BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

387 related articles for article (PubMed ID: 32604042)

  • 1. Deep learning combined with radiomics may optimize the prediction in differentiating high-grade lung adenocarcinomas in ground glass opacity lesions on CT scans.
    Wang X; Zhang L; Yang X; Tang L; Zhao J; Chen G; Li X; Yan S; Li S; Yang Y; Kang Y; Li Q; Wu N
    Eur J Radiol; 2020 Aug; 129():109150. PubMed ID: 32604042
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Prediction of solid and micropapillary components in lung invasive adenocarcinoma: radiomics analysis from high-spatial-resolution CT data with 1024 matrix.
    Ninomiya K; Yanagawa M; Tsubamoto M; Sato Y; Suzuki Y; Hata A; Kikuchi N; Yoshida Y; Yamagata K; Doi S; Ogawa R; Tokuda Y; Kido S; Tomiyama N
    Jpn J Radiol; 2024 Jun; 42(6):590-598. PubMed ID: 38413550
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Radiomics signature: a biomarker for the preoperative discrimination of lung invasive adenocarcinoma manifesting as a ground-glass nodule.
    Fan L; Fang M; Li Z; Tu W; Wang S; Chen W; Tian J; Dong D; Liu S
    Eur Radiol; 2019 Feb; 29(2):889-897. PubMed ID: 29967956
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Computer-aided diagnosis of ground glass pulmonary nodule by fusing deep learning and radiomics features.
    Hu X; Gong J; Zhou W; Li H; Wang S; Wei M; Peng W; Gu Y
    Phys Med Biol; 2021 Mar; 66(6):065015. PubMed ID: 33596552
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Imaging Phenotyping Using Radiomics to Predict Micropapillary Pattern within Lung Adenocarcinoma.
    Song SH; Park H; Lee G; Lee HY; Sohn I; Kim HS; Lee SH; Jeong JY; Kim J; Lee KS; Shim YM
    J Thorac Oncol; 2017 Apr; 12(4):624-632. PubMed ID: 27923715
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Machine learning-based radiomics strategy for prediction of cell proliferation in non-small cell lung cancer.
    Gu Q; Feng Z; Liang Q; Li M; Deng J; Ma M; Wang W; Liu J; Liu P; Rong P
    Eur J Radiol; 2019 Sep; 118():32-37. PubMed ID: 31439255
    [TBL] [Abstract][Full Text] [Related]  

  • 7. CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma.
    Jiang C; Luo Y; Yuan J; You S; Chen Z; Wu M; Wang G; Gong J
    Eur Radiol; 2020 Jul; 30(7):4050-4057. PubMed ID: 32112116
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Marginal radiomics features as imaging biomarkers for pathological invasion in lung adenocarcinoma.
    Cho HH; Lee G; Lee HY; Park H
    Eur Radiol; 2020 May; 30(5):2984-2994. PubMed ID: 31965255
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Impact of feature selection methods and subgroup factors on prognostic analysis with CT-based radiomics in non-small cell lung cancer patients.
    Sugai Y; Kadoya N; Tanaka S; Tanabe S; Umeda M; Yamamoto T; Takeda K; Dobashi S; Ohashi H; Takeda K; Jingu K
    Radiat Oncol; 2021 Apr; 16(1):80. PubMed ID: 33931085
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Joint use of the radiomics method and frozen sections should be considered in the prediction of the final classification of peripheral lung adenocarcinoma manifesting as ground-glass nodules.
    Wang B; Tang Y; Chen Y; Hamal P; Zhu Y; Wang T; Sun Y; Lu Y; Bhuva MS; Meng X; Yang Y; Ai Z; Wu C; Sun X
    Lung Cancer; 2020 Jan; 139():103-110. PubMed ID: 31760351
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A combination of radiomic features, clinic characteristics, and serum tumor biomarkers to predict the possibility of the micropapillary/solid component of lung adenocarcinoma.
    Xing X; Li L; Sun M; Zhu X; Feng Y
    Ther Adv Respir Dis; 2024; 18():17534666241249168. PubMed ID: 38757628
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Preoperative CT-based Deep Learning Model for Predicting Disease-Free Survival in Patients with Lung Adenocarcinomas.
    Kim H; Goo JM; Lee KH; Kim YT; Park CM
    Radiology; 2020 Jul; 296(1):216-224. PubMed ID: 32396042
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Radiomics for lung adenocarcinoma manifesting as pure ground-glass nodules: invasive prediction.
    Sun Y; Li C; Jin L; Gao P; Zhao W; Ma W; Tan M; Wu W; Duan S; Shan Y; Li M
    Eur Radiol; 2020 Jul; 30(7):3650-3659. PubMed ID: 32162003
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Prediction of micropapillary and solid pattern in lung adenocarcinoma using radiomic values extracted from near-pure histopathological subtypes.
    Chen LW; Yang SM; Wang HJ; Chen YC; Lin MW; Hsieh MS; Song HL; Ko HJ; Chen CM; Chang YC
    Eur Radiol; 2021 Jul; 31(7):5127-5138. PubMed ID: 33389033
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A radiomics study to predict invasive pulmonary adenocarcinoma appearing as pure ground-glass nodules.
    Cai J; Liu H; Yuan H; Wu Y; Xu Q; Lv Y; Li J; Fu J; Ye J
    Clin Radiol; 2021 Feb; 76(2):143-151. PubMed ID: 33187676
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Predicting Ki-67 labeling index level in early-stage lung adenocarcinomas manifesting as ground-glass opacity nodules using intra-nodular and peri-nodular radiomic features.
    Zhu M; Yang Z; Zhao W; Wang M; Shi W; Cheng Z; Ye C; Zhu Q; Liu L; Liang Z; Chen L
    Cancer Med; 2022 Nov; 11(21):3982-3992. PubMed ID: 35332684
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Solid Attenuation Components Attention Deep Learning Model to Predict Micropapillary and Solid Patterns in Lung Adenocarcinomas on Computed Tomography.
    Chen LW; Yang SM; Chuang CC; Wang HJ; Chen YC; Lin MW; Hsieh MS; Antonoff MB; Chang YC; Wu CC; Pan T; Chen CM
    Ann Surg Oncol; 2022 Nov; 29(12):7473-7482. PubMed ID: 35789301
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Radiomics signature on CECT as a predictive factor for invasiveness of lung adenocarcinoma manifesting as subcentimeter ground glass nodules.
    Chen W; Li M; Mao D; Ge X; Wang J; Tan M; Ma W; Huang X; Lu J; Li C; Hua Y; Wu H
    Sci Rep; 2021 Feb; 11(1):3633. PubMed ID: 33574448
    [TBL] [Abstract][Full Text] [Related]  

  • 19. The predictive value of CT-based radiomics in differentiating indolent from invasive lung adenocarcinoma in patients with pulmonary nodules.
    She Y; Zhang L; Zhu H; Dai C; Xie D; Xie H; Zhang W; Zhao L; Zou L; Fei K; Sun X; Chen C
    Eur Radiol; 2018 Dec; 28(12):5121-5128. PubMed ID: 29869172
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 20.