The application and progress of radiomics in prostate cancer
Zhang Hongtao1, 2, Yu Hongkai3, Wang Haiyi1, Ye Huiyi1
1 Department of Radiology, Chinese PLA General Hospital, Beijing 100853, China; 2 Department of Radiology, 307 Hospital, PLA; 3 Department of Urinary Surgery, Chinese PLA General Hospital
Abstract:Medical images can be converted into excavated data using computer software. Mass quantitative radiomics features can be extracted for the analysis of clinical information. Currently it is used in the study of various tumors. The range of application includes diagnosis of tumor, clinical grading and staging, evaluation of curative effect, prognosis analysis and gene analysis. This article overviews the method of radiomics and its application and progress in prostate cancer.
张洪涛, 俞鸿凯, 王海屹, 叶慧义. 影像组学在前列腺癌中的应用进展[J]. 微创泌尿外科杂志, 2018, 7(4): 282-286.
Zhang Hongtao, Yu Hongkai, Wang Haiyi, Ye Huiyi. The application and progress of radiomics in prostate cancer. JOURNAL OF MINIMALLY INVASIVE UROLOGY, 2018, 7(4): 282-286.
[1] 陶晶,孙忠全.前列腺癌TMPRSS2-ETS融合基因的研究进展.中国男科学杂志,2015,29(8):55-58,65. [2] 冯晓源.精准医疗,影像先行.中华放射学杂志,2016,50(1):1-2. [3] Schuster SC. Next-generation sequencing transforms Today's biology. Nat Methods, 2008,5(1):16-18. [4] Kumar V, Gu Y, Basu S, et al. Radiomics: the process and the challenges. Magn Reson Imaging, 2012,30(9):1234-1248. [5] Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer, 2012,48(4):441-446. [6] 吴亚平,林予松,顾建钦,等.影像组学的研究进展与挑战.中华放射学杂志,2017,51(12):983-985. [7] 陈瑾,王海屹,叶慧义.纹理分析在肿瘤影像学中的研究进展.中华放射学杂志,2017,51(12):979-982. [8] Cunliffe A, Armato SG, Castillo R, et al. Lung texture in serial thoracic computed tomography scans: correlation of radiomics-based features with radiation therapy dose and radiation pneumonitis development. Int J Radiat Oncol Biol Phys, 2015,91(5):1048-1056. [9] Mackin D, Fave X, Zhang LF, et al. Measuring computed tomography scanner variability of radiomics features. Invest Radiol, 2015,50(11):757-765. [10] Figueroa RL, Zeng-Treitler Q, Kandula S, et al. Predicting sample size required for classification performance. BMC Med Inform Decis Mak, 2012,12:8. [11] Chalkidou A, O'Doherty MJ, Marsden PK. False discovery rates in PET and CT studies with texture features: a systematic review. PLoS One, 2015,10(5):e0124165. [12] 张利文,方梦捷,藏亚丽,等.影像组学的发展与应用.中华放射学杂志,2017,51(1):75-77. [13] 吴佩琪,刘再毅,何兰,等.影像组学与大数据结合的研究现状.中华放射学杂志,2017,51(7):554-558. [14] Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology, 2016,278(2):563-577. [15] Gu Y, Kumar V, Hall LO, et al. Automated delineation of lung tumors from CT images using a single click ensemble segmentation approach. Pattern Recognit, 2013,46(3):692-702. [16] Zikic D, Glocker B, Konukoglu E, et al. Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel MR. Med Image Comput Comput Assist Interv, 2012,15(Pt 3):369-376. [17] 王敏,宋彬,黄子星,等.大数据时代的精准影像医学:放射组学.中国普外基础与临床杂志,2016,23(6):752-755. [18] Rahmim A, Schmidtlein CR, Jackson AA, et al. A novel metric for qua.pngication of homogeneous and heterogeneous tumors in PET for enhanced clinical outcome prediction. Phys Med Biol, 2016,61(1):227-242. [19] 谢凯,孙鸿飞,林涛,等.影像组学中特征提取研究进展.中国医学影像技术,2017,33(12):1792-1796. [20] Voulodimos A, Doulamis N, Doulamis A, et al. Deep learning for computer vision: a brief review. Comput Intell Neurosci, 2018:7068349. [21] Sidhu HS, Benigno S, Ganeshan B, et al. "Textural analysis of multiparametric MRI detects transition zone prostate cancer". Eur Radiol, 2017,27(6):2348-2358. [22] Wibmer A, Hricak H, Gondo T, et al. Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores. Eur Radiol, 2015,25(10):2840-2850. [23] Liang CS, Huang YQ, He L, et al. The development and validation of a CT-based radiomics signature for the preoperative discrimination of stage Ⅰ-Ⅱ and stage Ⅲ-Ⅳ colorectal cancer. Oncotarget, 2016,7(21):31401-31412. [24] Rosenkrantz AB, Triolo MJ, Melamed JA, et al. Whole-Lesion apparent diffusion coefficient metrics as a marker of percentage gleason 4 component within gleason 7 prostate cancer at radical prostatectomy. J Magn Reson Imaging, 2015,41(3):708-714. [25] Nie K, Shi L, Chen Q, et al. Rectal cancer: assessment of neoadjuvant chemoradiation outcome based on radiomics of multiparametric MRI. Clin Cancer Res, 2016,22(21):5256-5264. [26] Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun, 2014,5:4006. [27] Gnep K, Fargeas A, Gutierrez-Carvajal RE, et al. Haralick textural features on T-2-Weighted MRI are associated with biochemical recurrence following radiotherapy for peripheral Zone prostate cancer. J Am Coll Radiol, 2017,45(1):103-117. [28] Mazurowski MA. Radiogenomics: what it is and why it is important. J Am Coll Radiol, 2015,12(8):862-866. [29] Lee Y, Lee HJ, Kim YT, et al. Imaging characteristics of stage I non-small cell lung cancer on CT and FDG-PET: relationship with epidermal growth factor receptor protein expression status and survival. Korean J Radiol, 2013,14(2):375-383. [30] Yamamoto S, Korn RL, Oklu R, et al. ALK molecular phenotype in non-small cell lung cancer: CT radiogenomic characterization. Radiology, 2014,272(2):568-576. [31] Rizzo S, Petrella F, Buscarino V, et al. CT radiogenomic characterization of EGFR, K-RAS, and ALK mutations in Non-Small cell lung cancer. Eur Radiol, 2016,26(1):32-42. [32] Tomlins SA, Rhodes DR, Perner S, et al. Recurrent fusion of TMPRSS2 and ETS transcription factor genes in prostate cancer. Science, 2005,310(5748):644-648. [33] 孙钢.放射组学的兴起及其在消化系统肿瘤中的应用.中华消化病与影像杂志(电子版),2017,7(4):145-149. [34] 李振辉,李鹍,张大福.放射组学在消化道肿瘤中的应用.放射学实践,2017,32(3):298-301. [35] Verma V, Simone CB 2nd, Krishnan S, et al. The rise of radiomics and implications for oncologic management. J Natl Cancer Inst, 2017,109(7). doi:10.1093/jnci/djx055. [36] 高微波,朱海涛,孙应实.乳腺癌影像基因组学研究现状与进展.中华放射学杂志,2017 51(12):990-992.