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Published on: April 12, 2017
Algorithms in the First-Line Treatment of Metastatic Clear Cell Renal Cell Carcinoma--Analysis Using Diagnostic Nodes
Christian Rothermundt1, Alexandra Bailey2, Linda Cerbone2
1Division of Haematology and Oncology, Kantonsspital St. Gallen, St. Gallen, Switzerland; Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA; Department of Medical Oncology, San Camillo and Forlanini Hospitals, Rome, Italy; Department of Oncology, Cambridge University Hospitals National Health Service Foundation, Cambridge, United Kingdom; Gustave Roussy, Villejuif, France; Hämatologie, Hämostaseologie, Onkologie und Stammzelltransplantation, Medizinische Hochschule Hannover, Hannover, Germany; The Royal Marsden Hospital, London, United Kingdom; Department of Oncology, Akershus University Hospital and Medical Faculty of University of Oslo, Oslo, Norway; Policlinico San Matteo Pavia Fondazione IRCCS, Pavia, Italy; Department of Solid Tumor Oncology, Cleveland Clinic, Cleveland, Ohio, USA; Abteilung für Onkologie, Allgemeines Krankenhaus-Universitätskliniken, Wien, Austria; Faculty of Medicine, University of Oslo, Oslo, Norway; Department of Radiation Oncology, Kantonsspital St. Gallen, St. Gallen, Switzerland christian.rothermundt@kssg.ch.
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.
Insights
Expert analysis of metastatic clear cell renal cell carcinoma (mccRCC) treatment algorithms reveals varied decision criteria among specialists. This highlights diverse interpretations of clinical trial data in first-line therapy selection for mccRCC patients.
Area of Science:
- Oncology
- Clinical Trial Analysis
- Medical Decision Making
Background:
- Emergence of targeted therapies for metastatic clear cell renal cell carcinoma (mccRCC).
- Lack of clear decision criteria in existing guidelines for first-line mccRCC treatment.
- Need to analyze expert treatment algorithms for optimal therapy selection.
Purpose of the Study:
- To extract and analyze decision criteria for optimal first-line therapy in mccRCC.
- To compare treatment algorithms from multiple expert institutions.
- To identify common and discordant decision-making factors.
Main Methods:
- Analysis of treatment algorithms from 11 expert institutions for mccRCC.
- Deduction of decision trees and identification of treatment options.
- Utilized diagnostic nodes for automated cross-comparison of decision trees.
Main Results:
- Identified heterogeneity in clinical scenarios and treatment recommendations for first-line mccRCC.
- Commonly recommended treatments included sunitinib, pazopanib, and others, alongside best supportive care.
- Key decision criteria included performance status, MSKCC risk group, metastasis pattern, organ insufficiency, and age.
Conclusions:
- Diagnostic nodes effectively compared treatment algorithms for first-line mccRCC.
- Demonstrated significant heterogeneity in expert decision criteria and treatment strategies for mccRCC.
- Highlighted differing interpretations and implementation of clinical trial data in practice.
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