肿瘤微环境（TME）在肿瘤发生、肿瘤进展和新兴癌症治疗的抗癌免疫潜力中发挥着重要作用。然而，了解患者间 TME 异质性仍然是有效药物开发的一个挑战。本文将机器学习 (ML) 的最新进展应用于生存分析，对接受根治性手术切除和术后免疫病理学检查的 NSCLC 患者进行回顾性研究。比较机器学习方法在识别预后亚型方面的有效性。校准了六种生存模型（包括 Cox 回归和五种生存机器学习方法），并根据 PD-L1 表达、CD3 表达和十个基线患者特征预测 NSCLC 患者的生存。使用合成患者数据增强来描绘每种方法的生物标志物空间的预后子区域，并在模型之间进行比较以获得总体生存一致性。该研究共纳入 423 名接受明确手术切除的 NSCLC 患者（46% 为女性；中位年龄[分位数范围]：67[60-73]）。 219 名 (52%) 患者在观察期内经历了事件，最长随访时间为 10 年，中位随访时间为 78 个月。随机生存森林 (RSF) 实现了最高的预测精度，C 指数为 0.84。由此产生的生物标志物亚型表明，PD-L1 高表达且 CD3 计数低的患者在手术切除后五年内死亡风险较高。© 2024。作者。

The tumor microenvironment (TME) plays a fundamental role in tumorigenesis, tumor progression, and anti-cancer immunity potential of emerging cancer therapeutics. Understanding inter-patient TME heterogeneity, however, remains a challenge to efficient drug development. This article applies recent advances in machine learning (ML) for survival analysis to a retrospective study of NSCLC patients who received definitive surgical resection and immune pathology following surgery. ML methods are compared for their effectiveness in identifying prognostic subtypes. Six survival models, including Cox regression and five survival machine learning methods, were calibrated and applied to predict survival for NSCLC patients based on PD-L1 expression, CD3 expression, and ten baseline patient characteristics. Prognostic subregions of the biomarker space are delineated for each method using synthetic patient data augmentation and compared between models for overall survival concordance. A total of 423 NSCLC patients (46% female; median age [inter quantile range]: 67 [60-73]) treated with definite surgical resection were included in the study. And 219 (52%) patients experienced events during the observation period consisting of a maximum follow-up of 10 years and median follow up 78 months. The random survival forest (RSF) achieved the highest predictive accuracy, with a C-index of 0.84. The resultant biomarker subtypes demonstrate that patients with high PD-L1 expression combined with low CD3 counts experience higher risk of death within five-years of surgical resection.© 2024. The Author(s).