Machine Learning in Lung Cancer Radiomics

doi:10.1007/s11633-022-1364-x

CORC > 自动化研究所 > 中国科学院自动化研究所 > 学术期刊 > International Journal of Automation and Computing

	Machine Learning in Lung Cancer Radiomics
	Jiaqi Li 2
刊名	Machine Intelligence Research
	2023
卷号	20 期号:6 页码:753-782
关键词	Machine learning, lung cancer, radiomics, medical image, clinical application
ISSN号	2731-538X
DOI	10.1007/s11633-022-1364-x
英文摘要	Lung cancer is the leading cause of cancer-related deaths worldwide. Medical imaging technologies such as computed tomography (CT) and positron emission tomography (PET) are routinely used for non-invasive lung cancer diagnosis. In clinical practice, physicians investigate the characteristics of tumors such as the size, shape and location from CT and PET images to make decisions. Recently, scientists have proposed various computational image features that can capture more information than that directly perceivable by human eyes, which promotes the rise of radiomics. Radiomics is a research field on the conversion of medical images into high-dimensional features with data-driven methods to help subsequent data mining for better clinical decision support. Radiomic analysis has four major steps: image preprocessing, tumor segmentation, feature extraction and clinical prediction. Machine learning, including the high profile deep learning, facilitates the development and application of radiomic methods. Various radiomic methods have been proposed recently, such as the construction of radiomic signatures, tumor habitat analysis, cluster pattern characterization and end-to-end prediction of tumor properties. These methods have been applied in many studies aiming at lung cancer diagnosis, treatment and monitoring, shedding light on future non-invasive evaluations of the nodule malignancy, histological subtypes, genomic properties and treatment responses. In this review, we summarized and categorized the studies on the general workflow, methods for clinical prediction and clinical applications of machine learning in lung cancer radiomic studies, introduced some commonly-used software tools, and discussed the limitations of current methods and possible future directions.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/54166]
专题	自动化研究所_学术期刊_International Journal of Automation and Computing
作者单位	1.School of Medicine, Tsinghua University, Beijing 100084, China 2.Bioinformatics Division, Beijing National Research Center for Information Science and Technology (BNRIST) and Ministry of Education Key Laboratory of Bioinformatics, Department of Automation, Tsinghua University, Beijing 100084, China
推荐引用方式 GB/T 7714	Jiaqi Li. Machine Learning in Lung Cancer Radiomics[J]. Machine Intelligence Research,2023,20(6):753-782.
APA	Jiaqi Li.(2023).Machine Learning in Lung Cancer Radiomics.Machine Intelligence Research,20(6),753-782.
MLA	Jiaqi Li."Machine Learning in Lung Cancer Radiomics".Machine Intelligence Research 20.6(2023):753-782.