长期以来，关于浸润性肺腺癌的分级系统一直没有达成共识。直到2020年10月，提出了一种新的分级系统，用于量化肺腺癌的组织学亚型和比例的整体特征。本研究旨在基于正电子发射断层扫描/计算机断层扫描（PET/CT）开发深度学习分级标志（DLGS），以个性化手术治疗临床I期浸润性肺腺癌，并探索其预测的生物学基础。共纳入4个医疗中心的2638例临床I期浸润性肺腺癌患者进行回顾性构建和验证DLGS。通过受试者工作特征曲线下面积（AUC）评估DLGS的预测性能，通过DLGS定义的风险组进行存活分析，并比较风险组之间的组织学模式、基因型变异、遗传通路和免疫细胞浸润等方面来探索其生物学基础。 DLGS预测三级别的AUC值分别为0.862、0.844和0.851（验证集n=497、外部队列n=382和前瞻队列n=600），显著优于PET模型的0.814、0.810和0.806，CT模型的0.813、0.795和0.824以及临床模型的0.762、0.734和0.751。此外，对于DLGS定义的高风险人群，与亚叶切除相比，肺叶切除术具有更好的预后（总生存 p=0.085）和无复发生存（p=0.038），系统性淋巴结清扫与有限淋巴结清扫相比具有更好的预后（总生存 p=0.001，无复发生存 p=0.041）。 DLGS具有预测组织学分级和个性化手术治疗临床I期浸润性肺腺癌的潜力。其在其他地区的适用性应通过更大规模的国际研究进一步验证。© 2023该作者、由 Springer Verlag GmbH Germany 独家授权，隶属于 Springer Nature。

No consensus on a grading system for invasive lung adenocarcinoma had been built over a long period of time. Until October 2020, a novel grading system was proposed to quantify the whole landscape of histologic subtypes and proportions of pulmonary adenocarcinomas. This study aims to develop a deep learning grading signature (DLGS) based on positron emission tomography/computed tomography (PET/CT) to personalize surgical treatments for clinical stage I invasive lung adenocarcinoma and explore the biologic basis under its prediction.A total of 2638 patients with clinical stage I invasive lung adenocarcinoma from 4 medical centers were retrospectively included to construct and validate the DLGS. The predictive performance of the DLGS was evaluated by the area under the receiver operating characteristic curve (AUC), its potential to optimize surgical treatments was investigated via survival analyses in risk groups defined by the DLGS, and its biological basis was explored by comparing histologic patterns, genotypic alternations, genetic pathways, and infiltration of immune cells in microenvironments between risk groups.The DLGS to predict grade 3 achieved AUCs of 0.862, 0.844, and 0.851 in the validation set (n = 497), external cohort (n = 382), and prospective cohort (n = 600), respectively, which were significantly better than 0.814, 0.810, and 0.806 of the PET model, 0.813, 0.795, and 0.824 of the CT model, and 0.762, 0.734, and 0.751 of the clinical model. Additionally, for DLGS-defined high-risk population, lobectomy yielded an improved prognosis compared to sublobectomy p = 0.085 for overall survival [OS] and p = 0.038 for recurrence-free survival [RFS]) and systematic nodal dissection conferred a superior prognosis to limited nodal dissection (p = 0.001 for OS and p = 0.041 for RFS).The DLGS harbors the potential to predict the histologic grade and personalize the surgical treatments for clinical stage I invasive lung adenocarcinoma. Its applicability to other territories should be further validated by a larger international study.© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.