The Performance Comparison of 18F-FDG PET/MRI and 18F-FDG PET/CT for the Identification of Pancreatic Neoplasms

医学 PET-CT 核医学 恶性肿瘤 正电子发射断层摄影术 放射科 病变 胰腺 磁共振成像 病理 内科学
作者
Haiqun Xing,Haiyan Ding,Bo Hou,Zhixin Hao,Ya Hu,Wenjia Zhu,Sayuan Liang,Feng Feng,Fang Li,Yupei Zhao,Li Huo
出处
期刊:Molecular Imaging and Biology [Springer Science+Business Media]
卷期号:24 (3): 489-497 被引量:1
标识
DOI:10.1007/s11307-021-01687-w
摘要

PurposeTo determine the optimal imaging tool for clinical evaluation of pancreatic neoplasm by comparing the performance of 18F-FDG PET/MRI and PET/CT.ProceduresPatients with suspected pancreatic neoplasms underwent PET/MRI and PET/CT in the same day prior to resection or endoscopic ultrasound-guided fine-needle aspiration. Histology served as the golden standard of lesion classification. Visual assessment on lesion type and lesion malignancy via PET/MRI and PET/CT images was compared. Standard uptake values (SUVs) of PET images from the two scanners were measured and their correlations were further evaluated.ResultsThirty-nine patients were included for the final analysis. In visual assessment, we found MRI achieved better performance than CT in differentiating solid and cystic neoplasms, with accuracy of 100% vs. 87%, respectively. In visual malignancy diagnosis, the accuracy of PET/CT was 92.3% for overall lesions and 90.9% for cysts, while the accuracy of PET/MRI was 92.3% and 86.4%, respectively. Besides, semi-quantitative analysis achieved better specificity than visual assessment for both hybrid modalities (100% vs. 87.5% for PET/CT; 100% vs. 81.5% for PET/MR). Furthermore, strong correlation of SUV was found between PET/CT and PET/MRI, with Pearson’s correlation coefficients > 0.82.ConclusionsIn this study, we found PET/MRI and PET/CT, both using 18F-FDG as tracer, had comparable overall performance in identification of pancreatic neoplasms. Interestingly, for patients who had suspected pancreatic neoplasm but invisible FDG uptake, PET/MRI had shown exceptionally better performance, probably because MR images could detect tiny abnormal structures to improve diagnosis.
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