Differentiation of high-grade glioma and primary central nervous system lymphoma: Multiparametric imaging of the enhancing tumor and peritumoral regions based on hybrid 18F-FDG PET/MRI. Issue 150 (May 2022)

• Record Type: Journal Article
• Authors: Zhang, Shu; Wang, Jie; Wang, Kai; Li, Xiaotong; Zhao, Xiaobin; Chen, Qian; Zhang, Wei; Ai, Lin
• Is Part Of: European journal of radiology. Issue 150(2022)
• Publication Date: 2022-05
• Subjects: High-grade glioma -- Primary central nervous system lymphoma -- FDG PET -- Diffusion-weighted imaging -- Perfusion-weighted imaging -- Differential diagnosis; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2022.110235
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: rSUVmax, rCBFmax and rADCmin in the peritumoral region, as well as enhancing tumor, showed significant difference between HGG and PCNSL. Among the quantitative parameters, rSUVmax from the enhancing tumor has the best diagnostic ability for differentiation of HGG and PCNSL. Multiparametric 18 F-FDG PET/MRI diagnostic model based on conventional MRI features and quantitative parameters can improve diagnostic accuracy. Abstract: Objectives: To investigate the value of the 18 F-FDG PET/MRI multiparametric model in the differentiation of high-grade glioma (HGG) and primary central nervous system lymphoma (PCNSL), with emphasis on the quantitative analysis of the enhancing tumor (ET) and non-enhancing peritumoral region (PTR). Methods: Forty-five patients with HGG and 20 patients with PCNSL who underwent simultaneous 18 F-FDG PET, arterial spin labelling perfusion-weighted imaging and diffusion-weighted imaging with hybrid PET/MRI before treatment were retrospectively enrolled. The relative maximum standardized uptake value (rSUVmax ), relative maximum cerebral blood flow (rCBFmax ) and relative minimum apparent diffusion coefficient (rADCmin ) in both the ET and NPR were calculated and compared between HGG and PCNSL. Multivariate logistic regression was used to determine the best logistic regression model (LRM) for classification. Receiver operating curve analysis was used to assess diagnostic performance. Results: In the ET, HGG showed significantly lower rSUVmax values but higher rCBFmax and rADCmin than PCNSL (all P < 0.05). In the PTR, HGG demonstrated significantly higher rSUVmax and rCBFmax but lower rADCmin than PCNSL (all P < 0.05). Multivariate logistic regression based on quantitative parameters revealed that the LRM consisting of rSUVmax_ET, rADCmin_ET and rCBFmax_PTR had significantly improved diagnostic performance in differentiating HGG from PCNSL than single parameter alone, with an AUC of 0.980 and an accuracy of 95.4%. Multivariate logistic regression incorporating quantitative parameters and conventional MRI features revealed that the LRM consisting of rSUVmax_ET, rCBFmax_PTR and enhancement pattern yielded a slightly higher AUC of 0.989 and an identical accuracy of 95.4%. No significant difference in AUCs was detected between the two LRMs ( P = 0.233). Conclusions: Multiparametric 18 F-FDG PET/MRI diagnostic model based on conventional MRI features and quantitative analysis of the enhancing tumors and peritumoral regions is superior to single parameter in the differentiation of HGG and PCNSL, which should be considered in the clinical practice.