Radiology（imaging） and imaging-guided interventions, which provide multi-parametric morphologic and functional information, are playing an increasingly significant role in precision medicine. Radiologists are traine...Radiology（imaging） and imaging-guided interventions, which provide multi-parametric morphologic and functional information, are playing an increasingly significant role in precision medicine. Radiologists are trained to understand the imaging phenotypes, transcribe those observations（phenotypes） to correlate with underlying diseases and to characterize the images. However, in order to understand and characterize the molecular phenotype（to obtain genomic information） of solid heterogeneous tumours, the advanced sequencing of those tissues using biopsy is required. Thus, radiologists image the tissues from various views and angles in order to have the complete image phenotypes, thereby acquiring a huge amount of data. Deriving meaningful details from all these radiological data becomes challenging and raises the big data issues. Therefore, interest in the application of radiomics has been growing in recent years as it has the potential to provide significant interpretive and predictive information for decision support. Radiomics is a combination of conventional computer-aided diagnosis, deep learning methods, and human skills, and thus can be used for quantitative characterization of tumour phenotypes. This paper discusses the overview of radiomics workflow, the results of various radiomics-based studies conducted using various radiological images such as computed tomography（CT）, magnetic resonance imaging（MRI）, and positron-emission tomography（PET）, the challenges we are facing, and the potential contribution of radiomics towards precision medicine.展开更多
文摘Radiology（imaging） and imaging-guided interventions, which provide multi-parametric morphologic and functional information, are playing an increasingly significant role in precision medicine. Radiologists are trained to understand the imaging phenotypes, transcribe those observations（phenotypes） to correlate with underlying diseases and to characterize the images. However, in order to understand and characterize the molecular phenotype（to obtain genomic information） of solid heterogeneous tumours, the advanced sequencing of those tissues using biopsy is required. Thus, radiologists image the tissues from various views and angles in order to have the complete image phenotypes, thereby acquiring a huge amount of data. Deriving meaningful details from all these radiological data becomes challenging and raises the big data issues. Therefore, interest in the application of radiomics has been growing in recent years as it has the potential to provide significant interpretive and predictive information for decision support. Radiomics is a combination of conventional computer-aided diagnosis, deep learning methods, and human skills, and thus can be used for quantitative characterization of tumour phenotypes. This paper discusses the overview of radiomics workflow, the results of various radiomics-based studies conducted using various radiological images such as computed tomography（CT）, magnetic resonance imaging（MRI）, and positron-emission tomography（PET）, the challenges we are facing, and the potential contribution of radiomics towards precision medicine.
文摘目的基于机器学习建立并验证放射组学预测非小细胞肺癌(non-small cell lung cancer,NSCLC)表皮生长因子受体(epidermal growth factor receptor,EGFR)基因突变模型。方法收集462例病理证实的NSCLC且术前行CT和明了EGFR基因状态的患者。从患者术前薄层CT中提取107个放射组学特征。采用随机森林(random forest)建立机器学习模型预测NSCLC的EGFR突变状态,并采用5-折叠交叉验证进行校正。结果462例NSCLC患者中,EGFR突变型214例(46.3%)。单因素分析发现5个特征以及吸烟状况和性别与EGFR突变相关。利用这5个放射组学特征以及吸烟状态和性别构建随机森林模型在训练集对EGFR突变的ROC曲线下面积(he area under the ROC curve,AUC)为0.774,敏感性为74.5%,特异性为79.1%。在验证集中AUC为0.756,敏感性为79.7%,特异性为65.7%。结论基于机器学习的放射组学模型能较好的预测NSCLC的EGFR的突变,有助于临床医生术前治疗方案的选择。
