[Radiomics: the process and applications in tumor research].
[Radiomics: the process and applications in tumor research].
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DOI：
10.3760/cma.j.issn.0253-3766.2018.11.001
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发表时间：
2018-11-23
期刊：
影响因子：
--
通讯作者：
Ye, Z X
中科院分区：
文献类型：
--
作者：
Li, Q;Ye, Z X
放射组学可以通过高通量计算从医学图像中提取无数定量特征，以进行诊断和预测。放射组学的实践涉及图像采集、识别和分割感兴趣的体积、提取和分析定量特征以及分类或预测模型开发。与传统的视觉解读医学图像相比，计算机技术从放射组学角度对医学图像进行深度挖掘，使得特征提取更加高效、相对客观、特征类型丰富。然而，放射组学分析需要高图像质量和一致的扫描参数。提取的特征仅限于分割区域。放射组学在肿瘤筛查、早期诊断、准确分级和分期、治疗和预后、分子特征等方面具有广阔的前景。结合传统的医学图像视觉解读，放射组学有助于肿瘤的诊断和预测。
Radiomics enables extraction of innumerable quantitative features from medical images with high-throughput computing for diagnosis and prediction. The practice of radiomics involves image acquisition, identifying and segmenting the volumes of interest, extracting and analyzing of quantitative features, and classification or prediction model development. Compared with traditional visual interpretation of medical images, the deep mining of medical images by computer technology from radiomics makes feature uptake more efficient, relatively objective and rich in feature types. Whereas, radiomic analysis requires high image quality and consistent scan parameters. The features extracted are confined to the segmented area. Radiomics is promising in tumor screening, early diagnosis, accurate grading and staging, treatment and prognosis, molecular characteristics and so on. Combined with traditional visual interpretation of medical images, radiomics is helpful in tumor diagnosis and prediction.