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 *

381 related articles for article (PubMed ID: 34102564)

  • 21. [CT diagnosis of different pathological types of ground-glass nodules].
    Gao F; Ge XJ; Li M; Chen Y; Lyu F; Hua Y; Ren Q; Qi L
    Zhonghua Zhong Liu Za Zhi; 2014 Mar; 36(3):188-92. PubMed ID: 24785278
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Deep learning-based differentiation of invasive adenocarcinomas from preinvasive or minimally invasive lesions among pulmonary subsolid nodules.
    Park S; Park G; Lee SM; Kim W; Park H; Jung K; Seo JB
    Eur Radiol; 2021 Aug; 31(8):6239-6247. PubMed ID: 33555355
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Application of deep learning (3-dimensional convolutional neural network) for the prediction of pathological invasiveness in lung adenocarcinoma: A preliminary study.
    Yanagawa M; Niioka H; Hata A; Kikuchi N; Honda O; Kurakami H; Morii E; Noguchi M; Watanabe Y; Miyake J; Tomiyama N
    Medicine (Baltimore); 2019 Jun; 98(25):e16119. PubMed ID: 31232960
    [TBL] [Abstract][Full Text] [Related]  

  • 24. CNN models discriminating between pulmonary micro-nodules and non-nodules from CT images.
    Monkam P; Qi S; Xu M; Han F; Zhao X; Qian W
    Biomed Eng Online; 2018 Jul; 17(1):96. PubMed ID: 30012167
    [TBL] [Abstract][Full Text] [Related]  

  • 25. A triple-classification for the evaluation of lung nodules manifesting as pure ground-glass sign: a CT-based radiomic analysis.
    Yu Z; Xu C; Zhang Y; Ji F
    BMC Med Imaging; 2022 Jul; 22(1):133. PubMed ID: 35896975
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Toward an Expert Level of Lung Cancer Detection and Classification Using a Deep Convolutional Neural Network.
    Zhang C; Sun X; Dang K; Li K; Guo XW; Chang J; Yu ZQ; Huang FY; Wu YS; Liang Z; Liu ZY; Zhang XG; Gao XL; Huang SH; Qin J; Feng WN; Zhou T; Zhang YB; Fang WJ; Zhao MF; Yang XN; Zhou Q; Wu YL; Zhong WZ
    Oncologist; 2019 Sep; 24(9):1159-1165. PubMed ID: 30996009
    [TBL] [Abstract][Full Text] [Related]  

  • 27. The value of various peritumoral radiomic features in differentiating the invasiveness of adenocarcinoma manifesting as ground-glass nodules.
    Wu L; Gao C; Ye J; Tao J; Wang N; Pang P; Xiang P; Xu M
    Eur Radiol; 2021 Dec; 31(12):9030-9037. PubMed ID: 34037830
    [TBL] [Abstract][Full Text] [Related]  

  • 28. A CT-based deep learning model for subsolid pulmonary nodules to distinguish minimally invasive adenocarcinoma and invasive adenocarcinoma.
    Chen X; Feng B; Chen Y; Duan X; Liu K; Li K; Zhang C; Liu X; Long W
    Eur J Radiol; 2021 Dec; 145():110041. PubMed ID: 34837794
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Convolutional neural networks versus radiologists in characterization of small hypoattenuating hepatic nodules on CT: a critical diagnostic challenge in staging of colorectal carcinoma.
    Khalili K; Lawlor RL; Pourafkari M; Lu H; Tyrrell P; Kim TK; Jang HJ; Johnson SA; Martel AL
    Sci Rep; 2020 Sep; 10(1):15248. PubMed ID: 32943654
    [TBL] [Abstract][Full Text] [Related]  

  • 30. [Diagnostic value of contrast-enhanced CT scans in identifying lung adenocarcinomas manifesting as ground glass nodules].
    Sun YL; Gao F; Gao P; Jin L; Li C; Hua YQ; Li M
    Zhonghua Zhong Liu Za Zhi; 2018 Jul; 40(7):534-538. PubMed ID: 30060363
    [No Abstract]   [Full Text] [Related]  

  • 31. Computer-Aided Diagnosis of Ground-Glass Opacity Nodules Using Open-Source Software for Quantifying Tumor Heterogeneity.
    Li M; Narayan V; Gill RR; Jagannathan JP; Barile MF; Gao F; Bueno R; Jayender J
    AJR Am J Roentgenol; 2017 Dec; 209(6):1216-1227. PubMed ID: 29045176
    [TBL] [Abstract][Full Text] [Related]  

  • 32. [Comparison of Two-dimensional and Three-dimensional Features of Chest CT
 in the Diagnosis of Invasion of Pulmonary Ground Glass Nodules].
    Wang H; Yang H; Liu Z; Chen L; Xu X; Zhu Q
    Zhongguo Fei Ai Za Zhi; 2022 Oct; 25(10):723-729. PubMed ID: 36167458
    [TBL] [Abstract][Full Text] [Related]  

  • 33. A radiomics nomogram for invasiveness prediction in lung adenocarcinoma manifesting as part-solid nodules with solid components smaller than 6 mm.
    Zhang T; Zhang C; Zhong Y; Sun Y; Wang H; Li H; Yang G; Zhu Q; Yuan M
    Front Oncol; 2022; 12():900049. PubMed ID: 36033463
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Determining the invasiveness of ground-glass nodules using a 3D multi-task network.
    Yu Y; Wang N; Huang N; Liu X; Zheng Y; Fu Y; Li X; Wu H; Xu J; Cheng J
    Eur Radiol; 2021 Sep; 31(9):7162-7171. PubMed ID: 33665717
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Computed tomography radiomics-based distinction of invasive adenocarcinoma from minimally invasive adenocarcinoma manifesting as pure ground-glass nodules with bubble-like signs.
    Jiang Y; Xiong Z; Zhao W; Zhang J; Guo Y; Li G; Li Z
    Gen Thorac Cardiovasc Surg; 2022 Oct; 70(10):880-890. PubMed ID: 35301662
    [TBL] [Abstract][Full Text] [Related]  

  • 36. [A preliminary investigation on a deep learning convolutional neural networks based pulmonary tuberculosis CT diagnostic model].
    Wu SC; Wang XJ; Ji JY; Geng G; Zhang ZH; Hou DL
    Zhonghua Jie He He Hu Xi Za Zhi; 2021 May; 44(5):450-455. PubMed ID: 34865365
    [No Abstract]   [Full Text] [Related]  

  • 37. Development and validation of a 3D-convolutional neural network model based on chest CT for differentiating active pulmonary tuberculosis from community-acquired pneumonia.
    Han D; Chen Y; Li X; Li W; Zhang X; He T; Yu Y; Dou Y; Duan H; Yu N
    Radiol Med; 2023 Jan; 128(1):68-80. PubMed ID: 36574111
    [TBL] [Abstract][Full Text] [Related]  

  • 38. HRCT features distinguishing pre-invasive from invasive pulmonary adenocarcinomas appearing as ground-glass nodules.
    Zhang Y; Shen Y; Qiang JW; Ye JD; Zhang J; Zhao RY
    Eur Radiol; 2016 Sep; 26(9):2921-8. PubMed ID: 26662263
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Multi-classification model incorporating radiomics and clinic-radiological features for predicting invasiveness and differentiation of pulmonary adenocarcinoma nodules.
    Sun H; Zhang C; Ouyang A; Dai Z; Song P; Yao J
    Biomed Eng Online; 2023 Nov; 22(1):112. PubMed ID: 38037082
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Efficacy of exponentiation method with a convolutional neural network for classifying lung nodules on CT images by malignancy level.
    Usuzaki T; Takahashi K; Takagi H; Ishikuro M; Obara T; Yamaura T; Kamimoto M; Majima K
    Eur Radiol; 2023 Dec; 33(12):9309-9319. PubMed ID: 37477673
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 20.