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Enhancing Treatment Decisions for Advanced Non-Small Cell Lung Cancer with Epidermal Growth Factor Receptor
Hakan Şat Bozcuk1,2, Leyla Sert3, Muhammet Ali Kaplan3
1Independent Researcher, Antalya 07100, Turkey.
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.
Insights
A new web app uses reinforcement learning to help choose the best TKI therapy for EGFR-mutant non-small cell lung cancer (NSCLC). This tool aids clinicians in personalizing treatment decisions for improved patient outcomes.
Area of Science:
- Oncology
- Artificial Intelligence
- Bioinformatics
Background:
- Optimal tyrosine kinase inhibitor (TKI) selection remains challenging for advanced EGFR-mutant non-small cell lung cancer (NSCLC).
- Higher-generation TKIs improve progression-free survival but require personalized treatment strategies.
Purpose of the Study:
- To develop a reinforcement learning (RL)-powered web application for guiding initial TKI treatment decisions in EGFR-mutant advanced NSCLC.
- To create a clinical decision support tool for personalized oncology care.
Main Methods:
- Retrospective collection of clinical and mutational data from 318 advanced NSCLC patients across 14 centers.
- Utilized Extra Trees Classifier (ETC) for predicting progression-free survival and identifying key predictive features.
- Integrated top features into a Deep Q-Network (DQN) RL algorithm to recommend optimal TKI generation and treatment line, embedded in a web application.
Main Results:
- The ETC model achieved an AUC of 0.73, identifying neutrophil-to-lymphocyte ratio, age, and TKI treatment line as crucial features.
- The DQN RL algorithm demonstrated superior predictive performance with an AUC of 0.80.
- The web application provides tailored TKI recommendations based on patient-specific data, supporting clinical decision-making.
Conclusions:
- A reinforcement learning-based web application shows significant potential in optimizing TKI treatment selection for EGFR-mutant advanced NSCLC.
- This approach highlights the utility of RL in enhancing precision medicine and decision support in cancer care.
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