ArticleEuropean radiology2019
Differentiating kidney stones from phleboliths in unenhanced low-dose computed tomography using radiomics and machine learning.
Article in European radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 3 of them syntheses that pooled it.
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Who cites it
35 citing papers in PubMed, 3 syntheses or guidelines pooled it, 83 citations in OpenAlex.
- CT-based radiomics for predicting stone-free rate after ESWL: a systematic review and meta-analysis.World journal of urology · 2025Pooled it
- Artificial intelligence in urolithiasis: a systematic review of utilization and effectiveness.World journal of urology · 2024Pooled it
- The Ascent of Artificial Intelligence in Endourology: a Systematic Review Over the Last 2 Decades.Current urology reports · 2021Pooled it
- Machine learning integrating CT radiomics and clinical variables for preoperative prediction of renal uric acid stones: a model development and validation study.Translational andrology and urology · 2026Article
- Machine learning model integrating radiomics and clinical features for predicting postoperative bleeding after percutaneous nephrolithotomy.European journal of medical research · 2026Article
- An optimized bidirectional recurrent neural network for kidney stone detection based on developed bald eagle search method in CT scan images.Scientific reports · 2025Article
- Predicting ESWL success for ureteral stones: a radiomics-based machine learning approach.BMC medical imaging · 2025Article
- Employee loyalty evaluation using machine learning in technology-based small and medium-sized enterprises.Scientific reports · 2025Article
- Radiomics Analysis of Whole-Kidney Non-Contrast CT for Early Identification of Chronic Kidney Disease Stages 1-3.Bioengineering (Basel, Switzerland) · 2025Article
- Integrated radiomics and deep learning model for identifying medullary sponge kidney stones.Frontiers in medicine · 2025Article
- Automatic kidney stone identification: an adaptive feature-weighted LSTM model based on urine and blood routine analysis.Urolithiasis · 2024Article
- Develop a radiomics-based machine learning model to predict the stone-free rate post-percutaneous nephrolithotomy.Urolithiasis · 2024Article
- Identification of kidney stones in KUB X-ray images using VGG16 empowered with explainable artificial intelligence.Scientific reports · 2024Article
- Predicting Risk Stratification in Early-Stage Endometrial Carcinoma: Significance of Multiparametric MRI Radiomics Model.Journal of imaging informatics in medicine · 2024Article
- Review
- Transforming urinary stone disease management by artificial intelligence-based methods: A comprehensive review.Asian journal of urology · 2023Review
- Diagnostic accuracy of ultrasound-based multimodal radiomics modeling for fibrosis detection in chronic kidney disease.European radiology · 2023Article
- Theranostic roles of machine learning in clinical management of kidney stone disease.Computational and structural biotechnology journal · 2023Review
- Identifying ureteral stent encrustation using machine learning based on CT radiomics features: a bicentric study.Frontiers in medicine · 2023Article
- Ureteral calculi lithotripsy for single ureteral calculi: can DNN-assisted model help preoperatively predict risk factors for sepsis?European radiology · 2022Article
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Authors and funding
7 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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