The authors reply:

We thank Zhang et al.1 Zhang L. Zhang B. A machine learning–based radiomic model for predicting urinary infection stone. Kidney Int. 2021; 100: 1142 Abstract Full Text Full Text PDF Scopus (2) Google Scholar for their interest in our study. 2 Zheng J. Yu H. Batur J. et al. A multicenter study to develop a non-invasive radiomic model to identify urinary infection stone in vivo using machine-learning. Kidney Int. 2021; 100: 870-880 Abstract Full Text Full Text PDF Scopus (16) Google Scholar Usually, feature reproducibility assessment is implemented for data dimension reduction. However, because the margins of a urinary stone in computed tomography images are clear, satisfactory interobserver feature extraction reproducibility was achieved in our study, with interclass correlation coefficients ranging from 0.848 to 1.000. Therefore, all extracted radiomics features were used for the subsequent modeling. Moreover, the 24 selected features had only a low pairwise correlation (mean absolute Spearman, ρ = 0.196), indicating that these features provide complementary information. 3 Grossmann P. Narayan V. Chang K. et al. Quantitative imaging biomarkers for risk stratification of patients with recurrent glioblastoma treated with bevacizumab. Neuro Oncol. 2017; 19: 1688-1697 Crossref PubMed Scopus (78) Google Scholar We compared the performances of 4 feature selection methods and chose the optimal model in our study. This approach was also used in other radiomics studies. 4 Xu L. Yang P. Liang W. et al. A radiomics approach based on support vector machine using MR images for preoperative lymph node status evaluation in intrahepatic cholangiocarcinoma. Theranostics. 2019; 9: 5374-5385 Crossref PubMed Scopus (93) Google Scholar ,5 Saadani H. van der Hiel B. Aalbersberg E.A. et al. Metabolic biomarker-based BRAFV600 mutation association and prediction in melanoma. J Nucl Med. 2019; 60: 1545-1552 Crossref PubMed Scopus (19) Google Scholar The favorable performance of our radiomics model in the validation sets also indicated the reliability of this method. The method recommended by Zhang et al. is also reasonable, which needs further investigation. A multicenter study to develop a non-invasive radiomic model to identify urinary infection stone in vivo using machine-learningKidney InternationalVol. 100Issue 4PreviewUrolithiasis 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. Full-Text PDF A machine learning–based radiomic model for predicting urinary infection stoneKidney InternationalVol. 100Issue 5PreviewWe read with great interest the article by Zheng et al.,1 published in Kidney International. This study leveraged a noninvasive radiomic model to preoperatively predict infection stones. Despite the encouraging results, several methodological issues should be noted. A robust radiomic biomarker across various image acquisitions and feature selection methods is crucial for the reliability of subsequent modeling. The authors should include the radiomic features that did not show significant differences due to machine and acquisition parameters. Full-Text PDF