Two major treatment strategies employed in non-small cell lung cancer, NSCLC, are tyrosine kinase inhibitors, TKIs, and immune checkpoint inhibitors, ICIs. The choice of strategy is based on heterogeneous biomarkers that can dynamically change during therapy. Thus, there is a compelling need to identify comprehensive biomarkers that can be used longitudinally to help guide therapy choice. Herein, we report a ¹⁸F-FDG-PET/CT-based deep learning model, which demonstrates high accuracy in EGFR mutation status prediction across patient cohorts from different institutions. A deep learning score (EGFR-DLS) was significantly and positively associated with longer progression free survival (PFS) in patients treated with EGFR-TKIs, while EGFR-DLS is significantly and negatively associated with higher durable clinical benefit, reduced hyperprogression, and longer PFS among patients treated with ICIs. Thus, the EGFR-DLS provides a non-invasive method for precise quantification of EGFR mutation status in NSCLC patients, which is promising to identify NSCLC patients sensitive to EGFR-TKI or ICI-treatments.

非小细胞肺癌 (NSCLC) 的两种主要治疗策略是酪氨酸激酶抑制剂 (TKI) 和免疫检查点抑制剂 (ICI)。策略的选择基于在治疗过程中可以动态变化的异质生物标志物。因此，迫切需要确定可纵向使用的综合生物标志物，以帮助指导治疗选择。在此，我们报告了一种基于¹⁸ F-FDG-PET/CT 的深度学习模型，该模型证明了来自不同机构的患者队列的EGFR突变状态预测的高精度。深度学习评分 (EGFR-DLS) 与接受 EGFR-TKI 治疗的患者的较长无进展生存期 (PFS) 呈显着正相关，而 EGFR-DLS 与更高的持久临床获益、减少超进展和延长生存时间呈显着负相关。接受 ICI 治疗的患者的 PFS。因此，EGFR-DLS 提供了一种非侵入性方法，用于精确定量 NSCLC 患者的EGFR突变状态，有望识别对 EGFR-TKI 或 ICI 治疗敏感的 NSCLC 患者。