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Utility of a Computed Tomography-Based Navigation System (O-Arm) for En Bloc Partial Vertebrectomy for Lung Cancer Adjacent to the Thoracic Spine: Technical Case Report

Overview of attention for article published in Asian Spine Journal, April 2016
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Title
Utility of a Computed Tomography-Based Navigation System (O-Arm) for En Bloc Partial Vertebrectomy for Lung Cancer Adjacent to the Thoracic Spine: Technical Case Report
Published in
Asian Spine Journal, April 2016
DOI 10.4184/asj.2016.10.2.360
Pubmed ID
Authors

Kazuyoshi Kobayashi, Shiro Imagama, Zenya Ito, Kei Ando, Kohei Yokoi, Naoki Ishiguro

Abstract

We describe successful vertebrectomy from a posterior approach using a computed tomography (CT)-based navigation system (O-arm) in a 53-year-old man with adenocarcinoma of the posterior apex of the right lung with invasion of the adjacent rib, thoracic wall, and T2 and T3 vertebral bodies. En bloc partial vertebrectomy for lung cancer adjacent to the thoracic spine was planned using O-arm. First, laminectomy was performed from right T2 to T3, and pedicles and transverse processes of T2 to T3 were resected. O-arm was used to confirm the location of the cutting edge in the T2 to 3 right vertebral internal body, and osteotomy to the anterior cortex was performed with a chisel. Next, the patient was placed in a left decubitus position. The surgical specimen was extracted en bloc. This case shows that O-arm can be used reliably and easily in vertebrectomy from a posterior approach and can facilitate en bloc resection.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 33%
Student > Ph. D. Student 1 11%
Student > Postgraduate 1 11%
Student > Bachelor 1 11%
Unknown 3 33%
Readers by discipline Count As %
Medicine and Dentistry 6 67%
Unknown 3 33%