ArticleAbdominal radiology (New York)2021
Prediction of burden and management of renal calculi from whole kidney radiomics: a multicenter study.
Article in Abdominal radiology (New York), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Ureteral calculi lithotripsy for single ureteral calculi: can DNN-assisted model help preoperatively predict risk factors for sepsis?European radiology · 2022Article
- Radiomics in Urolithiasis: Systematic Review of Current Applications, Limitations, and Future Directions.Journal of clinical medicine · 2022Review
- Application of Radiomics Model of CT Images in the Identification of Ureteral Calculus and Phlebolith.International journal of clinical practice · 2022Article
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8 authors.
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Abstract
purposeTo assess if autosegmentation-assisted radiomics can predict disease burden, hydronephrosis, and treatment strategies in patients with renal calculi.
methodsThe local ethical committee-approved, retrospective study included 202 adult patients (mean age: 53 ± 17 years; male: 103; female: 99) who underwent clinically indicated, non-contrast abdomen-pelvis CT for suspected or known renal calculi. All CT examinations were reviewed to determine the presence (n = 123 patients) or absence (n = 79) of renal calculi. On CT images with renal calculi, each kidney stone was annotated and measured (maximum dimension, Hounsfield unit (HU), and combined and dominant stone volumes) using a HU threshold-based segmentation. We recorded the presence of hydronephrosis, number of renal calculi, and treatment strategies. Deidentified CT images were processed with the radiomics prototype (Radiomics, Frontier, Siemens Healthineers), which automatically segmented each kidney to obtain 1690 first-, shape-, and higher-order radiomics. Data were analyzed using multiple logistic regression analysis with areas under the curve (AUC) as output.
resultsAmong 202 patients, only 28 patients (18%) needed procedural treatment (lithotripsy or ureteroscopic stone extraction). Gray-level co-occurrence matrix (GLCM) and gray-level run length matrix (GLRLM) differentiated patients with and without procedural treatment (AUC 0.91, 95% CI 0.85-0.92). Higher-order radiomics (gray-level size zone matrix - GLSZM) differentiated kidneys with and without hydronephrosis (AUC: 0.99, p < 0.001) as well those with different stone volumes (AUC up to 0.89, 95% CI 0.89-0.92).
conclusionAutomated segmentation and radiomics of entire kidneys can assess hydronephrosis presence, stone burden, and treatment strategies for renal calculi with AUCs > 0.85.
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