Renal cell carcinoma (RCC) is a biologically heterogeneous disease, with many small renal masses (SRMs) exhibiting an indolent natural history, while others progress more rapidly to become life-threatening. Existing multiphase contrast-enhanced imaging methods, such as computed tomography or magnetic resonance imaging, cannot definitively distinguish between benign and malignant solid tumors or identify histologic subtype, and early results of molecular imaging studies (positron emission tomography [PET]) in the evaluation of SRMs have not improved on these established modalities. Alternative molecular markers/agents recognizing aberrant cellular pathways of cellular oxidative metabolism, DNA synthesis, and tumor hypoxia tracers are currently under development and investigation for RCC assessment, but to date none are yet clinically applicable or available. In contrast, immuno-PET offers highly selective binding to cancer-specific antigens, and might identify radiographically recognizable and distinct molecular targets. A phase I proof-of-concept study first demonstrated the ability of immuno-PET to discriminate between clear-cell RCC (ccRCC) and non-ccRCC, utilizing a chimeric monoclonal antibody to carbonic anhydrase IX (cG250, girentuximab) labeled with (124)I ((124)I-girentuximab PET); the study examined patients with renal masses who subsequently underwent standard surgical resection. A follow-up phase III multicenter trial confirmed that (124)I-cG250-PET can accurately and noninvasively identify ccRCC with high sensitivity (86%), specificity (87%), and positive predictive value (95%). In the challenge to appropriately match treatment of an incidentally identified SRM to its biological potential, this highly accurate and histologically specific molecular imaging modality demonstrates the ability of imaging to provide clinically important preoperative diagnostic information, which can result in optimal and personalized therapy.
Keywords: PET; clear cell; diagnosis; immuno-PET; kidney; molecular imaging; neoplasms.