Jiaqi Li, Zhuofeng Li, Lei Wei, Xuegong Zhang. Machine Learning in Lung Cancer Radiomics. Machine Intelligence Research. https://doi.org/10.1007/s11633-022-1364-x
Citation: Jiaqi Li, Zhuofeng Li, Lei Wei, Xuegong Zhang. Machine Learning in Lung Cancer Radiomics. Machine Intelligence Research. https://doi.org/10.1007/s11633-022-1364-x

Machine Learning in Lung Cancer Radiomics

doi: 10.1007/s11633-022-1364-x
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  • Author Bio:

    Jiaqi Li received the B. Eng. degree in automation from Beihang University, China in 2018. He is currently a Ph. D. degree candidate in bioinformatics at Department of Automation, Tsinghua University, China. His research interests include machine learning and its application for medical image analysis. E-mail: li-jq18@mails.tsinghua.edu.cn ORCID iD: 0000-0001-8967-5714

    Zhuofeng Li received the B. Eng. degree in automation from Tsinghua University, China in 2018. He is currently a master student in automation, Tsinghua University, China. His research interests include machine learning and medical image analysis. E-mail: lzf22@mails.tsinghua.edu.cn

    Lei Wei received the B. Eng. and Ph. D. degrees in automation from Tsinghua University, China in 2013 and 2019, respectively. He worked at Department of Automation, Tsinghua University as a postdoctoral fellow from 2019 to 2021, and then joint Beijing National Research Center for Information Science and Technology (BNRIST), Tsinghua University as an assistant research fellow. His research interests include systems biology, synthetic biology and single-cell bioinformatics. E-mail: weilei92@tsinghua.edu.cn ORCID iD: 0000-0002-1546-6458

    Xuegong Zhang received the B. Eng. degree in industrial automation and the Ph. D. degree in pattern recognition and intelligent systems from Tsinghua University, China in 1989 and 1994, respectively. He joined Faculty of Department of Automation, Tsinghua University, China in 1994. From 2001 to 2002, he worked at Harvard School of Public Health as a visiting scientist. He is currently professor of pattern recognition and bioinformatics in Department of Automation, Tsinghua University, and is adjunct professor in School of Life Sciences and School of Medicine, Tsinghua University. He is the director of Bioinformatics Division, Beijing National Research Center for Information Science and Technology (BNRIST). He was elected as the Fellow of the International Society for Computational Biology (ISCB) and of the Chinese Association of Artificial Intelligence (CAAI) in 2020. His research interests include machine learning methods and their applications in bioinformatics and medical data analysis, including digital twin systems of life, intelligent health and the informatics architecture of human cellular and molecular portraitures. E-mail: zhangxg@tsinghua.edu.cn (Corresponding author) ORCID iD: 0000-0002-9684-5643

  • Received Date: 2022-05-13
  • Accepted Date: 2022-08-06
  • Publish Online: 2023-02-18
  • 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.


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