ArticleAcademic radiology2019
Robustness of Textural Features to Predict Stone Fragility Across Computed Tomography Acquisition and Reconstruction Parameters.
Article in Academic radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- A scoping review of robustness concepts for machine learning in healthcare.NPJ digital medicine · 2025Article
- Prediction of burden and management of renal calculi from whole kidney radiomics: a multicenter study.Abdominal radiology (New York) · 2021Article
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3 authors.
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Abstract
RATIONALE AND
objectivesPrevious studies have demonstrated that quantitative relationships exist between stone fragility at lithotripsy and morphological features extracted from computed tomography (CT) scans. The goal of this study was to determine if variations in scanner model, patient size, radiation dose, or reconstruction parameters impact the accuracy of the prediction of renal stone fragility in an in vitro model. MATERIALS AND
methodsSixty-seven kidney stones were scanned using routine single and dual energy stone protocols, mimicking average, and large patient habitus. Low dose scans were also performed. Each scan was reconstructed with routine protocol parameters, and with thinner (0.6 mm) or thicker (3 mm) images, two different reconstruction kernels, and iterative reconstruction at two strengths. Fragility of each stone was measured in a controlled ex vivo experiment. A single predictive model was developed from a reference CT protocol configuration and applied to data from each CT acquisition and reconstruction parameter tested to obtain estimated stone comminution times.
resultsNone of the investigated protocols showed a significant variation in the accuracy of stone fragility classification, except for the ones with the most aggressive iterative reconstruction and/or with thicker images. In these protocols, a number of stone fragility assessments changed from fragile to hard (or vice versa), compared to their ground truth measurement.
conclusionPrediction accuracy of stone fragility models developed from CT data is robust to expected variations in CT stone protocols used for quantification tasks. This finding facilitates their future adoption to different clinical practices.
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