Rationale and Objectives
To evaluate the performance of a dedicated computed tomography (CT) software called
“bone reading” generating rib unfolded images for improved detection of rib metastases
in patients with lung cancer in comparison to readings of 5- and 1-mm axial CT images
and 18F-Fluordeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT).
Materials and Methods
Ninety consecutive patients who underwent 18F-FDG-PET/CT and chest CT scanning between 2012 and 2014 at our institution were analyzed
retrospectively. Chest CT scans with 5- and 1-mm slice thickness were interpreted
blindly and separately focused on the detection of rib metastases (location, number,
cortical vs. medullary, and osteoblastic vs. sclerotic). Subsequent image analysis
of unfolded 1 mm–based CT rib images was performed. For all three data sets the reading
time was registered. Finally, results were compared to those of FDG-PET. Validation
was based on FDG-PET positivity for osteolytic and mixed osteolytic/osteoblastic focal
rib lesions and follow-up for sclerotic PET-negative lesions.
Results
A total of 47 metastatic rib lesions were found on FDG-PET/CT plus another 30 detected
by CT bone reading and confirmed by follow-up CT. Twenty-nine lesions were osteolytic,
14 were mixed osteolytic/osteoblastic, and 34 were sclerotic. On a patient-based analysis,
CT (5 mm), CT (1 mm), and CT (1-mm bone reading) yielded a sensitivity, specificity,
and accuracy of 76.5/97.3/93, 81.3/97.3/94, and 88.2/95.9/92, respectively. On segment-based
(unfolded rib) analysis, the sensitivity, specificity, and accuracy of the three evaluations
were 47.7/95.7/67, 59.5/95.8/77, and 94.8/88.2/92, respectively. Reading time for
5 mm/1 mm axial images and unfolded images was 40.5/50.7/21.56 seconds, respectively.
Conclusions
The use of unfolded rib images in patients with lung cancer improves sensitivity and
specificity of rib metastasis detection in comparison to 5- and 1-mm CT slice reading.
Moreover, it may reduce the reading time.
Key Words
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Article info
Publication history
Accepted:
December 6,
2014
Received:
September 23,
2014
Footnotes
Disclosure: Hendrik Ditt is a staff member of Siemens AG and other authors have nothing to disclose.
Identification
Copyright
© 2015 AUR. Published by Elsevier Inc. All rights reserved.
