These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

279 related articles for article (PubMed ID: 33643902)

  • 1. Deep CNN Model Using CT Radiomics Feature Mapping Recognizes EGFR Gene Mutation Status of Lung Adenocarcinoma.
    Zhang B; Qi S; Pan X; Li C; Yao Y; Qian W; Guan Y
    Front Oncol; 2020; 10():598721. PubMed ID: 33643902
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Computed Tomography-Based Radiomics Signature: A Potential Indicator of Epidermal Growth Factor Receptor Mutation in Pulmonary Adenocarcinoma Appearing as a Subsolid Nodule.
    Yang X; Dong X; Wang J; Li W; Gu Z; Gao D; Zhong N; Guan Y
    Oncologist; 2019 Nov; 24(11):e1156-e1164. PubMed ID: 30936378
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Prediction of epidermal growth factor receptor (
    Zhang G; Shang L; Cao Y; Zhang J; Li S; Qian R; Liu H; Zhang Z; Pu H; Man Q; Kong W
    Quant Imaging Med Surg; 2024 Aug; 14(8):6048-6059. PubMed ID: 39144003
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Predicting EGFR mutation status in lung adenocarcinoma presenting as ground-glass opacity: utilizing radiomics model in clinical translation.
    Cheng B; Deng H; Zhao Y; Xiong J; Liang P; Li C; Liang H; Shi J; Li J; Xiong S; Lai T; Chen Z; Wu J; Qian T; Huan W; Ng MTA; He J; Liang W
    Eur Radiol; 2022 Sep; 32(9):5869-5879. PubMed ID: 35348863
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Deep learning predicts epidermal growth factor receptor mutation subtypes in lung adenocarcinoma.
    Song J; Ding C; Huang Q; Luo T; Xu X; Chen Z; Li S
    Med Phys; 2021 Dec; 48(12):7891-7899. PubMed ID: 34669994
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Transfer learning-based PET/CT three-dimensional convolutional neural network fusion of image and clinical information for prediction of EGFR mutation in lung adenocarcinoma.
    Shao X; Ge X; Gao J; Niu R; Shi Y; Shao X; Jiang Z; Li R; Wang Y
    BMC Med Imaging; 2024 Mar; 24(1):54. PubMed ID: 38438844
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Optimal
    Zuo Y; Liu Q; Li N; Li P; Zhang J; Song S
    Front Oncol; 2023; 13():1173355. PubMed ID: 37223682
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Hybrid deep multi-task learning radiomics approach for predicting EGFR mutation status of non-small cell lung cancer in CT images.
    Gong J; Fu F; Ma X; Wang T; Ma X; You C; Zhang Y; Peng W; Chen H; Gu Y
    Phys Med Biol; 2023 Dec; 68(24):. PubMed ID: 37972417
    [No Abstract]   [Full Text] [Related]  

  • 9. Identifying EGFR mutations in lung adenocarcinoma by noninvasive imaging using radiomics features and random forest modeling.
    Jia TY; Xiong JF; Li XY; Yu W; Xu ZY; Cai XW; Ma JC; Ren YC; Larsson R; Zhang J; Zhao J; Fu XL
    Eur Radiol; 2019 Sep; 29(9):4742-4750. PubMed ID: 30778717
    [TBL] [Abstract][Full Text] [Related]  

  • 10. PET/CT Based EGFR Mutation Status Classification of NSCLC Using Deep Learning Features and Radiomics Features.
    Huang W; Wang J; Wang H; Zhang Y; Zhao F; Li K; Su L; Kang F; Cao X
    Front Pharmacol; 2022; 13():898529. PubMed ID: 35571081
    [No Abstract]   [Full Text] [Related]  

  • 11. Application of CT radiomics features to predict the EGFR mutation status and therapeutic sensitivity to TKIs of advanced lung adenocarcinoma.
    Yang C; Chen W; Gong G; Li Z; Qiu Q; Yin Y
    Transl Cancer Res; 2020 Nov; 9(11):6683-6690. PubMed ID: 35117278
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Radiomics signature: A potential and incremental predictor for EGFR mutation status in NSCLC patients, comparison with CT morphology.
    Tu W; Sun G; Fan L; Wang Y; Xia Y; Guan Y; Li Q; Zhang D; Liu S; Li Z
    Lung Cancer; 2019 Jun; 132():28-35. PubMed ID: 31097090
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Deep learning-radiomics integrated noninvasive detection of epidermal growth factor receptor mutations in non-small cell lung cancer patients.
    Kim S; Lim JH; Kim CH; Roh J; You S; Choi JS; Lim JH; Kim L; Chang JW; Park D; Lee MW; Kim S; Heo J
    Sci Rep; 2024 Jan; 14(1):922. PubMed ID: 38195717
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Toward automatic prediction of EGFR mutation status in pulmonary adenocarcinoma with 3D deep learning.
    Zhao W; Yang J; Ni B; Bi D; Sun Y; Xu M; Zhu X; Li C; Jin L; Gao P; Wang P; Hua Y; Li M
    Cancer Med; 2019 Jul; 8(7):3532-3543. PubMed ID: 31074592
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Convolutional Neural Network Addresses the Confounding Impact of CT Reconstruction Kernels on Radiomics Studies.
    Yoon JH; Sun SH; Xiao M; Yang H; Lu L; Li Y; Schwartz LH; Zhao B
    Tomography; 2021 Dec; 7(4):877-892. PubMed ID: 34941646
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Machine Learning-Based Radiomics for Prediction of Epidermal Growth Factor Receptor Mutations in Lung Adenocarcinoma.
    Lu J; Ji X; Wang L; Jiang Y; Liu X; Ma Z; Ning Y; Dong J; Peng H; Sun F; Guo Z; Ji Y; Xing J; Lu Y; Lu D
    Dis Markers; 2022; 2022():2056837. PubMed ID: 35578691
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Using combined CT-clinical radiomics models to identify epidermal growth factor receptor mutation subtypes in lung adenocarcinoma.
    Huo JW; Luo TY; Diao L; Lv FJ; Chen WD; Yu RZ; Li Q
    Front Oncol; 2022; 12():846589. PubMed ID: 36059655
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Computed tomography-based radiomics quantification predicts epidermal growth factor receptor mutation status and efficacy of first-line targeted therapy in lung adenocarcinoma.
    Jiang M; Yang P; Li J; Peng W; Pu X; Chen B; Li J; Wang J; Wu L
    Front Oncol; 2022; 12():985284. PubMed ID: 36052262
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Development of a Nomogram Based on 3D CT Radiomics Signature to Predict the Mutation Status of EGFR Molecular Subtypes in Lung Adenocarcinoma: A Multicenter Study.
    Zhang G; Deng L; Zhang J; Cao Y; Li S; Ren J; Qian R; Peng S; Zhang X; Zhou J; Zhang Z; Kong W; Pu H
    Front Oncol; 2022; 12():889293. PubMed ID: 35574401
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Deep learning analysis to predict
    Yoon HJ; Choi J; Kim E; Um SW; Kang N; Kim W; Kim G; Park H; Lee HY
    Front Oncol; 2022; 12():951575. PubMed ID: 36119545
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 14.