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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.

Background and Aims: During liver transplantation, hepatocellular carcinoma (HCC) recurrence remains a critical challenge for patient survival. Targeted therapies, such as sorafenib and regorafenib, have been utilized to manage relapsed HCC in this unique setting. This study aimed to assess the efficacy of Sorafenib and Regorafenib in patients with HCC who experienced recurrence after liver transplantation. We focused on survival outcomes, treatment responses, and the management of side effects in this patient group., Methods: We conducted a retrospective analysis of 73 patients who experienced HCC recurrence post-liver transplantation between 2012 and 2022 across 11 oncology centers in Turkey. Patients were categorized according to Child-Pugh classification and treated with sorafenib as first-line therapy and Regorafenib in case of progression. Survival rates were analyzed using the Kaplan-Meier method, and risk factors were evaluated using Cox regression analysis., Results: Of the 73 patients included in the study, 62 were male (84.9%), and 11 were female (15.1%), with a mean age of 61.5 ± 10.9 years. All patients received sorafenib as first-line treatment. Among patients who experienced progression with sorafenib or discontinued treatment due to toxicity, 45.2% ( n = 33) continued treatment with regorafenib. The median progression-free survival (PFS1) time with sorafenib was 5.6 months, and the one-year survival rate was 24.3%. The median progression-free survival (PFS2) time with regorafenib, which was administered as second-line treatment, was also calculated as 5.9 months. Overall survival (OS) duration was determined as 35.9 months. The most common side effects associated with both drugs included fatigue, hand and foot syndrome, and hypertension. Significantly better survival outcomes were shown in the Child-Pugh A group compared to other patients., Conclusions: These results suggest that Sorafenib and Regorafenib treatments offer a survival advantage in patients with relapsed HCC post-transplantation. However, individualized treatment strategies and close follow-up are crucial for optimizing outcomes. Further studies are needed to refine therapeutic protocols and enhance the care of this specific patient group.