Upper urinary tract urothelial carcinoma is staged using the TNM classification of malignant tumors. Preoperative TNM is important for treatment planning. Computed tomography urography is now widely used for clinical survey of upper urinary tract carcinoma because of its diagnostic accuracy. Computed tomography urography is recommended as the first‐line imaging procedure in several guidelines. Several reports stated that computed tomography urography is also useful for staging. However, no educational and practical reviews detailing the T staging of upper urinary tract urothelial carcinomas using imaging are available. We discuss the scanning protocol, T staging using computed tomography urography, limitations, magnetic resonance imaging, computed tomography comparison and pitfalls in imaging of upper urinary tract urothelial carcinoma. A recent study reported the high diagnostic accuracy of computed tomography urography with respect to T3 or higher stage tumors. To date, images that show a Tis–T2 stage have not been reported, but various studies are ongoing. Although magnetic resonance imaging has lower spatial resolution than computed tomography urography, magnetic resonance imaging can be carried out without radiation exposure or contrast agents. Magnetic resonance imaging also offers the unique ability of diffusion‐weighted imaging without contrast agent use. Some researchers reported that diffusion‐weighted imaging is useful not only for detecting lesions, but for predicting the T stage and tumor grade. We recommend the appropriate use of computed tomography and magnetic resonance while considering the limitations of each modality and the pitfalls in upper urinary tract urothelial carcinoma imaging.
Deep learning (DL), part of a broader family of machine learning methods, is based on learning data representations rather than task-specific algorithms. Deep learning can be used to improve the image quality of clinical scans with image noise reduction. We review the ability of DL to reduce the image noise, present the advantages and disadvantages of computed tomography image reconstruction, and examine the potential value of new DL-based computed tomography image reconstruction.
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