肺腺癌CT影像分子分型研究进展

研究表明在明确驱动基因后进行特异性靶向治疗,肺癌患者的中位生存期显著延长。而除高通量测序技术和荧光原位杂交等分子生物学技术外,影像基因组学的出现,也为肺腺癌分子分型预测提供了一种无创的新方法。本文对肺腺癌计算机断层扫描（computed tomography,CT）影像分子分型的研究进展进行综述。首先,介绍肺腺癌分子分型的研究背景及肺腺癌主要的基因突变类型;然后,重点介绍两种主要的研究方法,即CT语义特征与肺腺癌分子亚型的相关性分析和基于机器学习的肺腺癌分子分型预测模型;最后,总结了该领域现阶段面临的主要问题,并对未来的研究方向做出展望。肺腺癌CT影像分子分型研究已经取得了一定成果,但仍存在很多问题。相关性分析与基于影像组学的预测模型研究由于样本各异且受过多人为干预,导致研究结果差异大,甚至有部分文献得到的结论截然相反。而基于深度学习的预测模型研究采用端到端的神经网络模型,人为参与极少,降低了研究难度,但尚处于起步阶段,构建的模型大多相对简单,远不能达到临床应用标准。今后的研究应聚焦于结合多种医学图像构建肺腺癌分子分型的大样本深度学习预测模型,同时结合临床信息、语义特征及影像组学特征,实现肺腺癌分子分型的无创、精准预测。     

Machine Learning-Based Radiomics Signatures for EGFR and KRAS Mutations Prediction in Non-Small-Cell Lung Cancer

Early identification of epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations is crucial for selecting a therapeutic strategy for patients with non-small-cell lung cancer (NSCLC). We proposed a machine learning-based model for feature selection and prediction of EGFR and KRAS mutations in patients with NSCLC by including the least number of the most semantic radiomics features. We included a cohort of 161 patients from 211 patients with NSCLC from The Cancer Imaging Archive (TCIA) and analyzed 161 low-dose computed tomography (LDCT) images for detecting EGFR and KRAS mutations. A total of 851 radiomics features, which were classified into 9 categories, were obtained through manual segmentation and radiomics feature extraction from LDCT. We evaluated our models using a validation set consisting of 18 patients derived from the same TCIA dataset. The results showed that the genetic algorithm plus XGBoost classifier exhibited the most favorable performance, with an accuracy of 0.836 and 0.86 for detecting EGFR and KRAS mutations, respectively. We demonstrated that a noninvasive machine learning-based model including the least number of the most semantic radiomics signatures could robustly predict EGFR and KRAS mutations in patients with NSCLC.     

Prostate Cancer Radiogenomics—From Imaging to Molecular Characterization

Radiomics and genomics represent two of the most promising fields of cancer research, designed to improve the risk stratification and disease management of patients with prostate cancer (PCa). Radiomics involves a conversion of imaging derivate quantitative features using manual or automated algorithms, enhancing existing data through mathematical analysis. This could increase the clinical value in PCa management. To extract features from imaging methods such as magnetic resonance imaging (MRI), the empiric nature of the analysis using machine learning and artificial intelligence could help make the best clinical decisions. Genomics information can be explained or decoded by radiomics. The development of methodologies can create more-efficient predictive models and can better characterize the molecular features of PCa. Additionally, the identification of new imaging biomarkers can overcome the known heterogeneity of PCa, by non-invasive radiological assessment of the whole specific organ. In the future, the validation of recent findings, in large, randomized cohorts of PCa patients, can establish the role of radiogenomics. Briefly, we aimed to review the current literature of highly quantitative and qualitative results from well-designed studies for the diagnoses, treatment, and follow-up of prostate cancer, based on radiomics, genomics and radiogenomics research.