Knowledge Commons of Institute of Automation,CAS
Machine Learning in Lung Cancer Radiomics | |
Jiaqi Li1; Zhuofeng Li1; Lei Wei1; Xuegong Zhang1,2 | |
发表期刊 | Machine Intelligence Research
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ISSN | 2731-538X |
2023 | |
卷号 | 20期号:6页码:753-782 |
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. |
关键词 | Machine learning, lung cancer, radiomics, medical image, clinical application |
DOI | 10.1007/s11633-022-1364-x |
语种 | 英语 |
七大方向——子方向分类 | 其他 |
国重实验室规划方向分类 | 其他 |
是否有论文关联数据集需要存交 | 否 |
中文导读 | https://mp.weixin.qq.com/s/crdbsP2PEk2V10I_N6iMXQ |
视频解析 | https://www.bilibili.com/video/BV1Sy41187BJ/ |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/56009 |
专题 | 学术期刊_Machine Intelligence Research |
作者单位 | 1.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 |
推荐引用方式 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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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
MIR-2022-05-156.pdf(7595KB) | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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