孙晓梦, 万遂人. 基于机器学习方法的前列腺癌DWI多参数分析及其应用[J]. 中国生物医学工程学报, 2019, 38(4): 508-512.
Sun Xiaomeng, Wan Suiren. Multi-Parameter Analysis and Application of Diffusion Weighted Imaging in Prostate Cancer Based on Machine Learning. Chinese Journal of Biomedical Engineering, 2019, 38(4): 508-512.
[1] Torre LA, Bray F, Siegel RL, et al. Global cancer statistics, 2012[J]. CA Cancer J Clin, 2015, 65 (2): 87-108. [2] Chen LH, Liu AL, Song QW, et al. Diffusion kurtosis imaging in differential diagnosis of prostate cancer and benign prostatic hyperplasia [J]. Chin J Med Imaging Technol, 2016, 32(7): 1097-1101. [3] Weinreb JC, Barentsz JO, Choyke PL, et al. PI-RADS prostate imaging - Reporting and data system: 2015, Version 2[J]. European Urology, 2016,69 (1): 16-40. [4] Taylor DG, Bushell MC. The spatial mapping of translational diffusion coefficients by the NMR imaging technique[J]. Phys Med Biol, 1985,30 (4): 345-349. [5] 刘书林, 方向军, 刘芬. 前列腺癌多参数MRI应用现状及进展[J]. 磁共振成像, 2017, 8(5):394-400. [6] Jensen JH, Helpern JA, Ramani A, et al. Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging [J]. Magn Reson Med, 2005, 53 (6): 1432-1440. [7] Jensen JH, Helpern JA. MRI quantification of non-Gaussian water diffusion by kurtosis analysis[J]. NMR Biomed, 2010,23 (7): 698-710. [8] Liu X, Zhou L, Peng W, et al. Comparison of stretched-Exponential and monoexponential model diffusion-Weighted imaging in prostate cancer and normal tissues[J]. Journal of Magnetic Resonance Imaging, 2015, 42(4):1078-1085. [9] Bihan DL, Mangin JF, Poupon C, et al. Diffusion tensor imaging: Concepts and applications[J]. JMRI, 2001, 13(4):534-546. [10] Hagmann P, Jonasson L, Maeder P, et al. Understanding diffusion MR imaging techniques: From scalar diffusion-weighted imaging to diffusion tensor imaging and beyond1[J]. Radiographics, 2006, 26(Suppl 1):S205- S223. [11] Veraart J, Poot DHJ, Hecke WV, et al. More accurate estimation of diffusion tensor parameters using diffusion kurtosis imaging[J]. Magnetic Resonance in Medicine, 2011, 65(1):138-145. [12] Dow M, Koh,David J, Collins MRO. Intravoxel incoherent motion in body diffusion-weighted MRI: reality and challenges[J]. AJR, 2011, 196(6):1351-61. [13] Cortes C, Vapnik V. Support-vector networks[J]. Machine Learning, 1995,20, 273-297. [14] 屠睿博, 陈中华, 王洪凯. 基于随机森林算法的小鼠Micro-CT 影像中骨骼关节特征点定位[J]. 中国生物医学工程学报, 2017, 36(3):257-266. [15] Li P, Hu X, Liang Q, et al. Concept drifting detection on noisy streaming data in random ensemble decision trees[M]// Machine Learning and Data Mining in Pattern Recognition. Beilin: Springer Berlin Heidelberg, 2009. [16] Xiao LH, Chen PR, Gou ZP, et al. Prostate cancer prediction using the random forest algorithm that takes into account transrectal ultrasound findings, age, and serum levels of prostate-specific antigen[J]. Asian Journal of Andrology, 2017, 19(5):586-590. [17] Garyfallidis E, Brett M, Amirbekian B, et al. DIPY, a library for the analysis of diffusion MRI data[J]. Frontiers in Neuroinformatics, 2014,8:8. [18] Hanley JA. The meaning and use of the areas under a receiver operating characteristic (ROC) curve[J]. Radiology, 1982, 143:26-29. [19] Quentin M, Pentanq G, Schimmoller L, et al. Feasibility of diffusional kurtosis tensor imaging in prostate MRI for the assessment of prostate cancer: preliminary results [J]. Magn Reson Imaging, 2014, 32(7): 880-885. [20] Suo S, Chen X, Wu L, et al. Non-Gaussian water diffusion kurtosis imaging of prostate cancer [J]. Magnetic Resonance Imaging, 2014, 32(5): 421-427. [21] Vargas HA, Lawrence EM, Mazaheri Y, et al. Updates in advanced diffusion-weighted magnetic resonance imaging techniques in the evaluation of prostate cancer [J]. World J Radiol, 2015, 7(8): 184-188. [22] Japkowicz N, Stephen S. The class imbalance problem: A systematic study[M]. Amstenlam: IOS Press, 2002.
