表皮生长因子受体 (EGFR) 酪氨酸激酶抑制剂 (TKI) 是 EGFR 突变非小细胞肺癌 (NSCLC) 的一线治疗。大约一半 EGFR 突变 NSCLC 患者接受 EGFR-TKI 治疗并在 1 年内发展病情进展。因此，对接受 EGFR-TKI 治疗的患者进行肿瘤进展的早期预测可以促进患者管理和治疗策略的发展。我们提出了一种深度学习方法，基于定量计算机断层扫描 (CT) 特征和临床数据，预测 EGFR-TKI 治疗后晚期 NSCLC 患者的无进展生存期 (PFS)。 从治疗前胸部 CT 图像中提取了 593 个放射学特征。基于 270 例 IIIB-IV 期 EGFR 突变 NSCLC 患者的 CT 放射学和临床特征，提出了深度生存模型应用于 EGFR-TKI 治疗的进展风险分层。使用 DeepSurv 模型计算了在 3、12、18 和 24 个月的时间点上相应的 PFS 预测和个性化 PFS 曲线的估计。 综合临床和放射学特征的模型比单独使用临床特征的模型表现更好。该模型在预测 PFS 方面的曲线下面积分别为 0.76、0.77、0.76 和 0.86。个性化 PFS 曲线显示良好 (PFS > 中位数) 和差 (PFS < 中位数) 肿瘤控制组之间存在显著差异 (p < 0.003)。 DeepSurv 模型为 EGFR-TKI 治疗提供了可靠的多时间点 PFS 预测。个性化的 PFS 曲线可以帮助精确和个性化地预测肿瘤进展。所提出的深度学习方法有望改善 EGFR 突变 NSCLC 患者的预处理个性化管理。© 2023. 作者 (们)。

The epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) are a first-line therapy for non-small cell lung cancer (NSCLC) with EGFR mutations. Approximately half of the patients with EGFR-mutated NSCLC are treated with EGFR-TKIs and develop disease progression within 1 year. Therefore, the early prediction of tumor progression in patients who receive EGFR-TKIs can facilitate patient management and development of treatment strategies. We proposed a deep learning approach based on both quantitative computed tomography (CT) characteristics and clinical data to predict progression-free survival (PFS) in patients with advanced NSCLC after EGFR-TKI treatment.A total of 593 radiomic features were extracted from pretreatment chest CT images. The DeepSurv models for the progression risk stratification of EGFR-TKI treatment were proposed based on CT radiomic and clinical features from 270 stage IIIB-IV EGFR-mutant NSCLC patients. Time-dependent PFS predictions at 3, 12, 18, and 24 months and estimated personalized PFS curves were calculated using the DeepSurv models.The model combining clinical and radiomic features demonstrated better prediction performance than the clinical model. The model achieving areas under the curve of 0.76, 0.77, 0.76, and 0.86 can predict PFS at 3, 12, 18, and 24 months, respectively. The personalized PFS curves showed significant differences (p < 0.003) between groups with good (PFS > median) and poor (PFS < median) tumor control.The DeepSurv models provided reliable multi-time-point PFS predictions for EGFR-TKI treatment. The personalized PFS curves can help make accurate and individualized predictions of tumor progression. The proposed deep learning approach holds promise for improving the pre-TKI personalized management of patients with EGFR-mutated NSCLC.© 2023. The Author(s).