DIAGNOSTIC ACCURACY OF CT PYELOGRAM FOR DETECTION OF URINARY TRACT STONES COMPOSITION
DOI:
https://doi.org/10.71000/fy3f1097Keywords:
Urolithiasis, Calcium Oxalate, Computed Tomography, , Diagnostic Imaging, Hounsfield Unit, Kidney Calculi, Stone CompositionAbstract
Background: Urolithiasis is a globally prevalent condition and a leading cause of urological consultation, second only to prostatic diseases in many regions. The increasing burden of stone disease, partly attributed to dietary and lifestyle changes, has emphasized the need for early and accurate diagnostic tools. Since the composition of urinary tract stones directly influences treatment planning and recurrence prevention, the non-invasive prediction of stone type is crucial. CT pyelography has emerged as a potential modality to aid in this differentiation.
Objective: To determine the diagnostic accuracy of CT pyelogram for detecting urinary tract stone composition, using histopathology as the gold standard.
Methods: This descriptive cross-sectional study was conducted over six months (August 1, 2024, to January 31, 2025) at the Department of Radiology, Ziauddin University, Karachi. A total of 185 patients aged 18–60 years, diagnosed with urolithiasis on ultrasound with evidence of hydronephrosis, were included through non-probability consecutive sampling. CT pyelogram was performed for each patient to assess stone size, location, and Hounsfield Unit (HU). Stone retrieval was followed by histopathological analysis for chemical composition. Data were analyzed using SPSS version 22, and diagnostic accuracy parameters were calculated.
Results: The mean age of patients was 44.24 ± 13.44 years. Histopathology confirmed calcium stones in 71.3% (132/185) of cases. CT pyelogram demonstrated a sensitivity of 92.4%, specificity of 96.2%, positive predictive value of 98.4%, negative predictive value of 83.6%, and overall diagnostic accuracy of 88.1% in determining stone composition.
Conclusion: CT pyelogram proved to be a highly accurate, non-invasive modality for predicting urinary stone composition, with significant clinical value in guiding appropriate treatment strategies for renal and ureteral calculi.
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Copyright (c) 2025 Saadia Ali, Nasir Ali Rahimnajjad, Aneeqa Qureshi, Anum Naz, Rafay Gul, Samita Asad, Syed Hameed-Ul-Hassan Shah (Author)
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