Oncotarget

Research Papers:

Heuristic value-based framework for lung cancer decision-making

Isa Mambetsariev, Rebecca Pharaon, Arin Nam, Kevin Knopf, Benjamin Djulbegovic, Victoria M. Villaflor, Everett E. Vokes and Ravi Salgia _

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Oncotarget. 2018; 9:29877-29891. https://doi.org/10.18632/oncotarget.25643

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Abstract

Isa Mambetsariev1,*, Rebecca Pharaon1,*, Arin Nam1,*, Kevin Knopf2, Benjamin Djulbegovic3, Victoria M. Villaflor4, Everett E. Vokes5 and Ravi Salgia1

1Department of Medical Oncology and Therapeutics Research, City of Hope, Duarte, CA, USA

2California Pacific Medical Center Research Institute, San Francisco, CA, USA

3Department of Clinical Supportive Care, City of Hope, Duarte, CA, USA

4Department of Medicine (Hematology and Oncology), Northwestern University, Chicago, IL, USA

5Department of Medicine, University of Chicago, Chicago, IL, USA

*These authors have contributed equally to this work

Correspondence to:

Ravi Salgia, email: rsalgia@coh.org

Keywords: non-small cell lung cancer; heuristics; fast-and-frugal trees; genomics; framework

Received: January 18, 2018     Accepted: June 04, 2018     Published: July 06, 2018

ABSTRACT

Heuristics and the application of fast-and-frugal trees may play a role in establishing a clinical decision-making framework for value-based oncology. We determined whether clinical decision-making in oncology can be structured heuristically based on the timeline of the patient’s treatment, clinical intuition, and evidence-based medicine. A group of 20 patients with advanced non-small cell lung cancer (NSCLC) were enrolled into the study for extensive treatment analysis and sequential decision-making. The extensive clinical and genomic data allowed us to evaluate the methodology and efficacy of fast-and-frugal trees as a way to quantify clinical decision-making. The results of the small cohort will be used to further advance the heuristic framework as a way of evaluating a large number of patients within registries. Among the cohort whose data was analyzed, substitution and amplification mutations occurred most frequently. The top five most prevalent genomic alterations were TP53 (45%), ALK (40%), LRP1B (30%), CDKN2A (25%), and MYC (25%). These 20 cases were analyzed by this clinical decision-making process and separated into two distinctions: 10 straightforward cases that represented a clearer decision-making path and 10 complex cases that represented a more intricate treatment pathway. The myriad of information from each case and their distinct pathways was applied to create the foundation of a framework for lung cancer decision-making as an aid for oncologists. In late-stage lung cancer patients, the fast-and-frugal heuristics can be utilized as a strategy of quantifying proper decision-making with limited information.


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