Automated Kidney Stone Composition Analysis with Photon-Counting Detector CT, a Performance Study-A Phantom Study.

作者信息

Dillinger Daniel, Waldeck Stephan, Overhoff Daniel, Faby Sebastian, Jürgens Markus, Schmidt Bernhard, Hesse Albrecht, Schoch Justine, Schmelz Hans, Stoll Rico, Nestler Tim

机构信息

Department of Vascular and Endovascular Surgery, Federal Armed Services Hospital Koblenz, Koblenz, Germany (D.D.); Department of Radiology and Neuroradiology, Federal Armed Services Hospital Koblenz, Koblenz, Germany (D.D., S.W., D.O.).

Department of Radiology and Neuroradiology, Federal Armed Services Hospital Koblenz, Koblenz, Germany (D.D., S.W., D.O.); Institute of Neuroradiology, University Medical Centre Johannes Gutenberg University Mainz, Mainz, Germany (S.W.).

出版信息

Acad Radiol. 2025 Apr;32(4):2005-2012. doi: 10.1016/j.acra.2024.10.045. Epub 2024 Nov 15.

BACKGROUND

For treatment of urolithiasis, the stone composition is of particular interest, as uric acid (UA) stones can be treated by chemolitholysis. In this ex vivo study, we employed an advanced composition analysis approach for urolithiasis utilizing spectral data obtained from a photon-counting detector CT (PCDCT) to differentiate UA and non-UA stones. Our primary objective was to assess the accuracy of this analysis method.

METHODS

A total of 148 urinary stones with a known composition that was measured by the standard reference method infrared spectroscopy (reference) were placed in an abdomen phantom and scanned in the PCDCT. Our objectives were to assess the stone detection rates of PCDCT and the accuracy of the prediction of the stone composition in UA vs non-UA compared to the reference.

RESULTS

Automated detection recognized 86.5% of all stones, with best detection rate for stones larger > 5 mm in diameter (95.4%, 88.8% for stones larger than 3 mm, 94.7% for stones larger than 4 mm). Depending on the volume, we found a recognition rate of 92.8% for stones larger than 20 mm and 94.0% for stones with more than 30 mm. Prediction of UA composition showed an overall sensitivity and a positive predictive value of 66.7% and a specificity and negative predictive value of 94.5%. Best diagnostic values volume wise were found by only including stones with a larger volume than 30 mm, there we found a sensitivity of 91.7%, and a specificity of 92.4%. Sensitivity in dependance of the largest diameter was best for stones larger than 5 mm (85.7%), but specificity decreased with increasing diameter (to 91.3%).

CONCLUSION

Automated urinary stone composition analysis with PCDCT showed a good automated detection rate of 86.5% up to 95.4% depending on stone diameter. The differentiation between non-UA and UA stones is performed with an NPV of 94.5% and a PPV of 66.7%. The prediction probability of non-UA stones was very good. This means the automatic detection and differentiation algorithm can identify the patients which will not profit from chemolitholysis.