ArticleKidney international2021
A multicenter study to develop a non-invasive radiomic model to identify urinary infection stone in vivo using machine-learning.
Article in Kidney international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 2 of them syntheses that pooled it.
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Who cites it
40 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- 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
- A retrospective study using machine learning to develop predictive model to identify urinary infection stones in vivo.Urolithiasis · 2023Trial
- Construction of a LASSO regression-based predictive model for recurrence of urinary tract stones.Translational andrology and urology · 2026Article
- A study on metabolic differences between infectious and non-infectious stones in patients with urine culture positive for Proteus mirabilis.BMC urology · 2026Article
- Machine learning model integrating radiomics and clinical features for predicting postoperative bleeding after percutaneous nephrolithotomy.European journal of medical research · 2026Article
- Noninvasive CT radiomics-clinical model accurately classifies anhydrous uric acid stones: a multicenter study.World journal of urology · 2025Article
- Pre-operatively predicting kidney stone recurrence: integrating radiomic features and clinical variables using machine learning.BMC medical imaging · 2025Article
- Inconsistency in stone composition among bilateral urolithiasis patients: a multicenter retrospective study.Urolithiasis · 2025Article
- Review
- Radiomics application using non-contrast computed tomography for predicting uric acid kidney stones.BMC medical imaging · 2025Article
- An interpretable machine learning model integrating computed tomography radiomics and clinical features for predicting the urosepsis after percutaneous nephrolithotomy.Biomedical engineering online · 2025Article
- Construction and validation of a cross-sectional risk classification model for hypoproteinemia in single-center maintenance hemodialysis patient.Scientific reports · 2025Article
- Review
- Trends and sex disparities in the burden of urolithiasis in 204 countries and territories, 1990-2021.Chinese medical journal · 2025Article
- Prediction of the composition of urinary calculi using artificial intelligence.Pakistan journal of medical sciences · 2025Article
- Development of machine learning-based differential diagnosis model and risk prediction model of organ damage for severe Mycoplasma pneumoniae pneumonia in children.Scientific reports · 2025Article
- Kidney Stones as Minerals: How Methods from Geology Could Inform Urolithiasis Treatment.Journal of clinical medicine · 2025Review
- Predictive value of the stone-free rate after percutaneous nephrolithotomy based on multiple machine learning models.Frontiers in medicine · 2025Article
- From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
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14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Urolithiasis is a common urological disease, and treatment strategy options vary between different stone types. However, accurate detection of stone composition can only be performed in vitro. The management of infection stones is particularly challenging with yet no effective approach to pre-operatively identify infection stones from non-infection stones. Therefore, we aimed to develop a radiomic model for preoperatively identifying infection stones with multicenter validation. In total, 1198 eligible patients with urolithiasis from three centers were divided into a training set, an internal validation set, and two external validation sets. Stone composition was determined by Fourier transform infrared spectroscopy. A total of 1316 radiomic features were extracted from the pre-treatment Computer Tomography images of each patient. Using the least absolute shrinkage and selection operator algorithm, we identified a radiomic signature that achieved favorable discrimination in the training set, which was confirmed in the validation sets. Moreover, we then developed a radiomic model incorporating the radiomic signature, urease-producing bacteria in urine, and urine pH based on multivariate logistic regression analysis. The nomogram showed favorable calibration and discrimination in the training and three validation sets (area under the curve [95% confidence interval], 0.898 [0.840-0.956], 0.832 [0.742-0.923], 0.825 [0.783-0.866], and 0.812 [0.710-0.914], respectively). Decision curve analysis demonstrated the clinical utility of the radiomic model. Thus, our proposed radiomic model can serve as a non-invasive tool to identify urinary infection stones in vivo, which may optimize disease management in urolithiasis and improve patient prognosis.
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