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 *

90 related articles for article (PubMed ID: 29261471)

  • 1. Using Computer Analysis to Predict Likelihood of Cancer in Lung Nodules.
    MacMahon H
    Radiology; 2018 Jan; 286(1):296-297. PubMed ID: 29261471
    [No Abstract]   [Full Text] [Related]  

  • 2. Improvement in detection of pulmonary nodules: digital image processing and computer-aided diagnosis.
    MacMahon H
    Radiographics; 2000; 20(4):1169-77. PubMed ID: 10903706
    [No Abstract]   [Full Text] [Related]  

  • 3. Cloud-Based NoSQL Open Database of Pulmonary Nodules for Computer-Aided Lung Cancer Diagnosis and Reproducible Research.
    Ferreira Junior JR; Oliveira MC; de Azevedo-Marques PM
    J Digit Imaging; 2016 Dec; 29(6):716-729. PubMed ID: 27440183
    [TBL] [Abstract][Full Text] [Related]  

  • 4. [CAD for identifying malignant lung nodules in early diagnosis: a survey].
    Li B; Tian L; Ou S
    Sheng Wu Yi Xue Gong Cheng Xue Za Zhi; 2009 Oct; 26(5):1141-5, 1157. PubMed ID: 19947507
    [TBL] [Abstract][Full Text] [Related]  

  • 5. JOURNAL CLUB: Computer-Aided Detection of Lung Nodules on CT With a Computerized Pulmonary Vessel Suppressed Function.
    Lo SB; Freedman MT; Gillis LB; White CS; Mun SK
    AJR Am J Roentgenol; 2018 Mar; 210(3):480-488. PubMed ID: 29336601
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Improved lung nodule diagnosis accuracy using lung CT images with uncertain class.
    Wang Z; Xin J; Sun P; Lin Z; Yao Y; Gao X
    Comput Methods Programs Biomed; 2018 Aug; 162():197-209. PubMed ID: 29903487
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Data analysis of the Lung Imaging Database Consortium and Image Database Resource Initiative.
    Wang W; Luo J; Yang X; Lin H
    Acad Radiol; 2015 Apr; 22(4):488-95. PubMed ID: 25601306
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Radiologists' performance for differentiating benign from malignant lung nodules on high-resolution CT using computer-estimated likelihood of malignancy.
    Li F; Aoyama M; Shiraishi J; Abe H; Li Q; Suzuki K; Engelmann R; Sone S; Macmahon H; Doi K
    AJR Am J Roentgenol; 2004 Nov; 183(5):1209-15. PubMed ID: 15505279
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Pulmonary nodule detection, characterization, and management with multidetector computed tomography.
    Brandman S; Ko JP
    J Thorac Imaging; 2011 May; 26(2):90-105. PubMed ID: 21508732
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Toward Understanding the Size Dependence of Shape Features for Predicting Spiculation in Lung Nodules for Computer-Aided Diagnosis.
    Niehaus R; Raicu DS; Furst J; Armato S
    J Digit Imaging; 2015 Dec; 28(6):704-17. PubMed ID: 25708891
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A weighted rule based method for predicting malignancy of pulmonary nodules by nodule characteristics.
    Kaya A; Can AB
    J Biomed Inform; 2015 Aug; 56():69-79. PubMed ID: 26008877
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A Segmentation Framework of Pulmonary Nodules in Lung CT Images.
    Mukhopadhyay S
    J Digit Imaging; 2016 Feb; 29(1):86-103. PubMed ID: 26055544
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Limited-Range Few-View CT: Using Historical Images for ROI Reconstruction in Solitary Lung Nodules Follow-up Examination.
    Zhang W; Song Y; Chen Y; Ma J; Sun J; Zhao J
    IEEE Trans Med Imaging; 2017 Dec; 36(12):2569-2577. PubMed ID: 29192886
    [TBL] [Abstract][Full Text] [Related]  

  • 14. A method for evaluating the performance of computer-aided detection of pulmonary nodules in lung cancer CT screening: detection limit for nodule size and density.
    Kobayashi H; Ohkubo M; Narita A; Marasinghe JC; Murao K; Matsumoto T; Sone S; Wada S
    Br J Radiol; 2017 Feb; 90(1070):20160313. PubMed ID: 27897029
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Solid, part-solid, or non-solid?: classification of pulmonary nodules in low-dose chest computed tomography by a computer-aided diagnosis system.
    Jacobs C; van Rikxoort EM; Scholten ET; de Jong PA; Prokop M; Schaefer-Prokop C; van Ginneken B
    Invest Radiol; 2015 Mar; 50(3):168-73. PubMed ID: 25478740
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Limited value of shape, margin and CT density in the discrimination between benign and malignant screen detected solid pulmonary nodules of the NELSON trial.
    Xu DM; van Klaveren RJ; de Bock GH; Leusveld A; Zhao Y; Wang Y; Vliegenthart R; de Koning HJ; Scholten ET; Verschakelen J; Prokop M; Oudkerk M
    Eur J Radiol; 2008 Nov; 68(2):347-52. PubMed ID: 17920800
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Three-dimensional volumetric assessment with thoracic CT: a reliable approach for noncalcified lung nodules?
    Mazzei MA; Scialpi M; Mazzei FG; Giacobone G; Volterrani L
    Radiology; 2010 Feb; 254(2):634; author reply 635. PubMed ID: 20093537
    [No Abstract]   [Full Text] [Related]  

  • 18. Software system detects lung nodules.
    FDA Consum; 2004; 38(5):5. PubMed ID: 15595133
    [No Abstract]   [Full Text] [Related]  

  • 19. Pulmonary nodules: a quantitative method of diagnosis by evaluating nodule perimeter difference to approximate oval using three-dimensional CT images.
    Kamiya H; Murayama S; Kakinohana Y; Miyara T
    Clin Imaging; 2011; 35(2):123-6. PubMed ID: 21377050
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Computer-aided diagnosis of the solitary pulmonary nodule.
    Shah SK; McNitt-Gray MF; Rogers SR; Goldin JG; Suh RD; Sayre JW; Petkovska I; Kim HJ; Aberle DR
    Acad Radiol; 2005 May; 12(5):570-5. PubMed ID: 15866129
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 5.