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    Endocrine Adverse Events in Patients Treated with Immune Checkpoint Inhibitors: A Comprehensive Analysis
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025) Dökmetaş, Meriç; Muğlu, Harun; Özcan, Erkan; Bayram Kuvvet, Buket; Helvacı, Kaan; Kalacı, Ender; Kahraman, Seda; Aykan, Musa Barış; Çiçin, İrfan; Selçukbiricik, Fatih; Ölmez, Ömer Fatih; Bilici, Ahmet
    Background and Objectives: Immune checkpoint inhibitors (ICIs) have revolutionized cancer therapy, but their use is associated with a spectrum of immune-related adverse events (irAEs), including endocrine disorders. This study aims to investigate the incidence, timing, treatment modalities, and impact of ICI-related endocrine side effects in cancer patients. Materials and Methods: This retrospective study analyzed 139 cancer patients treated with ICIs between 2016 and 2022. Data regarding endocrine irAEs, including hypothyroidism, hyperthyroidism, hypophysitis, and diabetes mellitus, were collected. The study examined the timing of irAE onset, management approaches, and the association between irAEs and treatment outcomes. Results: The most common endocrine irAE was hypothyroidism (65.5%), followed by hyperthyroidism (2.3%), hypophysitis (8.6%), and diabetes mellitus (0.7%). These disorders typically emerged within the first six months of ICI therapy. Most cases were managed conservatively or with hormone replacement therapy. Patients who developed endocrine irAEs exhibited a higher objective response rate (ORR) and clinical benefit rate (CBR) compared to those without irAEs. Conclusions: Endocrine dysfunction is a significant toxicity of ICI therapy. Early recognition, prompt diagnosis, and appropriate management are crucial to minimize their impact on patient health and quality of life. This study highlights the potential association between irAEs and improved clinical outcomes. Further research is needed to elucidate the underlying mechanisms and identify predictive biomarkers for irAE development. © 2025 by the authors.
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    Enhancing Treatment Decisions for Advanced Non-Small Cell Lung Cancer with Epidermal Growth Factor Receptor Mutations: A Reinforcement Learning Approach †
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025) Bozcuk, Hakan Şat; Sert, Leyla; Kaplan, Muhammet Ali; Tatlı, Ali Murat; Karaca, Mustafa; Muğlu, Harun; Bilici, Ahmet; Kılıçtaş, Bilge Şah; Artaç, Mehmet; Erel, Pınar; Yumuk, Perran Fulden; Bilgin, Burak; Şendur, Mehmet Ali Nahit; Kılıçkap, Saadettin; Taban, Hakan; Ballı, Sevinç; Demirkazık, Ahmet; Akdağ, Fatma; Hacıbekiroğlu, İlhan; Güzel, Halil Göksel; Koçer, Murat; Gürsoy, Pınar; Köylü, Bahadır; Selçukbiricik, Fatih; Karakaya, Gökhan; Alemdar, Mustafa Serkan
    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. © 2025 by the authors.