Prediction of burden and management of renal calculi from whole kidney radiomics: a multicenter study
| Autor: | Ruhani Doda Khera, Bernardo Bizzo, Bernhard Schmidt, Shadi Ebrahimian, Mannudeep K. Kalra, Andrew N. Primak, Fatemeh Homayounieh, Sanjay Saini |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
medicine.medical_specialty
Renal calculi Urology medicine.medical_treatment Hydronephrosis Lithotripsy urologic and male genital diseases 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Radiomics Urolithiasis Internal medicine Hounsfield scale medicine Humans Radiology Nuclear Medicine and imaging Kidney Radiological and Ultrasound Technology business.industry Gastroenterology Retrospective cohort study Kidneys Ureters Bladder Retroperitoneum Disease burden Hepatology medicine.disease medicine.anatomical_structure 030220 oncology & carcinogenesis Kidney stones Laparoscopy Radiology business CT |
| Zdroj: | Abdominal Radiology (New York) |
| ISSN: | 2366-0058 2366-004X |
| Popis: | Purpose To assess if autosegmentation-assisted radiomics can predict disease burden, hydronephrosis, and treatment strategies in patients with renal calculi. Methods The 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. Results Among 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 0.85. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink