Nils Große Hokamp1, Simon Lennartz2,3, Johannes Salem4, Daniel Pinto Dos Santos2, Axel Heidenreich4, David Maintz2, Stefan Haneder2. 1. Faculty of Medicine and University Hospital Cologne, Institute for Diagnostic and Interventional Radiology, University of Cologne, Kerpener Str. 62, 50937, Cologne, Germany. Nils.Grosse-Hokamp@uk-koeln.de. 2. Faculty of Medicine and University Hospital Cologne, Institute for Diagnostic and Interventional Radiology, University of Cologne, Kerpener Str. 62, 50937, Cologne, Germany. 3. Else Kröner Forschungskolleg Clonal Evolution in Cancer, University Hospital Cologne, Cologne, Germany. 4. Faculty of Medicine and University Hospital Cologne, Department of Urology, University of Cologne, Cologne, Germany.
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.
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.
Authors: Andrew B Chen; Taseen Haque; Sidney Roberts; Sirisha Rambhatla; Giovanni Cacciamani; Prokar Dasgupta; Andrew J Hung Journal: Urol Clin North Am Date: 2021-10-23 Impact factor: 2.766
Authors: Lei Tang; Wuchao Li; Xianchun Zeng; Rongpin Wang; Xiushu Yang; Guangheng Luo; Qijian Chen; Lihui Wang; Bin Song Journal: Ann Transl Med Date: 2021-07