Renal cell carcinoma (RCC) is a cause of significant morbidity and mortality, with an estimated 35,000 new cases and 12,480 deaths in the United States in 2003. Recent advances in imaging technology, pathology, urology, and oncology permit early diagnosis of RCC and facilitate optimal management. The 2004 World Health Organization classification for renal neoplasms recognizes several distinct histologic subtypes of RCC. These subtypes include clear cell RCC, papillary RCC, chromophobe RCC, hereditary cancer syndromes, multilocular cystic RCC, collecting duct carcinoma, medullary carcinoma, mucinous tubular and spindle cell carcinoma, neuroblastoma-associated RCC, Xp11.2 translocation-TFE3 carcinoma, and unclassified lesions. Different histologic subtypes of RCC have characteristic histomorphologic and biologic profiles. Clear cell RCC is the most common subtype and has a less favorable prognosis (stage for stage) than do papillary RCC and chromophobe RCC. Collecting duct carcinoma and renal medullary carcinoma are associated with aggressive clinical behavior and a poor prognosis.

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Unenhanced helical CT accurately determines the presence or absence of ureterolithiasis in patients with acute flank pain. CT precisely identifies stone size and location. When ureterolithiasis is absent, other causes of acute flank pain can be identified. In most cases additional imaging is not required.

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The recent proliferation of multi-detector row computed tomography (CT) has led to an increase in the creation and interpretation of images in planes other than the traditional axial plane. Powerful three-dimensional (3D) applications improve the utility of detailed CT data but also create confusion among radiologists, technologists, and referring clinicians when trying to describe a particular method or type of image. Designing examination protocols that optimize data quality and radiation dose to the patient requires familiarity with the concepts of beam collimation and section collimation as they apply to multi-detector row CT. A basic understanding of the time-limited nature of projection data and the need for thin-section axial reconstruction for 3D applications is necessary to use the available data effectively in clinical practice. The axial reconstruction data can be used to create nonaxial two-dimensional images by means of multiplanar reformation. Multiplanar images can be thickened into slabs with projectional techniques such as average, maximum, and minimum intensity projection; ray sum; and volume rendering. By assigning a full spectrum of opacity values and applying color to the tissue classification system, volume rendering provides a robust and versatile data set for advanced imaging applications.