To Örebro University

Computed tomography (CT) is the mainstay imaging method for urinary stones.

The aim of this thesis was to optimize the information obtained from the initial CT scan to allow a well-founded diagnosis and prognosis, and to guide the clinician as early and as far as possible in the further treatment of urinary stone disease.

We examined CT scan parameters with regards to their importance for prediction of spontaneous ureteral stone passage, the impact of interreader variability of stone size estimates on this prediction, and the predictive accuracy of a semi-automated, three-dimensional (3D) segmentation algorithm. We also developed and tested the ability of a machine learning algorithm to classify pelvic calcifications into ureteral stones and phleboliths.

Using single-energy CT, three quantitative methods for classification of stone composition into uric acid and non-uric acid stones in vivo were prospectively validated, using dual-energy CT as reference.

Our results show that spontaneous ureteral stone passage can be predicted with high accuracy, with knowledge of stone size and position. The interreader variability in the size estimation has a large impact on the predicted outcome, but can be eliminated through a 3D segmentation algorithm. Which size estimate we use is of minor importance, but it is important that we use the chosen estimate consistently. A machine learning algorithm can differentiate distal ureteral stones from phleboliths, but more than local features are needed to reach optimal discrimination.

A single-energy CT method can distinguish uric acid from non-uric acid stones in vivo with accuracy comparable to dual-energy CT.

In conclusion, single-energy CT not only detects a urinary stone, but can also provide us with a prediction regarding spontaneous stone passage and a classification of stone type into uric acid and non-uric acid.