Postoperative surveillance protocols for renal cell carcinoma
Renal Cancer: Contemporary Management, Page: 283-305
2013
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Book Chapter Description
Patients with renal cell carcinoma (RCC) are at risk of recurrence even after definitive surgical extirpation. Without prompt identification and treatment of recurrent disease, prognosis is poor. Literature suggests that early intervention in the form of metastasectomy and/or targeted therapy can lead to prolonged survival in those who develop recurrent or metastatic RCC. Therefore, surveillance in the postoperative setting is of paramount importance. Currently, there is no single consensus on the optimal surveillance strategy following radical or partial nephrectomy, or ablative therapies to treat RCC. The purpose of this chapter is to provide a comprehensive review of the existing data on postoperative surveillance strategies and how prognostic factors and patterns of recurrence have contributed to the establishment of such protocols. The specific components of surveillance are described and several specific evidence-based recommendations are reviewed, as well as the current 2012 National Comprehensive Cancer Network (NCCN) guidelines and a surveillance algorithm based on the UCLA integrated scoring system (UISS). Briefly, new frontiers in surveillance are also discussed, such as the use of PET imaging and the incorporation of molecular biomarkers into existing surveillance models.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84956559208&origin=inward; http://dx.doi.org/10.1007/978-1-4614-7236-0_17; https://link.springer.com/10.1007/978-1-4614-7236-0_17; https://dx.doi.org/10.1007/978-1-4614-7236-0_17; https://link.springer.com/chapter/10.1007/978-1-4614-7236-0_17
Springer Science and Business Media LLC
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