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Normal model construction for statistical image analysis of torso FDG-PET images based on anatomical standardization by CT images from FDG-PET/CT devices

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Abstract

Purpose

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 (\(p < 0.001\)).

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.

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Acknowledgements

This research work was funded in part by Grant-in-Aid for Scientific Research on Innovative Areas (26108005) and in part by Grant-in-Aid for Scientific Research (C26330134), MEXT, Japan.

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Authors and Affiliations

  1. Department of Intelligent Image Information, Graduate School of Medicine, Gifu University, 1-1 Yanagido, Gifu, Gifu, 501-1194, Japan

    Kenshiro Takeda, Takeshi Hara, Xiangrong Zhou & Hiroshi Fujita

  2. Faculty of Health Science, Gifu University of Medical Science, Seki, Japan

    Tetsuro Katafuchi

  3. Department of Radiology, Daiyukai General Hospital, Ichinomiya, Japan

    Masaya Kato & Satoshi Ito

  4. Department of Radiology, Nippon Medical School, Tokyo, Japan

    Keiichi Ishihara & Shinichiro Kumita

Authors
  1. Kenshiro Takeda
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  2. Takeshi Hara
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  3. Xiangrong Zhou
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  4. Tetsuro Katafuchi
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  5. Masaya Kato
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  6. Satoshi Ito
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  7. Keiichi Ishihara
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  8. Shinichiro Kumita
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  9. Hiroshi Fujita
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Corresponding author

Correspondence to Takeshi Hara.

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Conflict of interest

Takeshi Hara has received research grants from Nihon Medi-Physics Co. Ltd. Kenshiro Takeda, Xiangrong Zhou, Tetsuro Katafuchi, Masaya Kato, Satoshi Ito, Keiichi Ishihara, Shinichiro Kumita and Hiroshi Fujita declare that they have no conflict of interest.

Ethical approval

All procedures performed in the studies involving human participants were in accordance with the ethical standards of the institutional and national research committees, and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The Institutional Review Board at Gifu University approved this study (#23-131, #28-114). For this type of study, formal consent is not required.

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Takeda, K., Hara, T., Zhou, X. et al. Normal model construction for statistical image analysis of torso FDG-PET images based on anatomical standardization by CT images from FDG-PET/CT devices. Int J CARS 12, 777–787 (2017). https://doi.org/10.1007/s11548-017-1526-4

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  • DOI: https://doi.org/10.1007/s11548-017-1526-4

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