准确预测非小细胞肺癌（NSCLC）患者完全手术切除后的无骨转移生存期（BMFS）可能有助于制定适当的随访计划。本研究的目的是建立并验证术前基于 CT 的深度学习 (DL) 特征，以预测 NSCLC 患者的 BMFS。我们对 1547 名接受完整手术切除的 NSCLC 患者进行了回顾性分析，随后在两家医院进行了至少 36 个月的监测。我们使用 3D 卷积神经网络从多参数 CT 图像构建了 DL 特征，并将该特征与临床影像因素集成，以建立深度学习临床影像特征 (DLCS)。我们使用 Harrell 一致性指数（C 指数）和时间相关的接收器操作特性来评估性能。我们还使用 DLCS 评估了不同临床阶段 NSCLC 患者骨转移 (BM) 的风险。 DL 签名成功预测了 BM，验证队列的 C 指数分别为 0.799 和 0.818。 DLCS 的性能优于 DL 签名，相应的 C 指数为 0.806 和 0.834。 1 年、2 年和 3 年的曲线下面积范围（内部验证队列）为 0.820-0.865，外部验证队列为 0.860-0.884。此外，DLCS 成功地将不同临床分期的 NSCLC 患者分为 BM 高风险组和低风险组（p<0.05）。基于 CT 的 DL 可以预测接受完全手术切除的 NSCLC 患者的 BMFS，并可能有助于评估不同临床阶段患者的 BM 风险。© 2024。作者。

Accurate prediction of bone metastasis-free survival (BMFS) after complete surgical resection in patients with non-small cell lung cancer (NSCLC) may facilitate appropriate follow-up planning. The aim of this study was to establish and validate a preoperative CT-based deep learning (DL) signature to predict BMFS in NSCLC patients. We performed a retrospective analysis of 1547 NSCLC patients who underwent complete surgical resection, followed by at least 36 months of monitoring at two hospitals. We constructed a DL signature from multiparametric CT images using 3D convolutional neural networks, and we integrated this signature with clinical-imaging factors to establish a deep learning clinical-imaging signature (DLCS). We evaluated performance using Harrell's concordance index (C-index) and the time-dependent receiver operating characteristic. We also assessed the risk of bone metastasis (BM) in NSCLC patients at different clinical stages using DLCS. The DL signature successfully predicted BM, with C-indexes of 0.799 and 0.818 for the validation cohorts. DLCS outperformed the DL signature with corresponding C-indexes of 0.806 and 0.834. Ranges for area under the curve at 1, 2, and 3 years were 0.820-0.865 for internal and 0.860-0.884 for external validation cohorts. Furthermore, DLCS successfully stratified patients with different clinical stages of NSCLC as high- and low-risk groups for BM (p < 0.05). CT-based DL can predict BMFS in NSCLC patients undergoing complete surgical resection, and may assist in the assessment of BM risk for patients at different clinical stages.© 2024. The Author(s).