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Atypical primary central nervous system lymphoma and glioblastoma: multiparametric differentiation based on non-enhancing volume, apparent diffusion coefficient, and arterial spin labeling
OBJECTIVES: To evaluate the multiparametric diagnostic performance with non-enhancing tumor volume, apparent diffusion coefficient (ADC), and arterial spin labeling (ASL) to differentiate between atypical primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM). METHODS: One hundred an...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10326108/ https://www.ncbi.nlm.nih.gov/pubmed/37171492 http://dx.doi.org/10.1007/s00330-023-09681-2 |
Sumario: | OBJECTIVES: To evaluate the multiparametric diagnostic performance with non-enhancing tumor volume, apparent diffusion coefficient (ADC), and arterial spin labeling (ASL) to differentiate between atypical primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM). METHODS: One hundred and fifty-eight patients with pathologically confirmed typical PCNSL (n = 59), atypical PCNSL (hemorrhage, necrosis, or heterogeneous contrast enhancement, n = 29), and GBM (n = 70) were selected. Relative minimum ADC (rADC(min)), mean (rADC(mean)), maximum (rADC(max)), and rADC(max-min) (rADC(dif)) were obtained by standardization of the contralateral white matter. Maximum cerebral blood flow (CBF(max)) was obtained according to the ASL-CBF map. The regions of interests (ROIs) were manually delineated on the inner side of the tumor to further generate a 3D-ROI and obtain the non-enhancing tumor (nET) volume. The area under the curve (AUC) was used to evaluate the diagnostic performance. RESULTS: Atypical PCNSLs showed significantly lower rADC(max), rADC(mean), and rADC(dif) than that of GBMs. GBMs showed significantly higher CBF(max) and nET volume ratios than that of atypical PCNSLs. Combined three-variable models with rADC(mean), CBF(max), and nET volume ratio were superior to one- and two-variable models. The AUC of the three-variable model was 0.96, and the sensitivity and specificity were 90% and 96.55%, respectively. CONCLUSION: The combined evaluation of rADC(mean), CBF(max), and nET volume allowed for reliable differentiation between atypical PCNSL and GBM. KEY POINTS: • Atypical PCNSL is easily misdiagnosed as glioblastoma, which leads to unnecessary surgical resection. • The nET volume, ADC, and ASL-derived parameter (CBF) were lower for atypical PCNSL than that for glioblastoma. • The combination of multiple parameters performed well (AUC = 0.96) in the discrimination between atypical PCNSL and glioblastoma. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00330-023-09681-2. |
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