评估集成放射组学和转录组学分析是否能为非小细胞肺癌（NSCLC）中放射组学特征的分子注释和有效风险分层提供新的见解。共纳入三个数据集中的627位NSCLC患者。从分割的三维肿瘤体积中提取放射组学特征，并进行Z-score归一化以进行进一步分析。在转录组学水平上，使用基因集变异分析（GSVA）算法评估了186条途径和28种免疫细胞类型。根据其放射组学特征和途径富集得分，将NSCLC患者分为亚组，使用共识聚类法。亚组特异性放射组学特征用于验证聚类表现和预后价值。利用Kaplan-Meier生存分析和对数秩检验、单变量和多变量Cox分析探索亚组之间的生存差异。根据放射组学和途径富集特征，确定了三个放射转录组亚型。三个放射转录组亚型分别具有特定的分子特征：RTS1（增殖亚型）、RTS2（代谢亚型）和RTS3（免疫激活亚型）。RTS3显示了大多数免疫细胞的增加浸润。通过验证队列，证明了RTS分层策略具有显著的预后价值。生存分析表明，RTS策略可以按照预后对NSCLC患者进行分层（p = 0.009），并且在调整其他临床参数后，RTS策略仍然是独立的预后指标。该放射转录组研究提供了一种NSCLC分层策略，可为放射组学特征的分子注释和预后预测提供信息。• 放射转录组亚型（RTS）可以用于分层分子异质性患者。• RTS显示了分子表型与放射组学特征之间的关系。• RTS算法可用于识别预后差的患者。© 2023.作者（专属许可欧洲放射学会）。

To assess whether integrative radiomics and transcriptomics analyses could provide novel insights for radiomic features' molecular annotation and effective risk stratification in non-small cell lung cancer (NSCLC).A total of 627 NSCLC patients from three datasets were included. Radiomics features were extracted from segmented 3-dimensional tumour volumes and were z-score normalized for further analysis. In transcriptomics level, 186 pathways and 28 types of immune cells were assessed by using the Gene Set Variation Analysis (GSVA) algorithm. NSCLC patients were categorized into subgroups based on their radiomic features and pathways enrichment scores using consensus clustering. Subgroup-specific radiomics features were used to validate clustering performance and prognostic value. Kaplan-Meier survival analysis with the log-rank test and univariable and multivariable Cox analyses were conducted to explore survival differences among the subgroups.Three radiotranscriptomics subtypes (RTSs) were identified based on the radiomics and pathways enrichment profiles. The three RTSs were characterized as having specific molecular hallmarks: RTS1 (proliferation subtype), RTS2 (metabolism subtype), and RTS3 (immune activation subtype). RTS3 showed increased infiltration of most immune cells. The RTS stratification strategy was validated in a validation cohort and showed significant prognostic value. Survival analysis demonstrated that the RTS strategy could stratify NSCLC patients according to prognosis (p = 0.009), and the RTS strategy remained an independent prognostic indicator after adjusting for other clinical parameters.This radiotranscriptomics study provides a stratification strategy for NSCLC that could provide information for radiomics feature molecular annotation and prognostic prediction.• Radiotranscriptomics subtypes (RTSs) could be used to stratify molecularly heterogeneous patients. • RTSs showed relationships between molecular phenotypes and radiomics features. • The RTS algorithm could be used to identify patients with poor prognosis.© 2023. The Author(s), under exclusive licence to European Society of Radiology.