{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T08:13:58Z","timestamp":1791360838048,"version":"4.3.1"},"reference-count":22,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T00:00:00Z","timestamp":1747699200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Medicina"],"abstract":"<jats:p>Background and Objectives: Upper tract urothelial carcinoma (UTUC) is one of the most underdiagnosed but, at the same time, one of the most lethal cancers. In this review article, we investigated the application of artificial intelligence and novel technologies in the prompt identification of high-grade UTUC to prevent metastases and facilitate timely treatment. Materials and Methods: We conducted an extensive search of the literature from the Pubmed, Google scholar and Cochrane library databases for studies investigating the application of artificial intelligence for the diagnosis of UTUC, according to the PRISMA guidelines. After the exclusion of non-associated and non-English studies, we included 12 articles in our review. Results: Artificial intelligence systems have been shown to enhance post-radical nephroureterectomy urine cytology reporting, in order to facilitate the early diagnosis of bladder recurrence, as well as improve diagnostic accuracy in atypical cells, by being trained on annotated cytology images. In addition to this, by extracting textural radiomics features from data from computed tomography urograms, we can develop machine learning models to predict UTUC tumour grade and stage in small-size and especially high-grade tumours. Random forest models have been shown to have the best performance in predicting high-grade UTUC, while hydronephrosis is the most significant independent factor for high-grade tumours. ChatGPT, although not mature enough to provide information on diagnosis and treatment, can assist in improving patients\u2019 understanding of the disease\u2019s epidemiology and risk factors. Computer vision models, in real time, can augment visualisation during endoscopic ureteral tumour diagnosis and ablation. A deep learning workflow can also be applied in histopathological slides to predict UTUC protein-based subtypes. Conclusions: Artificial intelligence has been shown to greatly facilitate the timely diagnosis of high-grade UTUC by improving the diagnostic accuracy of urine cytology, CT Urograms and ureteroscopy visualisation. Deep learning systems can become a useful and easily accessible tool in physicians\u2019 armamentarium to deal with diagnostic uncertainties in urothelial cancer.<\/jats:p>","DOI":"10.3390\/medicina61050923","type":"journal-article","created":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T08:41:12Z","timestamp":1747730472000},"page":"923","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Artificial Intelligence and Novel Technologies for the Diagnosis of Upper Tract Urothelial Carcinoma"],"prefix":"10.3390","volume":"61","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1972-0206","authenticated-orcid":false,"given":"Nikolaos","family":"Kostakopoulos","sequence":"first","affiliation":[{"name":"1st Department of Urology, Metropolitan General Athens, 15562 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vasileios","family":"Argyropoulos","sequence":"additional","affiliation":[{"name":"1st Department of Urology, Metropolitan General Athens, 15562 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0390-1467","authenticated-orcid":false,"given":"Themistoklis","family":"Bellos","sequence":"additional","affiliation":[{"name":"2nd Department of Urology, University of Athens, 15126 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7028-1544","authenticated-orcid":false,"given":"Stamatios","family":"Katsimperis","sequence":"additional","affiliation":[{"name":"2nd Department of Urology, University of Athens, 15126 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Athanasios","family":"Kostakopoulos","sequence":"additional","affiliation":[{"name":"1st Department of Urology, Metropolitan General Athens, 15562 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Masson-Lecomte, A., Birtle, A., Pradere, B., Capoun, O., Comp\u00e9rat, E., Dom\u00ednguez-Escrig, J.L., Liedberg, F., Makaroff, L., Mariappan, P., and Moschini, M. 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