Automated extraction of information of lung cancer staging from unstructured reports of PET-CT interpretation: natural language processing with deep-learning

被引:0
作者
Hyung Jun Park
Namu Park
Jang Ho Lee
Myeong Geun Choi
Jin-Sook Ryu
Min Song
Chang-Min Choi
机构
[1] University of Ulsan College of Medicine,Department of Pulmonary and Critical Care Medicine, Asan Medical Center
[2] University of Washington,Department of Biomedical Informatics and Medical Education, School of Medicine
[3] Mokdong Hospital,Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, College of Medicine
[4] Ewha Womans University,Department of Nuclear Medicine, Asan Medical Center
[5] University of Ulsan College of Medicine,Department of Digital Analytics
[6] Yonsei University,Department of Oncology, Asan Medical Center
[7] University of Ulsan College of Medicine,Department of Information Medicine, Asan Medical Center
[8] University of Ulsan College of Medicine,undefined
来源
BMC Medical Informatics and Decision Making | / 22卷
关键词
Natural language processing; Auto-annotation; Deep learning; Lung cancer; Pseudo-labelling;
D O I
暂无
中图分类号
学科分类号
摘要
引用
收藏
相关论文
共 41 条
  • [31] Evaluating the accuracy of lung-RADS score extraction from radiology reports: Manual entry versus natural language processing
    Gandomi, Amir
    Hasan, Eusha
    Chusid, Jesse
    Paul, Subroto
    Inra, Matthew
    Makhnevich, Alex
    Raoof, Suhail
    Silvestri, Gerard
    Bade, Brett C.
    Cohen, Stuart L.
    INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS, 2024, 191
  • [32] An [18F]FDG-PET/CT deep learning method for fully automated detection of pathological mediastinal lymph nodes in lung cancer patients
    David Wallis
    Michaël Soussan
    Maxime Lacroix
    Pia Akl
    Clément Duboucher
    Irène Buvat
    European Journal of Nuclear Medicine and Molecular Imaging, 2022, 49 : 881 - 888
  • [33] An [18F]FDG-PET/CT deep learning method for fully automated detection of pathological mediastinal lymph nodes in lung cancer patients
    Wallis, David
    Soussan, Michael
    Lacroix, Maxime
    Akl, Pia
    Duboucher, Clement
    Buvat, Irene
    EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, 2022, 49 (03) : 881 - 888
  • [34] A novel staging system derived from natural language processing of pathology reports to predict prognostic outcomes of pancreatic cancer: a retrospective cohort study
    Li, Bo
    Wang, Beilei
    Zhuang, Pengjie
    Cao, Hongwei
    Wu, Shengyong
    Tan, Zhendong
    Gao, Suizhi
    Li, Penghao
    Jing, Wei
    Shao, Zhuo
    Zheng, Kailian
    Wu, Lele
    Gao, Bai
    Wang, Yang
    Jiang, Hui
    Guo, Shiwei
    He, Liang
    Yang, Yan
    Jin, Gang
    INTERNATIONAL JOURNAL OF SURGERY, 2023, 109 (11) : 3476 - 3489
  • [35] Automated classification of cancer morphology from Italian pathology reports using Natural Language Processing techniques: A rule-based approach
    Lindaa, Hammami
    Alessia, Paglialonga
    Giancarlo, Pruneri
    Michele, Torresani
    Milenaa, Sant
    Carlo, Bono
    Gianluca, Caiani Enrico
    Paolo, Baili
    JOURNAL OF BIOMEDICAL INFORMATICS, 2021, 116
  • [36] Deep-Learning-Based Natural Language Processing of Serial Free-Text Radiological Reports for Predicting Rectal Cancer Patient Survival
    Kim, Sunkyu
    Lee, Choong-kun
    Choi, Yonghwa
    Baek, Eun Sil
    Choi, Jeong Eun
    Lim, Joon Seok
    Kang, Jaewoo
    Shin, Sang Joon
    FRONTIERS IN ONCOLOGY, 2021, 11
  • [37] Deep Learning-Based Feature Extraction from Whole-Body PET/CT Employing Maximum Intensity Projection Images: Preliminary Results of Lung Cancer Data
    Joonhyung Gil
    Hongyoon Choi
    Jin Chul Paeng
    Gi Jeong Cheon
    Keon Wook Kang
    Nuclear Medicine and Molecular Imaging, 2023, 57 : 216 - 222
  • [38] Deep Learning-Based Feature Extraction from Whole-Body PET/CT Employing Maximum Intensity Projection Images: Preliminary Results of Lung Cancer Data
    Gil, Joonhyung
    Choi, Hongyoon
    Paeng, Jin Chul
    Cheon, Gi Jeong
    Kang, Keon Wook
    NUCLEAR MEDICINE AND MOLECULAR IMAGING, 2023, 57 (05) : 216 - 222
  • [39] Development and Validation of a Modified Three-Dimensional U-Net Deep-Learning Model for Automated Detection of Lung Nodules on Chest CT Images From the Lung Image Database Consortium and Japanese Datasets
    Suzuki, Kazuhiro
    Otsuka, Yujiro
    Nomura, Yukihiro
    Kumamaru, Kanako K.
    Kuwatsuru, Ryohei
    Aoki, Shigeki
    ACADEMIC RADIOLOGY, 2022, 29 : S11 - S17
  • [40] Segmentation-Free Outcome Prediction from Head and Neck Cancer PET/CT Images: Deep Learning-Based Feature Extraction from Multi-Angle Maximum Intensity Projections (MA-MIPs)
    Toosi, Amirhosein
    Shiri, Isaac
    Zaidi, Habib
    Rahmim, Arman
    CANCERS, 2024, 16 (14)