Enhancing Treatment Decisions for Advanced Non-Small Cell Lung Cancer with Epidermal Growth Factor Receptor Mutations: A Reinforcement Learning Approach †

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dc.authorscopusidSaadettin Kılıçkap / 8665552100
dc.authorscopusidMustafa Serkan Alemdar / 57221110826
dc.authorwosidSaadettin Kılıçkap / AAP-3732-2021
dc.authorwosidMustafa Serkan Alemdar / CAQ-5436-2022
dc.contributor.authorBozcuk, Hakan Şat
dc.contributor.authorSert, Leyla
dc.contributor.authorKaplan, Muhammet Ali
dc.contributor.authorTatlı, Ali Murat
dc.contributor.authorKaraca, Mustafa
dc.contributor.authorMuğlu, Harun
dc.contributor.authorBilici, Ahmet
dc.contributor.authorKılıçtaş, Bilge Şah
dc.contributor.authorArtaç, Mehmet
dc.contributor.authorErel, Pınar
dc.contributor.authorYumuk, Perran Fulden
dc.contributor.authorBilgin, Burak
dc.contributor.authorŞendur, Mehmet Ali Nahit
dc.contributor.authorKılıçkap, Saadettin
dc.contributor.authorTaban, Hakan
dc.contributor.authorBallı, Sevinç
dc.contributor.authorDemirkazık, Ahmet
dc.contributor.authorAkdağ, Fatma
dc.contributor.authorHacıbekiroğlu, İlhan
dc.contributor.authorGüzel, Halil Göksel
dc.contributor.authorKoçer, Murat
dc.contributor.authorGürsoy, Pınar
dc.contributor.authorKöylü, Bahadır
dc.contributor.authorSelçukbiricik, Fatih
dc.contributor.authorKarakaya, Gökhan
dc.contributor.authorAlemdar, Mustafa Serkan
dc.date.accessioned2025-04-18T09:44:59Z
dc.date.available2025-04-18T09:44:59Z
dc.date.issued2025
dc.departmentİstinye Üniversitesi, Tıp Fakültesi, Dahili Tıp Bilimleri Bölümü
dc.description.abstractBackground: Although higher-generation TKIs are associated with improved progression-free survival in advanced NSCLC patients with EGFR mutations, the optimal selection of TKI treatment remains uncertain. To address this gap, we developed a web application powered by a reinforcement learning (RL) algorithm to assist in guiding initial TKI treatment decisions. Methods: Clinical and mutational data from advanced NSCLC patients were retrospectively collected from 14 medical centers. Only patients with complete data and sufficient follow-up were included. Multiple supervised machine learning models were tested, with the Extra Trees Classifier (ETC) identified as the most effective for predicting progression-free survival. Feature importance scores were calculated by the ETC, and features were then integrated into a Deep Q-Network (DQN) RL algorithm. The RL model was designed to select optimal TKI generation and a treatment line for each patient and was embedded into an open-source web application for experimental clinical use. Results: In total, 318 cases of EGFR-mutant advanced NSCLC were analyzed, with a median patient age of 63. A total of 52.2% of patients were female, and 83.3% had ECOG scores of 0 or 1. The top three most influential features identified were neutrophil-to-lymphocyte ratio (log-transformed), age (log-transformed), and the treatment line of TKI administration, as tested by the ETC algorithm, with an area under curve (AUC) value of 0.73, whereas the DQN RL algorithm achieved a higher AUC value of 0.80, assigning distinct Q-values across four TKI treatment categories. This supports the decision-making process in the web-based ‘EGFR Mutant NSCLC Treatment Advisory System’, where clinicians can input patient-specific data to receive tailored recommendations. Conclusions: The RL-based web application shows promise in assisting TKI treatment selection for EGFR-mutant advanced NSCLC patients, underscoring the potential for reinforcement learning to enhance decision-making in oncology care. © 2025 by the authors.
dc.identifier.citationBozcuk, H. Ş., Sert, L., Kaplan, M. A., Tatlı, A. M., Karaca, M., Muğlu, H., ... & Alemdar, M. S. (2025). Enhancing Treatment Decisions for Advanced Non-Small Cell Lung Cancer with Epidermal Growth Factor Receptor Mutations: A Reinforcement Learning Approach. Cancers, 17(2), 233.
dc.identifier.doi10.3390/cancers17020233
dc.identifier.issn20726694
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85215677631
dc.identifier.scopusqualityQ1
dc.identifier.urihttp://dx.doi.org/10.3390/cancers17020233
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6846
dc.identifier.volume17
dc.identifier.wosWOS:001403762100001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorKılıçkap, Saadettin
dc.institutionauthorAlemdar, Mustafa Serkan
dc.institutionauthoridSaadettin Kılıçkap / 0000-0003-1637-7390
dc.institutionauthoridMustafa Serkan Alemdar / 0000-0002-7663-6182
dc.language.isoen
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.relation.ispartofCancers
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectArtificial İntelligence
dc.subjectDeep Learning
dc.subjectMachine Learning
dc.subjectEpidermal Growth Factor Receptor
dc.subjectMutation
dc.subjectNon-small Cell Lung Cancer
dc.subjectTyrosine Kinase İnhibitors
dc.titleEnhancing Treatment Decisions for Advanced Non-Small Cell Lung Cancer with Epidermal Growth Factor Receptor Mutations: A Reinforcement Learning Approach †
dc.typeArticle

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