放射基因组学
医学
无线电技术
肺癌筛查
肺癌
癌症
全国肺筛查试验
放射科
标准化
医学物理学
病理
内科学
计算机科学
操作系统
作者
Rajat Thawani,Michael McLane,Niha Beig,Soumya Ghose,Prateek Prasanna,Vamsidhar Velcheti,Anant Madabhushi
出处
期刊:Lung Cancer
[Elsevier BV]
日期:2018-01-01
卷期号:115: 34-41
被引量:332
标识
DOI:10.1016/j.lungcan.2017.10.015
摘要
Lung cancer is responsible for a large proportion of cancer-related deaths across the globe, with delayed detection being perhaps the most significant factor for its high mortality rate. Though the National Lung Screening Trial argues for screening of certain at-risk populations, the practical implementation of these screening efforts has not yet been successful and remains in high demand. Radiomics refers to the computerized extraction of data from radiologic images, and provides unique potential for making lung cancer screening more rapid and accurate using machine learning algorithms. The quantitative features analyzed express subvisual characteristics of images which correlate with pathogenesis of diseases. These features are broadly classified into four categories: intensity, structure, texture/gradient, and wavelet, based on the types of image attributes they capture. Many studies have been done to show correlation between these features and the malignant potential of a nodule on a chest CT. In cancer patients, these nodules also have features that can be correlated with prognosis and mutation status. The major limitations of radiomics are the lack of standardization of acquisition parameters, inconsistent radiomic methods, and lack of reproducibility. Researchers are working on overcoming these limitations, which would make radiomics more acceptable in the medical community.
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