{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T03:45:50Z","timestamp":1791776750684,"version":"4.3.4"},"reference-count":31,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T00:00:00Z","timestamp":1786579200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T00:00:00Z","timestamp":1786579200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100003083","name":"European Association of Urology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003083","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100016396","name":"Association Fran\u00e7aise d'Urologie","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100016396","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["bjui-journals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["BJU International"],"published-print":{"date-parts":[[2026,11]]},"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To predict spontaneous stone passage (SSP) in uncomplicated acute ureteric colic (AC) using non\u2010contrast computed tomography (NCCT)\u2010based radiomics and machine learning (ML).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Patients and Methods<\/jats:title>\n                    <jats:p>This retrospective single\u2010centre study included consecutive patients presenting with stone\u2010related uncomplicated AC between January 2022 and March 2024, confirmed by NCCT. Initial and 4\u2010week follow\u2010up clinical and imaging data were collected, including SSP status. After anonymisation, a semi\u2010automated density\u2010based stone segmentation was performed using 3DSlicer. Radiomic features were extracted (PyRadiomics_v3.1.1). Significant features were selected using the Least Absolute Shrinkage and Selection Operator (LASSO) method. After data splitting (training [80%]\u2013validation [20%]), nine ML models were trained with hyperparameter fine\u2010tuning to predict SSP on the validation set. The primary performance metric was the area under the curve (AUC).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 428 patients were included, with a median (interquartile range [IQR]) age of 45\u2009(32\u201356)\u2009years. Most ureteric stones were solitary, with a median (IQR) maximum stone diameter (MSD) of 5.3\u2009(4.1\u20136)\u2009mm and stone volume of 68.8\u2009(22\u201378.1)\u2009mm\n                      <jats:sup>3<\/jats:sup>\n                      . The majority of the stones were proximal rather than distal: 59.6%, were located in the upper ureter, 9.1%, in the middle, and 30.8% in the lower ureter. A pelvic phlebolith was present in 50.9% of cases. The overall SSP rate was 47.9%. Following LASSO selection, seven radiomic features were used for model training and validation: image\u2010original_mean (mean value of all voxels), image\u2010original_maximum (maximum voxel value), original_glrlm_RunEntropy (heterogeneity in grey level run lengths), original_gldm_DependenceVariance (structure irregularity), original_gldm_LargeDependenceEmphasis (voxel homogeneity), original_gldm_LargeDependenceHighGreyLevelEmphasis (homogenous regions with high voxel intensity), original_gldm_LargeDependenceLowGreyLevelEmphasis (homogenous regions with low voxel intensity), along with SSP status. The random forest algorithm achieved the best performance of all tested models with an AUC of 0.79 (95% confidence interval 0.68\u20130.88) on the validation set (accuracy\u2009=\u20090.72, F1\u2010score\u2009=\u20090.72, sensitivity\u2009=\u20090.69, specificity\u2009=\u20090.76).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Predicting SSP in uncomplicated AC is feasible using radiomics features extracted from NCCT. Hybrid models, including the stone location could improve their performance. External validation is required, and developing an automated ureteric stone detection algorithm would be necessary to create a fully stand\u2010alone clinical tool.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/bju.70408","type":"journal-article","created":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T02:55:34Z","timestamp":1786676134000},"page":"841-851","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Spontaneous stone passage prediction in acute ureteric colic using radiomics and machine learning"],"prefix":"10.1111","volume":"138","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3055-2984","authenticated-orcid":false,"given":"Frederic","family":"Panthier","sequence":"first","affiliation":[{"name":"Department of Urology, Westmoreland Street Hospital UCLH NHS Foundation Trust  London UK"},{"name":"Service d'Urologie, Assistance\u2010Publique H\u00f4pitaux de Paris, H\u00f4pital Tenon Sorbonne Universit\u00e9  Paris France"},{"name":"GRC n\u00b020, Groupe de Recherche Clinique sur la Lithiase Urinaire, H\u00f4pital Tenon Sorbonne Universit\u00e9  Paris France"},{"name":"PIMM, UMR 8006 CNRS\u2010Arts et M\u00e9tiers ParisTech  Paris France"},{"name":"Progressive Endourological Association for Research and Leading Solutions (PEARLS)  Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4124-789X","authenticated-orcid":false,"given":"Zine\u2010Eddine","family":"Khene","sequence":"additional","affiliation":[{"name":"Department of Urology Rennes University Hospital  Rennes France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniela","family":"Velinova","sequence":"additional","affiliation":[{"name":"Department of Urology, Westmoreland Street Hospital UCLH NHS Foundation Trust  London 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