BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

601 related articles for article (PubMed ID: 32757455)

  • 1. Estimation of malignancy of pulmonary nodules at CT scans: Effect of computer-aided diagnosis on diagnostic performance of radiologists.
    Liu J; Zhao L; Han X; Ji H; Liu L; He W
    Asia Pac J Clin Oncol; 2021 Jun; 17(3):216-221. PubMed ID: 32757455
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Neural network-based computer-aided diagnosis in distinguishing malignant from benign solitary pulmonary nodules by computed tomography.
    Chen H; Wang XH; Ma DQ; Ma BR
    Chin Med J (Engl); 2007 Jul; 120(14):1211-5. PubMed ID: 17697569
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. Integrating PET and CT information to improve diagnostic accuracy for lung nodules: A semiautomatic computer-aided method.
    Nie Y; Li Q; Li F; Pu Y; Appelbaum D; Doi K
    J Nucl Med; 2006 Jul; 47(7):1075-80. PubMed ID: 16818939
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Computer-aided diagnosis of lung nodules on CT scans: ROC study of its effect on radiologists' performance.
    Way T; Chan HP; Hadjiiski L; Sahiner B; Chughtai A; Song TK; Poopat C; Stojanovska J; Frank L; Attili A; Bogot N; Cascade PN; Kazerooni EA
    Acad Radiol; 2010 Mar; 17(3):323-32. PubMed ID: 20152726
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Pulmonary nodules at chest CT: effect of computer-aided diagnosis on radiologists' detection performance.
    Awai K; Murao K; Ozawa A; Komi M; Hayakawa H; Hori S; Nishimura Y
    Radiology; 2004 Feb; 230(2):347-52. PubMed ID: 14752180
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Computer-aided detection (CAD) in lung cancer screening at chest MDCT: ROC analysis of CAD versus radiologist performance.
    Fraioli F; Bertoletti L; Napoli A; Pediconi F; Calabrese FA; Masciangelo R; Catalano C; Passariello R
    J Thorac Imaging; 2007 Aug; 22(3):241-6. PubMed ID: 17721333
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Computer-assisted detection of pulmonary nodules: performance evaluation of an expert knowledge-based detection system in consensus reading with experienced and inexperienced chest radiologists.
    Marten K; Seyfarth T; Auer F; Wiener E; Grillhösl A; Obenauer S; Rummeny EJ; Engelke C
    Eur Radiol; 2004 Oct; 14(10):1930-8. PubMed ID: 15235812
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Evaluating the performance of a deep learning-based computer-aided diagnosis (DL-CAD) system for detecting and characterizing lung nodules: Comparison with the performance of double reading by radiologists.
    Li L; Liu Z; Huang H; Lin M; Luo D
    Thorac Cancer; 2019 Feb; 10(2):183-192. PubMed ID: 30536611
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Solitary pulmonary nodule diagnosis on CT: results of an observer study.
    Shah SK; McNitt-Gray MF; De Zoysa KR; Sayre JW; Kim HJ; Batra P; Behrashi A; Brown K; Greaser LE; Park JM; Roback DK; Wu C; Zaragoza E; Goldin JG; Suh RD; Brown MS; Aberle DR
    Acad Radiol; 2005 Apr; 12(4):496-501. PubMed ID: 15831424
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Efficiency of a computer-aided diagnosis (CAD) system with deep learning in detection of pulmonary nodules on 1-mm-thick images of computed tomography.
    Kozuka T; Matsukubo Y; Kadoba T; Oda T; Suzuki A; Hyodo T; Im S; Kaida H; Yagyu Y; Tsurusaki M; Matsuki M; Ishii K
    Jpn J Radiol; 2020 Nov; 38(11):1052-1061. PubMed ID: 32592003
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Neural network ensemble-based computer-aided diagnosis for differentiation of lung nodules on CT images: clinical evaluation.
    Chen H; Xu Y; Ma Y; Ma B
    Acad Radiol; 2010 May; 17(5):595-602. PubMed ID: 20167513
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Pulmonary nodules: estimation of malignancy at thin-section helical CT--effect of computer-aided diagnosis on performance of radiologists.
    Awai K; Murao K; Ozawa A; Nakayama Y; Nakaura T; Liu D; Kawanaka K; Funama Y; Morishita S; Yamashita Y
    Radiology; 2006 Apr; 239(1):276-84. PubMed ID: 16467210
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Computer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose CT by use of massive training artificial neural network.
    Suzuki K; Li F; Sone S; Doi K
    IEEE Trans Med Imaging; 2005 Sep; 24(9):1138-50. PubMed ID: 16156352
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Validation of a deep learning computer aided system for CT based lung nodule detection, classification, and growth rate estimation in a routine clinical population.
    Murchison JT; Ritchie G; Senyszak D; Nijwening JH; van Veenendaal G; Wakkie J; van Beek EJR
    PLoS One; 2022; 17(5):e0266799. PubMed ID: 35511758
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Pulmonary nodules on multi-detector row CT scans: performance comparison of radiologists and computer-aided detection.
    Rubin GD; Lyo JK; Paik DS; Sherbondy AJ; Chow LC; Leung AN; Mindelzun R; Schraedley-Desmond PK; Zinck SE; Naidich DP; Napel S
    Radiology; 2005 Jan; 234(1):274-83. PubMed ID: 15537839
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Artificial Intelligence Tool for Assessment of Indeterminate Pulmonary Nodules Detected with CT.
    Kim RY; Oke JL; Pickup LC; Munden RF; Dotson TL; Bellinger CR; Cohen A; Simoff MJ; Massion PP; Filippini C; Gleeson FV; Vachani A
    Radiology; 2022 Sep; 304(3):683-691. PubMed ID: 35608444
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Radiologists with and without deep learning-based computer-aided diagnosis: comparison of performance and interobserver agreement for characterizing and diagnosing pulmonary nodules/masses.
    Wataya T; Yanagawa M; Tsubamoto M; Sato T; Nishigaki D; Kita K; Yamagata K; Suzuki Y; Hata A; Kido S; Tomiyama N;
    Eur Radiol; 2023 Jan; 33(1):348-359. PubMed ID: 35751697
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Computer-aided detection of pulmonary nodules: a comparative study using the public LIDC/IDRI database.
    Jacobs C; van Rikxoort EM; Murphy K; Prokop M; Schaefer-Prokop CM; van Ginneken B
    Eur Radiol; 2016 Jul; 26(7):2139-47. PubMed ID: 26443601
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Usefulness of an artificial neural network for differentiating benign from malignant pulmonary nodules on high-resolution CT: evaluation with receiver operating characteristic analysis.
    Matsuki Y; Nakamura K; Watanabe H; Aoki T; Nakata H; Katsuragawa S; Doi K
    AJR Am J Roentgenol; 2002 Mar; 178(3):657-63. PubMed ID: 11856693
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 31.