Épidémiologie des calculs urinaires dans le Sud de la France : étude rétrospective monocentrique

Ménard, Olivier; Murez, T.; Bertrand, Jean‐Marie; Daille, A.M.; Cabaniols, L.; Robert, M; Thuret, Rodolphe

doi:10.1016/j.purol.2016.04.005

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…The ratio of mixed stone in this study was comparable to statistics of Asian countries such as China and India, where the mixed stone ratios are approximately 66.8% and 74.8%, respectively 44,45 . Characterization of mixed stone compositions is therefore more clinically relevant and meaningful in Asian countries, where mixed stones appear to be more common than other regions of the world 2,46 …”

Section: Discussionsupporting

confidence: 77%

“…44,45 Characterization of mixed stone compositions is therefore more clinically relevant and meaningful in Asian countries, where mixed stones appear to be more common than other regions of the world. 2,46 In the proposed fusion based multi-label classification framework, we cross-validated various models (n = 250), and applied fusion to the top-ranked models (n = 10) to yield the final consensus differentiation. This architecture possesses several advantages: (1) different classification models might exhibit varying performances, even on the same classification task, 47 and cross-validation on different models could help screen out the best models; (2) with feature selection embedded in each model, model cross-validation also enable selection of the top-ranked ESA parameters, which are most relevant of stone composition differentiation; and (3) fusion of different classification models usually yields superior performances when compared with their average.…”

Section: Discussionmentioning

confidence: 99%

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Machine learning based multi‐label classification of single or mixed‐composition urinary stones in in vivo spectral CT

Tang

Liang

et al. 2023

Medical Physics

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Background Urinary stones comprise both single and mixed compositions. Knowledge of the stone composition helps the urologists choose appropriate medical interventions for patients. The parameters from the spectral computerized tomography (CT) analysis have potential values for identification of the urinary stone compositions. Purpose The present study aims to identify the compositions of urinary stones in vivo using parameters from spectral CT and machine learning, based on multi‐label classification modeling. Methods This retrospective study collected 252 urinary stone samples with single/mixed compositions (including carbapatite [CP], calcium oxalate monohydrate [COM], calcium oxalate dehydrate [COD], uric acid [UA], and struvite [STR]), which were confirmed by ex vivo infrared spectroscopy. Parameters were extracted from an energy spectrum analysis (ESA) of the spectral CT, including the effective atomic number (Zeff), Zeff histogram, CT values at a given x‐ray energy level, and material densities. These ESA parameters were utilized for composition analysis via a multi‐label classification fusion framework, where 250 multi‐label models were built and the classification decisions from the top performance models were integrated by a multi‐criterion weighted fusion (MCWF) approach in order to reach a consensus prediction. An example‐based metric Accexam$Ac{c_{exam}}$ and label‐based metric Acclabel$Ac{c_{label}}$ were used for global and label‐wise accuracy evaluations, respectively. The top‐ranked parameters associated with discriminating the stone composition were also identified. Results The multi‐label classification fusion framework achieved an overall Accexam$Ac{c_{exam}}$ of 81.2%, with Acclabel$Ac{c_{label}}$ of 86.7% (CP), 90.6% (COM), 80.6% (COD), 95.0% (UA), and 94.4% (STR) for each composition on the independent testing cohort 1, and Accexam$Ac{c_{exam}}$ of 76.4% with Acclabel$Ac{c_{label}}$ of 80.5% (CP), 88.7% (COM), 74.9% (COD), 94.4% (UA), and 98.5% (STR) on the independent testing cohort 2. Conclusion The parameters extracted from the ESA on spectral CT can be utilized to characterize single or mixed stone compositions via multi‐label classification modeling. The generalization capability of the proposed methodology still requires further verification.

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Section: Discussionsupporting

confidence: 77%