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• W4321352078 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." @default.
• W4321352078 created "2023-02-20" @default.
• W4321352078 creator A5019276915 @default.
• W4321352078 creator A5048544566 @default.
• W4321352078 creator A5081242795 @default.
• W4321352078 creator A5085335073 @default.
• W4321352078 date "2023-02-18" @default.
• W4321352078 modified "2023-09-27" @default.
• W4321352078 title "Machine Learning in Lung Cancer Radiomics" @default.
• W4321352078 cites W130099911 @default.
• W4321352078 cites W1498436455 @default.
• W4321352078 cites W1829208371 @default.
• W4321352078 cites W1884191083 @default.
• W4321352078 cites W1901129140 @default.
• W4321352078 cites W1903029394 @default.
• W4321352078 cites W1966327575 @default.
• W4321352078 cites W1987012261 @default.
• W4321352078 cites W1992112921 @default.
• W4321352078 cites W1992122542 @default.
• W4321352078 cites W2009775937 @default.
• W4321352078 cites W2016974693 @default.
• W4321352078 cites W2026616100 @default.
• W4321352078 cites W2028911404 @default.
• W4321352078 cites W2035252444 @default.
• W4321352078 cites W2071949631 @default.
• W4321352078 cites W2079961815 @default.
• W4321352078 cites W2085487226 @default.
• W4321352078 cites W2093542953 @default.
• W4321352078 cites W2103004421 @default.
• W4321352078 cites W2108598243 @default.
• W4321352078 cites W2113524221 @default.
• W4321352078 cites W2118142823 @default.
• W4321352078 cites W2118246710 @default.
• W4321352078 cites W2127890285 @default.
• W4321352078 cites W2128739912 @default.
• W4321352078 cites W2130410032 @default.
• W4321352078 cites W2132157071 @default.
• W4321352078 cites W2135046866 @default.
• W4321352078 cites W2140124489 @default.
• W4321352078 cites W2143426320 @default.
• W4321352078 cites W2157380596 @default.
• W4321352078 cites W2166084034 @default.
• W4321352078 cites W2166401924 @default.
• W4321352078 cites W2174661749 @default.
• W4321352078 cites W2214074259 @default.
• W4321352078 cites W2251438188 @default.
• W4321352078 cites W2293531514 @default.
• W4321352078 cites W2307308593 @default.
• W4321352078 cites W2331032341 @default.
• W4321352078 cites W2333277922 @default.
• W4321352078 cites W2342045095 @default.
• W4321352078 cites W2394599079 @default.
• W4321352078 cites W2414064355 @default.
• W4321352078 cites W2461805626 @default.
• W4321352078 cites W2464708700 @default.
• W4321352078 cites W2470491115 @default.
• W4321352078 cites W2490901342 @default.
• W4321352078 cites W2507987841 @default.
• W4321352078 cites W2513408092 @default.
• W4321352078 cites W2517065464 @default.
• W4321352078 cites W2525205678 @default.
• W4321352078 cites W2533326786 @default.
• W4321352078 cites W2552342083 @default.
• W4321352078 cites W2552734365 @default.
• W4321352078 cites W2559588208 @default.
• W4321352078 cites W2566863750 @default.
• W4321352078 cites W2586629973 @default.
• W4321352078 cites W2589577297 @default.
• W4321352078 cites W2592929672 @default.
• W4321352078 cites W2606244205 @default.
• W4321352078 cites W2616814208 @default.
• W4321352078 cites W2736731826 @default.
• W4321352078 cites W2737453412 @default.
• W4321352078 cites W2742614046 @default.
• W4321352078 cites W2745640325 @default.
• W4321352078 cites W2753207486 @default.
• W4321352078 cites W2759538409 @default.
• W4321352078 cites W2761668583 @default.
• W4321352078 cites W2763355946 @default.
• W4321352078 cites W2767128594 @default.
• W4321352078 cites W2767236661 @default.
• W4321352078 cites W2767988301 @default.
• W4321352078 cites W2768158636 @default.
• W4321352078 cites W2776547207 @default.
• W4321352078 cites W2784371446 @default.
• W4321352078 cites W2786078834 @default.
• W4321352078 cites W2788633781 @default.
• W4321352078 cites W2788673917 @default.
• W4321352078 cites W2789242863 @default.
• W4321352078 cites W2790477006 @default.
• W4321352078 cites W2801894005 @default.
• W4321352078 cites W2803760365 @default.
• W4321352078 cites W2806191962 @default.
• W4321352078 cites W2806986802 @default.
• W4321352078 cites W2861648048 @default.
• W4321352078 cites W2883636763 @default.
• W4321352078 cites W2888945068 @default.
• W4321352078 cites W2897755679 @default.
• W4321352078 cites W2899440009 @default.
• W4321352078 cites W2900850487 @default.

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