Algorithms in the first-line treatment of metastatic clear cell renal cell carcinoma—analysis using diagnostic nodes

Christian Rothermundt, Alexandra Bailey, Linda Cerbone, Tim Eisen, Bernard Escudier, Silke Gillessen, Viktor Grünwald, James Larkin, David McDermott, Jan Oldenburg, Camillo Porta, Brian Rini, Manuela Schmidinger, Cora Sternberg, Paul M. Putora

Research output: Contribution to journalArticlepeer-review


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 ofmccRCCfromexpertsof11institutionswereobtained,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-a 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.

Original languageEnglish
Pages (from-to)1028-1035
Number of pages8
JournalThe oncologist
Issue number9
Publication statusPublished - Aug 3 2015


  • Algorithm
  • Decision criteria
  • Renal cell carcinoma
  • Treatment

ASJC Scopus subject areas

  • Cancer Research
  • Oncology


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