Dose independent characterization of renal stones by means of dual energy computed tomography and machine learning: an ex-vivo study.
Eur Radiol
; 30(3): 1397-1404, 2020 Mar.
Article
en En
| MEDLINE
| ID: mdl-31773296
ABSTRACT
OBJECTIVES:
To predict the main component of pure and mixed kidney stones using dual-energy computed tomography and machine learning.METHODS:
200 kidney stones with a known composition as determined by infrared spectroscopy were examined using a non-anthropomorphic phantom on a spectral detector computed tomography scanner. Stones were of either pure (monocrystalline, n = 116) or compound (dicrystalline, n = 84) composition. Image acquisition was repeated twice using both, normal and low-dose protocols, respectively (ND/LD). Conventional images and low and high keV virtual monoenergetic images were reconstructed. Stones were semi-automatically segmented. A shallow neural network was trained using data from ND1 acquisition split into training (70%), testing (15%) and validation-datasets (15%). Performance for ND2 and both LD acquisitions was tested. Accuracy on a per-voxel and a per-stone basis was calculated.RESULTS:
Main components were Whewellite (n = 80), weddellite (n = 21), Ca-phosphate (n = 39), cysteine (n = 20), struvite (n = 13), uric acid (n = 18) and xanthine stones (n = 9). Stone size ranged from 3 to 18 mm. Overall accuracy for predicting the main component on a per-voxel basis attained by ND testing dataset was 91.1%. On independently tested acquisitions, accuracy was 87.1-90.4%.CONCLUSIONS:
Even in compound stones, the main component can be reliably determined using dual energy CT and machine learning, irrespective of dose protocol. KEY POINTS ⢠Spectral Detector Dual Energy CT and Machine Learning allow for an accurate prediction of stone composition. ⢠Ex-vivo study demonstrates the dose independent assessment of pure and compound stones. ⢠Lowest accuracy is reported for compound stones with struvite as main component.Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Cálculos Renales
/
Tomografía Computarizada por Rayos X
/
Redes Neurales de la Computación
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Eur Radiol
Asunto de la revista:
RADIOLOGIA
Año:
2020
Tipo del documento:
Article
País de afiliación:
Alemania