Lung Tumor Motion and its Impact on Deep Learning Prediction of Local Recurrence

被引:0
|
作者
Teo, P. T. [1 ]
Randall, J. W. [2 ]
Bajaj, A. [3 ]
Lou, B. [4 ]
Shah, J. [4 ]
Gopalakrishnan, M. [3 ]
Kamen, A. [5 ]
Das, I. J. [3 ]
Abazeed, M. [2 ]
机构
[1] Northwestern Mem Hosp, Dept Radiat Oncol, Chicago, IL 60611 USA
[2] Northwestern Univ, Dept Radiat Oncol, Chicago, IL 60611 USA
[3] Northwestern Univ, Dept Radiat Oncol, Feinberg Sch Med, Chicago, IL 60611 USA
[4] Siemens Healthineers, Malvern, PA USA
[5] Siemens Healthineers, Princeton, NJ USA
关键词
D O I
暂无
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
2277
引用
收藏
页码:E126 / E126
页数:1
相关论文
共 50 条
  • [41] Radiomics in Deep Learning - Feature Augmentation for Lung Cancer Prediction
    Masquelin, A. H.
    Whitney, D.
    Stevenson, C.
    Spira, A.
    Bates, J. H.
    Estepar, R. San Jose
    Kinsey, C.
    [J]. AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE, 2020, 201
  • [42] Performance evaluation of deep learning techniques for lung cancer prediction
    Deepapriya, B. S.
    Kumar, Parasuraman
    Nandakumar, G.
    Gnanavel, S.
    Padmanaban, R.
    Anbarasan, Anbarasa Kumar
    Meena, K.
    [J]. SOFT COMPUTING, 2023, 27 (13) : 9191 - 9198
  • [43] Deep learning-based solid component measuring enabled interpretable prediction of tumor invasiveness for lung adenocarcinoma
    Sun, Jiajing
    Zhang, Li
    Hu, Bingyu
    Du, Zhicheng
    Cho, William C.
    Witharana, Pasan
    Sun, Hua
    Ma, Dehua
    Ye, Minhua
    Chen, Jiajun
    Wang, Xiaozhuang
    Yang, Jiancheng
    Zhu, Chengchu
    Shen, Jianfei
    [J]. LUNG CANCER, 2023, 186
  • [44] Dual energy CT image prediction on primary tumor of lung cancer for nodal metastasis using deep learning
    Wang, You-Wei
    Chen, Chii-Jen
    Huang, Hsu-Cheng
    Wang, Teh-Chen
    Chen, Hsin-Ming
    Shih, Jin-Yuan
    Chen, Jin-Shing
    Huang, Yu-Sen
    Chang, Yeun-Chung
    Chang, Ruey-Feng
    [J]. COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, 2021, 91
  • [45] Integration of clinical features and deep learning on pathology for the prediction of breast cancer recurrence assays and risk of recurrence
    Frederick M. Howard
    James Dolezal
    Sara Kochanny
    Galina Khramtsova
    Jasmine Vickery
    Andrew Srisuwananukorn
    Anna Woodard
    Nan Chen
    Rita Nanda
    Charles M. Perou
    Olufunmilayo I. Olopade
    Dezheng Huo
    Alexander T. Pearson
    [J]. npj Breast Cancer, 9
  • [46] Integration of clinical features and deep learning on pathology for the prediction of breast cancer recurrence assays and risk of recurrence
    Howard, Frederick M.
    Dolezal, James
    Kochanny, Sara
    Khramtsova, Galina
    Vickery, Jasmine
    Srisuwananukorn, Andrew
    Woodard, Anna
    Chen, Nan
    Nanda, Rita
    Perou, Charles M.
    Olopade, Olufunmilayo I.
    Huo, Dezheng
    Pearson, Alexander T.
    [J]. NPJ BREAST CANCER, 2023, 9 (01)
  • [47] Prediction of distant melanoma recurrence from primary tumor digital H&E images using deep learning.
    Robinson, Eric
    Kulkarni, Prathamesh M.
    Pradhan, Jaya Sarin
    Gartrell, Robyn Denise
    Yang, Chen
    Rizk, Emanuelle M.
    Acs, Balazs
    Rohr, Bethany
    Phelps, Robert
    Ferringer, Tammie
    Horst, Basil
    Rimm, David L.
    Wang, Jing
    Saenger, Yvonne M.
    [J]. JOURNAL OF CLINICAL ONCOLOGY, 2019, 37 (15)
  • [48] A Manifold Learning based Video Prediction approach for Deep Motion Transfer
    Cai, Yuliang
    Mohan, Sumit
    Niranjan, Adithya
    Jain, Nilesh
    Cloninger, Alex
    Das, Srinjoy
    [J]. 2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW 2021), 2021, : 4214 - 4221
  • [49] Research on Prediction Method of Ship Rolling Motion Based on Deep Learning
    Wang, Yuchao
    Zhang, Mingyue
    Fu, Huixuan
    Wang, Qiusu
    [J]. PROCEEDINGS OF THE 39TH CHINESE CONTROL CONFERENCE, 2020, : 7182 - 7187
  • [50] Roll motion prediction using a hybrid deep learning and ARIMA model
    Suhermi, Novri
    Suhartono
    Prastyo, Dedy Dwi
    Ali, Baharuddin
    [J]. INNS CONFERENCE ON BIG DATA AND DEEP LEARNING, 2018, 144 : 251 - 258