Accurate identification of primary central nervous system lymphoma (PCNSL) and its differentiation from other brain tumors remain difficult but are essential for treatment. In this study, we investigated whether 13N-ammonia combined with 18F-FDG could distinguish PCNSL from solid gliomas effectively.
Ten consecutive patients with final diagnosis of PCNSL (5 female and 5 male patients; mean [SD] age, 59.10 [12.47] years; range, 43–74 years) and another fifteen consecutive patients with solid glioma lesions (5 female and 10 male patients; mean [SD] age, 46.73 [19.61] years; range, 14–72 years) were included in this study. PET/CT imaging was performed for all of them with both 18F-FDG and 13N-ammonia as tracers. Tumor-to-gray matter (T/G) ratios were calculated for the evaluation of tumor uptake. Both Student t test and discriminant analysis were recruited to assess the differential efficacy of these 2 tracers.
The T/G ratios of 18F-FDG in PCNSL lesions were higher than in solid gliomas (3.26 [1.18] vs 1.56 [0.41], P < 0.001), whereas the T/G ratios of 13N-ammonia in PCNSL lesions were lower than in solid gliomas significantly (1.38 [0.20] vs 2.11 [0.69], P < 0.001). All the lesions of PCNSL displayed higher T/G ratios of 18F-FDG than 13N-ammonia, whereas 14 (77.8%) of 18 glioma lesions showed contrary results. Tumor classification by means of canonical discriminant analysis yielded an overall accuracy of 96.9%, and only one glioma lesion was misclassified into the PCNSL group.
PCNSLs and solid gliomas have different metabolic profiles on 13N-ammonia and 18F-FDG imaging. The combination of these 2 tracers can distinguish these 2 clinical entities effectively and make an accurate prediction of PCNSL.
From the Department of Nuclear Medicine, the First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
Received for publication February 23, 2012; revision accepted September 10, 2012.
Conflicts of interest and sources of funding: none declared.
Reprints: Xiangsong Zhang, PhD, Department of Nuclear Medicine, the First Affiliated Hospital of Sun Yat-Sen University, 58#, ZhongshanEr Rd, Guangzhou, China, 510080. E-mail: email@example.com.