Literature DB >> 26192552

3D ECG- and respiratory-gated non-contrast-enhanced (CE) perfusion MRI for postoperative lung function prediction in non-small-cell lung cancer patients: A comparison with thin-section quantitative computed tomography, dynamic CE-perfusion MRI, and perfusion scan.

Yoshiharu Ohno1,2, Shinichiro Seki3, Hisanobu Koyama3, Takeshi Yoshikawa1,2, Sumiaki Matsumoto1,2, Daisuke Takenaka3,4, Yoshimori Kassai5, Masao Yui5, Kazuro Sugimura3.   

Abstract

PURPOSE: To compare predictive capabilities of non-contrast-enhanced (CE)- and dynamic CE-perfusion MRIs, thin-section multidetector computed tomography (CT) (MDCT), and perfusion scan for postoperative lung function in non-small cell lung cancer (NSCLC) patients.
MATERIALS AND METHODS: Sixty consecutive pathologically diagnosed NSCLC patients were included and prospectively underwent thin-section MDCT, non-CE-, and dynamic CE-perfusion MRIs and perfusion scan, and had their pre- and postoperative forced expiratory volume in one second (FEV1 ) measured. Postoperative percent FEV1 (po%FEV1 ) was then predicted from the fractional lung volume determined on semiquantitatively assessed non-CE- and dynamic CE-perfusion MRIs, from the functional lung volumes determined on quantitative CT, from the number of segments observed on qualitative CT, and from uptakes detected on perfusion scans within total and resected lungs. Predicted po%FEV1 s were then correlated with actual po%FEV1 s, which were %FEV1 s measured postoperatively. The limits of agreement were also determined.
RESULTS: All predicted po%FEV1 s showed significant correlation (0.73 ≤ r ≤ 0.93, P < 0.0001) and limits of agreement with actual po%FEV1 (non-CE-perfusion MRI: 0.3 ± 10.0%, dynamic CE-perfusion MRI: 1.0 ± 10.8%, perfusion scan: 2.2 ± 14.1%, quantitative CT: 1.2 ± 9.0%, qualitative CT: 1.5 ± 10.2%).
CONCLUSION: Non-CE-perfusion MRI may be able to predict postoperative lung function more accurately than qualitatively assessed MDCT and perfusion scan.
© 2014 Wiley Periodicals, Inc.

Entities:  

Keywords:  lung; lung cancer; lung function; magnetic resonance (MR); perfusion

Mesh:

Year:  2014        PMID: 26192552     DOI: 10.1002/jmri.24800

Source DB:  PubMed          Journal:  J Magn Reson Imaging        ISSN: 1053-1807            Impact factor:   4.813


  4 in total

Review 1.  Overview of MRI for pulmonary functional imaging.

Authors:  Yoshiharu Ohno; Satomu Hanamatsu; Yuki Obama; Takahiro Ueda; Hirotaka Ikeda; Hidekazu Hattori; Kazuhiro Murayama; Hiroshi Toyama
Journal:  Br J Radiol       Date:  2021-02-02       Impact factor: 3.629

2.  Predicting Postoperative Lung Function Following Lung Cancer Resection: A Systematic Review and Meta-analysis.

Authors:  Nicola K Oswald; James Halle-Smith; Rana Mehdi; Peter Nightingale; Babu Naidu; Alice M Turner
Journal:  EClinicalMedicine       Date:  2019-09-10

Review 3.  State-of-the-art MR Imaging for Thoracic Diseases.

Authors:  Yumi Tanaka; Yoshiharu Ohno; Satomu Hanamatsu; Yuki Obama; Takahiro Ueda; Hirotaka Ikeda; Akiyoshi Iwase; Takashi Fukuba; Hidekazu Hattori; Kazuhiro Murayama; Takeshi Yoshikawa; Daisuke Takenaka; Hisanobu Koyama; Hiroshi Toyama
Journal:  Magn Reson Med Sci       Date:  2021-04-29       Impact factor: 2.760

4.  Comparison of predicted postoperative forced expiratory volume in the first second (FEV1) using lung perfusion scintigraphy with observed forced expiratory volume in the first second (FEV1) post lung resection.

Authors:  Boon Mathew; Sudipta Nag; Archi Agrawal; Priya Ranganathan; Nilendu C Purandare; Sneha Shah; Ameya Puranik; Venkatesh Rangarajan
Journal:  World J Nucl Med       Date:  2020-01-29
  4 in total

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