Quantitative evaluation of encrustations in Double-J ureteral stents with micro-computed tomography and semantic segmentation

August 04, 2021

Shaokai Zheng (1), Pedro Amado (1), Bernhard Kiss (2), Fabian Stangl (2), Andreas Haeberlin (2), Daniel Sidler (2), Dominik Obrist (1), Fiona Burkhard (2), Francesco Clavica (1)
Research Square/Scientific Reports, August 2021. DOI: 10.21203/rs.3.rs-763507/v1


Double-J ureteral stents; micro-computed tomography; semantic segmentation; Quantitative evaluation; encrustations


Accurate evaluations of stent encrustation patterns, such as volume distribution, from different patient groups are valuable for clinical management and the development of better stents. This study compared stent encrustation patterns from stone and kidney transplant patients. Twenty-three double-J ureteral stents were collected at a single center from patients with stone disease or underwent kidney transplantation. Encrustations on stent samples were quantified by means of micro‑computed tomography and semantic segmentation using Convolutional Neural Network models. Luminal encrustation volume per stent unit was derived to represent encrustation level, which did not differ between patient groups in the first six weeks. However, stone patients showed higher encrustation levels over prolonged indwelling times (p = 0.036). Along the stent shaft body, the stone group showed higher encrustation levels near the ureteropelvic junction compared to the ureterovesical junction (p = 0.013), whereas the transplant group showed no such difference. Possible explanations were discussed regarding vesicoureteral refluxes. In both patient groups, stent pigtails were more susceptible to encrustations, and no difference between renal and bladder pigtail was identified. Our results suggest that excessively long stents with superfluous pigtails should be avoided.

How Our Software Was Used

Dragonfly was used for 3D segmentation.

Author Affiliation

(1) University of Bern.
(2) University Hospital of Bern.