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Prediction of spontaneous ureteral stone passage: Automated 3D-measurements perform equal to radiologists, and linear measurements equal to volumetric
Örebro University, School of Medical Sciences. Department of Radiology.ORCID iD: 0000-0001-8949-119X
Örebro University, School of Medical Sciences. Department of Radiology.ORCID iD: 0000-0003-3253-8967
Örebro University, School of Medical Sciences. Örebro University Hospital. Department of Radiology.ORCID iD: 0000-0003-0137-9991
Örebro University, School of Medical Sciences. Department of Radiology.ORCID iD: 0000-0002-1346-1450
2018 (English)In: European Radiology, ISSN 0938-7994, E-ISSN 1432-1084, Vol. 28, no 6, p. 2474-2483Article in journal (Refereed) Published
Abstract [en]

OBJECTIVES: To compare the ability of different size estimates to predict spontaneous passage of ureteral stones using a 3D-segmentation and to investigate the impact of manual measurement variability on the prediction of stone passage.

METHODS: We retrospectively included 391 consecutive patients with ureteral stones on non-contrast-enhanced CT (NECT). Three-dimensional segmentation size estimates were compared to the mean of three radiologists' measurements. Receiver-operating characteristic (ROC) analysis was performed for the prediction of spontaneous passage for each estimate. The difference in predicted passage probability between the manual estimates in upper and lower stones was compared.

RESULTS: The area under the ROC curve (AUC) for the measurements ranged from 0.88 to 0.90. Between the automated 3D algorithm and the manual measurements the 95% limits of agreement were 0.2 ± 1.4 mm for the width. The manual bone window measurements resulted in a > 20 percentage point (ppt) difference between the readers in the predicted passage probability in 44% of the upper and 6% of the lower ureteral stones.

CONCLUSIONS: All automated 3D algorithm size estimates independently predicted the spontaneous stone passage with similar high accuracy as the mean of three readers' manual linear measurements. Manual size estimation of upper stones showed large inter-reader variations for spontaneous passage prediction.

KEY POINTS:• An automated 3D technique predicts spontaneous stone passage with high accuracy.• Linear, areal and volumetric measurements performed similarly in predicting stone passage.• Reader variability has a large impact on the predicted prognosis for stone passage.

Place, publisher, year, edition, pages
Springer, 2018. Vol. 28, no 6, p. 2474-2483
Keywords [en]
Computed tomography, Ureteral calculi, Kidney stone, Ureter, Renal colic
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
URN: urn:nbn:se:oru:diva-64712DOI: 10.1007/s00330-017-5242-9ISI: 000431653200024PubMedID: 29368161Scopus ID: 2-s2.0-85040930069OAI: oai:DiVA.org:oru-64712DiVA, id: diva2:1179953
Note

Funding Agency

Research Committee of Region Orebro County 

Available from: 2018-02-02 Created: 2018-02-02 Last updated: 2024-01-16Bibliographically approved
In thesis
1. Non-enhanced single-energy computed tomography of urinary stones
Open this publication in new window or tab >>Non-enhanced single-energy computed tomography of urinary stones
2021 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

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.

Place, publisher, year, edition, pages
Örebro: Örebro University, 2021. p. 85
Series
Örebro Studies in Medicine, ISSN 1652-4063 ; 229
Keywords
Diagnostic, CT, urinary stone, kidney stone, urolithiasis, phlebolith, uric acid, spontaneous passage, CNN, artificial intelligence
National Category
Surgery
Identifiers
urn:nbn:se:oru:diva-86874 (URN)978-91-7529-368-4 (ISBN)
Public defence
2021-02-05, Örebro universitet, Campus USÖ, hörsal C3, Södra Grev Rosengatan 32, Örebro, 09:00 (Swedish)
Opponent
Supervisors
Available from: 2020-10-28 Created: 2020-10-28 Last updated: 2021-01-14Bibliographically approved

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Jendeberg, JohanGeijer, HåkanAlshamari, MuhammedLidén, Mats

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