Bright diffusion sign: A sensitive and specific radiologic biomarker for multinodular and vacuolating neuronal tumor

Arim Pak; Hye Jeong Choi; Sung-Hye You; Kyung-Sook Yang; Byungjun Kim; Sue-Hee Choi; Sang Heum Kim; Jung Youn Kim; Bo Kyu Kim; Sang Eun Park; Inseon Ryoo; Hye Na Jung

doi:10.1016/j.neurad.2023.11.006

Bright diffusion sign: A sensitive and specific radiologic biomarker for multinodular and vacuolating neuronal tumor

J Neuroradiol. 2023 Dec 8:S0150-9861(23)00268-7. doi: 10.1016/j.neurad.2023.11.006. Online ahead of print.

Authors

Affiliations

¹ Department of Radiology, Anam Hospital, Korea University College of Medicine, Seoul, South Korea.
² Department of Radiology, CHA Bundang Medical Center, CHA University, Seongnam, South Korea.
³ Department of Radiology, Anam Hospital, Korea University College of Medicine, Seoul, South Korea. Electronic address: yshneuro@gmail.com.
⁴ Department of Biostatistics, Korea University College of Medicine, Seoul, South Korea.
⁵ Department of Radiology, Guro Hospital, Korea University College of Medicine, Seoul, South Korea.

PMID: 38168545
DOI: 10.1016/j.neurad.2023.11.006

Abstract

Background and purpose: Accurate differentiation between multinodular and vacuolating neuronal tumor (MVNT) and dysembryoplastic neuroepithelial tumor (DNET) is important for treatment decision-making. We aimed to develop an accurate radiologic diagnostic model for differentiating MVNT from DNET using T2WI and diffusion-weighted imaging (DWI).

Materials and methods: A total of 56 patients (mean age, 47.48±17.78 years; 31 women) diagnosed with MVNT (n = 37) or DNET (n = 19) who underwent brain MRI, including T2WI and DWI, were included. Two board-certified neuroradiologists performed qualitative (bubble appearance, cortical involvement, bright diffusion sign, and bright apparent diffusion coefficient [ADC] sign) and quantitative (nDWI and nADC) assessments. A diagnostic tree model was developed with significant and reliable imaging findings using an exhaustive chi-squared Automatic Interaction Detector (CHAID) algorithm.

Results: In visual assessment, the imaging features that showed high diagnostic accuracy and interobserver reliability were the bright diffusion sign and absence of cortical involvement (bright diffusion sign: accuracy, 94.64 %; sensitivity, 91.89 %; specificity, 100.00 %; interobserver agreement, 1.00; absence of cortical involvement: accuracy, 92.86 %; sensitivity, 89.19 %; specificity, 100.00 %; interobserver agreement, 1.00). In quantitative analysis, nDWI was significantly higher in MVNT than in DENT (1.52 ± 0.34 vs. 0.91 ± 0.27, p < 0.001), but the interobserver agreement was fair (intraclass correlation coefficient = 0.321). The overall diagnostic accuracy of the tree model with visual assessment parameters was 98.21 % (55/56).

Conclusion: The bright diffusion sign and absence of cortical involvement are accurate and reliable imaging findings for differentiating MVNT from DNET. By using simple, intuitive, and reliable imaging findings, such as the bright diffusion sign, MVNT can be accurately differentiated from DNET.

Keywords: Diffusion-weighted imaging; Dysembryoplastic neuroepithelial tumor; Multinodular and vacuolating neuronal tumor.