Connection

Rozemarijn Vliegenthart to Solitary Pulmonary Nodule

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This is a "connection" page, showing publications Rozemarijn Vliegenthart has written about Solitary Pulmonary Nodule.
Rozemarijn Vliegenthart
Connection Strength
5.774
Solitary Pulmonary Nodule
  1. CT characteristics of solid pulmonary nodules of never smokers versus smokers: A population-based study. Eur J Radiol. 2022 Sep; 154:110410.
    View in: PubMed
    Score: 0.731
  2. Evaluation of a novel deep learning-based classifier for perifissural nodules. Eur Radiol. 2021 Jun; 31(6):4023-4030.
    View in: PubMed
    Score: 0.658
  3. Relationship between nodule count and lung cancer probability in baseline CT lung cancer screening: The NELSON study. Lung Cancer. 2017 11; 113:45-50.
    View in: PubMed
    Score: 0.525
  4. Response. Radiology. 2014 Apr; 271(1):311.
    View in: PubMed
    Score: 0.414
  5. Features of resolving and nonresolving indeterminate pulmonary nodules at follow-up CT: the NELSON study. Radiology. 2014 Mar; 270(3):872-9.
    View in: PubMed
    Score: 0.404
  6. Optimisation of volume-doubling time cutoff for fast-growing lung nodules in CT lung cancer screening reduces false-positive referrals. Eur Radiol. 2013 Jul; 23(7):1836-45.
    View in: PubMed
    Score: 0.386
  7. The additional diagnostic value of virtual bronchoscopy navigation in patients with pulmonary nodules - The NAVIGATOR study. Lung Cancer. 2023 03; 177:37-43.
    View in: PubMed
    Score: 0.191
  8. Performance of a deep learning-based lung nodule detection system as an alternative reader in a Chinese lung cancer screening program. Eur J Radiol. 2022 Jan; 146:110068.
    View in: PubMed
    Score: 0.176
  9. Deep learning-based pulmonary nodule detection: Effect of slab thickness in maximum intensity projections at the nodule candidate detection stage. Comput Methods Programs Biomed. 2020 Nov; 196:105620.
    View in: PubMed
    Score: 0.159
  10. Clinical characteristics and work-up of small to intermediate-sized pulmonary nodules in a Chinese dedicated cancer hospital. Cancer Biol Med. 2020 02 15; 17(1):199-207.
    View in: PubMed
    Score: 0.156
  11. A Subsolid Nodules Imaging Reporting System (SSN-IRS) for Classifying 3 Subtypes of Pulmonary Adenocarcinoma. Clin Lung Cancer. 2020 07; 21(4):314-325.e4.
    View in: PubMed
    Score: 0.155
  12. New Fissure-Attached Nodules in Lung Cancer Screening: A Brief Report From The NELSON Study. J Thorac Oncol. 2020 01; 15(1):125-129.
    View in: PubMed
    Score: 0.152
  13. New Subsolid Pulmonary Nodules in Lung Cancer Screening: The NELSON Trial. J Thorac Oncol. 2018 09; 13(9):1410-1414.
    View in: PubMed
    Score: 0.139
  14. Relationship between the number of new nodules and lung cancer probability in incidence screening rounds of CT lung cancer screening: The NELSON study. Lung Cancer. 2018 11; 125:103-108.
    View in: PubMed
    Score: 0.138
  15. Influence of lung nodule margin on volume- and diameter-based reader variability in CT lung cancer screening. Br J Radiol. 2018 Oct; 91(1090):20170405.
    View in: PubMed
    Score: 0.133
  16. Disagreement of diameter and volume measurements for pulmonary nodule size estimation in CT lung cancer screening. Thorax. 2018 08; 73(8):779-781.
    View in: PubMed
    Score: 0.133
  17. Quantification of growth patterns of screen-detected lung cancers: The NELSON study. Lung Cancer. 2017 06; 108:48-54.
    View in: PubMed
    Score: 0.127
  18. Follow-up of CT-derived airway wall thickness: Correcting for changes in inspiration level improves reliability. Eur J Radiol. 2016 Nov; 85(11):2008-2013.
    View in: PubMed
    Score: 0.123
  19. Interscan variation of semi-automated volumetry of subsolid pulmonary nodules. Eur Radiol. 2015 Apr; 25(4):1040-7.
    View in: PubMed
    Score: 0.108
  20. The impact of radiologists' expertise on screen results decisions in a CT lung cancer screening trial. Eur Radiol. 2015 Mar; 25(3):792-9.
    View in: PubMed
    Score: 0.108
  21. Detection and quantification of the solid component in pulmonary subsolid nodules by semiautomatic segmentation. Eur Radiol. 2015 Feb; 25(2):488-96.
    View in: PubMed
    Score: 0.107
  22. Small irregular pulmonary nodules in low-dose CT: observer detection sensitivity and volumetry accuracy. AJR Am J Roentgenol. 2014 Mar; 202(3):W202-9.
    View in: PubMed
    Score: 0.103
  23. A practical approach to radiological evaluation of CT lung cancer screening examinations. Cancer Imaging. 2013 Sep 23; 13(3):391-9.
    View in: PubMed
    Score: 0.100
  24. Systematic error in lung nodule volumetry: effect of iterative reconstruction versus filtered back projection at different CT parameters. AJR Am J Roentgenol. 2012 Dec; 199(6):1241-6.
    View in: PubMed
    Score: 0.094
  25. Sensitivity and accuracy of volumetry of pulmonary nodules on low-dose 16- and 64-row multi-detector CT: an anthropomorphic phantom study. Eur Radiol. 2013 Jan; 23(1):139-47.
    View in: PubMed
    Score: 0.092
  26. Smooth or attached solid indeterminate nodules detected at baseline CT screening in the NELSON study: cancer risk during 1 year of follow-up. Radiology. 2009 Jan; 250(1):264-72.
    View in: PubMed
    Score: 0.071
  27. Limited value of shape, margin and CT density in the discrimination between benign and malignant screen detected solid pulmonary nodules of the NELSON trial. Eur J Radiol. 2008 Nov; 68(2):347-52.
    View in: PubMed
    Score: 0.066
  28. Comparison of three software systems for semi-automatic volumetry of pulmonary nodules on baseline and follow-up CT examinations. Acta Radiol. 2014 Jul; 55(6):691-8.
    View in: PubMed
    Score: 0.025
Connection Strength

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