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Cho NS, Sanvito F, Le VL, Oshima S, Teraishi A, Yao J, Telesca D, Raymond C, Pope WB, Nghiemphu PL, Lai A, Cloughesy TF, Salamon N, Ellingson BM. Quantification of T2-FLAIR Mismatch in Nonenhancing Diffuse Gliomas Using Digital Subtraction. AJNR Am J Neuroradiol 2024; 45:188-197. [PMID: 38238098 DOI: 10.3174/ajnr.a8094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Accepted: 11/10/2023] [Indexed: 02/09/2024]
Abstract
BACKGROUND AND PURPOSE The T2-FLAIR mismatch sign on MR imaging is a highly specific imaging biomarker of isocitrate dehydrogenase (IDH)-mutant astrocytomas, which lack 1p/19q codeletion. However, most studies using the T2-FLAIR mismatch sign have used visual assessment. This study quantified the degree of T2-FLAIR mismatch using digital subtraction of fluid-nulled T2-weighted FLAIR images from non-fluid-nulled T2-weighted images in human nonenhancing diffuse gliomas and then used this information to assess improvements in diagnostic performance and investigate subregion characteristics within these lesions. MATERIALS AND METHODS Two cohorts of treatment-naïve, nonenhancing gliomas with known IDH and 1p/19q status were studied (n = 71 from The Cancer Imaging Archive (TCIA) and n = 34 in the institutional cohort). 3D volumes of interest corresponding to the tumor were segmented, and digital subtraction maps of T2-weighted MR imaging minus T2-weighted FLAIR MR imaging were used to partition each volume of interest into a T2-FLAIR mismatched subregion (T2-FLAIR mismatch, corresponding to voxels with positive values on the subtraction maps) and nonmismatched subregion (T2-FLAIR nonmismatch corresponding to voxels with negative values on the subtraction maps). Tumor subregion volumes, percentage of T2-FLAIR mismatch volume, and T2-FLAIR nonmismatch subregion thickness were calculated, and 2 radiologists assessed the T2-FLAIR mismatch sign with and without the aid of T2-FLAIR subtraction maps. RESULTS Thresholds of ≥42% T2-FLAIR mismatch volume classified IDH-mutant astrocytoma with a specificity/sensitivity of 100%/19.6% (TCIA) and 100%/31.6% (institutional); ≥25% T2-FLAIR mismatch volume showed 92.0%/32.6% and 100%/63.2% specificity/sensitivity, and ≥15% T2-FLAIR mismatch volume showed 88.0%/39.1% and 93.3%/79.0% specificity/sensitivity. In IDH-mutant astrocytomas with ≥15% T2-FLAIR mismatch volume, T2-FLAIR nonmismatch subregion thickness was negatively correlated with the percentage T2-FLAIR mismatch volume (P < .0001) across both cohorts. The percentage T2-FLAIR mismatch volume was higher in grades 3-4 compared with grade 2 IDH-mutant astrocytomas (P < .05), and ≥15% T2-FLAIR mismatch volume IDH-mutant astrocytomas were significantly larger than <15% T2-FLAIR mismatch volume IDH-mutant astrocytoma (P < .05) across both cohorts. When evaluated by 2 radiologists, the additional use of T2-FLAIR subtraction maps did not show a significant difference in interreader agreement, sensitivity, or specificity compared with a separate evaluation of T2-FLAIR and T2-weighted MR imaging alone. CONCLUSIONS T2-FLAIR digital subtraction maps may be a useful, automated tool to obtain objective segmentations of tumor subregions based on quantitative thresholds for classifying IDH-mutant astrocytomas using the percentage T2 FLAIR mismatch volume with 100% specificity and exploring T2-FLAIR mismatch/T2-FLAIR nonmismatch subregion characteristics. Conversely, the addition of T2-FLAIR subtraction maps did not enhance the sensitivity or specificity of the visual T2-FLAIR mismatch sign assessment by experienced radiologists.
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Affiliation(s)
- Nicholas S Cho
- From the UCLA Brain Tumor Imaging Laboratory (N.S.C., F.S., V.L.L., S.O., A.T., J.Y., C.R., B.M.E.), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California
- Department of Radiological Sciences (N.S.C., F.S., V.L.L., S.O., A.T., J.Y., C.R., W.B.P., N.S., B.M.E.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
- Department of Bioengineering (N.S.C., V.L.L., B.M.E.), Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, California
- Medical Scientist Training Program (N.S.C.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Francesco Sanvito
- From the UCLA Brain Tumor Imaging Laboratory (N.S.C., F.S., V.L.L., S.O., A.T., J.Y., C.R., B.M.E.), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California
- Department of Radiological Sciences (N.S.C., F.S., V.L.L., S.O., A.T., J.Y., C.R., W.B.P., N.S., B.M.E.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Viên Lam Le
- From the UCLA Brain Tumor Imaging Laboratory (N.S.C., F.S., V.L.L., S.O., A.T., J.Y., C.R., B.M.E.), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California
- Department of Radiological Sciences (N.S.C., F.S., V.L.L., S.O., A.T., J.Y., C.R., W.B.P., N.S., B.M.E.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
- Department of Bioengineering (N.S.C., V.L.L., B.M.E.), Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, California
| | - Sonoko Oshima
- From the UCLA Brain Tumor Imaging Laboratory (N.S.C., F.S., V.L.L., S.O., A.T., J.Y., C.R., B.M.E.), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California
- Department of Radiological Sciences (N.S.C., F.S., V.L.L., S.O., A.T., J.Y., C.R., W.B.P., N.S., B.M.E.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Ashley Teraishi
- From the UCLA Brain Tumor Imaging Laboratory (N.S.C., F.S., V.L.L., S.O., A.T., J.Y., C.R., B.M.E.), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California
- Department of Radiological Sciences (N.S.C., F.S., V.L.L., S.O., A.T., J.Y., C.R., W.B.P., N.S., B.M.E.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Jingwen Yao
- From the UCLA Brain Tumor Imaging Laboratory (N.S.C., F.S., V.L.L., S.O., A.T., J.Y., C.R., B.M.E.), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California
- Department of Radiological Sciences (N.S.C., F.S., V.L.L., S.O., A.T., J.Y., C.R., W.B.P., N.S., B.M.E.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Donatello Telesca
- Department of Biostatistics (D.T.), Fielding School of Public Health, University of California Los Angeles, Los Angeles, California
| | - Catalina Raymond
- From the UCLA Brain Tumor Imaging Laboratory (N.S.C., F.S., V.L.L., S.O., A.T., J.Y., C.R., B.M.E.), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California
- Department of Radiological Sciences (N.S.C., F.S., V.L.L., S.O., A.T., J.Y., C.R., W.B.P., N.S., B.M.E.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Whitney B Pope
- Department of Radiological Sciences (N.S.C., F.S., V.L.L., S.O., A.T., J.Y., C.R., W.B.P., N.S., B.M.E.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Phioanh L Nghiemphu
- UCLA Neuro-Oncology Program (P.L.N., A.L., T.F.C.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
- Department of Neurology (P.L.N., A.L., T.F.C.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Albert Lai
- UCLA Neuro-Oncology Program (P.L.N., A.L., T.F.C.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
- Department of Neurology (P.L.N., A.L., T.F.C.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Timothy F Cloughesy
- UCLA Neuro-Oncology Program (P.L.N., A.L., T.F.C.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
- Department of Neurology (P.L.N., A.L., T.F.C.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Noriko Salamon
- Department of Radiological Sciences (N.S.C., F.S., V.L.L., S.O., A.T., J.Y., C.R., W.B.P., N.S., B.M.E.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Benjamin M Ellingson
- From the UCLA Brain Tumor Imaging Laboratory (N.S.C., F.S., V.L.L., S.O., A.T., J.Y., C.R., B.M.E.), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, California
- Department of Radiological Sciences (N.S.C., F.S., V.L.L., S.O., A.T., J.Y., C.R., W.B.P., N.S., B.M.E.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
- Department of Bioengineering (N.S.C., V.L.L., B.M.E.), Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, California
- Department of Neurosurgery (B.M.E.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
- Department of Psychiatry and Biobehavioral Sciences (B.M.E.), David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
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Kurokawa R, Hagiwara A, Kurokawa M, Ellingson BM, Baba A, Moritani T. Diffusion histogram profiles predict molecular features of grade 4 in histologically lower-grade adult diffuse gliomas following WHO classification 2021. Eur Radiol 2024; 34:1367-1375. [PMID: 37581661 PMCID: PMC10853353 DOI: 10.1007/s00330-023-10071-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 06/30/2023] [Accepted: 07/06/2023] [Indexed: 08/16/2023]
Abstract
OBJECTIVES In the latest World Health Organization classification 2021, grade 4 adult diffuse gliomas can be diagnosed with several molecular features even without histological evidence of necrosis or microvascular proliferation. We aimed to explore whole tumor histogram-derived apparent diffusion coefficient (ADC) histogram profiles for differentiating between the presence (Mol-4) and absence (Mol-2/3) of grade 4 molecular features in histologically lower-grade gliomas. METHODS Between June 2019 and October 2022, 184 adult patients with diffuse gliomas underwent MRI. After excluding 121 patients, 18 (median age, 64.5 [range, 37-84 years]) Mol-4 and 45 (median 40 [range, 18-73] years) Mol-2/3 patients with histologically lower-grade gliomas were enrolled. Whole tumor volume-of-interest-derived ADC histogram profiles were calculated and compared between the two groups. Stepwise logistic regression analysis with Akaike's information criterion using the ADC histogram profiles with p values < 0.01 and age at diagnosis was used to identify independent variables for predicting the Mol-4 group. RESULTS The 90th percentile (p < 0.001), median (p < 0.001), mean (p < 0.001), 10th percentile (p = 0.014), and entropy (p < 0.001) of normalized ADC were lower, and kurtosis (p < 0.001) and skewness (p = 0.046) were higher in the Mol-4 group than in the Mol-2/3 group. Multivariate logistic regression analysis revealed that the entropy of normalized ADC and age at diagnosis were independent predictive parameters for the Mol-4 group with an area under the curve of 0.92. CONCLUSION ADC histogram profiles may be promising preoperative imaging biomarkers to predict molecular grade 4 among histologically lower-grade adult diffuse gliomas. CLINICAL RELEVANCE STATEMENT This study highlighted the diagnostic usefulness of ADC histogram profiles to differentiate histologically lower grade adult diffuse gliomas with the presence of molecular grade 4 features and those without. KEY POINTS • ADC histogram profiles to predict molecular CNS WHO grade 4 status among histologically lower-grade adult diffuse gliomas were evaluated. • Entropy of ADC and age were independent predictive parameters for molecular grade 4 status. • ADC histogram analysis is useful for predicting molecular grade 4 among histologically lower-grade gliomas.
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Affiliation(s)
- Ryo Kurokawa
- Division of Neuroradiology, Department of Radiology, University of Michigan, 1500 E. Medical Center Dr, Ann Arbor, MI, 48109, USA.
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
| | - Akifumi Hagiwara
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
- Department of Radiology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Mariko Kurokawa
- Division of Neuroradiology, Department of Radiology, University of Michigan, 1500 E. Medical Center Dr, Ann Arbor, MI, 48109, USA
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA
| | - Akira Baba
- Division of Neuroradiology, Department of Radiology, University of Michigan, 1500 E. Medical Center Dr, Ann Arbor, MI, 48109, USA
| | - Toshio Moritani
- Division of Neuroradiology, Department of Radiology, University of Michigan, 1500 E. Medical Center Dr, Ann Arbor, MI, 48109, USA
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Pons-Escoda A, Garcia-Ruiz A, Naval-Baudin P, Martinez-Zalacain I, Castell J, Camins A, Vidal N, Bruna J, Cos M, Perez-Lopez R, Oleaga L, Warnert E, Smits M, Majos C. Differentiating IDH-mutant astrocytomas and 1p19q-codeleted oligodendrogliomas using DSC-PWI: high performance through cerebral blood volume and percentage of signal recovery percentiles. Eur Radiol 2024:10.1007/s00330-024-10611-z. [PMID: 38282078 DOI: 10.1007/s00330-024-10611-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/13/2023] [Accepted: 01/01/2024] [Indexed: 01/30/2024]
Abstract
OBJECTIVE Presurgical differentiation between astrocytomas and oligodendrogliomas remains an unresolved challenge in neuro-oncology. This research aims to provide a comprehensive understanding of each tumor's DSC-PWI signatures, evaluate the discriminative capacity of cerebral blood volume (CBV) and percentage of signal recovery (PSR) percentile values, and explore the synergy of CBV and PSR combination for pre-surgical differentiation. METHODS Patients diagnosed with grade 2 and 3 IDH-mutant astrocytomas and IDH-mutant 1p19q-codeleted oligodendrogliomas were retrospectively retrieved (2010-2022). 3D segmentations of each tumor were conducted, and voxel-level CBV and PSR were extracted to compute mean, minimum, maximum, and percentile values. Statistical comparisons were performed using the Mann-Whitney U test and the area under the receiver operating characteristic curve (AUC-ROC). Lastly, the five most discriminative variables were combined for classification with internal cross-validation. RESULTS The study enrolled 52 patients (mean age 45-year-old, 28 men): 28 astrocytomas and 24 oligodendrogliomas. Oligodendrogliomas exhibited higher CBV and lower PSR than astrocytomas across all metrics (e.g., mean CBV = 2.05 and 1.55, PSR = 0.68 and 0.81 respectively). The highest AUC-ROCs and the smallest p values originated from CBV and PSR percentiles (e.g., PSRp70 AUC-ROC = 0.84 and p value = 0.0005, CBVp75 AUC-ROC = 0.8 and p value = 0.0006). The mean, minimum, and maximum values yielded lower results. Combining the best five variables (PSRp65, CBVp70, PSRp60, CBVp75, and PSRp40) achieved a mean AUC-ROC of 0.87 for differentiation. CONCLUSIONS Oligodendrogliomas exhibit higher CBV and lower PSR than astrocytomas, traits that are emphasized when considering percentiles rather than mean or extreme values. The combination of CBV and PSR percentiles results in promising classification outcomes. CLINICAL RELEVANCE STATEMENT The combination of histogram-derived percentile values of cerebral blood volume and percentage of signal recovery from DSC-PWI enhances the presurgical differentiation between astrocytomas and oligodendrogliomas, suggesting that incorporating these metrics into clinical practice could be beneficial. KEY POINTS • The unsupervised selection of percentile values for cerebral blood volume and percentage of signal recovery enhances presurgical differentiation of astrocytomas and oligodendrogliomas. • Oligodendrogliomas exhibit higher cerebral blood volume and lower percentage of signal recovery than astrocytomas. • Cerebral blood volume and percentage of signal recovery combined provide a broader perspective on tumor vasculature and yield promising results for this preoperative classification.
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Affiliation(s)
- Albert Pons-Escoda
- Radiology Department, Feixa Llarga SN, Hospital Universitari de Bellvitge, 08907, Barcelona, Spain.
- Neuro-oncology Unit, Feixa Llarga SN, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, 08907, Barcelona, Spain.
- Facultat de Medicina i Ciències de La Salut, Universitat de Barcelona (UB), Carrer de Casanova 143, 08036, Barcelona, Spain.
- Diagnostic Imaging and Nuclear Medicine Research Group, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, Feixa Llarga SN, 08907, Barcelona, Spain.
| | - Alonso Garcia-Ruiz
- Radiomics Group, Vall d'Hebron Institut d'Oncologia- VHIO, Carrer de Natzaret, 115-117, 08035, Barcelona, Spain
| | - Pablo Naval-Baudin
- Radiology Department, Feixa Llarga SN, Hospital Universitari de Bellvitge, 08907, Barcelona, Spain
- Diagnostic Imaging and Nuclear Medicine Research Group, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, Feixa Llarga SN, 08907, Barcelona, Spain
| | - Ignacio Martinez-Zalacain
- Radiology Department, Feixa Llarga SN, Hospital Universitari de Bellvitge, 08907, Barcelona, Spain
- Diagnostic Imaging and Nuclear Medicine Research Group, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, Feixa Llarga SN, 08907, Barcelona, Spain
| | - Josep Castell
- Radiology Department, Feixa Llarga SN, Hospital Universitari de Bellvitge, 08907, Barcelona, Spain
| | - Angels Camins
- Radiology Department, Feixa Llarga SN, Hospital Universitari de Bellvitge, 08907, Barcelona, Spain
| | - Noemi Vidal
- Neuro-oncology Unit, Feixa Llarga SN, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, 08907, Barcelona, Spain
- Pathology Department, Feixa Llarga SN, Hospital Universitari de Bellvitge, 08907, Barcelona, Spain
| | - Jordi Bruna
- Neuro-oncology Unit, Feixa Llarga SN, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, 08907, Barcelona, Spain
| | - Monica Cos
- Radiology Department, Feixa Llarga SN, Hospital Universitari de Bellvitge, 08907, Barcelona, Spain
| | - Raquel Perez-Lopez
- Radiomics Group, Vall d'Hebron Institut d'Oncologia- VHIO, Carrer de Natzaret, 115-117, 08035, Barcelona, Spain
| | - Laura Oleaga
- Radiology Department, Hospital Clinic de Barcelona, Villarroel 170, 08036, Barcelona, Spain
| | - Esther Warnert
- Department of Radiology & Nuclear Medicine, Erasmus MC, Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
- Erasmus MC Cancer Institute, Erasmus MC, Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Marion Smits
- Department of Radiology & Nuclear Medicine, Erasmus MC, Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
- Erasmus MC Cancer Institute, Erasmus MC, Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
- Medical Delta, Delft, The Netherlands
| | - Carles Majos
- Radiology Department, Feixa Llarga SN, Hospital Universitari de Bellvitge, 08907, Barcelona, Spain
- Neuro-oncology Unit, Feixa Llarga SN, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, 08907, Barcelona, Spain
- Diagnostic Imaging and Nuclear Medicine Research Group, Institut d'Investigació Biomèdica de Bellvitge- IDIBELL, Feixa Llarga SN, 08907, Barcelona, Spain
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Pak A, Choi HJ, You SH, Yang KS, Kim B, Choi SH, Kim SH, Kim JY, Kim BK, Park SE, Ryoo I, Jung HN. Bright diffusion sign: A sensitive and specific radiologic biomarker for multinodular and vacuolating neuronal tumor. J Neuroradiol 2023:S0150-9861(23)00268-7. [PMID: 38168545 DOI: 10.1016/j.neurad.2023.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 11/27/2023] [Indexed: 01/05/2024]
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.
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Affiliation(s)
- Arim Pak
- Department of Radiology, Anam Hospital, Korea University College of Medicine, Seoul, South Korea
| | - Hye Jeong Choi
- Department of Radiology, CHA Bundang Medical Center, CHA University, Seongnam, South Korea
| | - Sung-Hye You
- Department of Radiology, Anam Hospital, Korea University College of Medicine, Seoul, South Korea.
| | - Kyung-Sook Yang
- Department of Biostatistics, Korea University College of Medicine, Seoul, South Korea
| | - Byungjun Kim
- Department of Radiology, Anam Hospital, Korea University College of Medicine, Seoul, South Korea
| | - Sue-Hee Choi
- Department of Radiology, CHA Bundang Medical Center, CHA University, Seongnam, South Korea
| | - Sang Heum Kim
- Department of Radiology, CHA Bundang Medical Center, CHA University, Seongnam, South Korea
| | - Jung Youn Kim
- Department of Radiology, CHA Bundang Medical Center, CHA University, Seongnam, South Korea
| | - Bo Kyu Kim
- Department of Radiology, Anam Hospital, Korea University College of Medicine, Seoul, South Korea
| | - Sang Eun Park
- Department of Radiology, Anam Hospital, Korea University College of Medicine, Seoul, South Korea
| | - Inseon Ryoo
- Department of Radiology, Guro Hospital, Korea University College of Medicine, Seoul, South Korea
| | - Hye Na Jung
- Department of Radiology, Guro Hospital, Korea University College of Medicine, Seoul, South Korea
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Cho NS, Sanvito F, Thakuria S, Wang C, Hagiwara A, Nagaraj R, Oshima S, Lopez Kolkovsky AL, Lu J, Raymond C, Liau LM, Everson RG, Patel KS, Kim W, Yang I, Bergsneider M, Nghiemphu PL, Lai A, Nathanson DA, Cloughesy TF, Ellingson BM. Multi-nuclear sodium, diffusion, and perfusion MRI in human gliomas. J Neurooncol 2023; 163:417-427. [PMID: 37294422 PMCID: PMC10322966 DOI: 10.1007/s11060-023-04363-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 06/02/2023] [Indexed: 06/10/2023]
Abstract
PURPOSE There is limited knowledge about the associations between sodium and proton MRI measurements in brain tumors. The purpose of this study was to quantify intra- and intertumoral correlations between sodium, diffusion, and perfusion MRI in human gliomas. METHODS Twenty glioma patients were prospectively studied on a 3T MRI system with multinuclear capabilities. Three mutually exclusive tumor volumes of interest (VOIs) were segmented: contrast-enhancing tumor (CET), T2/FLAIR hyperintense non-enhancing tumor (NET), and necrosis. Median and voxel-wise associations between apparent diffusion coefficient (ADC), normalized relative cerebral blood volume (nrCBV), and normalized sodium measurements were quantified for each VOI. RESULTS Both relative sodium concentration and ADC were significantly higher in areas of necrosis compared to NET (P = 0.003 and P = 0.008, respectively) and CET (P = 0.02 and P = 0.02). Sodium concentration was higher in CET compared to NET (P = 0.04). Sodium and ADC were higher in treated compared to treatment-naïve gliomas within NET (P = 0.006 and P = 0.01, respectively), and ADC was elevated in CET (P = 0.03). Median ADC and sodium concentration were positively correlated across patients in NET (r = 0.77, P < 0.0001) and CET (r = 0.84, P < 0.0001), but not in areas of necrosis (r = 0.45, P = 0.12). Median nrCBV and sodium concentration were negatively correlated across patients in areas of NET (r=-0.63, P = 0.003). Similar associations were observed when examining voxel-wise correlations within VOIs. CONCLUSION Sodium MRI is positively correlated with proton diffusion MRI measurements in gliomas, likely reflecting extracellular water. Unique areas of multinuclear MRI contrast may be useful in future studies to understand the chemistry of the tumor microenvironment.
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Affiliation(s)
- Nicholas S Cho
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, USA
- Medical Scientist Training Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Francesco Sanvito
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Shruti Thakuria
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Chencai Wang
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Akifumi Hagiwara
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Raksha Nagaraj
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Sonoko Oshima
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Alfredo L Lopez Kolkovsky
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- NMR Laboratory, Neuromuscular Investigation Center, Institute of Myology, Paris, France
| | - Jianwen Lu
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Linda M Liau
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Richard G Everson
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Kunal S Patel
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Won Kim
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Isaac Yang
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Marvin Bergsneider
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Phioanh L Nghiemphu
- UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Albert Lai
- UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - David A Nathanson
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Timothy F Cloughesy
- UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- UCLA Brain Tumor Imaging Laboratory (BTIL) Professor of Radiology, Psychiatry, and Neurosurgery Departments of Radiological Sciences, Psychiatry, and Neurosurgery David Geffen School of Medicine, University of California, Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA.
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