Temporal subtraction method for lung nodule detection on successive thoracic CT soft-copy images.
OBJECTIVE
To assess the effects of a new computed tomographic (CT) temporal subtraction (TS) method on radiologist performance in lung nodule detection on thin-section CT images.


METHODS
The institutional review board approved this study, and the informed consent requirement was waived. Fifty pairs (current and previous CT images) of standard-dose 2-mm thin-section CT images and corresponding CT TS images were used for an observer performance study. Two thoracic radiologists identified 30 nodules ranging in size from 5 to 19 mm, and these nodules served as the reference standard of actionable nodules (noncalcified nodules larger than 4 mm). Eight radiologists (four attending radiologists, four radiology residents) participated in this observer study. Ratings and locations of lesions determined by observers were used to assess the significance of differences between radiologists' performances without and with the CT TS images in jacknife free-response receiver operating characteristics analysis.


RESULTS
Average figure of merit values increased significantly for all radiologists (from 0.838 without CT TS images to 0.894 with CT TS images [P = .033]). Average sensitivity for detection of actionable nodules was improved from 73.4% to 83.4%, with a false-positive rate of 0.15 per case, by using CT TS images. The reading time with CT TS images was not significantly different from that without.


CONCLUSIONS
The novel CT TS method would increase observer performance for lung nodule detection without considerably extending the reading time.
From the Department of Radiology, University of Occupational and Environmental Health School of Medicine, 1-1 Iseigaoka, Yahatanishi-ku, Kitakyushu 807-8555, Japan (T.A., S.M., M.F., H.T., H.O., Y.H., Y.K.); Graduate School of Engineering, Kyushu Institute of Technology, Kitakyushu, Japan (S.M., H.K.); and Department of Medical Physics, Division of Advanced Biomedical Sciences, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan (S.K., J.S.).
Takatoshi
T
Aoki
Seiichi
S
Murakami
Hyoungseop
H
Kim
Masami
M
Fujii
Hiroyuki
H
Takahashi
Hodaka
H
Oki
Yoshiko
Y
Hayashida
Shigehiko
S
Katsuragawa
Junji
J
Shiraishi
Yukunori
Y
Korogi
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013902:Radiography, Thoracic; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D012984:Software; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*