Chen Wei, Qiao Xu, Yin Shang, Zhang Xianru, Xu Xin
School and Hospital of Stomatology, Cheeloo College of Medicine, Shandong University, Shandong Key Laboratory of Oral Tissue Regeneration, Shandong Engineering Laboratory for Dental Materials and Oral Tissue Regeneration, Jinan, China.
Department of Biomedical Engineering, Shandong University, Jinan, China.
J Oncol. 2022 Aug 27;2022:5131170. doi: 10.1155/2022/5131170. eCollection 2022.
The objectives of our study were to assess the association of radiological imaging and gene expression with patient outcomes in non-small cell lung cancer (NSCLC) and construct a nomogram by combining selected radiomic, genomic, and clinical risk factors to improve the performance of the risk model.
A total of 116 cases of NSCLC with CT images, gene expression, and clinical factors were studied, wherein 87 patients were used as the training cohort, and 29 patients were used as an independent testing cohort. Handcrafted radiomic features and deep-learning genomic features were extracted and selected from CT images and gene expression analysis, respectively. Two risk scores were calculated through Cox regression models for each patient based on radiomic features and genomic features to predict overall survival (OS). Finally, a fusion survival model was constructed by incorporating these two risk scores and clinical factors.
The fusion model that combined CT images, gene expression data, and clinical factors effectively stratified patients into low- and high-risk groups. The C-indexes for OS prediction were 0.85 and 0.736 in the training and testing cohorts, respectively, which was better than that based on unimodal data.
Combining radiomics and genomics can effectively improve OS prediction for NSCLC patients.
我们研究的目的是评估非小细胞肺癌（NSCLC）中放射影像学和基因表达与患者预后的关联，并通过结合选定的放射组学、基因组学和临床风险因素构建列线图，以提高风险模型的性能。
共研究了116例具有CT图像、基因表达和临床因素的NSCLC病例，其中87例患者作为训练队列，29例患者作为独立测试队列。分别从CT图像和基因表达分析中提取并选择手工制作的放射组学特征和深度学习基因组特征。基于放射组学特征和基因组特征，通过Cox回归模型为每位患者计算两个风险评分，以预测总生存期（OS）。最后，通过纳入这两个风险评分和临床因素构建了一个融合生存模型。
结合CT图像、基因表达数据和临床因素的融合模型有效地将患者分为低风险组和高风险组。训练队列和测试队列中OS预测的C指数分别为0.85和0.736，优于基于单峰数据的模型。
结合放射组学和基因组学可以有效改善NSCLC患者的OS预测。
