Journal Article

Differentiating kidney stones from phleboliths in unenhanced low-dose computed tomography using radiomics and machine learning

Hofmeister, Jeremy, Martin, Steve P, Klein, Jacques, De Perrot, Thomas, Burgermeister, Simon, Gregoire Feutry, Montet, Xavier

2019

Overview

ObjectivesDistinguishing between kidney stones and phleboliths can constitute a diagnostic challenge in patients undergoing unenhanced low-dose CT (LDCT) for acute flank pain. We sought to investigate the accuracy of radiomics and a machine-learning classifier in differentiating between kidney stones and phleboliths on LDCT.MethodsRadiomics features were extracted following a semi-automatic segmentation of kidney stones and phleboliths for two independent consecutive cohorts of patients undergoing LDCT for acute flank pain.Radiomics features from the first cohort of patients (n = 369) were ultimately used to train a machine-learning model designed to distinguish kidney stones (n = 211) from phleboliths (n = 201). Classification performance was assessed on the second independent cohort (i.e., testing set) (kidney stones n = 24; phleboliths n = 23) using positive and negative predictive values (PPV and NPV), area under the receiver operating curves (AUC), and permutation testing.ResultsOur machine-learning classification model trained on radiomics features achieved an overall accuracy of 85.1% on the independent testing set, with an AUC of 0.902, PPV of 81.5%, and NPV of 90.0%. Classification accuracy was significantly better than chance on permutation testing (p < 0.05, permutation p value).ConclusionRadiomics and machine learning enable accurate differentiation between kidney stones and phleboliths on LDCT in patients presenting with acute flank pain.Key Points• Combining a machine-learning algorithm with radiomics features extracted for abdominopelvic calcification on LDCT offers a highly accurate method for discriminating phleboliths from kidney stones.• Our radiomics and machine-learning model proved robust for CT acquisition and reconstruction protocol when tested in comparison with an external independent cohort of patients with acute flank pain.• The high performance of the radiomics-based automatic classification model in differentiating phleboliths from kidney stones indicates its potential as a future diagnostic tool for equivocal abdominopelvic calcifications in the setting of suspected renal colic.

• Publisher: Springer Nature B.V
• Subject: Accuracy / Algorithms / Artificial intelligence / Calcification / Calculi / Classification / Colic / Computed tomography / Diagnostic software / Diagnostic systems / Feature extraction / Kidney stones / Kidneys / Learning algorithms / Machine learning / Nephrolithiasis / Pain / Patients / Permutations / Radiomics / Segmentation