Normal model construction for statistical image analysis of torso FDG-PET images based on anatomical standardization by CT images from FDG-PET/CT devices.
OBJECTIVE
A better understanding of the standardized uptake value (SUV) ranges of fludeoxyglucose positron emission tomography (FDG-PET) is crucial for radiologists. We have developed a statistical image analysis method for FDG-PET imaging of the torso, based on comparisons with normal data. The purpose of this study was to verify the accuracy of the normal model and usefulness of the statistical image analysis method by using typical cancer cases in the liver, lungs, and abdomen.


METHODS
Our study and the data collection (49 normal and 34 abnormal cases, in terms of PET/CT findings) were approved by the institutional review board. Our scheme consisted of the following steps: (1) normal model construction, (2) anatomical standardization of patient images, and (3) Z-score calculation to show the results of the statistical image analysis. To validate the Z-score index, we sampled 3603 and 1270 voxels in normal organs and abnormal regions, respectively, from the liver, lungs, and the abdomen. We then obtained the SUV and Z-score for each region. A receiver operating characteristics (ROC) analysis-based method was performed to evaluate the discrimination performances of the SUV and Z-score.


RESULTS
The discrimination performances of the SUV and Z-score for the objective regions of interest (ROIs) were evaluated by the areas under the ROC curves (AUCs). As a result of the ROC analysis and statistical tests, all AUCs were found to be larger than 0.98. When the ROIs in the objective regions were combined, the mean AUCs of the Z-score and SUV were 0.99 and 0.98, respectively, the difference being statistically significant ([Formula: see text]).


CONCLUSIONS
The results suggested the possibility of applying a quantitative image reading method for torso FDG-PET imaging. Furthermore, a combination of the SUV and Z-score may provide increased accuracy of the determination methods, such as computer-aided detection and diagnosis.
Department of Intelligent Image Information, Graduate School of Medicine, Gifu University, 1-1 Yanagido, Gifu, Gifu, 501-1194, Japan.
Kenshiro
K
Takeda
Department of Intelligent Image Information, Graduate School of Medicine, Gifu University, 1-1 Yanagido, Gifu, Gifu, 501-1194, Japan. takeshi.hara@mac.com.
Takeshi
http://orcid.org/0000-0002-0235-238X
T
Hara
Department of Intelligent Image Information, Graduate School of Medicine, Gifu University, 1-1 Yanagido, Gifu, Gifu, 501-1194, Japan.
Xiangrong
X
Zhou
Faculty of Health Science, Gifu University of Medical Science, Seki, Japan.
Tetsuro
T
Katafuchi
Department of Radiology, Daiyukai General Hospital, Ichinomiya, Japan.
Masaya
M
Kato
Department of Radiology, Daiyukai General Hospital, Ichinomiya, Japan.
Satoshi
S
Ito
Department of Radiology, Nippon Medical School, Tokyo, Japan.
Keiichi
K
Ishihara
Department of Radiology, Nippon Medical School, Tokyo, Japan.
Shinichiro
S
Kumita
Department of Intelligent Image Information, Graduate School of Medicine, Gifu University, 1-1 Yanagido, Gifu, Gifu, 501-1194, Japan.
Hiroshi
H
Fujita
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D005260:Female; D019788:Fluorodeoxyglucose F18 / Q000737:chemistry; D006801:Humans; D008099:Liver / Q000000981:diagnostic imaging; D008168:Lung / Q000000981:diagnostic imaging; D008297:Male; D008875:Middle Aged; D000072078:Positron Emission Tomography Computed Tomography / Q000379:methods*; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D011860:Radiography, Abdominal; D019275:Radiopharmaceuticals / Q000737:chemistry; D015203:Reproducibility of Results; D060726:Torso / Q000000981:diagnostic imaging*; D055815:Young Adult
D019275:Radiopharmaceuticals; D019788:Fluorodeoxyglucose F18
PET/CT; ROC; SUV; Torso FDG-PET; Z-score