Page 309 - 《软件学报》2021年第12期

邓成龙 等:基于随机森林的宫颈鳞癌放化疗疗效预测 3973

[22] Harry VN, Semple SI, Gilbert FJ, et al. Diffusion-weighted magnetic resonance imaging in the early detection of response to
chemoradiation in cervical cancer. Gynecologic Oncology, 2008,111(2):213−220.
[23] Kim HS, Kim CK, Park BK, et al. Evaluation of therapeutic response to concurrent chemoradiotherapy in patients with cervical
cancer using diffusion-weighted MR imaging. Journal of Magnetic Resonance Imaging, 2013,37(1):187−193.
[24] McVeigh PZ, Syed AM, Milosevic M, et al. Diffusion-weighted MRI in cervical cancer. European Radiology, 2008,18(5):
1058−1064.
[25] Zhang Y, Chen JY, Xie CM, et al. Diffusion-weighted magnetic resonance imaging for prediction of response of advanced cervical
cancer to chemoradiation. Journal of Computer Assisted Tomography, 2011,35(1):102−107.
[26] Levy A, Caramella C, Chargari C, et al. Accuracy of diffusion-weighted echo-planar MR imaging and ADC mapping in the
evaluation of residual cervical carcinoma after radiation therapy. Gynecologic Oncology, 2011,123(1):110−115.
[27] Somoye G, Harry V, Semple S, et al. Early diffusion weighted magnetic resonance imaging can predict survival in women with
locally advanced cancer of the cervix treated with combined chemo-radiation. European Radiology, 2012,22(11):2319−2327.
[28] Yoon JW, Lee YJ, Lee I, et al. 18F-FDG PET/CT in the assessment of metabolic response to chemoradiation therapy in locally
advanced cervical cancer. Gynecologic Oncology, 2016,141:198−199.
[29] Mayadev J, Enserro D, Brady W, et al. Positron emission tomography as an imaging biomarker after chemoradiation and
immunotherapy for cervical cancer patients treated on GOG 9929. Gynecologic Oncology, 2019,154(1):e8.
[30] Zhang XH. Research of tumor segmentation and curative effect prediction for cervical cancer based on MRI image [Master’s
Thesis]. Beijing: Institute of Software, Chinese Academy of Sciences, 2019 (in Chinese with English abstract).
[31] Wang H, Kang XD, Liu LL, et al. Progress of magnetic resonance image on denoising. Chinese Journal of Magnetic Resonance
Imaging, 2012,3(3):231−236 (in Chinese with English abstract).
[32] Gerig G, Kübler O, Kikinis R, et al. Nonlinear anisotropic filtering of MRI data. IEEE Trans. on Medical Imaging, 1992,11(2):
221−232.
[33] Gao JJ, Li CM, Feng CL, et al. Non-locally regularized segmentation of multiple sclerosis lesion from multi-channel MRI data.
Magnetic Resonance Imaging, 2014,32(8):1058−1066.
[34] Guo M, Ma YL, Zhu T. A method of medical ultrasonic image denoising and enhancement based on wavelet transform. Chinese
Journal of Medical Imaging Technology, 2006,22(9):1435−1437 (in Chinese with English abstract).
[35] Joshi RL, Fischer TR. Comparison of generalized Gaussian and Laplacian modeling in DCT image coding. IEEE Signal Processing
Letters, 1995,2(5):81−82.
[36] Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J,
Wells WM, et al, eds. Proc. of the 18th Medical Image Computing and Computer-Assisted Intervention (MICCAI 2015). 2015.
234−241.
[37] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. In: Proc. of the IEEE Conf. on Computer Vision and
Pattern Recognition. 2016. 770−778.
[38] National Cancer Institute. Cervical cancer (PDQ®): Treatment. Bethesda, MD. 2006. http://www.cancer.gov/cancertopics/pdq/
treatment/cervical/healthprofessional
[39] Yan B, Zhao TT, Liang XF, et al. DWI quantitative measurement in diagnosis of uterine cervix cancer. Chinese Journal of Medical
Imaging Technology, 2016,32(2):284−287 (in Chinese with English abstract).
[40] Szegedy C, Ioffe S, Vanhoucke V, et al. Inception-v4, inception-resnet and the impact of residual connections on learning. In: Proc.
of the 31st AAAI Conf. on Artificial Intelligence. 2017.
[41] Kamnitsas K, Ledig C, Newcombe VF, et al. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion
segmentation. Medical Image Analysis, 2017,36:61−78.

附中文参考文献:
[11] 李霞,段晶晶,孔为民.宫颈癌放化疗敏感性及交叉耐受性研究进展.肿瘤学杂志,2019,25(1):67−70.
[16] 刘伟锋,徐凡,吴梅.DCE-MRI 4D-Tissue 技术定量参数评估宫颈癌放化疗疗效的预测价值.实用肿瘤学杂志,2016,30(3):
225−228.