방사선의학 웹진

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Abstract
We aimed to characterize solitary pulmonary nodule (SPN) using imaging parameters for F-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) or enhanced CT corrected by tumor shadow disappearance rate (TDR) to reflect the tissue density.We enrolled 51 patients with an SPN who underwent PET/CT and chest CT with enhancement. The FDG uptake of SPN was evaluated using maximum standardized uptake value (SUVmax) on PET/CT. The mean Hounsfield unit (HU) for each SPN was evaluated over the region of interest on nonenhanced and enhanced CT images. The change in mean HU (HUpeak-pre) was quantified by subtracting the mean HU of the preenhanced CT from that of the post-enhanced CT. TDR was defined as the ratio of the tumor area, which disappears at a mediastinal window, to the tumor area of the lung window. We investigated which parameters (SUVmax or HUpeak-pre) could contribute to the characterization of SPN classified by TDR value and whether diagnostic performance could be improved using TDR-corrected imaging parameters.For SPN with higher tissue density (TDR <42%, n = 22), high value of SUVmax (≥3.1) was a significant factor to predict malignancy (P = .006). High value of HUpeak-pre (≥38) was a significant factor to characterize SPN (P = .002) with lower tissue density (TDR ≥42%, n = 29). The combined approach using TDR-corrected parameters had better predictive performance to characterize SPN than SUVmax only (P = .031).Applying imaging parameters such as SUVmax or HUpeak-pre in consideration of tissue density calculated with TDR could contribute to accurate characterization of SPN.

Diagnostic flow-chart for the characterization of solitary pulmonary nodule (SPN) using F-18 FDG PET/CT and enhanced CT based on the tumor shadow disappearance rate (TDR).

Affiliations

Ki Seong Park  1 , Hyun Ju Seon  2 , Ju-Sik Yun  3 , Su Woong Yoo  4 , Changho Lee  4   5 , Sae-Ryung Kang  4 , Jahae Kim  1   5   6 , Sang-Geon Cho  1 , Ho-Chun Song  1   5 , Hee-Seung Bom  4   5 , Jung-Joon Min  4   5 , Seong Young Kwon  4   5
1 Department of Nuclear Medicine, Chonnam National University Hospital, Gwangju, Republic of Korea.
2 Department of Radiology, Chosun University Hospital, Gwangju, Republic of Korea.
3 Department of Thoracic and Cardiovascular Surgery, Chonnam National University Hwasun Hospital, Jeonnam, Republic of Korea.
4 Department of Nuclear Medicine, Chonnam National University Hwasun Hospital, Jeonnam, Republic of Korea.
5 Department of Nuclear Medicine, Chonnam National University Medical School, Jeonnam, Republic of Korea.
6 Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, Republic of Korea.