4.5 Article

Histogram of kidney stones on non-contrast computed tomography to predict successful stone dusting during retrograde intrarenal surgery

Journal

WORLD JOURNAL OF UROLOGY
Volume 39, Issue 9, Pages 3563-3569

Publisher

SPRINGER
DOI: 10.1007/s00345-021-03659-9

Keywords

Kidney calculi; Endoscopy; Laser lithotripsy; Diagnostic imaging; Computed tomography

Funding

  1. Kyung Hee University [KHU-20182183]

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This study aimed to predict the success of kidney stone dusting during retrograde intrarenal surgery by measuring stone density parameters and histograms. Successfully dusted stones had wider HU range and higher standard deviation, with histograms showing a wide and rugged pattern.
Purpose To predict successful dusting of kidney stones during retrograde intrarenal surgery (RIRS) using stone density parameters and histograms measured with non-contrast computed tomography imaging. Methods Medical records of 49 patients who underwent retrograde intrarenal surgery between January 2018 and January 2019 at Kyung Hee University Hospital were reviewed, and the data of 55 stones were evaluated. Patient age, sex, mean stone density, the highest and lowest measured Hounsfield unit (HU), standard deviation and range of the measured HUs, volume of the most measured HU, and success of dusting were evaluated. Histograms of the measured HUs were created and cutoff values for successful dusting were analyzed. Results Thirty-two stones were successfully dusted during surgery. Dusted stones had a wider range of HU and higher standard deviation. The volume of the most measured HU was smaller in the dusted stones. Successful dusting could be predicted when the volume of the most measured HU was < 8.9 mm(3), with range >= 853, or when the volume of the most measured HU was < 8.9 mm(3), with range < 853, and the mean stone density was < 355. The histograms of HUs of the dusted stones were wide and rugged, while those of dusting failed stones were narrow and peaked. Conclusion Evaluation of stone HU histograms showed differences in distribution and proportion. This will help predict surgical outcomes and prepare for intraoperative complications.

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