Advances of radiomics in non-neoplastic disease
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摘要: 影像组学自兴起以来在肿瘤学诊断、鉴别及预后方面取得了不少研究成果。在肿瘤学方面不断发展的同时,在临床非肿瘤性病变诊断方面影像组学也发挥其高通量、大数据方面的优势,取得了可喜的进步。相关报道围绕脑体积精确测量,注意缺陷多动障碍、精神分裂症、肥厚性心肌病及高血压性心脏病鉴别、急性冠脉综合征和粥样斑块特点以及肝硬化等疾病的诊断。研究结果显示,相对以往常规的影像方法,影像组学显示出更加精准的诊断优势。尽管相关研究方式不尽相同,多项研究结果显示ICC及AUC值可达0.9左右甚至接近1。现将上述研究详细综述如下。Abstract: Radiomics has made many achievements in the diagnosis, differentiation and prognosis of oncology. With the continuous development on oncology, it has made gratifying progress in the field of clinical non-neoplastic disease with its advantages of high throughput and big data. The past reports analyzed measurements of brain volume, attention deficit hyperactivity disorder, Schizophrenia, differentiation of hypertrophic cardiomyopathy and hypertensive heart Disease, the characteristics of acute coronary syndrome and atheromatous plaque, and the diagnosis of diseases such as cirrhosis. The results showed that compared with the conventional imaging methods, radiomics showed more accurate diagnostic advantages. Although relevant research methods are different, several studies have shown that ICC and AUC values can reach around 0.9 or even close to 1. The above research is summarized in detail as follows.
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Key words:
- radiomics /
- non-neoplastic /
- disease
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[1] Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data[J]. Radiology, 2016, 278(2): 563-77. http://d.old.wanfangdata.com.cn/Periodical/dianzixb202004003 [2] Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis[J]. Eur J Cancer, 2012, 48(4): 441-6. http://d.old.wanfangdata.com.cn/Periodical/dianzixb202004003 [3] Liu ZY, Wang S, Dong D, et al. The applications of radiomics in precision diagnosis and treatment of oncology: opportunities and challenges[J]. Theranostics, 2019, 9(5): 1303-22. doi: 10.7150/thno.30309 [4] Foy JP, Durdux C, Giraud P, et al. RE: the rise of radiomics and implications for oncologic management[J]. J Natl Cancer Inst, 2018, 110(11): 1275-6. doi: 10.1093/jnci/djy037 [5] Limkin EJ, Sun R, Dercle L, et al. Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology[J]. Ann Oncol, 2017, 28(6): 1191-206. doi: 10.1093/annonc/mdx034 [6] Bi WL, Hosny A, Schabath MB, et al. Artificial intelligence in cancer imaging: Clinical challenges and applications[J]. CA Cancer J Clin, 2019, 69(2): 127-57. http://cn.bing.com/academic/profile?id=fcc600eef9b5b750026f3cf3a9d415b9&encoded=0&v=paper_preview&mkt=zh-cn [7] Verma V, Simone CB 2nd, Krishnan S, et al. The rise of radiomics and implications for oncologic management[J]. J Natl Cancer Inst, 2017, 109(7): 55-62. http://cn.bing.com/academic/profile?id=96385562eb59900d4f622894ffe0fa49&encoded=0&v=paper_preview&mkt=zh-cn [8] Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach[J]. Nat Commun, 2014, 5: 4006-13. doi: 10.1038/ncomms5006 [9] Adduru VR, Michael AM, Helguera M, et al. Leveraging clinical imaging archives for radiomics: reliability of automated methods for brain volume measurement[J]. Radiology, 2017, 284(3): 862-9. doi: 10.1148/radiol.2017161928 [10] Smith SM, Zhang YY, Jenkinson M, et al. Accurate, robust, and automated longitudinal and cross-sectional brain change analysis[J]. Neuroimage, 2002, 17(1): 479-89. doi: 10.1006/nimg.2002.1040 [11] Smith SM. Fast robust automated brain extraction[J]. Hum Brain Mapp, 2002, 17(3): 143-55. http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ028532798/ [12] Eritaia J, Wood SJ, Stuart GW, et al. An optimized method for estimating intracranial volume from magnetic resonance images[J]. Magn Reson Med, 2000, 44(6): 973-7. doi: 10.1002/1522-2594(200012)44:6<973::AID-MRM21>3.0.CO;2-H [13] Klauschen F, Goldman A, Barra V, et al. Evaluation of automated brain MR image segmentation and volumetry methods[J]. Hum Brain Mapp, 2009, 30(4): 1310-27. doi: 10.1002/hbm.20599 [14] Malone IB, Leung KK, Clegg S, et al. Accurate automatic estimation of total intracranial volume: a nuisance variable with less nuisance[J]. Neuroimage, 2015, 104: 366-72. doi: 10.1016/j.neuroimage.2014.09.034 [15] Sun HQ, Chen Y, Huang Q, et al. Psychoradiologic utility of MR imaging for diagnosis of attention deficit hyperactivity disorder: a radiomics analysis[J]. Radiology, 2018, 287(2): 620-30. doi: 10.1148/radiol.2017170226 [16] Port JD. Diagnosis of attention deficit hyperactivity disorder by using MR imaging and radiomics: a potential tool for clinicians[J]. Radiology, 2018, 287(2): 631-2. http://cn.bing.com/academic/profile?id=6667677e9ddf3545298263f37cc66698&encoded=0&v=paper_preview&mkt=zh-cn [17] Cui LB, Liu L, Wang HN, et al. Disease definition for schizophrenia by functional connectivity using radiomics strategy[J]. Schizophr Bull, 2018, 44(5): 1053-9. doi: 10.1093/schbul/sby007 [18] Neisius U, El-Rewaidy H, Nakamori S, et al. Radiomic analysis of myocardial native T1 imaging discriminates between hypertensive heart disease and hypertrophic cardiomyopathy[J]. JACC Cardiovasc Imaging, 2019, 12(10): 1946-54. doi: 10.1016/j.jcmg.2018.11.024 [19] Al-Mallah MH. Radiomics in hypertrophic cardiomyopathy: the new tool[J]. JACC Cardiovasc Imaging, 2019, 12(10): 1955-7. doi: 10.1016/j.jcmg.2019.02.004 [20] Leiner T. Detecting coronary plaque vulnerability using computed tomography radiomics: the one stop shop for plaque vulnerability?[J]. Eur Heart J Cardiovasc Imaging, 2019, 20(11): 1248-9. doi: 10.1093/ehjci/jez071 [21] Kolossváry M, Karády J, Szilveszter B, et al. Radiomic features are superior to conventional quantitative computed tomographic metrics to identify coronary plaques with napkin-ring sign[J]. Circ Cardiovasc Imaging, 2017, 10(12): e006843-9. http://cn.bing.com/academic/profile?id=8a9dd9f43ae797987880122751327304&encoded=0&v=paper_preview&mkt=zh-cn [22] Kolossváry M, Park J, Bang JI, et al. Identification of invasive and radionuclide imaging markers of coronary plaque vulnerability using radiomic analysis of coronary computed tomography angiography[J]. Eur Heart J Cardiovasc Imaging, 2019, 20(11): 1250-8. doi: 10.1093/ehjci/jez033 [23] Dey D, Commandeur F. Radiomics to identify high-risk atherosclerotic plaque from computed tomography: the power of quantification[J]. Circ Cardiovasc Imaging, 2017, 10(12): e007254-61. http://cn.bing.com/academic/profile?id=0d7bf8d52f54fb32f8e54ce5d7e28b19&encoded=0&v=paper_preview&mkt=zh-cn [24] Wang K, Lu X, Zhou H, et al. Deep learning radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study[J]. Gut, 2019, 68(4): 729-41. doi: 10.1136/gutjnl-2018-316204 [25] Park HJ, Lee SS, Park B, et al. Radiomics Analysis of Gadoxetic Acid-enhanced MRI for Staging Liver Fibrosis[J]. Radiology, 2019, 290(2): 380-7. http://cn.bing.com/academic/profile?id=549c95bc4dc41ee8bbea47230776f7b7&encoded=0&v=paper_preview&mkt=zh-cn
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