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.authorid0000-0001-9584-0827
dc.authorid0000-0002-0443-6966
dc.authorid0000-0003-0882-0524
dc.contributor.authorBozcuk, Hakan Sat
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.date.accessioned2025-02-22T14:08:40Z
dc.date.available2025-02-22T14:08:40Z
dc.date.issued2025
dc.departmentDicle Üniversitesi, Tıp Fakültesi, Dahili Tıp Bilimleri Bölümü, İç Hastalıklar Ana Bilim Dalıen_US
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.en_US
dc.identifier.citationBozcuk, H. Ş., Sert, L., Kaplan, M. A., Tatlı, A. M., Karaca, M., Muğlu, H. ve diğerleri. (2025). Enhancing treatment decisions for advanced non-small cell lung cancer with epidermal growth factor receptor mutations: A Reinforcement learning approach. Cancers, 17(2), 1-15.
dc.identifier.doi10.3390/cancers17020233
dc.identifier.issn2072-6694
dc.identifier.issue2en_US
dc.identifier.pmid39858018en_US
dc.identifier.scopus2-s2.0-85215677631en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.3390/cancers17020233
dc.identifier.urihttps://hdl.handle.net/11468/29563
dc.identifier.volume17en_US
dc.identifier.wosWOS:001403762100001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorSert, Leyla
dc.institutionauthorKaplan, Muhammet Ali
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofCancersen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_WOS_20250222
dc.subjectNon-small cell lung canceren_US
dc.subjectEpidermal growth factor receptoren_US
dc.subjectMutationen_US
dc.subjectTyrosine kinase inhibitorsen_US
dc.subjectDeep learningen_US
dc.subjectMachine learningen_US
dc.subjectArtificial intelligenceen_US
dc.titleEnhancing treatment decisions for advanced non-small cell lung cancer with epidermal growth factor receptor mutations: A Reinforcement learning approachen_US
dc.typeArticleen_US

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