Artificial Intelligence and Novel Technologies for the Diagnosis of Upper Tract Urothelial Carcinoma

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 and Google scholar databases for studies investigating the application of artificial intelligence for the diagnosis of UTUC. 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 early diagnosis of bladder recurrence, as well as improve diagnostic accuracy in atypical cells, by being trained in annotated cytology images. Apart from that, data from computed tomography urograms by extracting textural radiomics features, can develop machine learning models to predict UTUC tumors grade and stage in small size and especially high grade tumors. 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 tumors. ChatGPT, although not mature to provide information on diagnosis and treatment, can assist patients' understanding of the disease’s epidemiology and risk factors. Computer vision models in real-time can augment visualisation during endoscopic ureteral tumor diagnosis and ablation. Deep learning workflow can also be applied in histopathological slides to predict UTUC protein- based subtypes. Conclusion Artificial intelligence has been shown to greatly facilitate the timely diagnosis of high-grade UTUC by improving the diagnostic accuracy of urine cytology, CT Urogram and ureteroscopy visualisation. Deep learning systems can become a useful and easily accessible tool in physicians' armamentarium to deal with urothelial cancer diagnostic uncertainties.