Validation of the Twinkling Artifact and other Significant Factors Using Predictive Model for Diagnostic of Renal Stone

Authors

  • Rujiluck Rojthamrong Department of Radiology, Pranangklao Hospital

Keywords:

Twinkling artifact, Kidney stones, Ultrasound

Abstract

Background: Renal calculi, also known as renal stones, are a significant global health concern. While CT is the preferred method for diagnosing these conditions, it has drawbacks in terms of cost and radiation hazards. Ultrasound imaging is widely used for renal stone screening and initial diagnosis, with CT scans used for confirmation. The presence of the twinkling artifact enhances ultrasound sensitivity for detecting renal stones, but its accuracy should be considered in context. Relying solely on this artifact can lead to overdiagnosis of renal stones during ultrasound exams, resulting in unnecessary CT scans. In 2022, a research publication aimed to enhance the accuracy of the twinkling artifact in diagnosing renal stones. The study introduced the “kidney stone predictive model” as a proposed method to improve diagnostic precision in this area. Objective: To validate the diagnostic performance of the kidney stone predictive model for renal stone diagnosis. Methods: A cross-sectional study examined patients referred for CT scans to investigate kidney stones. On the same day, ultrasound imaging was conducted to document the twinkling artifact and its variability. CT scans were used as the reference standard for stone diagnosis. ROC curves were utilized to analyze the sizes of the twinkling artifact, facilitating calculations of prevalence and diagnostic performance. Results: Out of 82 twinkling artifacts, 37 (45.1%) were confirmed as renal stones by CT scans. The median size was 5.69 mm (gif.latex?\pm3.48). A twinkling artifact size of 5.0 mm showed overall accuracy of 79.3% sensitivity of 83.8% and specificity of 73.3%. The model demonstrated an accuracy of 82.9%, sensitivity of 75.7% and specificity of 88.9%. No significant differences were observed between the original and validation studies. Conclusions: The kidney stone predictive model demonstrates strong diagnostic performance and moderate interobserver agreement in diagnosing renal stones, making it an effective and accurate diagnostic tool.

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Published

15-12-2023

How to Cite

1.
Rojthamrong R. Validation of the Twinkling Artifact and other Significant Factors Using Predictive Model for Diagnostic of Renal Stone. J DMS [Internet]. 2023 Dec. 15 [cited 2024 Apr. 20];48(4):116-23. Available from: https://he02.tci-thaijo.org/index.php/JDMS/article/view/263435

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