用于预测 NSCLC EGFR 突变状态的多任务深度学习模型
Multi-task deep learning model for predicting EGFR mutation status in NSCLC.
作者
作者单位
- Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Hefei, China.
- Clinical Chest College of Anhui Medical University, Hefei, China.
- Department of Radiology, Fuyang People's Hospital, Fuyang, China.
- School of Information Science and Technology, University of Science and Technology of China, Hefei, China.
- Clinical Chest College of Anhui Medical University, Hefei, China. ahch_minxuhong@163.com.
- Department of Radiology, the First Affiliated Hospital of Anhui Medical University, Hefei, China. cjr.yuyongqiang@vip.163.com.
摘要
中文
多任务深度学习预测 EGFR 突变状态。表皮生长因子受体（EGFR）突变状态是非小细胞肺癌（NSCLC）管理中的关键生物标志物,在选择适合 EGFR 靶向治疗的患者中发挥重要作用。随着深度学习（DL）的发展,开发无创方法预测 EGFR 突变状态日益受到关注。本研究提出了一个多任务深度学习（MTDL）模型,利用 CT 图像预测 EGFR 突变状态（WHO 国际临床试验注册编号 ChiCTR2400083082）。我们的 MTDL 模型在准确预测 EGFR 突变状态方面取得了良好表现。此外,MTDL 评分与接受 EGFR 靶向治疗患者的生存期、相关基因表达模式及肿瘤微环境显著相关。这些结果表明,我们的方法有望作为一种准确且无创的生物标志物用于预测 EGFR 突变状态,从而辅助 NSCLC 患者的个体化治疗决策。
English
Multi-task DL for predicting EGFR mutation status Epidermal growth factor receptor (EGFR) mutation status is a critical biomarker in the management of non-small cell lung cancer (NSCLC), playing an essential role in selecting patients for EGFR-targeted treatment. With advancements in deep learning (DL), there is a growing interest in developing non-invasive methods for predicting EGFR mutation status. In this study, we present a multi-task deep learning (MTDL) model that utilizes CT images to predict EGFR mutation status (ChiCTR2400083082 in the WHO International Clinical Trials Registry). Our MTDL model achieved promising performance in accurately predicting EGFR mutation status. Additionally, the MTDL score was significantly associated with survival in patients receiving EGFR-targeted treatment, as well as relevant gene expression patterns and tumor microenvironment. These findings suggest that our method has the potential to serve as an accurate and non-invasive biomarker for predicting EGFR mutation status, thereby facilitating personalized treatment decisions for NSCLC patients.
分类与指标
- 研究类型
- AI/ML
- 病种
- 肺癌
- JCR 分区
- Q1
- 影响因子
- 18.0
- 新锐分区
- 1区