{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T10:19:48Z","timestamp":1775211588322,"version":"3.50.1"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T00:00:00Z","timestamp":1761696000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T00:00:00Z","timestamp":1761696000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"DOI":"10.1186\/s12880-025-01965-x","type":"journal-article","created":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T11:55:46Z","timestamp":1761738946000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Radiomics application using non-contrast computed tomography for predicting uric acid kidney stones"],"prefix":"10.1186","volume":"25","author":[{"given":"Yang","family":"Huang","sequence":"first","affiliation":[]},{"given":"Ning","family":"Li","sequence":"additional","affiliation":[]},{"given":"Xiaowei","family":"Han","sequence":"additional","affiliation":[]},{"given":"Shufeng","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Guozheng","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xisong","family":"Zhu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,29]]},"reference":[{"issue":"4","key":"1965_CR1","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1016\/j.eururo.2024.05.029","volume":"86","author":"A Skolarikos","year":"2024","unstructured":"Skolarikos A, Somani B, Neisius A, et al. Metabolic evaluation and recurrence prevention for urinary stone patients: an EAU guidelines update. Eur Urol. 2024;86(4):343\u201363.","journal-title":"Eur Urol"},{"issue":"3","key":"1965_CR2","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1016\/j.eururo.2015.07.041","volume":"69","author":"C T\u00fcrk","year":"2016","unstructured":"T\u00fcrk C, Pet\u0159\u00edk A, Sarica K, et al. EAU guidelines on interventional treatment for urolithiasis. Eur Urol. 2016;69(3):475\u201382.","journal-title":"Eur Urol"},{"issue":"3","key":"1965_CR3","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1016\/j.eururo.2015.07.040","volume":"69","author":"C T\u00fcrk","year":"2016","unstructured":"T\u00fcrk C, Pet\u0159\u00edk A, Sarica K, et al. EAU guidelines on diagnosis and conservative management of urolithiasis. Eur Urol. 2016;69(3):468\u201374.","journal-title":"Eur Urol"},{"key":"1965_CR4","doi-asserted-by":"publisher","first-page":"6431","DOI":"10.2147\/JIR.S433438","volume":"16","author":"Y Wang","year":"2023","unstructured":"Wang Y, Lu J. The management of diabetes with Hyperuricemia: can we hit two birds with one stone. J Inflamm Res. 2023;16:6431\u201341.","journal-title":"J Inflamm Res"},{"issue":"6","key":"1965_CR5","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1016\/j.semnephrol.2020.12.003","volume":"40","author":"E Adomako","year":"2020","unstructured":"Adomako E, Moe OW. Uric acid and urate in urolithiasis: the innocent bystander, instigator, and perpetrator. Semin Nephrol. 2020;40(6):564\u201373.","journal-title":"Semin Nephrol"},{"issue":"2","key":"1965_CR6","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1016\/j.urology.2012.02.019","volume":"80","author":"YC Jou","year":"2012","unstructured":"Jou YC, Fang CY, Chen SY, et al. Proteomic study of renal uric acid stone. Urology. 2012;80(2):260\u201366.","journal-title":"Urology"},{"key":"1965_CR7","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.ijcard.2015.08.109","volume":"213","author":"J Maiuolo","year":"2016","unstructured":"Maiuolo J, Oppedisano F, Gratteri S, Muscoli C, Mollace V. Regulation of uric acid metabolism and excretion. Int J Cardiol. 2016;213:8\u201314.","journal-title":"Int J Cardiol"},{"key":"1965_CR8","doi-asserted-by":"crossref","unstructured":"Neogi T, Jansen TL, Dalbeth N, et al. Gout classification criteria: an American college of rheumatology\/European league against rheumatism collaborative initiative. Ann Rheum Dis. 2015;74(10):1789\u201398.","DOI":"10.1136\/annrheumdis-2015-208237"},{"issue":"12","key":"1965_CR9","doi-asserted-by":"publisher","first-page":"1441","DOI":"10.1016\/j.acra.2007.09.016","volume":"14","author":"AN Primak","year":"2007","unstructured":"Primak AN, Fletcher JG, Vrtiska TJ, et al. Noninvasive differentiation of uric acid versus non-uric acid kidney stones using dual-energy CT. Acad Radiol. 2007;14(12):1441\u201347.","journal-title":"Acad Radiol"},{"issue":"2","key":"1965_CR10","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1148\/radiol.10100249","volume":"257","author":"G Hidas","year":"2010","unstructured":"Hidas G, Eliahou R, Duvdevani M, et al. Determination of renal stone composition with dual-energy CT: in vivo analysis and comparison with x-ray diffraction. Radiology. 2010;257(2):394\u2013401.","journal-title":"Radiology"},{"issue":"1","key":"1965_CR11","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1007\/s00330-023-10095-3","volume":"34","author":"Akinci D\u2019Antonoli","year":"2024","unstructured":"Akinci D\u2019Antonoli, Cuocolo R, Baessler B, Pinto Dos Santos D. Towards reproducible radiomics research: introduction of a database for radiomics studies. Eur Radiol. 2024;34(1):436\u201343.","journal-title":"Eur Radiol"},{"issue":"1","key":"1965_CR12","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1002\/med.21846","volume":"42","author":"J Guiot","year":"2022","unstructured":"Guiot J, Vaidyanathan A, Deprez L, et al. A review in radiomics: making personalized medicine a reality via routine imaging. Med Res Rev. 2022;42(1):426\u201340.","journal-title":"Med Res Rev"},{"issue":"1","key":"1965_CR13","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1007\/s00240-023-01506-7","volume":"52","author":"Y Nakamae","year":"2023","unstructured":"Nakamae Y, Deguchi R, Nemoto M, et al. AI prediction of extracorporeal shock wave lithotripsy outcomes for ureteral stones by machine learning-based analysis with a variety of stone and patient characteristics. Urolithiasis. 2023;52(1):9.","journal-title":"Urolithiasis"},{"key":"1965_CR14","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.ejrad.2018.08.018","volume":"107","author":"Y Xun","year":"2018","unstructured":"Xun Y, Li J, Geng Y, et al. Single extracorporeal shock-wave lithotripsy for proximal ureter stones: can CT texture analysis technique help predict the therapeutic effect. Eur J Radiol. 2018;107:84\u201389.","journal-title":"Eur J Radiol"},{"issue":"6","key":"1965_CR15","doi-asserted-by":"publisher","first-page":"3734","DOI":"10.1007\/s00330-020-07498-x","volume":"31","author":"R Wang","year":"2021","unstructured":"Wang R, Su Y, Mao C, Li S, You M, Xiang S. Laser lithotripsy for proximal ureteral calculi in adults: can 3D CT texture analysis help predict treatment success. Eur Radiol. 2021;31(6):3734\u201344.","journal-title":"Eur Radiol"},{"issue":"4","key":"1965_CR16","doi-asserted-by":"publisher","first-page":"829","DOI":"10.1016\/j.juro.2018.04.059","volume":"200","author":"M Mannil","year":"2018","unstructured":"Mannil M, von Spiczak J, Hermanns T, Poyet C, Alkadhi H, Fankhauser CD. Three-dimensional texture analysis with machine learning provides incremental predictive information for successful shock wave lithotripsy in patients with kidney stones. J Urol. 2018;200(4):829\u201336.","journal-title":"J Urol"},{"issue":"9","key":"1965_CR17","doi-asserted-by":"publisher","first-page":"792","DOI":"10.1016\/j.crad.2018.04.010","volume":"73","author":"GM Zhang","year":"2018","unstructured":"Zhang GM, Sun H, Shi B, Xu M, Xue HD, Jin ZY. Uric acid versus non-uric acid urinary stones: differentiation with single energy CT texture analysis. Clin Radiol. 2018;73(9):792\u201399.","journal-title":"Clin Radiol"},{"key":"1965_CR18","doi-asserted-by":"publisher","first-page":"109168","DOI":"10.1016\/j.compbiomed.2024.109168","volume":"182","author":"CJ Ejiyi","year":"2024","unstructured":"Ejiyi CJ, Cai D, Ejiyi MB, et al. Polynomial-SHAP analysis of liver disease markers for capturing of complex feature interactions in machine learning models. Comput Biol Med. 2024;182:109168.","journal-title":"Comput Biol Med"},{"issue":"2","key":"1965_CR19","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1007\/s11604-022-01349-z","volume":"41","author":"P Kaviani","year":"2023","unstructured":"Kaviani P, Primak A, Bizzo B, et al. Performance of threshold-based stone segmentation and radiomics for determining the composition of kidney stones from single-energy CT. Jpn J Radiol. 2023;41(2):194\u2013200.","journal-title":"Jpn J Radiol"},{"issue":"4","key":"1965_CR20","doi-asserted-by":"publisher","first-page":"870","DOI":"10.1016\/j.kint.2021.05.031","volume":"100","author":"J Zheng","year":"2021","unstructured":"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(4):870\u201380.","journal-title":"Kidney Int"},{"issue":"9","key":"1965_CR21","doi-asserted-by":"publisher","first-page":"1095","DOI":"10.1097\/CM9.0000000000002866","volume":"137","author":"J Zheng","year":"2024","unstructured":"Zheng J, Zhang J, Cai J, et al. Development of a radiomics model to discriminate ammonium uric acid stones from uric acid stones in vivo: a remedy for the diagnostic pitfall of dual-energy computed tomography. Chin Med J (engl). 2024;137(9):1095\u2013104.","journal-title":"Chin Med J (engl)"},{"issue":"3","key":"1965_CR22","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1159\/000538133","volume":"108","author":"L Jin","year":"2024","unstructured":"Jin L, Chen Z, Sun Y, Tian Z, Yi X, Huang Y. Advancements in uric acid stone detection: Integrating deep learning with CT imaging and clinical assessments in the upper urinary tract. Urol Int. 2024;108(3):234\u201341.","journal-title":"Urol Int"},{"key":"1965_CR23","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.urology.2019.04.020","volume":"130","author":"TY Tran","year":"2019","unstructured":"Tran TY, Bamberger JN, Blum KA, et al. Predicting the impacted ureteral stone with computed tomography. Urology. 2019;130:43\u201347.","journal-title":"Urology"},{"issue":"3","key":"1965_CR24","doi-asserted-by":"publisher","first-page":"580","DOI":"10.1148\/radiol.210822","volume":"304","author":"A Pourvaziri","year":"2022","unstructured":"Pourvaziri A, Parakh A, Cao J, et al. Comparison of four dual-energy CT scanner technologies for determining renal stone composition: a phantom approach. Radiology. 2022;304(3):580\u201389.","journal-title":"Radiology"},{"issue":"15","key":"1965_CR25","doi-asserted-by":"publisher","first-page":"2178","DOI":"10.1039\/C8CC10050K","volume":"55","author":"R Molloy","year":"2019","unstructured":"Molloy R, Sun W, Chen J, Zhou W. Structure and cleavage of monosodium urate monohydrate crystals. Chem Commun (camb). 2019;55(15):2178\u201381.","journal-title":"Chem Commun (camb)"},{"issue":"6","key":"1965_CR26","first-page":"534","volume":"22","author":"KS Wan","year":"2016","unstructured":"Wan KS, Liu CK, Ko MC, Lee WK, Huang CS. Nephrolithiasis among male patients with newly diagnosed gout. Hong Kong Med J. 2016;22(6):534\u201337.","journal-title":"Hong Kong Med J"},{"key":"1965_CR27","doi-asserted-by":"publisher","first-page":"e421","DOI":"10.5114\/pjr.2018.79588","volume":"83","author":"M St\u0119pie\u0144","year":"2018","unstructured":"St\u0119pie\u0144 M, Chrzan R, Gawlas W. In vitro analysis of urinary stone composition in dual-energy computed tomography. Pol J Radiol. 2018;83:e421\u201325.","journal-title":"Pol J Radiol"},{"issue":"3","key":"1965_CR28","doi-asserted-by":"publisher","first-page":"2370","DOI":"10.21037\/qims-23-922","volume":"14","author":"Z Xu","year":"2024","unstructured":"Xu Z, Li M, Li B, Shu H. Synthesis of virtual monoenergetic images from kilovoltage peak images using wavelet loss enhanced CycleGAN for improving radiomics features reproducibility. Quant Imag Med Surg. 2024;14(3):2370\u201390.","journal-title":"Quant Imag Med Surg"},{"key":"1965_CR29","doi-asserted-by":"publisher","first-page":"110055","DOI":"10.1016\/j.ejrad.2021.110055","volume":"146","author":"G Corrias","year":"2022","unstructured":"Corrias G, Micheletti G, Barberini L, Suri JS, Saba L. Texture analysis imaging \u201cwhat a clinical radiologist needs to know\u201d. Eur J Radiol. 2022;146:110055.","journal-title":"Eur J Radiol"},{"issue":"1","key":"1965_CR30","doi-asserted-by":"publisher","first-page":"19559","DOI":"10.1038\/s41598-023-46695-8","volume":"13","author":"VH Tang","year":"2023","unstructured":"Tang VH, Duong S, Nguyen C, et al. Wavelet radiomics features from multiphase CT images for screening hepatocellular carcinoma: analysis and comparison. Sci Rep. 2023;13(1):19559.","journal-title":"Sci Rep"},{"issue":"10","key":"1965_CR31","doi-asserted-by":"publisher","first-page":"4758","DOI":"10.21037\/qims-22-252","volume":"12","author":"Z Jiang","year":"2022","unstructured":"Jiang Z, Yin J, Han P, et al. Wavelet transformation can enhance computed tomography texture features: a multicenter radiomics study for grade assessment of COVID-19 pulmonary lesions. Quant Imag Med Surg. 2022;12(10):4758\u201370.","journal-title":"Quant Imag Med Surg"},{"issue":"4","key":"1965_CR32","doi-asserted-by":"publisher","first-page":"e15316","DOI":"10.1111\/ctr.15316","volume":"38","author":"R Yanagawa","year":"2024","unstructured":"Yanagawa R, Iwadoh K, Akabane M, et al. LightGBM outperforms other machine learning techniques in predicting graft failure after liver transplantation: creation of a predictive model through large-scale analysis. Clin transplant. 2024;38(4):e15316.","journal-title":"Clin transplant"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-025-01965-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-025-01965-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-025-01965-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T11:55:46Z","timestamp":1761738946000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-025-01965-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,29]]},"references-count":32,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1965"],"URL":"https:\/\/doi.org\/10.1186\/s12880-025-01965-x","relation":{},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,29]]},"assertion":[{"value":"21 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The research including clinical images received ethical approval for from the Ethics Committee of the Quzhou People\u2019s Hospital. All research procedures were carried out in accordance with the Declaration of Helsinki (2000) of the World Medical Association.and the informed consent form was signed by the patients or their families.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent of publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interests"}}],"article-number":"433"}}