Comparative analysis of glymphatic system alterations in multiple sclerosis and neuromyelitis optica spectrum disorder using MRI indices from diffusion tensor imaging.

OBJECTIVE: The glymphatic system is a glial-based perivascular network that promotes brain metabolic waste clearance. Glymphatic system dysfunction has been observed in both multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD), indicating the role of neuroinflammation in the glymphatic system. However, little is known about how the two diseases differently affect the human glymphatic system. The present study aims to evaluate the diffusion MRI-based measures of the glymphatic system by contrasting MS and NMOSD. METHODS: This prospective study included 63 patients with NMOSD (n = 21) and MS (n = 42) who underwent DTI. The fractional volume of extracellular-free water (FW) and an index of diffusion tensor imaging (DTI) along the perivascular space (DTI-ALPS) were used as indirect indicators of water diffusivity in the interstitial extracellular and perivenous spaces of white matter, respectively. Age and EDSS scores were adjusted. RESULTS: Using Bayesian hypothesis testing, we show that the present data substantially favor the null model of no differences between MS and NMOSD for the diffusion MRI-based measures of the glymphatic system. The inclusion Bayes factor (BF10) of model-averaged probabilities of the group (MS, NMOSD) was 0.280 for FW and 0.236 for the ALPS index. CONCLUSION: Together, these findings suggest that glymphatic alteration associated with MS and NMOSD might be similar and common as an eventual result, albeit the disease etiologies differ. PRACTITIONER POINTS: Previous literature indicates important glymphatic system alteration in MS and NMOSD. We explore the difference between MS and NMOSD using diffusion MRI-based measures of the glymphatic system. We show support for the null hypothesis of no difference between MS and NMOSD. This suggests that glymphatic alteration associated with MS and NMOSD might be similar and common etiology.

Dynamic Contrast-enhanced MRI Quantification of Altered Vascular Permeability in Autoimmune Encephalitis.

Background Blood-brain barrier (BBB) permeability change is a possible pathologic mechanism of autoimmune encephalitis. Purpose To evaluate the change in BBB permeability in patients with autoimmune encephalitis as compared with healthy controls by using dynamic contrast-enhanced (DCE) MRI and to explore its predictive value for treatment response in patients. Materials and Methods This single-center retrospective study included consecutive patients with probable or possible autoimmune encephalitis and healthy controls who underwent DCE MRI between April 2020 and May 2021. Automatic volumetric segmentation was performed on three-dimensional T1-weighted images, and volume transfer constant (Ktrans) values were calculated at encephalitis-associated brain regions. Ktrans values were compared between the patients and controls, with adjustment for age and sex with use of a nonparametric approach. The Wilcoxon rank sum test was performed to compare Ktrans values of the good (improvement in modified Rankin Scale [mRS] score of at least two points or achievement of an mRS score of ≤2) and poor (improvement in mRS score of less than two points and achievement of an mRS score >2) treatment response groups among the patients. Results Thirty-eight patients with autoimmune encephalitis (median age, 38 years [IQR, 29-59 years]; 20 [53%] female) and 17 controls (median age, 71 years [IQR, 63-77 years]; 12 [71%] female) were included. All brain regions showed higher Ktrans values in patients as compared with controls (P < .001). The median difference in Ktrans between the patients and controls was largest in the right parahippocampal gyrus (25.1 × 10-4 min-1 [95% CI: 17.6, 43.4]). Among patients, the poor treatment response group had higher baseline Ktrans values in both cerebellar cortices (P = .03), the left cerebellar cortex (P = .02), right cerebellar cortex (P = .045), left cerebral cortex (P = .045), and left postcentral gyrus (P = .03) than the good treatment response group. Conclusion DCE MRI demonstrated that BBB permeability was increased in all brain regions in patients with autoimmune encephalitis as compared with controls, and baseline Ktrans values were higher in patients with poor treatment response in the cerebellar cortex, left cerebral cortex, and left postcentral gyrus as compared with the good response group. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Filippi and Rocca in this issue.

Predicting hematoma expansion in acute spontaneous intracerebral hemorrhage: integrating clinical factors with a multitask deep learning model for non-contrast head CT.

PURPOSE: To predict hematoma growth in intracerebral hemorrhage patients by combining clinical findings with non-contrast CT imaging features analyzed through deep learning. METHODS: Three models were developed to predict hematoma expansion (HE) in 572 patients. We utilized multi-task learning for both hematoma segmentation and prediction of expansion: the Image-to-HE model processed hematoma slices, extracting features and computing a normalized DL score for HE prediction. The Clinical-to-HE model utilized multivariate logistic regression on clinical variables. The Integrated-to-HE model combined image-derived and clinical data. Significant clinical variables were selected using forward selection in logistic regression. The two models incorporating clinical variables were statistically validated. RESULTS: For hematoma detection, the diagnostic performance of the developed multi-task model was excellent (AUC, 0.99). For expansion prediction, three models were evaluated for predicting HE. The Image-to-HE model achieved an accuracy of 67.3%, sensitivity of 81.0%, specificity of 64.0%, and an AUC of 0.76. The Clinical-to-HE model registered an accuracy of 74.8%, sensitivity of 81.0%, specificity of 73.3%, and an AUC of 0.81. The Integrated-to-HE model, merging both image and clinical data, excelled with an accuracy of 81.3%, sensitivity of 76.2%, specificity of 82.6%, and an AUC of 0.83. The Integrated-to-HE model, aligning closest to the diagonal line and indicating the highest level of calibration, showcases superior performance in predicting HE outcomes among the three models. CONCLUSION: The integration of clinical findings with non-contrast CT imaging features analyzed through deep learning showed the potential for improving the prediction of HE in acute spontaneous intracerebral hemorrhage patients.

Deep learning-based reconstruction for acceleration of lumbar spine MRI: a prospective comparison with standard MRI.

OBJECTIVE: To compare the image quality and diagnostic performance between standard turbo spin-echo MRI and accelerated MRI with deep learning (DL)-based image reconstruction for degenerative lumbar spine diseases. MATERIALS AND METHODS: Fifty patients who underwent both the standard and accelerated lumbar MRIs at a 1.5-T scanner for degenerative lumbar spine diseases were prospectively enrolled. DL reconstruction algorithm generated coarse (DL_coarse) and fine (DL_fine) images from the accelerated protocol. Image quality was quantitatively assessed in terms of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) and qualitatively assessed using five-point visual scoring systems. The sensitivity and specificity of four radiologists for the diagnosis of degenerative diseases in both protocols were compared. RESULTS: The accelerated protocol reduced the average MRI acquisition time by 32.3% as compared to the standard protocol. As compared with standard images, DL_coarse and DL_fine showed significantly higher SNRs on T1-weighted images (T1WI; both p < .001) and T2-weighted images (T2WI; p = .002 and p < 0.001), higher CNRs on T1WI (both p < 0.001), and similar CNRs on T2WI (p = .49 and p = .27). The average radiologist assessment of overall image quality for DL_coarse and DL_fine was higher on sagittal T1WI (p = .04 and p < .001) and axial T2WI (p = .006 and p = .01) and similar on sagittal T2WI (p = .90 and p = .91). Both DL_coarse and DL_fine had better image quality of cauda equina and paraspinal muscles on axial T2WI (both p = .04 for cauda equina; p = .008 and p = .002 for paraspinal muscles). Differences in sensitivity and specificity for the detection of central canal stenosis and neural foraminal stenosis between standard and DL-reconstructed images were all statistically nonsignificant (p ≥ 0.05). CONCLUSION: DL-based protocol reduced MRI acquisition time without degrading image quality and diagnostic performance of readers for degenerative lumbar spine diseases. CLINICAL RELEVANCE STATEMENT: The deep learning (DL)-based reconstruction algorithm may be used to further accelerate spine MRI imaging to reduce patient discomfort and increase the cost efficiency of spine MRI imaging. KEY POINTS: • By using deep learning (DL)-based reconstruction algorithm in combination with the accelerated MRI protocol, the average acquisition time was reduced by 32.3% as compared with the standard protocol. • DL-reconstructed images had similar or better quantitative/qualitative overall image quality and similar or better image quality for the delineation of most individual anatomical structures. • The average radiologist's sensitivity and specificity for the detection of major degenerative lumbar spine diseases, including central canal stenosis, neural foraminal stenosis, and disc herniation, on standard and DL-reconstructed images, were similar.

Quantifying infarct core volume in ischemic stroke: What is the optimal threshold and parameters of computed tomography perfusion?

OBJECTIVE: Although computed tomography perfusion (CTP) is used to select and guide decision-making processes in patients with acute ischemic stroke, there is no clear standardization of the optimal threshold to predict ischemic core volume accurately. The infarct core volume with a relative cerebral blood flow(rCBF) threshold of < 30% is commonly used. We aimed to assess the volumetric agreement of the infarct core volume with different CTP parameters and thresholds using CTP software (RAPID, VITREA) and the infarct volume on diffusion-weighted imaging (DWI), with a short interval time (within 60 min) between CTP and follow-up DWI. MATERIALS AND METHODS: This retrospective study included 42 acute ischemic stroke patients with occlusion of the large artery in the anterior circulation between April 2017-November 2020. RAPID identified infarct core as tissue rCBF < 20-38%. VITREA defined the infarct core as cerebral blood volume (CBV) < 26-56%. Olea Sphere was used to measure infarct core volume on DWI. The CTP-infarct core volume with different thresholds of perfusion parameters (CBF threshold vs CBV threshold) were compared with DWI-infarct core volumes. RESULTS: The median time between CTP and DWI was 37.5min. The commonly used threshold of CBV< 41% (4.3 mL) resulted in lower median infarct core volume difference compared to the commonly used thresholds of rCBF < 30% (8.2mL). On the other hand, the optimal thresholds of CBV < 26% (-1.0mL; 95% CI, -53.9 to 58.1 mL; 0.945) resulted in the lowest median infarct core volume difference, narrowest limits of agreement, and largest interclass correlation coefficient compared with the optimal thresholds of rCBF < 38% (4.9 mL; 95% CI, -36.4 to 62.9 mL; 0.939). CONCLUSION: Our study found that the both optimal and commonly used thresholds of CBV provided a more accurate prediction of the infarct core volume in patients with AIS than rCBF.

Computed tomography complements ultrasound for the differential diagnosis of traumatic neuroma from recurrent tumor in patients with postoperative thyroid cancer.

OBJECTIVES: Traumatic neuromas (TNs) mimic recurrent tumors in US after total thyroidectomy (TT) and lateral neck dissection (LND) for thyroid cancer. We aimed to evaluate whether CT could complement US in the differential diagnosis of TNs from recurrent thyroid cancer in the dissected neck. MATERIAL AND METHODS: We retrospectively included a total of 97 consecutive US-detected lesions (28 TNs and 69 recurrent tumors) in patients with a previous history of TT and LND for thyroid cancer. The lesions were classified as benign, indeterminate, or suspicious according to the presence of benign or suspicious features on US and CT. Imaging features and categories on US and CT were compared between TNs and recurrent tumors. The diagnostic performances of US and CT for differentiating between TNs and recurrent tumors were calculated. RESULTS: On US, most TNs and recurrent tumors showed internal hyperechogenicity without hilar echogenicity or hilar vascularity and were categorized as suspicious lesions (23/28, 82.1% vs. 53/69, 76.8%). On CT, all TNs lacked strong enhancement without hilar fat or hilar vessel enhancement and were categorized as indeterminate lesions (28/28, 100%). In contrast, most recurrent tumors showed strong enhancement and were categorized as suspicious lesions (63/69, 91.3%). The addition of CT to US corrected 23 false-positive diagnoses in 28 TNs and 10 false-negative diagnoses in 69 recurrent tumors. CONCLUSIONS: CT complements US for the correct differentiation of TNs from recurrent tumors in postoperative thyroid cancer patients. The addition of CT to US may prevent unnecessary painful biopsy or surgery. KEY POINTS: • In the dissected neck, traumatic neuromas could mimic US suspicious LNs owing to its internal hyperechogenicity. • CT effectively differentiated traumatic neuromas from recurrent thyroid cancers by demonstrating significantly different enhancement patterns. • CT could complement US and may prevent unnecessary painful biopsy or surgery for US-detected lesions after thyroidectomy and neck dissection.

Prediction of hemorrhagic complications after ultrasound-guided biopsy of the thyroid and neck.

OBJECTIVES: Hemorrhage occasionally occurs after ultrasound (US)-guided biopsy of the thyroid and neck and sometimes leads to serious complications. We aimed to identify predictors of hemorrhagic complications after US-guided biopsy of the thyroid and neck. MATERIAL AND METHODS: In this retrospective study, we analyzed consecutive patients who underwent US-guided biopsy from April 2020 to November 2020. Procedure characteristics, US features, and peri- and post-procedural patient symptoms and signs were compared between patients with and without post-biopsy hemorrhage. Associations between clinical and imaging variables and post-biopsy hemorrhage were analyzed using univariate and multivariate regression analyses. RESULTS: A total of 305 patients who underwent US-guided biopsy of the thyroid and neck were included (219 women, 86 men; age range, 20-89 years). Seventeen (5.7%) cases of post-biopsy hemorrhage were detected 30 min after biopsy and manual compression. Among them, 10 developed hemorrhage at 30 min without immediate hemorrhage. In the multivariate analysis, a high tenderness score on the visual analog scale (VAS) at 30 min after biopsy (odds ratio [OR] 5.05, p < .001) was identified as an independent predictor of post-biopsy hemorrhage. In patients with hemorrhage at 30 min, tenderness scores significantly increased over 30 min of observation. CONCLUSIONS: High tenderness scores at 30 min after biopsy and manual compression were independent predictors of hemorrhage after US-guided biopsy of the thyroid and neck. The tenderness score could serve as a valuable marker to triage patients who require further observation and management after a US-guided biopsy of the thyroid and neck. KEY POINTS: • High tenderness scores at 30 min after compression were associated with the presence of delayed post-biopsy hemorrhage at 30 min. • Patients with hemorrhage at 30 min demonstrated a significant increase in tenderness scores over time. • High tenderness scores after biopsy site compression predicted the presence of delayed post-biopsy hemorrhage in the thyroid and neck.

Response prediction of vestibular schwannoma after gamma-knife radiosurgery using pretreatment dynamic contrast-enhanced MRI: a prospective study.

OBJECTIVES: There are few known predictive factors for response to gamma-knife radiosurgery (GKRS) in vestibular schwannoma (VS). We investigated the predictive role of pretreatment dynamic contrast-enhanced (DCE)-MRI parameters regarding the tumor response after GKRS in sporadic VS. METHODS: This single-center prospective study enrolled participants between April 2017 and February 2019. We performed a volumetric measurement of DCE-MRI-derived parameters before GKRS. The tumor volume was measured in a follow-up MRI. The pharmacokinetic parameters were compared between responders and nonresponders according to 20% or more tumor volume reduction. Stepwise multivariable logistic regression analyses were performed, and the diagnostic performance of DCE-MRI parameters for the prediction of tumor response was evaluated by receiver operating characteristic curve analysis. RESULTS: Ultimately, 35 participants (21 women, 52 ± 12 years) were included. There were 22 (62.9%) responders with a mean follow-up interval of 30.2 ± 5.7 months. Ktrans (0.036 min-1 vs. 0.057 min-1, p = .008) and initial area under the time-concentration curve within 90 s (IAUC90) (84.4 vs. 143.6, p = .003) showed significant differences between responders and nonresponders. Ktrans (OR = 0.96, p = .021) and IAUC90 (OR = 0.97, p = .004) were significant differentiating variables in each multivariable model with clinical variables for tumor response prediction. Ktrans showed a sensitivity of 81.8% and a specificity of 69.2%, and IAUC90 showed a sensitivity of 100% and a specificity of 53.8% for tumor response prediction. CONCLUSION: DCE-MRI (particularly Ktrans and IAUC90) has the potential to be a predictive factor for tumor response in VS after GKRS. KEY POINTS: •Pretreatment prediction of gamma-knife radiosurgery response in vestibular schwannoma is still challenging. •Dynamic contrast-enhanced MRI could have predictive value for the response of vestibular schwannoma after gamma-knife radiosurgery.

Contrast-enhanced MRI T1 Mapping for Quantitative Evaluation of Putative Dynamic Glymphatic Activity in the Human Brain in Sleep-Wake States.

Background Evaluation of the glymphatic system with intrathecal contrast material injection has limited clinical use. Purpose To investigate the feasibility of using serial intravenous contrast-enhanced T1 mapping in the quantitative evaluation of putative dynamic glymphatic activity in various brain regions and to demonstrate the effect of sleep on glymphatic activity in humans. Materials and Methods In this prospective study from May 2019 to February 2020, 25 healthy participants (mean age, 25 years ± 2 [standard deviation]; 15 men) underwent two cycles of MRI (day and night cycles). For each cycle, T1 maps were acquired at baseline and 0.5, 1, 1.5, 2, and 12 hours after intravenous contrast material injection. For the night cycle, participants had a normal night of sleep between 2 and 12 hours. The time (tmin) to reach the minimum T1 value (T1min), the absolute difference between baseline T1 and T1min (peak ΔT1), and the slope between two measurements at 2 and 12 hours (slope[2h-12h]) were determined from T1 value-time curves in cerebral gray matter (GM), cerebral white matter (WM), cerebellar GM, cerebellar WM, and putamen. Mixed-model analysis of variance (ANOVA), Friedman test, and repeated-measures ANOVA were used to assess the effect of sleep on slope(2h-12h) and to compare tmin and peak ΔT1 among different regions. Results The slope(2h-12h) increased from the day to night cycles in cerebral GM, cerebellar GM, and putamen (geometric mean ratio [night/day] = 1.4 [95% CI: 1.2, 1.7], 1.3 [95% CI: 1.1, 1.4], and 2.4 [95% CI: 1.6, 3.6], respectively; P = .001, P < .001, and P < .001, respectively). Median tmin values were 0.5 hour in cerebral and cerebellar GM and putamen for both cycles. Cerebellar GM had the highest mean peak ΔT1, followed by cerebral GM and putamen in both day (159 msec ± 6, 99 msec ± 4, and 62 msec ± 5, respectively) and night (152 msec ± 6, 104 msec ± 6, and 58 msec ± 4, respectively) cycles. Conclusion Clearance of a gadolinium-based contrast agent was greater after sleep compared with daytime wakefulness. These results suggest that sleep was associated with greater glymphatic clearance compared with wakefulness. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Anzai and Minoshima in this issue.

Differentiation between glioblastoma and primary CNS lymphoma: application of DCE-MRI parameters based on arterial input function obtained from DSC-MRI.

OBJECTIVE: This study aimed to evaluate whether arterial input functions (AIFs) obtained from dynamic susceptibility contrast (DSC)-MRI (AIFDSC) improve the reliability and diagnostic accuracy of dynamic contrast-enhanced (DCE)-derived pharmacokinetic (PK) parameters for differentiating glioblastoma from primary CNS lymphoma (PCNSL) compared with AIFs derived from DCE-MRI (AIFDCE). METHODS: This retrospective study included 172 patients with glioblastoma (n = 147) and PCNSL (n = 25). All patients had undergone preoperative DSC- and DCE-MRI. The volume transfer constant (Ktrans), volume of the vascular plasma space (vp), and volume of the extravascular extracellular space (ve) were acquired using AIFDSC and AIFDCE. The relative cerebral blood volume (rCBV) was obtained from DSC-MRI. Intraclass correlation coefficients (ICC) and ROC curves were used to assess the reliability and diagnostic accuracy of individual parameters. RESULTS: The mean Ktrans, vp, and ve values revealed better ICCs with AIFDSC than with AIFDCE (Ktrans, 0.911 vs 0.355; vp, 0.766 vs 0.503; ve, 0.758 vs 0.657, respectively). For differentiating all glioblastomas from PCNSL, the mean rCBV (AUC = 0.856) was more accurate than the AIFDSC-driven mean Ktrans, which had the largest AUC (0.711) among the DCE-derived parameters (p = 0.02). However, for glioblastomas with low rCBV (≤ 75th percentile of PCNSL; n = 30), the AIFDSC-driven mean Ktrans and vp were more accurate than rCBV (AUC: Ktrans, 0.807 vs rCBV, 0.515, p = 0.004; vp, 0.715 vs rCBV, p = 0.045). CONCLUSION: DCE-derived PK parameters using the AIFDSC showed improved reliability and diagnostic accuracy for differentiating glioblastoma with low rCBV from PCNSL. KEY POINTS: • An accurate differential diagnosis of glioblastoma and PCNSL is crucial because of different therapeutic strategies. • In contrast to the rCBV from DSC-MRI, another perfusion imaging technique, the DCE parameters for the differential diagnosis have been limited because of the low reliability of AIFs from DCE-MRI. • When we analyzed DCE-MRI data using AIFs from DSC-MRI (AIFDSC), AIFDSC-driven DCE parameters showed improved reliability and better diagnostic accuracy than rCBV for differentiating glioblastoma with low rCBV from PCNSL.

Revascularization Evaluation in Adult-Onset Moyamoya Disease after Bypass Surgery: Superselective Arterial Spin Labeling Perfusion MRI Compared with Digital Subtraction Angiography.

Background A superselective (SS) arterial spin labeling (ASL) MRI technique can be used to monitor the revascularization area as a supplementary or alternative modality to digital subtraction angiography (DSA), with the advantage of being noninvasive. Purpose To evaluate whether SS-ASL perfusion MRI could be used to visualize the revascularization area after combined direct and indirect bypass surgery in adults with moyamoya disease compared with DSA. Materials and Methods Patients diagnosed with moyamoya disease who underwent DSA and SS-ASL 6 months after surgery between June 2017 and November 2019 in a single institution were retrospectively evaluated. Subjective grading of the revascularization area and collateral grading in 10 Alberta Stroke Program Early CT Score (ASPECTS) locations were performed. The change in perfusion status in a subgroup that underwent both preoperative and postoperative SS-ASL studies was evaluated. Intermodality agreement was analyzed by using weighted κ statistics. Results Thirty-seven hemispheres from 33 patients (mean age, 39 years ± 12 [standard deviation]; 20 women) were evaluated. The intermodality agreement of the revascularization area grading was substantial (weighted κ = 0.70; 95% confidence interval [CI]: 0.37, 1.00). The overall intermodality agreement of the postoperative collateral grading in the 10 ASPECTS locations for all vessels was substantial (weighted κ = 0.77; 95% CI: 0.74, 0.80). For the presence of postoperative collateral supplied by the ipsilateral external carotid artery in 10 ASPECTS locations (a total of 370 locations) using DSA as a reference test, the SS-ASL showed a sensitivity of 92% (183 of 199 locations; 95% CI: 87%, 95%) and a specificity of 83% (142 of 171 locations; 95% CI: 77%, 88%). The overall intermodality agreement of the changes in perfusion status was moderate (weighted κ = 0.59; 95% CI: 0.54, 0.65). Conclusion Superselective arterial spin labeling imaging precisely depicted the revascularization territory in patients with moyamoya disease who underwent bypass surgery, and it showed the changes in the vascular supplying territories before and after bypass surgery. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Hendrikse in this issue.

Prognostication of anaplastic astrocytoma patients: application of contrast leakage information of dynamic susceptibility contrast-enhanced MRI and dynamic contrast-enhanced MRI.

PURPOSE: To examine the applicability of contrast leakage information from dynamic susceptibility contrast-enhanced (DSC) MRI and dynamic contrast-enhanced (DCE) MRI to determine which one is the most valuable surrogate imaging biomarker for predicting disease progression in anaplastic astrocytoma (AA) patients. MATERIALS AND METHODS: This study was approved by the institutional review board (IRB), which waived informed consent. A total of seventy-three AA patients who had undergone preoperative DCE and DSC MRI and received standard treatment, including partial resection or biopsy followed by radiation therapy, were included in this retrospective study. Based on Response Assessment in Neuro-Oncology (RANO), patients were sorted into progression (n = 21) and non-progression (n = 52) groups. Tumor boundaries were defined as high-signal intensity (SI) lesions on fluid-attenuated inversion recovery (FLAIR) imaging, where we analyzed mean pharmacokinetic parameters (Ktrans, Vp, and Ve) from DCE MRI and contrast leakage information (mean extraction fraction (EF)) from DSC MRI. RESULTS: Mean Ve and mean EF were significantly higher in patients with progression-free survival (PFS) < 18 months than in those with PFS ≥ 18 months. For distinguishing the group with PFS < 18 months, AUC values were calculated using the mean Ve value (AUC = 0.716). The Kaplan-Meier survival analysis revealed that mean Ve value was significantly correlated with PFS. In Cox proportional-hazards regression, only the mean Ve value was found to be significantly associated with PFS. CONCLUSION: We found that the mean Ve value based on high-SI tumor lesions on FLAIR imaging was capable of predicting outcomes of AA patients as a potential surrogate imaging biomarker. KEY POINTS: • Mean Ve(2.152 ± 1.857 vs. 1.173 ± 1.408) was significantly higher in anaplastic astrocytoma patients with PFS < 18 months that in those with PFS ≥ 18 months (p = 0.02). • Cox proportional-hazards regression showed that only mean Ve(p = 0.034) was significantly associated with PFS, regardless of IDH mutation status, in anaplastic astrocytoma patients.

Arterial spin labeling perfusion-weighted imaging aids in prediction of molecular biomarkers and survival in glioblastomas.

OBJECTIVES: Prediction of progression-free survival (PFS) and overall survival (OS) and early identification of molecular biomarkers with prognostic information are clinically important in glioblastoma (GBM) patients. We aimed to explore the utility of arterial spin labeling perfusion-weighted imaging (ASL-PWI) in the prediction of molecular biomarkers and survival in GBM patients. METHODS: We retrospectively analyzed 149 consecutive GBM patients, who had undergone maximal surgical resection or biopsy followed by concurrent chemoradiotherapy and adjuvant chemotherapy using temozolomide between November 2010 and June 2016. On preoperative ASL-PWI, cerebral blood flow (CBF) within contrast-enhancing (CE) and nonenhancing (NE) portions were evaluated both qualitatively (perfusion pattern[CE] and perfusion pattern[NE]) and quantitatively (nCBFCE and nCBFNE). ASL-PWI findings were correlated with molecular biomarkers, including isocitrate dehydrogenase (IDH) and O6-methylguanine-DNA methyltransferase (MGMT) methylation statuses, and survival, using the Mann-Whitney U-test, Spearman rank correlation, Kaplan-Meier analysis, and receiver operating characteristics analysis. RESULTS: nCBFCE was significantly higher in the IDH wild-type group than in the IDH mutant group (p = .013) and in the MGMT unmethylated group than in the methylated group (p = .047). Areas under the receiver operating characteristic curve were 0.678 for IDH mutation (p = .022) and 0.601 for MGMT promoter methylation (p = .043). Hyperperfusion was associated with the shortest median PFS for both perfusion pattern[CE] (7.6 months) and perfusion pattern[NE] (4.0 months). The perfusion pattern[NE] remained an independent predictor for PFS and OS even after adjusting for clinical and molecular predictors, unlike perfusion pattern[CE]. CONCLUSIONS: ASL-PWI can aid to predict survival and molecular biomarkers including IDH mutation and MGMT promoter methylation statuses in GBM patients. KEY POINTS: • ASL-PWI can aid to predict survival in GBM patients. • ASL-PWI can aid to predict IDH and MGMT promoter methylation statuses in GBM.

Differentiation of intrahepatic mass-forming cholangiocarcinoma from hepatocellular carcinoma on gadoxetic acid-enhanced liver MR imaging.

OBJECTIVES: To determine the different imaging features of intrahepatic mass-forming cholangiocarcinoma (IMCC) from hepatocellular carcinoma (HCC) on gadoxetic acid-enhanced magnetic resonance imaging (MRI). METHODS: This retrospective study was institutional review board approved and the requirement for informed consent was waived. Patients who underwent gadoxetic acid-enhanced MRI with histologically confirmed IMCCs (n = 46) or HCCs (n = 58) were included. Imaging features of IMCCs and HCCs on gadoxetic acid-enhanced MRI including T2- and T1-weighted, diffusion weighted images, dynamic study and hepatobiliary phase (HBP) images were analyzed. Univariate and multivariate logistic regression analyses were performed to identify relevant differentiating features between IMCCs and HCCs. RESULTS: Multivariate analysis revealed heterogeneous T2 signal intensity and a hypointense rim on the HBP as suggestive findings of IMCCs and the wash-in and "portal wash-out" enhancement pattern as well as focal T1 high signal intensity foci as indicative of HCCs (all, p < 0.05). When we combined any three of the above four imaging features, we were able to diagnose IMCCs with 94 % (43/46) sensitivity and 86 % (50/58) specificity. CONCLUSIONS: Combined interpretation of enhancement characteristics including HBP images, morphologic features, and strict application of the "portal wash-out" pattern helped more accurate discrimination of IMCCs from HCCs. KEY POINTS: • Analysis of enhancement characteristics helped accurate discrimination of IMCCs from HCCs. • Wash-out should be determined on the PVP of gadoxetic acid-enhanced MRI. • A hypointense rim on the HBP was a significant finding of IMCCs.

Hepatic steatosis in living liver donor candidates: preoperative assessment by using breath-hold triple-echo MR imaging and 1H MR spectroscopy.

PURPOSE: To evaluate the diagnostic performance of both breath-hold T2*-corrected triple-echo spoiled gradient-echo water-fat separation magnetic resonance (MR) imaging (triple-echo imaging) and high-speed T2-corrected multiecho hydrogen 1 ((1)H) MR spectroscopy in the assessment of macrovesicular hepatic steatosis in living liver donor candidates by using histologic assessment as a reference standard. MATERIALS AND METHODS: The institutional review board approved this retrospective study with waiver of the need to obtain informed consent. One hundred eighty-two liver donor candidates who had undergone preoperative triple-echo imaging and single-voxel (3 × 3 × 3 cm) MR spectroscopy performed with a 3.0-T imaging unit and who had also undergone histologic evaluation of macrovesicular steatosis were included in this study. In part 1 of the study (n = 84), the Pearson correlation coefficient was calculated. Receiver operating characteristic (ROC) curve analysis was performed to detect substantial (≥10%) macrovesicular steatosis. In part 2 of the study, with a different patient group (n = 98), diagnostic performance was evaluated by using the diagnostic cutoff values determined in part 1 of the study. RESULTS: The correlation coefficients of triple-echo MR imaging and MR spectroscopy with macrovesicular steatosis were 0.886 and 0.887, respectively. The areas under the ROC curve for detection of substantial macrovesicular steatosis were 0.959 and 0.988, with cutoff values of 4.93% and 5.79%, respectively, and without a significant difference (P = .328). In the part 2 study group, sensitivity and specificity were 90.9% (10 of 11) and 86.2% (75 of 87) for triple-echo MR imaging and 90.9% (10 of 11) and 86.2% (75 of 87) for MR spectroscopy, respectively. CONCLUSION: Either breath-hold triple-echo MR imaging or MR spectroscopy can be used to detect substantial macrovesicular steatosis in living liver donor candidates. In the future, this may allow selective biopsy in candidates who are expected to have substantial macrovesicular steatosis on the basis of MR-based hepatic fat fraction.

Quantification of infarct core volume in patients with acute ischemic stroke using cerebral metabolic rate of oxygen (CMRO2) in CT perfusion.

BACKGROUND AND PURPOSE: The cerebral metabolic rate of oxygen (CMRO2) is considered a robust marker of the infarct core in 15°-tracer- based positron emission tomography. We aimed to delineate the infarct core in patients with acute ischemic stroke using commonly used relative cerebral blood flow (rCBF) < 30% and oxygen metabolism parameter of CMRO2 on CT perfusion in comparison with pre-treatment diffusion- weighted imaging (DWI)-derived infarct core volume. MATERIALS AND METHODS: Patients with acute ischemic stroke who met the inclusion criteria were recruited. The CMRO2 and CBF maps in CT perfusion were automatically generated using post-processing software. The infarct core volume was quantified with relative (r) CMRO2 < 20% - 30% and rCBF < 30%. The optimal threshold was defined as those that demonstrated the smallest mean absolute error, lowest mean infarct core volume difference, narrowest 95% limit of agreement, and largest intraclass correlation coefficient (ICC) against the DWI. RESULTS: This study included 76 patients (mean age ± standard deviation, 69.97 ± 12.15 years, 43 males). The optimal thresholds of rCMRO2 < 26% resulted in the lowest mean infarct core volume difference, narrowest 95% limit of agreement, and largest ICC among different thresholds. Bland-Altman analysis demonstrated a volumetric bias of 1.96 mL between DWI and rCMRO2 < 26%, whereas in cases of DWI and rCBF < 30%, the bias was notably larger at 14.10 mL. The highest correlation was observed for rCMRO2 < 26% (ICC=0.936), whereas rCBF < 30% showed a slightly lower ICC of 0.934. CONCLUSIONS: CT perfusion-derived CMRO2 is a promising parameter for estimating the infarct core volume in patients with acute ischemic stroke. ABBREVIATIONS: CMRO2 = cerebral metabolic rate of oxygen.

Added prognostic value of 3D deep learning-derived features from preoperative MRI for adult-type diffuse gliomas.

BACKGROUND: To investigate the prognostic value of spatial features from whole-brain MRI using a three-dimensional (3D) convolutional neural network for adult-type diffuse gliomas. METHODS: In a retrospective, multicenter study, 1925 diffuse glioma patients were enrolled from 5 datasets: SNUH (n = 708), UPenn (n = 425), UCSF (n = 500), TCGA (n = 160), and Severance (n = 132). The SNUH and Severance datasets served as external test sets. Precontrast and postcontrast 3D T1-weighted, T2-weighted, and T2-FLAIR images were processed as multichannel 3D images. A 3D-adapted SE-ResNeXt model was trained to predict overall survival. The prognostic value of the deep learning-based prognostic index (DPI), a spatial feature-derived quantitative score, and established prognostic markers were evaluated using Cox regression. Model evaluation was performed using the concordance index (C-index) and Brier score. RESULTS: The MRI-only median DPI survival prediction model achieved C-indices of 0.709 and 0.677 (BS = 0.142 and 0.215) and survival differences (P < 0.001 and P = 0.002; log-rank test) for the SNUH and Severance datasets, respectively. Multivariate Cox analysis revealed DPI as a significant prognostic factor, independent of clinical and molecular genetic variables: hazard ratio = 0.032 and 0.036 (P < 0.001 and P = 0.004) for the SNUH and Severance datasets, respectively. Multimodal prediction models achieved higher C-indices than models using only clinical and molecular genetic variables: 0.783 vs. 0.774, P = 0.001, SNUH; 0.766 vs. 0.748, P = 0.023, Severance. CONCLUSIONS: The global morphologic feature derived from 3D CNN models using whole-brain MRI has independent prognostic value for diffuse gliomas. Combining clinical, molecular genetic, and imaging data yields the best performance.

Added value of dynamic contrast-enhanced MR imaging in deep learning-based prediction of local recurrence in grade 4 adult-type diffuse gliomas patients.

Local recurrences in patients with grade 4 adult-type diffuse gliomas mostly occur within residual non-enhancing T2 hyperintensity areas after surgical resection. Unfortunately, it is challenging to distinguish non-enhancing tumors from edema in the non-enhancing T2 hyperintensity areas using conventional MRI alone. Quantitative DCE MRI parameters such as Ktrans and Ve convey permeability information of glioblastomas that cannot be provided by conventional MRI. We used the publicly available nnU-Net to train a deep learning model that incorporated both conventional and DCE MRI to detect the subtle difference in vessel leakiness due to neoangiogenesis between the non-recurrence area and the local recurrence area, which contains a higher proportion of high-grade glioma cells. We found that the addition of Ve doubled the sensitivity while nonsignificantly decreasing the specificity for prediction of local recurrence in glioblastomas, which implies that the combined model may result in fewer missed cases of local recurrence. The deep learning model predictive of local recurrence may enable risk-adapted radiotherapy planning in patients with grade 4 adult-type diffuse gliomas.

Progressive reduction in basal ganglia explains and predicts cerebral structural alteration in type 2 diabetes.

Type 2 diabetes is consistently reported to be associated with reduced gray matter, mainly in the cortical-striatal-limbic networks. However, little is known about how the progression of diabetes affects cerebral gray matter. To investigate, we collected 543 age- and sex-matched participants of nondiabetes, prediabetes, and diabetes. Voxel-based morphometry using a linear trend model was performed to reveal brain regions associated with disease progression. The Granger causal network of structural covariance was used to assess the causal relationships of brain structural alterations according to disease progression. Multivariate pattern analysis was applied for the stage-specific predictions of hyperglycemia. We detected a linear trend of gray matter volume reduction in the basal ganglia with disease progression (P < 0.05, FWER corrected), which caused a reduction in bilateral temporal gyri, frontal pole, parahippocampus, and bilateral posterior cingulate/precuneus volumes. In addition, the gray matter pattern of the basal ganglia could predict patients with diabetes (accuracy 60.12%, p = 0.002). In conclusion, the basal ganglia is the brain area with progressive gray matter reduction as diabetes progress. The reduced volume in the basal ganglia causes widespread gray matter reductions throughout diabetes progression. These findings indicate that the basal ganglia play a key role in diabetes by affecting the cortical-striatal-limbic network.

Structural disconnection is associated with disability in the neuromyelitis optica spectrum disorder.

OBJECTIVES: Neuromyelitis optica spectrum disorder (NMOSD) is an autoimmune inflammatory disease of the central nervous system. Accumulating evidence suggests there is a distinct pattern of brain lesions characteristic of NMOSD, and brain MRI has potential prognostic implications. However, the question of how the brain lesions in NMOSD are associated with its distinct clinical course remains incompletely understood. Here, we aimed to investigate the association between neurological impairment and brain lesions via brain structural disconnection. METHODS: Twenty patients were diagnosed with NMOSD according to the 2015 International Panel for NMO Diagnosis criteria. The white matter lesions were manually drawn section by section. Whole-brain structural disconnection was estimated, and connectome-based predictive modeling (CPM) was used to estimate the patient's Expanded Disability Status Scale score (EDSS) from their disconnection severity matrix. Furthermore, correlational tractography was performed to assess the fractional anisotropy (FA) and axial diffusivity (AD) of white matter fibers, which negatively correlated with the EDSS score. RESULTS: CPM successfully predicted the EDSS using the disconnection severity matrix (r = 0.506, p = 0.028; q2 = 0.274). Among the important edges in the prediction process, the majority of edges connected the motor to the frontoparietal network. Correlational tractography identified a decreased FA and AD value according to EDSS scores in periependymal white matter tracts. DISCUSSION: Structural disconnection-based predictive modeling and local connectome analysis showed that frontoparietal and periependymal white matter disconnection is predictive and associated with the EDSS score of NMOSD patients.