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 *

362 related articles for article (PubMed ID: 33678583)

  • 1. Lung Cancer and Granuloma Identification Using a Deep Learning Model to Extract 3-Dimensional Radiomics Features in CT Imaging.
    Lin X; Jiao H; Pang Z; Chen H; Wu W; Wang X; Xiong L; Chen B; Huang Y; Li S; Li L
    Clin Lung Cancer; 2021 Sep; 22(5):e756-e766. PubMed ID: 33678583
    [TBL] [Abstract][Full Text] [Related]  

  • 2. One deep learning local-global model based on CT imaging to differentiate between nodular cryptococcosis and lung cancer which are hard to be diagnosed.
    Li S; Zhang G; Yin Y; Xie Q; Guo X; Cao K; Song Q; Guan J; Zhou X
    Comput Med Imaging Graph; 2021 Dec; 94():102009. PubMed ID: 34741847
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Differentiation of granulomatous nodules with lobulation and spiculation signs from solid lung adenocarcinomas using a CT deep learning model.
    Wen Y; Wu W; Liufu Y; Pan X; Zhang Y; Qi S; Guan Y
    BMC Cancer; 2024 Jul; 24(1):875. PubMed ID: 39039511
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Solitary solid pulmonary nodules: a CT-based deep learning nomogram helps differentiate tuberculosis granulomas from lung adenocarcinomas.
    Feng B; Chen X; Chen Y; Lu S; Liu K; Li K; Liu Z; Hao Y; Li Z; Zhu Z; Yao N; Liang G; Zhang J; Long W; Liu X
    Eur Radiol; 2020 Dec; 30(12):6497-6507. PubMed ID: 32594210
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Perinodular and Intranodular Radiomic Features on Lung CT Images Distinguish Adenocarcinomas from Granulomas.
    Beig N; Khorrami M; Alilou M; Prasanna P; Braman N; Orooji M; Rakshit S; Bera K; Rajiah P; Ginsberg J; Donatelli C; Thawani R; Yang M; Jacono F; Tiwari P; Velcheti V; Gilkeson R; Linden P; Madabhushi A
    Radiology; 2019 Mar; 290(3):783-792. PubMed ID: 30561278
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Radiomics nomogram for preoperative differentiation of lung tuberculoma from adenocarcinoma in solitary pulmonary solid nodule.
    Feng B; Chen X; Chen Y; Liu K; Li K; Liu X; Yao N; Li Z; Li R; Zhang C; Ji J; Long W
    Eur J Radiol; 2020 Jul; 128():109022. PubMed ID: 32371184
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Differentiating minimally invasive and invasive adenocarcinomas in patients with solitary sub-solid pulmonary nodules with a radiomics nomogram.
    Feng B; Chen X; Chen Y; Li Z; Hao Y; Zhang C; Li R; Liao Y; Zhang X; Huang Y; Long W
    Clin Radiol; 2019 Jul; 74(7):570.e1-570.e11. PubMed ID: 31056198
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Predicting N2 lymph node metastasis in presurgical stage I-II non-small cell lung cancer using multiview radiomics and deep learning method.
    Zhang H; Liao M; Guo Q; Chen J; Wang S; Liu S; Xiao F
    Med Phys; 2023 Apr; 50(4):2049-2060. PubMed ID: 36563341
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Development and Validation of a Deep Learning Radiomics Model to Predict High-Risk Pathologic Pulmonary Nodules Using Preoperative Computed Tomography.
    Ye G; Wu G; Li K; Zhang C; Zhuang Y; Liu H; Song E; Qi Y; Li Y; Yang F; Liao Y
    Acad Radiol; 2024 Apr; 31(4):1686-1697. PubMed ID: 37802672
    [TBL] [Abstract][Full Text] [Related]  

  • 10. CT-based radiomics signature for differentiating solitary granulomatous nodules from solid lung adenocarcinoma.
    Yang X; He J; Wang J; Li W; Liu C; Gao D; Guan Y
    Lung Cancer; 2018 Nov; 125():109-114. PubMed ID: 30429007
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Enhancing brain metastasis prediction in non-small cell lung cancer: a deep learning-based segmentation and CT radiomics-based ensemble learning model.
    Gong J; Wang T; Wang Z; Chu X; Hu T; Li M; Peng W; Feng F; Tong T; Gu Y
    Cancer Imaging; 2024 Jan; 24(1):1. PubMed ID: 38167564
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. Preoperative diagnosis of malignant pulmonary nodules in lung cancer screening with a radiomics nomogram.
    Liu A; Wang Z; Yang Y; Wang J; Dai X; Wang L; Lu Y; Xue F
    Cancer Commun (Lond); 2020 Jan; 40(1):16-24. PubMed ID: 32125097
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Radiomic signature based on CT imaging to distinguish invasive adenocarcinoma from minimally invasive adenocarcinoma in pure ground-glass nodules with pleural contact.
    Jiang Y; Che S; Ma S; Liu X; Guo Y; Liu A; Li G; Li Z
    Cancer Imaging; 2021 Jan; 21(1):1. PubMed ID: 33407884
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Development of a combined radiomics and CT feature-based model for differentiating malignant from benign subcentimeter solid pulmonary nodules.
    Liu J; Qi L; Wang Y; Li F; Chen J; Cui S; Cheng S; Zhou Z; Li L; Wang J
    Eur Radiol Exp; 2024 Jan; 8(1):8. PubMed ID: 38228868
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Preoperative CT-based radiomics combined with intraoperative frozen section is predictive of invasive adenocarcinoma in pulmonary nodules: a multicenter study.
    Wu G; Woodruff HC; Sanduleanu S; Refaee T; Jochems A; Leijenaar R; Gietema H; Shen J; Wang R; Xiong J; Bian J; Wu J; Lambin P
    Eur Radiol; 2020 May; 30(5):2680-2691. PubMed ID: 32006165
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Diagnosis of Invasive Lung Adenocarcinoma Based on Chest CT Radiomic Features of Part-Solid Pulmonary Nodules: A Multicenter Study.
    Wu G; Woodruff HC; Shen J; Refaee T; Sanduleanu S; Ibrahim A; Leijenaar RTH; Wang R; Xiong J; Bian J; Wu J; Lambin P
    Radiology; 2020 Nov; 297(2):451-458. PubMed ID: 32840472
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Development and validation of a deep learning signature for predicting lymph node metastasis in lung adenocarcinoma: comparison with radiomics signature and clinical-semantic model.
    Ma X; Xia L; Chen J; Wan W; Zhou W
    Eur Radiol; 2023 Mar; 33(3):1949-1962. PubMed ID: 36169691
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A combined non-enhanced CT radiomics and clinical variable machine learning model for differentiating benign and malignant sub-centimeter pulmonary solid nodules.
    Lin RY; Zheng YN; Lv FJ; Fu BJ; Li WJ; Liang ZR; Chu ZG
    Med Phys; 2023 May; 50(5):2835-2843. PubMed ID: 36810703
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Development of a CT-Based comprehensive model combining clinical, radiomics with deep learning for differentiating pulmonary metastases from noncalcified pulmonary hamartomas: a retrospective cohort study.
    Liu Y; Ren H; Pei Y; Shen L; Guo J; Zhou J; Li C; Liu Y
    Int J Surg; 2024 Aug; 110(8):4900-4910. PubMed ID: 38759692
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 19.