基因状态已成为预后预测的焦点。此外，深度学习经常应用于医学成像领域，以诊断、预测和评估癌症患者的治疗反应。然而，基于突变基因的深度学习生存（DLS）模型与患者无进展生存（PFS）或总生存（OS）预后直接相关的报道很少。此外，DLS 模型尚未应用于根据突变基因确定 IO 相关预后。在此，我们开发了一种深度学习方法来预测接受或不接受免疫治疗（IO）治疗的肺癌患者的预后。对来自不同中心的 6542 名患者的样本进行了基因组测序。基于多组体细胞突变的 DLS 模型经过训练和验证，可预测未接受 IO 治疗的患者的 OS 和接受 IO 治疗的患者的 PFS。在未接受 IO 治疗的患者中，使用以下方法训练 DLS 模型（低 DLS 与高 DLS）：训练 MSK-MET 队列（HR = 0.241 [0.213-0.273]，P < 0.001）并在相互验证 MSK-MET 队列中进行测试（HR = 0.175 [0.148-0.206]，P < 0.001）。然后使用 OncoSG、MSK-CSC 和 TCGA-LUAD 队列验证 DLS 模型（HR = 0.420 [0.272-0.649]，P < 0.001；HR = 0.550 [0.424-0.714]，P < 0.001；HR = 0.2 15 [ 0.159-0.291]，P < 0.001，分别）。随后，它在接受 IO 治疗的患者中进行了微调和重新训练。 DLS 模型（低 DLS 与高 DLS）可以预测 MIND、MSKCC 和 POPLAR/OAK 队列中的 PFS 和 OS（分别为 P < 0.001）。与肿瘤淋巴结转移分期、COX模型、肿瘤突变负荷和程序性死亡配体1表达相比，DLS模型在接受或不接受IO治疗的患者中具有最高的C指数。基于突变基因的DLS模型可以稳健地预测接受或不接受 IO 治疗的肺癌患者的预后。© 2023。作者。

Gene status has become the focus of prognosis prediction. Furthermore, deep learning has frequently been implemented in medical imaging to diagnose, prognosticate, and evaluate treatment responses in patients with cancer. However, few deep learning survival (DLS) models based on mutational genes that are directly associated with patient prognosis in terms of progression-free survival (PFS) or overall survival (OS) have been reported. Additionally, DLS models have not been applied to determine IO-related prognosis based on mutational genes. Herein, we developed a deep learning method to predict the prognosis of patients with lung cancer treated with or without immunotherapy (IO).Samples from 6542 patients from different centers were subjected to genome sequencing. A DLS model based on multi-panels of somatic mutations was trained and validated to predict OS in patients treated without IO and PFS in patients treated with IO.In patients treated without IO, the DLS model (low vs. high DLS) was trained using the training MSK-MET cohort (HR = 0.241 [0.213-0.273], P < 0.001) and tested in the inter-validation MSK-MET cohort (HR = 0.175 [0.148-0.206], P < 0.001). The DLS model was then validated with the OncoSG, MSK-CSC, and TCGA-LUAD cohorts (HR = 0.420 [0.272-0.649], P < 0.001; HR = 0.550 [0.424-0.714], P < 0.001; HR = 0.215 [0.159-0.291], P < 0.001, respectively). Subsequently, it was fine-tuned and retrained in patients treated with IO. The DLS model (low vs. high DLS) could predict PFS and OS in the MIND, MSKCC, and POPLAR/OAK cohorts (P < 0.001, respectively). Compared with tumor-node-metastasis staging, the COX model, tumor mutational burden, and programmed death-ligand 1 expression, the DLS model had the highest C-index in patients treated with or without IO.The DLS model based on mutational genes can robustly predict the prognosis of patients with lung cancer treated with or without IO.© 2023. The Author(s).