Preoperative assessment of localized pleural adhesion: Utility of software-assisted analysis on dynamic-ventilation computed tomography

滋賀医大　永谷先生の論文がEuropean Journal of Radiologyにアクセプトされました。いつもコツコツと論文化を進めておられ敬服します。

Preoperative assessment of localized pleural adhesion: Utility of software-assisted analysis on dynamic-ventilation computed tomography
European Journal of Radiology ( IF 2.687 ) Pub Date : 2020-10-22 , DOI: 10.1016/j.ejrad.2020.109347
Yukihiro Nagatani; Masayuki Hashimoto; Yasuhiko Oshio; Shigetaka Sato; Jun Hanaoka; Kentaro Fukunaga; Ryo Uemura; Makoto Yoshigoe; Norihisa Nitta; Noritoshi Usio; Shinsuke Tsukagoshi; Tatsuya Kimoto; Tsuneo Yamashiro; Hiroshi Moriya; Kiyoshi Murata; Yoshiyuki Watanabe
Purpose To assess the usefulness of software analysis using dynamic-ventilation CT for localized pleural adhesion (LPA).
Materials and methods Fifty-one patients scheduled to undergo surgery underwent both dynamic-ventilation CT and static chest CT as preoperative assessments. Five observers independently evaluated the presence and severity of LPA on a three-point scale (non, mild, and severe LPA) for 9 pleural regions (upper, middle, and lower pleural aspects on ventral, lateral, and dorsal areas) on the chest CT by three different methods by observing images from: static high-resolution CT (static image); dynamic-ventilation CT (movie image), and dynamic-ventilation CT while referring to the adhesion map (movie image with color map), which was created using research software to visualize movement differences between the lung surface and chest wall. The presence and severity of LPA was confirmed by intraoperative thoracoscopic findings. Parameters of diagnostic accuracy for LPA presence and severity were assessed among the three methods using Wilcoxon signed rank test in total and for each of the three pleural aspects.
Results Mild and severe LPA were confirmed in 14 and 8 patients. Movie image with color map had higher sensitivity (56.9 ± 10.7 %) and negative predictive value (NPV) (91.4 ± 1.7 %) in LPA detection than both movie image and static image. Additionally, for severe LPA, detection sensitivity was the highest with movie image with color map (82.5 ± 6.1 %), followed by movie image (58.8 ± 17.0 %) and static image (38.8 ± 13.9 %). For LPA severity, movie image with color map was similar to movie image and superior to static image in accuracy as well as underestimation and overestimation, with a mean value of 80.2 %.
Conclusion Software-assisted dynamic-ventilation CT may be a useful novel imaging approach to improve the detection performance of LPA.