Literature DB >> 25586709

Improved detection of bone metastases from lung cancer in the thoracic cage using 5- and 1-mm axial images versus a new CT software generating rib unfolding images: comparison with standard ¹⁸F-FDG-PET/CT.

Georg Homann1, Deedar F Mustafa2, Hendrik Ditt3, Werner Spengler4, Hans-Georg Kopp4, Konstantin Nikolaou2, Marius Horger2.   

Abstract

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 (18)F-Fluordeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT).
MATERIALS AND METHODS: Ninety consecutive patients who underwent (18)F-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.
Copyright © 2015 AUR. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  FDG-PET/CT; Lung cancer; bone metastasis; computed tomography; ribs; unfolded rib images

Mesh:

Substances:

Year:  2015        PMID: 25586709     DOI: 10.1016/j.acra.2014.12.005

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


  7 in total

1.  Improvement of diagnostic confidence for detection of multiple myeloma involvement of the ribs by a new CT software generating rib unfolded images: Comparison with 5- and 1-mm axial images.

Authors:  Georg Homann; Katja Weisel; Deedar Farhad Mustafa; Hendrik Ditt; Konstantin Nikolaou; Marius Horger
Journal:  Skeletal Radiol       Date:  2015-04-02       Impact factor: 2.199

2.  Improved MDCT monitoring of pelvic myeloma bone disease through the use of a novel longitudinal bone subtraction post-processing algorithm.

Authors:  Marius Horger; Wolfgang M Thaiss; Hendrik Ditt; Katja Weisel; Jan Fritz; Konstantin Nikolaou; Shu Liao; Christopher Kloth
Journal:  Eur Radiol       Date:  2016-11-23       Impact factor: 5.315

3.  Automatic rib unfolding in postmortem computed tomography: diagnostic evaluation of the OpenRib software compared with the autopsy in the detection of rib fractures.

Authors:  Martin Kolopp; Nicolas Douis; Ayla Urbaneja; Cédric Baumann; Pedro Augusto Gondim Teixeira; Alain Blum; Laurent Martrille
Journal:  Int J Legal Med       Date:  2019-11-16       Impact factor: 2.686

4.  Effect of Bone Reading CT software on radiologist performance in detecting bone metastases from breast cancer.

Authors:  Ji Y Ha; Kyung N Jeon; Kyungsoo Bae; Bong H Choi
Journal:  Br J Radiol       Date:  2017-03-03       Impact factor: 3.039

5.  Establishment of a regression model of bone metabolism markers for the diagnosis of bone metastases in lung cancer.

Authors:  Zhongliang Zhu; Guangyu Yang; Weizhong Wang; Yonglie Zhou; Zhenzhen Pang; Jiawei Liang
Journal:  World J Surg Oncol       Date:  2021-01-24       Impact factor: 2.754

6.  Value of Clinical Information on Radiology Reports in Oncological Imaging.

Authors:  Felix Schön; Rebecca Sinzig; Felix Walther; Christoph Georg Radosa; Heiner Nebelung; Maria Eberlein-Gonska; Ralf-Thorsten Hoffmann; Jens-Peter Kühn; Sophia Freya Ulrike Blum
Journal:  Diagnostics (Basel)       Date:  2022-06-30

7.  A Neural Network and Optimization Based Lung Cancer Detection System in CT Images.

Authors:  Chapala Venkatesh; Kadiyala Ramana; Siva Yamini Lakkisetty; Shahab S Band; Shweta Agarwal; Amir Mosavi
Journal:  Front Public Health       Date:  2022-06-07
  7 in total

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