Page 308 - 《软件学报》2021年第12期
P. 308

3972                                Journal of Software  软件学报 Vol.32, No.12, December 2021

         致谢   在此,我们向对本文工作提供帮助和支持的同行以及对本文提出宝贵意见的各位审稿专家表示衷心的
         感谢.

         References:
          [1]    Bray F, Ferlay J, Soerjomataram I,  et al. Global cancer  statistics  2018: GLOBOCAN estimates  of  incidence and mortality
             worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, 2018,68(6):394−424. https://doi.org/10.3322/caac.
             21492
          [2]    National Cancer Institute. HPV and cancer. http://www.cancer.gov/about-cancer/causes-prevention/risk/infectious-agents/hpv-fact-
             sheet
          [3]    Moscicki AB,  Schiffman M, Kjaer S,  et al. Chapter 5: Updating  the  natural  history  of HPV and anogenital cancer. Vaccine,
             2006,24(Suppl.):S3,S42−S51.
          [4]    Cheng X, Cai S, Li Z, et al. The prognosis of women with stage IB1~IIB node-positive cervical carcinoma after radical surgery.
             Word Journal of Surgical Oncology, 2004,2(1):1−8.
          [5]    Edge  SB, Compton  CC. The American joint committee  on cancer: The  7th  edition  of  the AJCC cancer  staging manual and  the
             future of TNM. Annals of Surgical Oncology, 2010,17(6):1471−1474.
          [6]    Bhatla N, Berek  JS,  Fredes MC,  et al. Revised FIGO  staging for  carcinoma of the  cervix uteri. Int’l Journal  of Gynecology  &
             Obstetrics, 2019, 145(1):129−135.
          [7]    Coit DG, Thompson JA, Algazi A, et al. Melanoma, version 2.2016, NCCN clinical practice guidelines in oncology. Journal of the
             National Comprehensive Cancer Network, 2016,14(4):450−473.
          [8]    Gien LT, Covens A. Fertility-sparing options for early stage cervical cancer. Gynecologic Oncology, 2010,117(2):350−357.
          [9]    Bogaerts  J, Ford  R, Sargent  D,  et al.  Individual  patient  data analysis to assess modifications  to the RECIST criteria. European
             Journal of Cancer, 2009,45(2):248−260.
         [10]    Patankar SS,  Tergas  AI, Deutsch I,  et al.  High versus low-dose rate brachytherapy for  cervical  cancer.  Gynecologic  Oncology,
             2015,136(3):534−541.
         [11]    Li X, Duan JJ, Kong WM. Advances in research on chemosensitivity and cross-tolerance of cervical cancer. Journal of Chinese
             Oncology, 2019,25(1):67−70 (in Chinese with English abstract).
         [12]    Bhosale PR, Iyer RB, Ramalingam P, et al. Is MRI helpful in assessing the distance of the tumour from the internal os in patients
             with cervical cancer below FIGO Stage IB2? Clinical Radiology, 2016, 71(6):515−522.
         [13]    Devine CE, Viswanathan  C, Faria  SD,  et al. Imaging and  staging  of cervical cancer.  Seminars  in  Ultrasound, CT and MRI,
             2019,40(4):280−286.
         [14]    Breiman L. Random forests. Machine Learning, 2001,45(1):5−32.
         [15]    Kim JH, Kim  CK, Park  BK,  et al. Dynamic contrast-enhanced  3-T MR  imaging in cervical cancer  before and after concurrent
             chemoradiotherapy. European Radiology, 2012,22(11):2533−2539.
         [16]    Liu WF, Xu  F, Wu M. Predictive  value  of DCE-MRI  4D-tissue  technical  quantitative  parameters in evaluating the efficacy  of
             radiotherapy  and  chemotherapy for  cervical  cancer. Practical Oncology Journal, 2016,30(3):225−228 (in Chinese  with  English
             abstract).
         [17]    Dong LX, Li X, Yang S, et al. Model study of texture analysis based on DW-MRI to predict the chemoradiosensitivity in cervical
             cancer. Medical Recapitulate, 2018,24(11):2270−2274 (in Chinese with English abstract).
         [18]    Fu ZZ, Peng Y, Cao LY, et al. Value of apparent diffusion coefficient (ADC) in assessing radiotherapy and chemotherapy success
             in cervical cancer. Magnetic Resonance Imaging, 2015,33(5):516−524.
         [19]    Kuang F, Yan Z, Wang J, et al. The value of diffusion-weighted MRI to evaluate the response to radiochemotherapy for cervical
             cancer. Magnetic Resonance Imaging, 2014,32(4):342−349.
         [20]    Makino H, Kato H, Furui T, et al. Predictive value of diffusion-weighted magnetic resonance imaging during chemoradiotherapy
             for uterine cervical cancer. Journal of Obstetrics and Gynaecology Research, 2014,40(4):1098−1104.
         [21]    Ma WL, Huan Y, Wei WC, et al. The applied research of 3.0 T MRI and DWI in monitoring therapeutic effect of uterine cervix
             cancer with radiotherapy. Journal of Clinical Radiology, 2009,28(12):67−70 (in Chinese with English abstract).
   303   304   305   306   307   308   309   310   311   312   313