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Samuel G. Armato to Tomography, X-Ray Computed

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This is a "connection" page, showing publications Samuel G. Armato has written about Tomography, X-Ray Computed.
Samuel G. Armato
Connection Strength
5.210
Tomography, X-Ray Computed
  1. Accuracy of the Vancouver Lung Cancer Risk Prediction Model Compared With That of Radiologists. Chest. 2019 07; 156(1):112-119.
    View in: PubMed
    Score: 0.334
  2. Correlation of patient survival with clinical tumor measurements in malignant pleural mesothelioma. Eur Radiol. 2019 Jun; 29(6):2981-2988.
    View in: PubMed
    Score: 0.328
  3. Toward Understanding the Size Dependence of Shape Features for Predicting Spiculation in Lung Nodules for Computer-Aided Diagnosis. J Digit Imaging. 2015 Dec; 28(6):704-17.
    View in: PubMed
    Score: 0.265
  4. Comparison of Two Deformable Registration Algorithms in the Presence of Radiologic Change Between Serial Lung CT Scans. J Digit Imaging. 2015 Dec; 28(6):755-60.
    View in: PubMed
    Score: 0.265
  5. Observer variability in mesothelioma tumor thickness measurements: defining minimally measurable lesions. J Thorac Oncol. 2014 Aug; 9(8):1187-94.
    View in: PubMed
    Score: 0.241
  6. Variability of tumor area measurements for response assessment in malignant pleural mesothelioma. Med Phys. 2013 Aug; 40(8):081916.
    View in: PubMed
    Score: 0.225
  7. Lung texture in serial thoracic CT scans: assessment of change introduced by image registration. Med Phys. 2012 Aug; 39(8):4679-90.
    View in: PubMed
    Score: 0.210
  8. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. Med Phys. 2011 Feb; 38(2):915-31.
    View in: PubMed
    Score: 0.189
  9. Characterization of mesothelioma and tissues present in contrast-enhanced thoracic CT scans. Med Phys. 2011 Feb; 38(2):942-7.
    View in: PubMed
    Score: 0.189
  10. Assessment of radiologist performance in the detection of lung nodules: dependence on the definition of "truth". Acad Radiol. 2009 Jan; 16(1):28-38.
    View in: PubMed
    Score: 0.164
  11. The Reference Image Database to Evaluate Response to therapy in lung cancer (RIDER) project: a resource for the development of change-analysis software. Clin Pharmacol Ther. 2008 Oct; 84(4):448-56.
    View in: PubMed
    Score: 0.161
  12. Current state and future directions of pleural mesothelioma imaging. Lung Cancer. 2008 Mar; 59(3):411-20.
    View in: PubMed
    Score: 0.152
  13. The Lung Image Database Consortium (LIDC): ensuring the integrity of expert-defined "truth". Acad Radiol. 2007 Dec; 14(12):1455-63.
    View in: PubMed
    Score: 0.152
  14. Two-dimensional extrapolation methods for texture analysis on CT scans. Med Phys. 2007 Sep; 34(9):3465-72.
    View in: PubMed
    Score: 0.149
  15. Variability in mesothelioma tumor response classification. AJR Am J Roentgenol. 2006 Apr; 186(4):1000-6.
    View in: PubMed
    Score: 0.135
  16. Computerized analysis of mesothelioma on CT scans. Lung Cancer. 2005 Jul; 49 Suppl 1:S41-4.
    View in: PubMed
    Score: 0.126
  17. Lung image database consortium: developing a resource for the medical imaging research community. Radiology. 2004 Sep; 232(3):739-48.
    View in: PubMed
    Score: 0.121
  18. Automated lung segmentation for thoracic CT impact on computer-aided diagnosis. Acad Radiol. 2004 Sep; 11(9):1011-21.
    View in: PubMed
    Score: 0.121
  19. Lung cancer: performance of automated lung nodule detection applied to cancers missed in a CT screening program. Radiology. 2002 Dec; 225(3):685-92.
    View in: PubMed
    Score: 0.107
  20. Automated detection of lung nodules in CT scans: preliminary results. Med Phys. 2001 Aug; 28(8):1552-61.
    View in: PubMed
    Score: 0.098
  21. Computerized detection of pulmonary nodules on CT scans. Radiographics. 1999 Sep-Oct; 19(5):1303-11.
    View in: PubMed
    Score: 0.086
  22. Imaging in pleural mesothelioma: A review of the 14th International Conference of the International Mesothelioma Interest Group. Lung Cancer. 2019 04; 130:108-114.
    View in: PubMed
    Score: 0.081
  23. Revised Modified Response Evaluation Criteria in Solid Tumors for Assessment of Response in Malignant Pleural Mesothelioma (Version 1.1). J Thorac Oncol. 2018 07; 13(7):1012-1021.
    View in: PubMed
    Score: 0.078
  24. Quality assurance and quantitative imaging biomarkers in low-dose CT lung cancer screening. Br J Radiol. 2018 Oct; 91(1090):20170401.
    View in: PubMed
    Score: 0.075
  25. Incorporation of pre-therapy 18 F-FDG uptake data with CT texture features into a radiomics model for radiation pneumonitis diagnosis. Med Phys. 2017 Jul; 44(7):3686-3694.
    View in: PubMed
    Score: 0.073
  26. Imaging in pleural mesothelioma: A review of the 12th International Conference of the International Mesothelioma Interest Group. Lung Cancer. 2015 Nov; 90(2):148-54.
    View in: PubMed
    Score: 0.065
  27. Computer-assisted staging of chronic rhinosinusitis correlates with symptoms. Int Forum Allergy Rhinol. 2015 Jul; 5(7):637-642.
    View in: PubMed
    Score: 0.063
  28. Role of the Quantitative Imaging Biomarker Alliance in optimizing CT for the evaluation of lung cancer screen-detected nodules. J Am Coll Radiol. 2015 Apr; 12(4):390-5.
    View in: PubMed
    Score: 0.063
  29. Radiologic-pathologic correlation of mesothelioma tumor volume. Lung Cancer. 2015 Mar; 87(3):278-82.
    View in: PubMed
    Score: 0.062
  30. Computer-aided nodule detection system: results in an unselected series of consecutive chest radiographs. Acad Radiol. 2015 Apr; 22(4):475-80.
    View in: PubMed
    Score: 0.062
  31. Lung texture in serial thoracic CT scans: correlation with radiologist-defined severity of acute changes following radiation therapy. Phys Med Biol. 2014 Sep 21; 59(18):5387-98.
    View in: PubMed
    Score: 0.061
  32. CT-based pulmonary artery measurements for the assessment of pulmonary hypertension. Acad Radiol. 2014 Apr; 21(4):523-30.
    View in: PubMed
    Score: 0.059
  33. Imaging in pleural mesothelioma: a review of the 11th International Conference of the International Mesothelioma Interest Group. Lung Cancer. 2013 Nov; 82(2):190-6.
    View in: PubMed
    Score: 0.056
  34. Lung texture in serial thoracic CT scans: registration-based methods to compare anatomically matched regions. Med Phys. 2013 Jun; 40(6):061906.
    View in: PubMed
    Score: 0.056
  35. Disease volumes as a marker for patient response in malignant pleural mesothelioma. Ann Oncol. 2013 Apr; 24(4):999-1005.
    View in: PubMed
    Score: 0.053
  36. The influence of initial outlines on manual segmentation. Med Phys. 2010 May; 37(5):2153-8.
    View in: PubMed
    Score: 0.045
  37. A modified gradient correlation filter for image segmentation: application to airway and bowel. Med Phys. 2009 Feb; 36(2):480-5.
    View in: PubMed
    Score: 0.041
  38. The Lung Image Database Consortium (LIDC) data collection process for nodule detection and annotation. Acad Radiol. 2007 Dec; 14(12):1464-74.
    View in: PubMed
    Score: 0.038
  39. The Lung Image Database Consortium (LIDC): a comparison of different size metrics for pulmonary nodule measurements. Acad Radiol. 2007 Dec; 14(12):1475-85.
    View in: PubMed
    Score: 0.038
  40. Evaluation of lung MDCT nodule annotation across radiologists and methods. Acad Radiol. 2006 Oct; 13(10):1254-65.
    View in: PubMed
    Score: 0.035
  41. Modeling of mesothelioma growth demonstrates weaknesses of current response criteria. Lung Cancer. 2006 May; 52(2):141-8.
    View in: PubMed
    Score: 0.034
  42. The radiologic measurement of mesothelioma. Hematol Oncol Clin North Am. 2005 Dec; 19(6):1053-66, vi.
    View in: PubMed
    Score: 0.033
  43. Vessel tree reconstruction in thoracic CT scans with application to nodule detection. IEEE Trans Med Imaging. 2005 Apr; 24(4):486-99.
    View in: PubMed
    Score: 0.032
  44. Assessment methodologies and statistical issues for computer-aided diagnosis of lung nodules in computed tomography: contemporary research topics relevant to the lung image database consortium. Acad Radiol. 2004 Apr; 11(4):462-75.
    View in: PubMed
    Score: 0.029
  45. Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography. Med Phys. 2003 Jul; 30(7):1602-17.
    View in: PubMed
    Score: 0.028
  46. The role of imaging in diagnosis and management of malignant peritoneal mesothelioma: a systematic review. Abdom Radiol (NY). 2022 05; 47(5):1725-1740.
    View in: PubMed
    Score: 0.026
  47. QIBA guidance: Computed tomography imaging for COVID-19 quantitative imaging applications. Clin Imaging. 2021 Sep; 77:151-157.
    View in: PubMed
    Score: 0.024
  48. Autosegmentation for thoracic radiation treatment planning: A grand challenge at AAPM 2017. Med Phys. 2018 Oct; 45(10):4568-4581.
    View in: PubMed
    Score: 0.020
  49. Clinical significance of noncalcified lung nodules in patients with breast cancer. Breast Cancer Res Treat. 2016 Sep; 159(2):265-71.
    View in: PubMed
    Score: 0.017
  50. Lung texture in serial thoracic computed tomography scans: correlation of radiomics-based features with radiation therapy dose and radiation pneumonitis development. Int J Radiat Oncol Biol Phys. 2015 Apr 01; 91(5):1048-56.
    View in: PubMed
    Score: 0.016
  51. Lung volume measurements as a surrogate marker for patient response in malignant pleural mesothelioma. J Thorac Oncol. 2013 Apr; 8(4):478-86.
    View in: PubMed
    Score: 0.014
  52. Optimization of response classification criteria for patients with malignant pleural mesothelioma. J Thorac Oncol. 2012 Nov; 7(11):1728-34.
    View in: PubMed
    Score: 0.013
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The connection strength for concepts is the sum of the scores for each matching publication.

Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.