CASIA OpenIR  > 学术期刊  > Machine Intelligence Research
Machine Learning in Lung Cancer Radiomics
Jiaqi Li1; Zhuofeng Li1; Lei Wei1; Xuegong Zhang1,2
Source PublicationMachine Intelligence Research
ISSN2731-538X
2023
Volume20Issue:6Pages:753-782
Abstract

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.

KeywordMachine learning, lung cancer, radiomics, medical image, clinical application
DOI10.1007/s11633-022-1364-x
Language英语
Sub direction classification其他
planning direction of the national heavy laboratory其他
Paper associated data
Chinese guidehttps://mp.weixin.qq.com/s/crdbsP2PEk2V10I_N6iMXQ
Video parsinghttps://www.bilibili.com/video/BV1Sy41187BJ/
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Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/56009
Collection学术期刊_Machine Intelligence Research
Affiliation1.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
2.School of Medicine, Tsinghua University, Beijing 100084, China
Recommended Citation
GB/T 7714
Jiaqi Li,Zhuofeng Li,Lei Wei,et al. Machine Learning in Lung Cancer Radiomics[J]. Machine Intelligence Research,2023,20(6):753-782.
APA Jiaqi Li,Zhuofeng Li,Lei Wei,&Xuegong Zhang.(2023).Machine Learning in Lung Cancer Radiomics.Machine Intelligence Research,20(6),753-782.
MLA Jiaqi Li,et al."Machine Learning in Lung Cancer Radiomics".Machine Intelligence Research 20.6(2023):753-782.
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