Prediction of Post-Radiotherapy Residual Disease From Pre-Treatment Imaging Using Deep Learning

被引：0
|
作者：
Huff, D. [1 ]
Bradshaw, T. [1 ]
Jeraj, R. [1 ,2 ]
机构：
[1] Univ Wisconsin Madison, Madison, WI USA
[2] Univ Ljubljana, Ljubljana, Slovenia
来源：
MEDICAL PHYSICS | 2018年 / 45卷 / 06期
关键词：
D O I：
暂无
中图分类号：
R8 [特种医学]; R445 [影像诊断学];
学科分类号：
1002 ; 100207 ; 1009 ;
摘要：
SU-H300-Ge
引用
收藏
页码：E162 / E163
页数：2
相关论文
共 50 条
  • [41] Comprehensive characterization of matched pre-treatment biopsies and residual disease of chemotherapy treated breast cancer
    Hoogstraat, M.
    Lips, E.
    Mulder, L.
    Nederlof, P.
    Sonke, G.
    Rodenhuis, S.
    Wesseling, J.
    Wessels, L. F. A.
    EUROPEAN JOURNAL OF CANCER, 2016, 61 : S30 - S30
  • [42] Pre-treatment Magnetic Resonance Imaging Features of Prostate Cancer as a Predictor of Response to External Beam Radiotherapy
    McKenna, A. D.
    Westphalen, A. C.
    Qayyum, A.
    Roach, M.
    Kurhanewicz, J.
    Coakley, F. V.
    AMERICAN JOURNAL OF ROENTGENOLOGY, 2007, 188 (05)
  • [43] Radiobiological Modeling of Tumor Response During Radiotherapy Based On Pre-Treatment Dynamic PET Imaging Data
    Crispin-Ortuzar, M.
    Grkovski, M.
    Beattie, B.
    Lee, N.
    Riaz, N.
    Humm, J.
    Jeong, J.
    Fontanella, A.
    Deasy, J.
    MEDICAL PHYSICS, 2016, 43 (06) : 3797 - 3797
  • [44] Detecting Errors in Radiotherapy Treatment Plans Using Deep Learning
    Ma, L.
    Nguyen, D.
    Yan, Y.
    Pompos, A.
    Tan, J.
    Lu, W.
    Hannan, R.
    Jiang, S.
    MEDICAL PHYSICS, 2018, 45 (06) : E273 - E273
  • [45] Predicting the Risk of Local Failure after Treatment for Head and Neck Squamous Cell Carcinoma Using Clinical and Post-Radiotherapy PET/CT Imaging Features
    Hughes, R. T.
    Restler, E. A.
    Smith, S.
    Shenker, R. F.
    Lanier, C. M.
    Schroeder, J. A.
    INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS, 2024, 120 (02): : E759 - E759
  • [46] A COMPARISON OF 2 APPROACHES TO THE TREATMENT OF NOCTURNAL ENURESIS AND THE PREDICTION OF EFFECTIVENESS USING PRE-TREATMENT VARIABLES
    BUTLER, RJ
    BREWIN, CR
    FORSYTHE, WI
    JOURNAL OF CHILD PSYCHOLOGY AND PSYCHIATRY AND ALLIED DISCIPLINES, 1988, 29 (04): : 501 - 509
  • [47] Prediction of Benefit from EGFR TKIs by Proteomic Analysis of Pre-treatment Serum
    Carbone, David P.
    JOURNAL OF THORACIC ONCOLOGY, 2009, 4 (09) : S121 - S122
  • [48] Prediction Alzheimer's disease from MRI images using deep learning
    Mggdadi, Esraa
    Al-Aiad, Ahmad
    Al-Ayyad, Muhammad Saleh
    Darabseh, Alaa
    2021 12TH INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION SYSTEMS (ICICS), 2021, : 120 - 125
  • [49] Plant Disease Prediction using Deep Learning and IoT
    Gupta, Akash Kumar
    Gupta, Kishan
    Jadhav, Jayant
    Deolekar, Rugved V.
    Nerurkar, Amit
    Deshpande, Sachin
    PROCEEDINGS OF THE 2019 6TH INTERNATIONAL CONFERENCE ON COMPUTING FOR SUSTAINABLE GLOBAL DEVELOPMENT (INDIACOM), 2019, : 902 - 907
  • [50] Pre-treatment apparent diffusion coefficient is imaging biomarker for prediction of response to chemoembolization in hepatocellular carcinoma
    Yuan, Zheng
    Li, Wen-Tao
    Peng, Wei-Jun
    EUROPEAN JOURNAL OF RADIOLOGY, 2013, 82 (12) : E901 - E902
12345