Title |
Preoperative prediction of microvascular invasion of hepatocellular carcinoma using 18F-FDG PET/CT: a multicenter retrospective cohort study
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Published in |
European Journal of Nuclear Medicine and Molecular Imaging, November 2017
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DOI | 10.1007/s00259-017-3880-4 |
Pubmed ID | |
Authors |
Seung Hyup Hyun, Jae Seon Eo, Bong-Il Song, Jeong Won Lee, Sae Jung Na, Il Ki Hong, Jin Kyoung Oh, Yong An Chung, Tae-Sung Kim, Mijin Yun |
Abstract |
The aim of this study was to assess the potential of tumor (18)F-fluorodeoxyglucose (FDG) avidity as a preoperative imaging biomarker for the prediction of microvascular invasion (MVI) of hepatocellular carcinoma (HCC). One hundred and fifty-eight patients diagnosed with Barcelona Clinic Liver Cancer stages 0 or A HCC (median age, 57 years; interquartile range, 50-64 years) who underwent (18)F-FDG positron emission tomography with computed tomography (PET/CT) before curative surgery at seven university hospitals were included. Tumor FDG avidity was measured by tumor-to-normal liver standardized uptake value ratio (TLR) of the primary tumor on FDG PET/CT imaging. Logistic regression analysis was performed to identify significant parameters associated with MVI. The predictive performance of TLR and other clinical variables was assessed using receiver operating characteristic (ROC) curve analysis. MVI was present in 76 of 158 patients with HCCs (48.1%). Multivariable logistic regression analysis revealed that TLR, serum alpha-fetoprotein (AFP) level, and tumor size were significantly associated with the presence of MVI (P < 0.001). Multinodularity was not significantly associated with MVI (P = 0.563). The area under the ROC curve (AUC) for predicting the presence of MVI was best with TLR (AUC = 0.704), followed by tumor size (AUC = 0.685) and AFP (AUC = 0.670). We were able to build an improved prediction model combining TLR, tumor size, and AFP by using multivariable logistic regression modeling (AUC = 0.756). Tumor FDG avidity measured by TLR on FDG PET/CT is a preoperative imaging biomarker for the prediction of MVI in patients with HCC. |
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Unknown | 2 | 100% |
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Members of the public | 2 | 100% |
Mendeley readers
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Unknown | 17 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 4 | 24% |
Student > Master | 4 | 24% |
Researcher | 3 | 18% |
Student > Doctoral Student | 1 | 6% |
Lecturer | 1 | 6% |
Other | 2 | 12% |
Unknown | 2 | 12% |
Readers by discipline | Count | As % |
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Medicine and Dentistry | 8 | 47% |
Computer Science | 2 | 12% |
Physics and Astronomy | 1 | 6% |
Linguistics | 1 | 6% |
Unknown | 5 | 29% |