Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Department of Oncology, Jiangyin People's Hospital, Jiangyin, China.
J Cancer Res Clin Oncol. 2023 Oct;149(13):12265-12274. doi: 10.1007/s00432-023-05104-3. Epub 2023 Jul 11.
The efficacy of epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) was affected by numerous factors. In the study, we developed and validated an artificial neural network (ANN) system based on clinical characteristics and next-generation sequencing (NGS) to support clinical decisions.
A multicenter retrospective non-interventional study was conducted. 240 patients from three hospitals with advanced non-small cell lung cancer (NSCLC) and EGFR mutation were tested by NGS before the first treatment. All patients received formal EGFR-TKIs treatment. Five different models were individually trained to predict the efficacy of EGFR-TKIs based on one medical center with 188 patients. Two independent cohorts from other medical centers were collected for external validation.
Compared with logistic regression, four machine learning methods showed better predicting abilities for EGFR-TKIs. The inclusion of NGS tests improved the predictive power of models. ANN performed best on the dataset with mutations TP53, RB1, PIK3CA, EGFR mutation sites, and tumor mutation burden (TMB). The prediction accuracy, recall and AUC were 0.82, 0.82, and 0.82, respectively in our final model. In the external validation set, ANN still showed good performance and differentiated patients with poor outcomes. Finally, a clinical decision support software based on ANN was developed and provided a visualization interface for clinicians.
This study provides an approach to assess the efficacy of NSCLC patients with first-line EGFR-TKI treatment. Software is developed to support clinical decisions.
表皮生长因子受体（EGFR）-酪氨酸激酶抑制剂（TKI）的疗效受多种因素影响。本研究基于临床特征和下一代测序（NGS）开发并验证了一种人工神经网络（ANN）系统，以支持临床决策。
进行了一项多中心回顾性非干预性研究。来自三所医院的 240 名晚期非小细胞肺癌（NSCLC）和 EGFR 突变患者在首次治疗前通过 NGS 进行了检测。所有患者均接受了正式的 EGFR-TKIs 治疗。分别使用五个不同的模型基于一个有 188 名患者的医疗中心预测 EGFR-TKIs 的疗效。另外两个来自其他医疗中心的独立队列被收集用于外部验证。
与逻辑回归相比，四种机器学习方法对 EGFR-TKIs 的预测能力更好。NGS 测试的纳入提高了模型的预测能力。ANN 在包含 TP53、RB1、PIK3CA、EGFR 突变部位和肿瘤突变负荷（TMB）突变的数据集上表现最佳。在我们的最终模型中，预测准确率、召回率和 AUC 分别为 0.82、0.82 和 0.82。在外部验证集中，ANN 仍然表现出良好的性能，能够区分预后不良的患者。最后，基于 ANN 开发了一个临床决策支持软件，并为临床医生提供了可视化界面。
本研究提供了一种评估 NSCLC 患者一线 EGFR-TKI 治疗效果的方法。开发了软件以支持临床决策。
