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Non-Small Cell Lung Cancer from Genomics to Therapeutics: A Framework for Community Practice Integration to Arrive at Personalized Therapy Strategies
Open AccessArticle

Complex Oncological Decision-Making Utilizing Fast-and-Frugal Trees in a Community Setting—Role of Academic and Hybrid Modeling

1
Department of Medical Oncology and Therapeutics Research, 1500 E Duarte Road, City of Hope National Medical Center, Duarte, CA 91010, USA
2
Newport Diagnostic Center, Newport Beach, CA 92660, USA
3
Department of Thoracic Surgery, Hoag Hospital, CA 92660, USA
4
Department of Medicine, City of Hope National Medical Center, Duarte, CA 91010, USA
5
Department of Hematology & Hematopoietic Cell Transplantation, City of Hope National Medical Center, Duarte, CA 91010, USA
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2020, 9(6), 1884; https://doi.org/10.3390/jcm9061884
Received: 15 May 2020 / Revised: 11 June 2020 / Accepted: 12 June 2020 / Published: 16 June 2020
Non-small cell lung cancer is a devastating disease and with the advent of targeted therapies and molecular testing, the decision-making process has become complex. While established guidelines and pathways offer some guidance, they are difficult to utilize in a busy community practice and are not always implemented in the community. The rationale of the study was to identify a cohort of patients with lung adenocarcinoma at a City of Hope community site (n = 11) and utilize their case studies to develop a decision-making framework utilizing fast-and-frugal tree (FFT) heuristics. Most patients had stage IV (N = 9, 81.8%) disease at the time of the first consultation. The most common symptoms at initial presentation were cough (N = 5, 45.5%), shortness of breath (N = 3, 27.2%), and weight loss (N = 3, 27.2%). The Eastern Cooperative Oncology Group (ECOG) performance status ranged from 0-1 in all patients in this study. Distribution of molecular drivers among the patients were as follows: EGFR (N = 5, 45.5%), KRAS (N = 2, 18.2%), ALK (N = 2, 18.2%), MET (N = 2, 18.2%), and RET (N = 1, 9.1%). Seven initial FFTs were developed for the various case scenarios, but ultimately the decisions were condensed into one FFT, a molecular stage IV FFT, that arrived at accurate decisions without sacrificing initial information. While these FFT decision trees may seem arbitrary to an experienced oncologist at an academic site, the simplicity of their utility is essential for community practice where patients often do not get molecular testing and are not assigned proper therapy. View Full-Text
Keywords: non-small cell lung cancer; actionable mutations; next-generation sequencing; fast-and-frugal trees; community practice; personalized medicine non-small cell lung cancer; actionable mutations; next-generation sequencing; fast-and-frugal trees; community practice; personalized medicine
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MDPI and ACS Style

Salgia, R.; Mambetsariev, I.; Tan, T.; Schwer, A.; Pearlstein, D.P.; Chehabi, H.; Baroz, A.; Fricke, J.; Pharaon, R.; Romo, H.; Waddington, T.; Babikian, R.; Buck, L.; Kulkarni, P.; Cianfrocca, M.; Djulbegovic, B.; Pal, S.K. Complex Oncological Decision-Making Utilizing Fast-and-Frugal Trees in a Community Setting—Role of Academic and Hybrid Modeling. J. Clin. Med. 2020, 9, 1884. https://doi.org/10.3390/jcm9061884

AMA Style

Salgia R, Mambetsariev I, Tan T, Schwer A, Pearlstein DP, Chehabi H, Baroz A, Fricke J, Pharaon R, Romo H, Waddington T, Babikian R, Buck L, Kulkarni P, Cianfrocca M, Djulbegovic B, Pal SK. Complex Oncological Decision-Making Utilizing Fast-and-Frugal Trees in a Community Setting—Role of Academic and Hybrid Modeling. Journal of Clinical Medicine. 2020; 9(6):1884. https://doi.org/10.3390/jcm9061884

Chicago/Turabian Style

Salgia, Ravi; Mambetsariev, Isa; Tan, Tingting; Schwer, Amanda; Pearlstein, Daryl P.; Chehabi, Hazem; Baroz, Angel; Fricke, Jeremy; Pharaon, Rebecca; Romo, Hannah; Waddington, Thomas; Babikian, Razmig; Buck, Linda; Kulkarni, Prakash; Cianfrocca, Mary; Djulbegovic, Benjamin; Pal, Sumanta K. 2020. "Complex Oncological Decision-Making Utilizing Fast-and-Frugal Trees in a Community Setting—Role of Academic and Hybrid Modeling" J. Clin. Med. 9, no. 6: 1884. https://doi.org/10.3390/jcm9061884

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