Handcrafted versus deep learning radiomics for prediction of cancer therapy response (vol 1, pg e106, 2019)

被引：0
|
作者：
Mak, R. H.
Aerts, H. J.
Hosny, A.
机构：
来源：
LANCET DIGITAL HEALTH | 2019年 / 1卷 / 04期
关键词：
D O I：
10.1016/S2589-7500(19)30078-0
中图分类号：
R-058 [];
学科分类号：
摘要：
引用
收藏
页码：E160 / E160
页数：1
相关论文
共 50 条
  • [21] Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics
    Jiang, Yuming
    Zhou, Kangneng
    Sun, Zepang
    Wang, Hongyu
    Xie, Jingjing
    Zhang, Taojun
    Sang, Shengtian
    Islam, Md Tauhidul
    Wang, Jen-Yeu
    Chen, Chuanli
    Yuan, Qingyu
    Xi, Sujuan
    Li, Tuanjie
    Xu, Yikai
    Xiong, Wenjun
    Wang, Wei
    Li, Guoxin
    Li, Ruijiang
    CELL REPORTS MEDICINE, 2023, 4 (08)
  • [22] Radiomics and deep learning methods for the prediction of 2-year overall survival in LUNG1 dataset(vol 18,: e0285188,2023)
    Braghetto, Anna
    Marturano, Francesca
    Paiusco, Marta
    Baiesi, Marco
    Bettinelli, Andrea
    SCIENTIFIC REPORTS, 2023, 13 (01)
  • [23] Combined deep learning and radiomics in pretreatment radiation esophagitis prediction for patients with esophageal cancer underwent volumetric modulated arc therapy
    Xie, Congying
    Yu, Xianwen
    Tan, Ninghang
    Zhang, Jicheng
    Su, Wanyu
    Ni, Weihua
    Li, Chenyu
    Zhao, Zeshuo
    Xiang, Ziqing
    Shao, Li
    Li, Heng
    Wu, Jianping
    Cao, Zhuo
    Jin, Juebin
    Jin, Xiance
    RADIOTHERAPY AND ONCOLOGY, 2024, 199
  • [24] Pretreatment ultrasound-based deep learning radiomics model for the early prediction of pathologic response to neoadjuvant chemotherapy in breast cancer
    Yu, Fei-Hong
    Miao, Shu-Mei
    Li, Cui-Ying
    Hang, Jing
    Deng, Jing
    Ye, Xin-Hua
    Liu, Yun
    EUROPEAN RADIOLOGY, 2023, 33 (08) : 5634 - 5644
  • [25] Pretreatment ultrasound-based deep learning radiomics model for the early prediction of pathologic response to neoadjuvant chemotherapy in breast cancer
    Fei-Hong Yu
    Shu-Mei Miao
    Cui-Ying Li
    Jing Hang
    Jing Deng
    Xin-Hua Ye
    Yun Liu
    European Radiology, 2023, 33 : 5634 - 5644
  • [26] A comparative study of online learning in response to the coronavirus disease 2019 pandemic versus conventional learning (Vol 42, pg 324, 2021)
    不详
    SAUDI MEDICAL JOURNAL, 2024, 45 (02) : 213 - 213
  • [27] Pathologic Complete Response Prediction after Neoadjuvant Chemoradiation Therapy for Rectal Cancer Using Radiomics and Deep Embedding Network of MRI
    Lee, Seunghyun
    Lim, Joonseok
    Shin, Jaeseung
    Kim, Sungwon
    Hwang, Heasoo
    APPLIED SCIENCES-BASEL, 2021, 11 (20):
  • [28] A Deep Learning Approach to Antibiotic Discovery (vol 180, pg 688.e1, 2020)
    Stokes, Jonathan M.
    Yang, Kevin
    Swanson, Kyle
    Jin, Wengong
    Cubillos-Ruiz, Andres
    Donghia, Nina M.
    MacNair, Craig R.
    French, Shawn
    Carfrae, Lindsey A.
    Bloom-Ackermann, Zohar
    Tran, Victoria M.
    Chiappino-Pepe, Anush
    Badran, Ahmed H.
    Andrews, Ian W.
    Chory, Emma J.
    Church, George M.
    Brown, Eric D.
    Jaakkola, Tommi S.
    Barzilay, Regina
    Collins, James J.
    CELL, 2020, 181 (02) : 475 - 483
  • [29] Ct-based subregional radiomics using hand-crafted and deep learning features for prediction of therapeutic response to anti-PD1 therapy in NSCLC
    Hu, Yue
    Jiang, Tao
    Wang, Huan
    Song, Jiangdian
    Yang, Zhiguang
    Wang, Yan
    Su, Juan
    Jin, Meiqi
    Chang, Shijie
    Deng, Kexue
    Jiang, Wenyan
    PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS, 2024, 117
  • [30] Comparison of radiomics-based machine-learning classifiers for the pretreatment prediction of pathologic complete response to neoadjuvant therapy in breast cancer
    Li, Xue
    Li, Chunmei
    Wang, Hong
    Jiang, Lei
    Chen, Min
    PEERJ, 2024, 12
12345