Enhancing treatment decisions for advanced non-small cell lung cancer with epidermal growth factor receptor mutations a reinforcement learning approach
| dc.contributor.author | Bozcuk, Hakan Şat | |
| dc.contributor.author | Sert, Leyla | |
| dc.contributor.author | Kaplan, Muhammet Ali | |
| dc.contributor.author | Tatlı, Ali Murat | |
| dc.contributor.author | Muğlu, Harun | |
| dc.contributor.author | Bilici, Ahmet | |
| dc.contributor.author | Alemdar, Mustafa Serkan | |
| dc.date.accessioned | 2026-04-13T11:31:14Z | |
| dc.date.available | 2026-04-13T11:31:14Z | |
| dc.date.issued | 2025 | |
| dc.department | İstanbul Medipol Üniversitesi, Tıp Fakültesi, Dahili Tıp Bilimleri Bölümü, İç Hastalıkları Ana Bilim Dalı | |
| dc.description.abstract | Background: 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. | |
| dc.identifier.citation | Bozcuk, H. Ş., Sert, L., Kaplan, M. A., Tatlı, A. M., Muğlu, H., Bilici, A. ... 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). http://dx.doi.org/10.3390/cancers17020233 | |
| dc.identifier.doi | 10.3390/cancers17020233 | |
| dc.identifier.issn | 2072-6694 | |
| dc.identifier.issue | 2 | |
| dc.identifier.pmid | 39858018 | |
| dc.identifier.scopus | 2-s2.0-85215677631 | |
| dc.identifier.scopusquality | Q1 | |
| dc.identifier.uri | http://dx.doi.org/10.3390/cancers17020233 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12511/13417 | |
| dc.identifier.volume | 17 | |
| dc.identifier.wos | WOS:001403762100001 | |
| dc.identifier.wosquality | Q2 | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.indekslendigikaynak | PubMed | |
| dc.institutionauthor | Bilici, Ahmet | |
| dc.institutionauthorid | 0000-0002-0443-6966 | |
| dc.language.iso | en | |
| dc.relation.ispartof | Cancers | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.rights | Attribution 4.0 International | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Artificial Intelligence | |
| dc.subject | Deep Learning | |
| dc.subject | Epidermal Growth Factor Receptor | |
| dc.subject | Machine Learning | |
| dc.subject | Mutation | |
| dc.subject | Non-Small Cell Lung Cancer | |
| dc.subject | Tyrosine Kinase Inhibitors | |
| dc.title | Enhancing treatment decisions for advanced non-small cell lung cancer with epidermal growth factor receptor mutations a reinforcement learning approach | |
| dc.type | Article |
