Literature DB >> 19734062

Comparison of 2D and 3D views for evaluation of flat lesions in CT colonography.

Antonella Lostumbo1, Christian Wanamaker, Joy Tsai, Kenji Suzuki, Abraham H Dachman.   

Abstract

RATIONALE AND
OBJECTIVES: Flat lesions in the colon may result in false-negative computed tomography colonography interpretations. It is unknown whether flat lesions are better measured on two-dimensional (2D) or three-dimensional (3D) images and which settings are optimal for enhanced reproducibility and decreased variability. We evaluated these factors to determine whether 2D or 3D is best for flat lesion measurements. METHODS AND MATERIALS: Eighty-eight lesions in 66 patients from a previously published clinical trial were analyzed. Lesions were viewed with four methods including 2D at three window/level settings and 3D endoluminal view. Lesions in either supine or prone were counted as one dataset. Long axis and height were measured. Criteria of "height" (<or=3 mm high) or "ratio" (height <or=half the long axis) were applied. A subset of lesions was subject to inter- and intra-observer variability analysis.
RESULTS: With the "height" criterion, more datasets were classified as flat in 2D flat (n = 76), 2D soft tissue (n = 82), and 3D (n = 73) views than in the 2D lung (n = 49) view. If long axis is used as the key metric, endoluminal 3D (12.1%) views significantly showed the least inter-observer variability compared to lung (18.9%) or soft tissue (20.2%) views. Intra-observer variability was low overall for all methods.
CONCLUSION: When characterizing lesions as flat, a consistent viewing method should be used. To minimize inter-observer variability (such as when following a patient over time), it is best to use the ratio criterion for flat lesion definition incorporating the single longest dimension on 3D views as the key metric.

Entities:  

Mesh:

Year:  2009        PMID: 19734062     DOI: 10.1016/j.acra.2009.07.004

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  7 in total

1.  Machine Learning in Computer-aided Diagnosis of the Thorax and Colon in CT: A Survey.

Authors:  Kenji Suzuki
Journal:  IEICE Trans Inf Syst       Date:  2013-04-01

Review 2.  Overview of deep learning in medical imaging.

Authors:  Kenji Suzuki
Journal:  Radiol Phys Technol       Date:  2017-07-08

3.  Quantitative radiology: automated measurement of polyp volume in computed tomography colonography using Hessian matrix-based shape extraction and volume growing.

Authors:  Mark L Epstein; Piotr R Obara; Yisong Chen; Junchi Liu; Amin Zarshenas; Nazanin Makkinejad; Abraham H Dachman; Kenji Suzuki
Journal:  Quant Imaging Med Surg       Date:  2015-10

4.  Seeing is believing: video classification for computed tomographic colonography using multiple-instance learning.

Authors:  Shijun Wang; Matthew T McKenna; Tan B Nguyen; Joseph E Burns; Nicholas Petrick; Berkman Sahiner; Ronald M Summers
Journal:  IEEE Trans Med Imaging       Date:  2012-05       Impact factor: 10.048

5.  A review of computer-aided diagnosis in thoracic and colonic imaging.

Authors:  Kenji Suzuki
Journal:  Quant Imaging Med Surg       Date:  2012-09

6.  Pixel-based machine learning in medical imaging.

Authors:  Kenji Suzuki
Journal:  Int J Biomed Imaging       Date:  2012-02-28

7.  The second ESGAR consensus statement on CT colonography.

Authors:  Emanuele Neri; Steve Halligan; Mikael Hellström; Philippe Lefere; Thomas Mang; Daniele Regge; Jaap Stoker; Stuart Taylor; Andrea Laghi
Journal:  Eur Radiol       Date:  2012-09-15       Impact factor: 5.315

  7 in total

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