Re: Quantitation of hypoechoic lesions for the prediction and Gleason grading of prostate cancer: a prospective study

被引:0
|
作者
Joseph M. Norris
Mark Emberton
Clement Orczyk
机构
[1] University College London,UCL Division of Surgery and Interventional Science
[2] University College London Hospitals NHS Foundation Trust,Department of Urology
[3] London Deanery of Urology,undefined
来源
World Journal of Urology | 2020年 / 38卷
关键词
Multiparametric MRI; Prostate cancer; Targeted biopsy;
D O I
暂无
中图分类号
学科分类号
摘要
引用
收藏
页码:803 / 804
页数:1
相关论文
共 50 条
  • [41] A prospective study of a urine and plasma biomarker test for the prediction of gleason ≥3+4 prostate cancer in a mixed cohort
    Poulsen, Mads Hvid
    Feddersen, Soren
    Albitar, Maher
    Poulsen, Charlotte Aaberg
    Lund, Martin
    Pedersen, Torben Brochner
    Mortensen, Mike Allan
    Lund, Lars
    SCANDINAVIAN JOURNAL OF UROLOGY, 2020, 54 (04) : 323 - 327
  • [42] MRI grading for the prediction of prostate cancer aggressiveness
    Boschheidgen, M.
    Schimmoeller, L.
    Arsov, C.
    Ziayee, F.
    Morawitz, J.
    Valentin, B.
    Radke, K. L.
    Giessing, M.
    Esposito, I
    Albers, P.
    Antoch, G.
    Ullrich, T.
    EUROPEAN RADIOLOGY, 2022, 32 (04) : 2351 - 2359
  • [43] MRI grading for the prediction of prostate cancer aggressiveness
    M. Boschheidgen
    L. Schimmöller
    C. Arsov
    F. Ziayee
    J. Morawitz
    B. Valentin
    K. L. Radke
    M. Giessing
    I. Esposito
    P. Albers
    G. Antoch
    T. Ullrich
    European Radiology, 2022, 32 : 2351 - 2359
  • [44] Improvement of Gleason Grading prediction in Prostate Cancer Stratification for Radical Prostatectomy: a Machine Learning-based Theronostic Multi-omics Study
    Ning, J.
    Spielvogel, C. P.
    Haberl, D.
    Trachtova, K.
    Stoiber, S.
    Rasul, S.
    Bystry, V.
    Gurnhofer, E.
    Timelthaler, G.
    Papp, L.
    Schlederer, M.
    Hacker, M.
    Haug, A.
    Kenner, L.
    EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, 2023, 50 (SUPPL 1) : S544 - S544
  • [45] Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study
    Bulten, Wouter
    Pinckaers, Hans
    van Boven, Hester
    Vink, Robert
    de Bel, Thomas
    van Ginneken, Bram
    van der Laak, Jeroen
    Hulsbergen-van de Kaa, Christina
    Litjens, Geert
    LANCET ONCOLOGY, 2020, 21 (02): : 233 - 241
  • [46] Development of a Deep Learning Algorithm for the Histopathologic Diagnosis and Gleason Grading of Prostate Cancer Biopsies: A Pilot Study
    Kott, Ohad
    Linsley, Drew
    Amin, Ali
    Karagounis, Andreas
    Jeffers, Carleen
    Golijanin, Dragan
    Serre, Thomas
    Gershman, Boris
    EUROPEAN UROLOGY FOCUS, 2021, 7 (02): : 347 - 351
  • [47] A study of the new five tiered prostate cancer Gleason grading system in a nationwide population cohort in Sweden
    Zhang, Jing
    Wu, Chin-Lee
    TRANSLATIONAL CANCER RESEARCH, 2016, 5 (03) : 233 - 233
  • [48] Re: Consensus Guidelines for Reporting Prostate Cancer Gleason Grade
    Egevad, Lars
    Samaratunga, Hemamali
    Srigley, John R.
    Delahunt, Brett
    JOURNAL OF UROLOGY, 2016, 196 (04): : 1321 - 1322
  • [49] Faster and Better: Artificial Intelligence Assisted Gleason Group Grading in Prostate Cancer
    Juhila, Juuso
    Kovala, Marja
    Noora, Neittaanmaki
    Puttonen, Henri
    Manninen, Anniina
    Wester, Anniina
    Blom, Sami
    Karjalainen, Marika
    LABORATORY INVESTIGATION, 2023, 103 (03) : S1295 - S1296
  • [50] Reproducibility of Gleason grading of prostate cancer can be improved by the use of reference images
    Egevad, L
    UROLOGY, 2001, 57 (02) : 291 - 295