論文

査読有り
2008年5月

Software-based fusion of PET and CT images for suspected recurrent lung cancer

MOLECULAR IMAGING AND BIOLOGY
  • Yuji Nakamoto
  • ,
  • Michio Senda
  • ,
  • Tomohisa Okada
  • ,
  • Setsu Sakamoto
  • ,
  • Tsuneo Saga
  • ,
  • Tatsuya Higashi
  • ,
  • Kaori Togashi

10
3
開始ページ
147
終了ページ
153
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1007/s11307-008-0131-x
出版者・発行元
SPRINGER

Purpose: The purpose of this study was to compare the diagnostic performance of the manual fusion of positron emission tomography (PET) and computed tomography (CT) images with that of CT alone and that of side-by-side PET and CT (PET/CT) in patients with suspected recurrent lung cancer.
Procedures: Fifty-three patients who had previously had surgery for lung cancer underwent a whole-body 2-deoxy-2-[F-18]fluoro-D-glucose (FDG)-PET scan, followed by a diagnostic CT scan. The PET and CT images were fused on a workstation. CT alone, PET/CT, and fused images were evaluated separately using a five-point grading scale (0 = definitely negative, 1=probably negative, 2=equivocal, 3=probably positive, and 4=definitely positive). Lesions of grade 3 or 4 were considered positive, and diagnostic accuracy and certainty were evaluated.
Results: Overall, 67 lesions in 33 patients were considered true positive pathologically or clinically. Of these 67 lesions, the evaluation of CT, PET/CT, and fused images detected 46, 55, and 66 lesions, respectively, with the number of grade 4 lesions detected being 38, 50, and 63, respectively. The diagnostic accuracy of CT, PET/CT, and fused images according to patients was 75%, 79%, and 87%, respectively.
Conclusion: These results suggest that interpreting fused images increased diagnostic certainty for detecting recurrence and provided more accurate diagnoses.

リンク情報
DOI
https://doi.org/10.1007/s11307-008-0131-x
PubMed
https://www.ncbi.nlm.nih.gov/pubmed/18293042
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000255680100004&DestApp=WOS_CPL
ID情報
  • DOI : 10.1007/s11307-008-0131-x
  • ISSN : 1536-1632
  • PubMed ID : 18293042
  • Web of Science ID : WOS:000255680100004

エクスポート
BibTeX RIS