{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T10:41:41Z","timestamp":1770288101958,"version":"3.49.0"},"reference-count":19,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T00:00:00Z","timestamp":1748563200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T00:00:00Z","timestamp":1748563200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Klinikum der Universit\u00e4t M\u00fcnchen"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Background<\/jats:title>\n            <jats:p>To investigate a non-invasive radiomics-based machine learning algorithm to differentiate upper urinary tract urothelial carcinoma (UTUC) from renal cell carcinoma (RCC) prior to surgical intervention.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>Preoperative computed tomography venous-phase datasets from patients that underwent procedures for histopathologically confirmed UTUC or RCC were retrospectively analyzed. Tumor segmentation was performed manually, and radiomic features were extracted according to the <jats:italic>International Image Biomarker Standardization Initiative<\/jats:italic>. Features were normalized using z-scores, and a predictive model was developed using the <jats:italic>least absolute shrinkage and selection operator<\/jats:italic> (LASSO). The dataset was split into a training cohort (70%) and a test cohort (30%).<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>A total of 236 patients [30.5% female, median age 70.5 years (IQR: 59.5\u201377), median tumor size 5.8\u00a0cm (range: 4.1\u20138.2\u00a0cm)] were included. For differentiating UTUC from RCC, the model achieved a sensitivity of 88.4% and specificity of 81% (AUC: 0.93, radiomics score cutoff: 0.467) in the training cohort. In the validation cohort, the sensitivity was 80.6% and specificity 80% (AUC: 0.87, radiomics score cutoff: 0.601). Subgroup analysis of the validation cohort demonstrated robust performance, particularly in distinguishing clear cell RCC from high-grade UTUC (sensitivity: 84%, specificity: 73.1%, AUC: 0.84) and high-grade from low-grade UTUC (sensitivity: 57.7%, specificity: 88.9%, AUC: 0.68). Limitations include the need for independent validation in future randomized controlled trials (RCTs).<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>Machine learning-based radiomics models can reliably differentiate between RCC and UTUC in preoperative CT imaging. With a suggested performance benefit compared to conventional imaging, this technology might be added to the current preoperative diagnostic workflow.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Clinical trial number<\/jats:title>\n            <jats:p>Local ethics committee no. 20\u2013179<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/s12880-025-01727-9","type":"journal-article","created":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T08:48:55Z","timestamp":1748594935000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Radiomics-based differentiation of upper urinary tract urothelial and renal cell carcinoma in preoperative computed tomography datasets"],"prefix":"10.1186","volume":"25","author":[{"given":"Julian","family":"Marcon","sequence":"first","affiliation":[]},{"given":"Philipp","family":"Weinhold","sequence":"additional","affiliation":[]},{"given":"Mona","family":"Rzany","sequence":"additional","affiliation":[]},{"given":"Matthias P.","family":"Fabritius","sequence":"additional","affiliation":[]},{"given":"Michael","family":"Winkelmann","sequence":"additional","affiliation":[]},{"given":"Alexander","family":"Buchner","sequence":"additional","affiliation":[]},{"given":"Lennert","family":"Eismann","sequence":"additional","affiliation":[]},{"given":"Jan-Friedrich","family":"Jokisch","sequence":"additional","affiliation":[]},{"given":"Jozefina","family":"Casuscelli","sequence":"additional","affiliation":[]},{"given":"Gerald B.","family":"Schulz","sequence":"additional","affiliation":[]},{"given":"Thomas","family":"Kn\u00f6sel","sequence":"additional","affiliation":[]},{"given":"Michael","family":"Ingrisch","sequence":"additional","affiliation":[]},{"given":"Jens","family":"Ricke","sequence":"additional","affiliation":[]},{"given":"Christian G.","family":"Stief","sequence":"additional","affiliation":[]},{"given":"Severin","family":"Rodler","sequence":"additional","affiliation":[]},{"given":"Philipp M.","family":"Kazmierczak","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,30]]},"reference":[{"issue":"1","key":"1727_CR1","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.eururo.2020.05.042","volume":"79","author":"M Roupr\u00eat","year":"2021","unstructured":"Roupr\u00eat M, et al. 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Our research was carried out in accordance with the Declaration of Helsinki of the World Medical Association, and informed consent to participate in the study was obtained from all patients.","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 for publication"}},{"value":"S.R. declares the following financial disclosures (Horst-J\u00fcrgen-L\u00fchl Foundation, Bayerisches Zentrum f\u00fcr Krebsforschung (BZKF), F\u00f6rderung f\u00fcr Forschung und Lehre (F\u00f6FoLe)).","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"196"}}