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.
Highlights
Lung cancer, the leading cause of cancer death among all ages, is expected to account for 234,030 newly diagnosed cases and 154,050 cancer related deaths in the United States in 2018 [1]
While years ago we relied solely on histopathology for information, the newer science of genomic-based classification of non-small cell lung cancer (NSCLC) has blurred the lines among histologic subtypes by demonstrating that lung cancer can be molecularly subclassified as either adenocarcinoma or neuroendocrine tumor based on its molecular markers [6]
This development of genomic-based characterization has shifted the focus of lung cancer treatment from merely general cytotoxic chemotherapy and radiation treatments to an integration of newer targeted therapeutics that can overcome the challenges of chemotherapeutic resistance and disease progression [7]
Summary
The leading cause of cancer death among all ages, is expected to account for 234,030 newly diagnosed cases and 154,050 cancer related deaths in the United States in 2018 [1]. While years ago we relied solely on histopathology for information, the newer science of genomic-based classification of NSCLC has blurred the lines among histologic subtypes by demonstrating that lung cancer can be molecularly subclassified as either adenocarcinoma or neuroendocrine tumor based on its molecular markers [6]. The discovery of various genetic driver mutations such as gain-of-function EGFR and KRAS mutations, ALK and ROS1 translocations, and MET amplifications/mutations has vastly improved the basic understanding of the biology of NSCLC [8] This has allowed for the development of targeted therapies for these mutations, such as EGFR tyrosine-kinase inhibitors which include erlotinib, gefitinib and afatinib, as well as newer third-generation TKIs such as osimertinib [9]. This new approach of genomic-based classification presents a challenge biologically and clinically as more kinase inhibitors are developed and the biological understanding of driver mutations and NSCLC is further accentuated
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