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.
背景：尽管在EGFR突变的晚期非小细胞肺癌（NSCLC）患者中，更高代次的酪氨酸激酶抑制剂（TKI）与更长的无进展生存期相关，但TKI治疗的最佳选择仍不确定。为填补这一空白，我们开发了一个基于强化学习（RL）算法的网络应用程序，以协助指导初始TKI治疗决策。方法：回顾性收集了来自14个医疗中心的晚期NSCLC患者的临床和突变数据。仅纳入数据完整且随访充足的患者。测试了多种监督机器学习模型，其中Extra Trees分类器（ETC）被确定为预测无进展生存期最有效的模型。通过ETC计算特征重要性评分，然后将特征整合到深度Q网络（DQN）强化学习算法中。该RL模型旨在为每位患者选择最佳的TKI代次和治疗线，并嵌入一个开源网络应用程序中，供实验性临床使用。结果：共分析了318例EGFR突变的晚期NSCLC病例，患者中位年龄为63岁。52.2%的患者为女性，83.3%的患者ECOG评分为0或1分。根据ETC算法测试，最具影响力的前三个特征为中性粒细胞与淋巴细胞比值（对数转换）、年龄（对数转换）以及TKI给药的治疗线，其曲线下面积（AUC）值为0.73；而DQN RL算法获得了更高的AUC值0.80，并在四个TKI治疗类别中分配了不同的Q值。这为基于网络的“EGFR突变NSCLC治疗咨询系统”的决策过程提供了支持，临床医生可输入患者特定数据以获取个性化建议。结论：基于强化学习的网络应用程序在协助EGFR突变晚期NSCLC患者的TKI治疗选择方面显示出潜力，突显了强化学习在优化肿瘤治疗决策中的潜在价值。
肿瘤(癌症)患者之家