Rationale and Objectives
To assess if vessel suppression (VS) improves nodule detection rate, interreader agreement,
and reduces reading time in oncologic chest computed tomography (CT).
Material and Methods
One-hundred consecutive oncologic patients (65 male; median age 60y) who underwent
contrast-enhanced chest CT were retrospectively included. For all exams, additional
VS series (ClearRead CT, Riverrain Technologies, Miamisburg) were reconstructed. Two
groups of three radiologists each with matched experience were defined. Each group
evaluated the SD-CT as well as VS-CT. Each reader marked the presence, size, and position
of pulmonary nodules and documented reading time. In addition, for the VS-CT the presence
of false positive nodules had to be stated. Cohen's Kappa (k) was used to calculate
the interreader-agreement between groups. Reading time was compared using paired t test.
Results
Nodule detection rate was significantly higher in VS-CT compared to the SD-CT (+21%;
p <0.001). Interreader-agreement was higher in the VS-CT (k = 0.431, moderate agreement)
compared to SD-CT (k = 0.209, fair agreement). Almost all VS-CT series had false positive
findings (97-99 out of 100). Average reading time was significantly shorter in the
VS-CT compared to the SD-CT (154 ± 134vs. 194 ± 126; 21%, p<0.001).
Conclusions
Vessel suppression increases nodule detection rate, improves interreader agreement,
and reduces reading time in chest CT of oncologic patients. Due to false positive
results a consensus reading with the SD-CT is essential.
Key Words
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References
- Detection of pulmonary nodules by multislice computed tomography: improved detection rate with reduced slice thickness.Eur Radiol. 2003; 13: 2378-2383
- Evaluation of pulmonary nodules and infection on chest CT with radiation dose equivalent to chest radiography: Prospective intra-individual comparison study to standard dose CT.Eur J Radiol. 2016; 85: 360-365
- Differentiating between subsolid and solid pulmonary nodules at CT: inter- and intraobserver agreement between experienced thoracic radiologists.Radiology. 2016; 278: 888-896
- Lung cancer: interobserver agreement on interpretation of pulmonary findings at low-dose CT screening.Radiology. 2008; 246: 265-272
- Lung cancer screening with sputum cytologic examination, chest radiography, and computed tomography: an update for the U.S. Preventive Services Task Force.Ann Internal Med. 2004; 140: 740-753
- Early lung cancer action project: initial findings on repeat screenings.Cancer. 2001; 92: 153-159
- Vessel suppressed chest Computed Tomography for semi-automated volumetric measurements of solid pulmonary nodules.Eur J Radiol. 2018; 101: 97-102
- Small irregular pulmonary nodules in low-dose CT: observer detection sensitivity and volumetry accuracy.AJR Am J Roentgenol. 2014; 202: W202-W209
- Volumetric measurements of pulmonary nodules at multi-row detector CT: in vivo reproducibility.Eur Radiol. 2004; 14: 86-92
- The measurement of observer agreement for categorical data.Biometrics. 1977; 33: 159-174
- Effect of CAD on radiologists' detection of lung nodules on thoracic CT scans: analysis of an observer performance study by nodule size.Acad Radiol. 2009; 16: 1518-1530
- False-positive reduction in computer-aided mass detection using mammographic texture analysis and classification.Comput Methods Prog Biomed. 2018; 160: 75-83
- CT colonography: investigation of the optimum reader paradigm by using computer-aided detection software.Radiology. 2008; 246: 463-471
Article Info
Publication History
Published online: February 06, 2020
Accepted:
January 9,
2020
Received in revised form:
January 9,
2020
Received:
November 21,
2019
Identification
Copyright
© 2020 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
