Prediction of Relapse-free Survival of NSCLC Patients Through Multimodal Data Fusion Using Deep Learning Model

被引:0
|
作者
Kim, H. R. [1 ]
Beck, K. [2 ]
Kang, J. H. [2 ]
Hong, H. [1 ]
机构
[1] Seoul Womens Univ, Seoul, South Korea
[2] Catholic Univ Korea, Seoul St Marys Hosp, Coll Med, Seoul, South Korea
关键词
Multimodal fusion; Deep-learning; relapse-free survival;
D O I
暂无
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
P4.07G.02
引用
收藏
页码:S387 / S387
页数:1
相关论文
共 50 条
  • [1] TransCDR: a deep learning model for enhancing the generalizability of drug activity prediction through transfer learning and multimodal data fusion
    Xia, Xiaoqiong
    Zhu, Chaoyu
    Zhong, Fan
    Liu, Lei
    BMC BIOLOGY, 2024, 22 (01)
  • [2] Establishment of a predictive model for GVHD-free, relapse-free survival after allogeneic HSCT using ensemble learning
    Iwasaki, Makoto
    Kanda, Junya
    Arai, Yasuyuki
    Kondo, Tadakazu
    Ishikawa, Takayuki
    Ueda, Yasunori
    Imada, Kazunori
    Akasaka, Takashi
    Yonezawa, Akihito
    Yago, Kazuhiro
    Nohgawa, Masaharu
    Anzai, Naoyuki
    Moriguchi, Toshinori
    Kitano, Toshiyuki
    Itoh, Mitsuru
    Arima, Nobuyoshi
    Takeoka, Tomoharu
    Watanabe, Mitsumasa
    Hirata, Hirokazu
    Asagoe, Kosuke
    Miyatsuka, Isao
    An, Le My
    Miyanishi, Masanori
    Takaori-Kondo, Akifumi
    BLOOD ADVANCES, 2022, 6 (08) : 2618 - 2627
  • [3] Soybean yield prediction from UAV using multimodal data fusion and deep learning
    Maimaitijiang, Maitiniyazi
    Sagan, Vasit
    Sidike, Paheding
    Hartling, Sean
    Esposito, Flavin
    Fritschi, Felix B.
    REMOTE SENSING OF ENVIRONMENT, 2020, 237
  • [4] Developing and validating a survival prediction model for NSCLC patients using distributed learning
    Jochems, A.
    Deist, T.
    El-Naqa, I.
    Kessler, M.
    Mayo, C.
    Reeves, J.
    Jolly, S.
    Matuszak, M.
    Ten Haken, R.
    Van Soes, J.
    Oberije, C.
    Faivre-Finn, C.
    Price, G.
    Lambin, P.
    Dekker, A.
    RADIOTHERAPY AND ONCOLOGY, 2017, 123 : S860 - S861
  • [5] Multi-gene prediction of distant relapse-free survival in early NSCLC: microarray expression-profiling study
    Jarzab, M.
    Jassem, E.
    Szymanowska, A.
    Rzyman, W.
    Oczko-Wojciechowska, M.
    Fujarewicz, K.
    Chmielik, E.
    Niklinska, W.
    Kozlowski, M.
    Jassem, J.
    EJC SUPPLEMENTS, 2007, 5 (04): : 363 - 363
  • [6] A competing risks cure frailty model: An application to relapse-free survival of breast cancer patients
    Ghavami, Vahid
    Mahmoudi, Mahmood
    Foroushani, Abbas Rahimi
    Baghishani, Hossein
    Yaseri, Mehdi
    Shandiz, Fatemeh Homaei
    PAKISTAN JOURNAL OF STATISTICS AND OPERATION RESEARCH, 2021, 17 (03) : 591 - 605
  • [7] Multimodal data integration using deep learning predicts overall survival of patients with glioma
    Yuan, Yifan
    Zhang, Xuan
    Wang, Yining
    Li, Hongyan
    Qi, Zengxin
    Du, Zunguo
    Chu, Ying-Hua
    Feng, Danyang
    Hu, Jie
    Xie, Qingguo
    Song, Jianping
    Liu, Yuqing
    Cai, Jiajun
    VIEW, 2024, 5 (05)
  • [8] Application of deep learning on whole-slide images to predict relapse-free survival of hepatocellular carcinoma patients following liver transplant
    Roberts, D.
    Schmauch, B.
    Moro, A.
    Sasaki, K.
    Sin-Chan, P.
    Aucejo, F.
    ANNALS OF ONCOLOGY, 2021, 32 : S826 - S827
  • [9] Fitting data of relapse-free survival after post-prostatectomy RT with a comprehensive TCP model
    Fiorino, C.
    Broggi, S.
    Fossati, N.
    Cozzarini, C.
    Goldner, G.
    Wiegel, T.
    Hinkelbein, W.
    Karnes, J. R.
    Boorjian, S. A.
    Haustermans, K.
    Joniau, S.
    Shariat, S.
    Montorsi, F.
    Van Poppel, H.
    Di Muzio, N. G.
    Calandrino, R.
    Briganti, A.
    RADIOTHERAPY AND ONCOLOGY, 2016, 119 : S416 - S416
  • [10] A Model of Normality Inspired Deep Learning Framework for Depression Relapse Prediction Using Audiovisual Data
    Othmani, Alice
    Zeghina, Assaad-Oussama
    Muzammel, Muhammad
    COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2022, 226