117 patients with irregular soft tissue structures of low echogenicity projecting into the bladder lumen from a fixed mural site on ultrasonography aged 20-60 years of either gender were included in the study. While patients already taking radiotherapy or immunotherapy or having any other contraindication to Magnetic resonance imaging were excluded. Magnetic resonance imaging was performed in every patient using 1.5 Tesla MR system. Magnetic Resonance Imaging findings were interpreted by consultant radiologist for presence or absence of urinary bladder carcinoma and further local staging of carcinoma if present. Magnetic resonance imaging findings were compared with histopathology results taken via cystoscopy. Results: All the patients were subjected to Magnetic resonance imaging abdomen pelvis and found that 60 were True Positive and 05 were False Positive. Among 52, MRI negative patients, 05 (False Negative) had urinary bladder carcinoma on histopathology whereas 47 (True Negative) had no carcinoma on histopathology (p=0.0001). Overall sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy of Magnetic resonance imaging in diagnosing and staging urinary bladder cancer, taking histopathology as gold standard was 92.31%, 90.38%, 92.31%, 90.38% and 91.45% respectively. Conclusion: MRI is a highly sensitive and accurate noninvasive
Authors
- Arsalan Tariq (ORCID: https://orcid.org/0009-0008-5371-8418)
- Sadaf Aziz
- Yasser Saeed Khan (ORCID: https://orcid.org/0000-0001-6575-5648)
- Munazzah Aziz
- Mubashrah Aziz
- Atif Latif
Institutions
- Combined Military Hospital (PK)
- National University of Medical Sciences (PK)
Publication Details
- Journal
- Pakistan Armed Forces Medical Journal
- Published
- 2024-11-18
- DOI
- https://doi.org/10.51253/pafmj.v74isuppl-2.10849
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00