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Algorithms in the First-Line Treatment of Metastatic Clear Cell Renal Cell Carcinoma-Analysis Using Diagnostic Nodes

Journal Paper/Review - Aug 3, 2015

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Rothermundt C, Bailey A, Cerbone L, Eisen T, Escudier B, Gillessen Sommer S, Grünwald V, Larkin J, McDermott D, Oldenburg J, Porta C, Rini B, Schmidinger M, Sternberg C, Putora P. Algorithms in the First-Line Treatment of Metastatic Clear Cell Renal Cell Carcinoma-Analysis Using Diagnostic Nodes. Oncologist 2015; 20:1028-35.
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Journal Paper/Review (English)
Journal
Oncologist 2015; 20
Publication Date
Aug 3, 2015
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Issn Electronic
1549-490X
Pages
1028-35
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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.