Algorithms in the First‐Line Treatment of Metastatic Clear Cell Renal Cell Carcinoma—Analysis Using Diagnostic Nodes. (3rd August 2015)

• Record Type: Journal Article
• Authors: Rothermundt, Christian; Bailey, Alexandra; Cerbone, Linda; Eisen, Tim; Escudier, Bernard; Gillessen, Silke; Grünwald, Viktor; Larkin, James; McDermott, David; Oldenburg, Jan; Porta, Camillo; Rini, Brian; Schmidinger, Manuela; Sternberg, Cora; Putora, Paul M.
• Is Part Of: Oncologist. Volume 20:Number 9(2015)
• Page Start: 1028
• Page End: 1035
• Publication Date: 2015-08-03
• Subjects: Algorithm -- Decision criteria -- Renal cell carcinoma -- Treatment; Oncology -- Periodicals; Tumors -- Periodicals; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Oncology; Tumors; Neoplasms; Electronic journals; Periodicals; Periodicals; 616.994
• DOI: 10.1634/theoncologist.2015-0145
• Languages: English
• ISSNs: 1083-7159

Abstract

Abstract : Background: With the advent of targeted therapies, many treatment options in the first‐line setting of metastatic clear cell renal cell carcinoma (mccRCC) have emerged. Guidelines and randomized trial reports usually do not elucidate the decision criteria for the different treatment options. In order to extract the decision criteria for the optimal therapy for patients, we performed an analysis of treatment algorithms from experts in the field. Materials and Methods: Treatment algorithms for the treatment of mccRCC from experts of 11 institutions were obtained, and decision trees were deduced. Treatment options were identified and a list of unified decision criteria determined. The final decision trees were analyzed with a methodology based on diagnostic nodes, which allows for an automated cross‐comparison of decision trees. The most common treatment recommendations were determined, and areas of discordance were identified. Results: The analysis revealed heterogeneity in most clinical scenarios. The recommendations selected for first‐line treatment of mccRCC included sunitinib, pazopanib, temsirolimus, interferon‐α combined with bevacizumab, high‐dose interleukin‐2, sorafenib, axitinib, everolimus, and best supportive care. The criteria relevant for treatment decisions were performance status, Memorial Sloan Kettering Cancer Center risk group, only or mainly lung metastases, cardiac insufficiency, hepatic insufficiency, age, and "zugzwang" (composite of multiple, related criteria). Conclusion: In the present study, we used diagnostic nodes to compare treatment algorithms in the first‐line treatment of mccRCC. The results illustrate the heterogeneity of the decision criteria and treatment strategies for mccRCC and how available data are interpreted and implemented differently among experts. Implications for Practice: The data provided in the present report should not be considered to serve as treatment recommendations for the management of treatment‐naïve patients with multiple metastases from metastatic clear cell renal cell carcinoma outside a clinical trial; however, the data highlight the different treatment options and the criteria used to select them. The diversity in decision making and how results from phase III trials can be interpreted and implemented differently in daily practice are demonstrated. Abstract : Treatment algorithms from experts of 11 institutions in the field were analyzed to extract the decision criteria and treatment recommendations for metastatic clear cell renal cell carcinoma (mccRCC), and decision trees were deduced. The analysis revealed heterogeneity in most clinical scenarios and treatment strategies for mccRCC and how the available data are interpreted and implemented differently among experts.