为了开发和验证一个预测早期复发的I期肺腺癌（LUAD）的预测模型，该模型将基于术前CT的放射组学特征与STAS（肺癌沿气道扩散）结合起来。我们回顾性地收集了具有I期LUAD术后病理诊断的患者的最新术前薄层胸部CT扫描和术后病理HE染色切片。手动分割出感兴趣的区域，从肿瘤区域和扩展3个像素单元、6个像素单元和12个像素单元的周围肿瘤区域提取放射组学特征，并使用卷积神经网络提取2D和3D深度学习图像特征。然后，我们构建了RAISm（与STAS相结合的放射组学模型）。接下来，在开发队列和验证队列中评估了RAISm的性能。我们回顾性地选择了2015年1月至2018年12月期间来自两个医疗中心的226名患者作为模型的开发队列，并将其随机分为训练集（72.6％，n = 164）和测试集（27.4％，n = 62）。从2019年6月至2019年12月，我们选择了51名患者作为验证队列。在训练队列中，RAISm在预测LUAD I期早期复发方面表现出极佳的区分性（AUC = 0.847，95％ CI 0.762-0.932），并在验证队列中得到证实（AUC = 0.817，95％ CI 0.625-1.000）。RAISm在区分性和临床净益方面优于单一模态标识和其他标识的组合。我们首次将术前CT放射组学与STAS结合起来，用于术后预测I期LUAD的复发，并在验证队列中证实了该模型的出色效果，表明其有助于术后治疗策略的潜力。© 2023. 国际癌症影像学学会（ICIS）。

To develop and validate a prediction model for early recurrence of stage I lung adenocarcinoma (LUAD) that combines radiomics features based on preoperative CT with tumour spread through air spaces (STAS).The most recent preoperative thin-section chest CT scans and postoperative pathological haematoxylin and eosin-stained sections were retrospectively collected from patients with a postoperative pathological diagnosis of stage I LUAD. Regions of interest were manually segmented, and radiomics features were extracted from the tumour and peritumoral regions extended by 3 voxel units, 6 voxel units, and 12 voxel units, and 2D and 3D deep learning image features were extracted by convolutional neural networks. Then, the RAdiomics Integrated with STAS model (RAISm) was constructed. The performance of RAISm was then evaluated in a development cohort and validation cohort.A total of 226 patients from two medical centres from January 2015 to December 2018 were retrospectively included as the development cohort for the model and were randomly split into a training set (72.6%, n = 164) and a test set (27.4%, n = 62). From June 2019 to December 2019, 51 patients were included in the validation cohort. RAISm had excellent discrimination in predicting the early recurrence of stage I LUAD in the training cohort (AUC = 0.847, 95% CI 0.762-0.932) and validation cohort (AUC = 0.817, 95% CI 0.625-1.000). RAISm outperformed single modality signatures and other combinations of signatures in terms of discrimination and clinical net benefits.We pioneered combining preoperative CT-based radiomics with STAS to predict stage I LUAD recurrence postoperatively and confirmed the superior effect of the model in validation cohorts, showing its potential to assist in postoperative treatment strategies.© 2023. International Cancer Imaging Society (ICIS).