Myelin Content in Mild Traumatic Brain Injury Patients with Post-Concussion Syndrome: Quantitative Assessment with a Multidynamic Multiecho Sequence

Yoo Roh-Eul,최승홍,Youn Sung-Won,Hwang Moonjung,Kim Eunkyung,Oh Byung-Mo,Lee Ji Ye,Hwang Inpyeong,Kang Koung Mi,Yun Tae Jin,Kim Ji-hoon,Sohn Chul-Ho 대한영상의학회 2022 Korean Journal of Radiology Vol.23 No.2

Objective: This study aimed to explore the myelin volume change in patients with mild traumatic brain injury (mTBI) with post-concussion syndrome (PCS) using a multidynamic multiecho (MDME) sequence and automatic whole-brain segmentation. Materials and Methods: Forty-one consecutive mTBI patients with PCS and 29 controls, who had undergone MRI including the MDME sequence between October 2016 and April 2018, were included. Myelin volume fraction (MVF) maps were derived from the MDME sequence. After three dimensional T1-based brain segmentation, the average MVF was analyzed at the bilateral cerebral white matter (WM), bilateral cerebral gray matter (GM), corpus callosum, and brainstem. The Mann–Whitney U-test was performed to compare MVF and myelin volume between patients with mTBI and controls. Myelin volume was correlated with neuropsychological test scores using the Spearman rank correlation test. Results: The average MVF at the bilateral cerebral WM was lower in mTBI patients with PCS (median [interquartile range], 25.2% [22.6%–26.4%]) than that in controls (26.8% [25.6%–27.8%]) (p = 0.004). The region-of-interest myelin volume was lower in mTBI patients with PCS than that in controls at the corpus callosum (1.87 cm3 [1.70–2.05 cm3] vs. 2.21 cm3 [1.86– 3.46 cm3]; p = 0.003) and brainstem (9.98 cm3 [9.45–11.00 cm3] vs. 11.05 cm3 [10.10–11.53 cm3]; p = 0.015). The total myelin volume was lower in mTBI patients with PCS than that in controls at the corpus callosum (0.45 cm3 [0.39–0.48 cm3] vs. 0.48 cm3 [0.45–0.54 cm3]; p = 0.004) and brainstem (1.45 cm3 [1.28–1.59 cm3] vs. 1.54 cm3 [1.42–1.67 cm3]; p = 0.042). No significant correlation was observed between myelin volume parameters and neuropsychological test scores, except for the total myelin volume at the bilateral cerebral WM and verbal learning test (delayed recall) (r = 0.425; p = 0.048). Conclusion: MVF quantified from the MDME sequence was decreased at the bilateral cerebral WM in mTBI patients with PCS. The total myelin volumes at the corpus callosum and brainstem were decreased in mTBI patients with PCS due to atrophic changes.

Blood-Brain Barrier Disruption in Mild Traumatic Brain Injury Patients with Post-Concussion Syndrome: Evaluation with Region-Based Quantification of Dynamic Contrast-Enhanced MR Imaging Parameters Using Automatic Whole-Brain Segmentation

Yoen Heera,Yoo Roh-Eul,Choi Seung Hong,Kim Eunkyung,오병모,Yang Dongjin,Hwang Inpyeong,Kang Koung Mi,Yun Tae Jin,Kim Ji-hoon,Sohn Chul-Ho 대한영상의학회 2021 Korean Journal of Radiology Vol.22 No.1

Objective: This study aimed to investigate the blood-brain barrier (BBB) disruption in mild traumatic brain injury (mTBI) patients with post-concussion syndrome (PCS) using dynamic contrast-enhanced (DCE) magnetic resonance (MR) imaging and automatic whole brain segmentation. Materials and Methods: Forty-two consecutive mTBI patients with PCS who had undergone post-traumatic MR imaging, including DCE MR imaging, between October 2016 and April 2018, and 29 controls with DCE MR imaging were included in this retrospective study. After performing three-dimensional T1-based brain segmentation with FreeSurfer software (Laboratory for Computational Neuroimaging), the mean Ktrans and vp from DCE MR imaging (derived using the Patlak model and extended Tofts and Kermode model) were analyzed in the bilateral cerebral/cerebellar cortex, bilateral cerebral/cerebellar white matter (WM), and brainstem. Ktrans values of the mTBI patients and controls were calculated using both models to identify the model that better reflected the increased permeability owing to mTBI (tendency toward higher Ktrans values in mTBI patients than in controls). The Mann-Whitney U test and Spearman rank correlation test were performed to compare the mean Ktrans and vp between the two groups and correlate Ktrans and vp with neuropsychological tests for mTBI patients. Results: Increased permeability owing to mTBI was observed in the Patlak model but not in the extended Tofts and Kermode model. In the Patlak model, the mean Ktrans in the bilateral cerebral cortex was significantly higher in mTBI patients than in controls (p = 0.042). The mean vp values in the bilateral cerebellar WM and brainstem were significantly lower in mTBI patients than in controls (p = 0.009 and p = 0.011, respectively). The mean Ktrans of the bilateral cerebral cortex was significantly higher in patients with atypical performance in the auditory continuous performance test (commission errors) than in average or good performers (p = 0.041). Conclusion: BBB disruption, as reflected by the increased Ktrans and decreased vp values from the Patlak model, was observed throughout the bilateral cerebral cortex, bilateral cerebellar WM, and brainstem in mTBI patients with PCS.

Assessment of Mild Cognitive Impairment in Elderly Subjects Using a Fully Automated Brain Segmentation Software

Kwon, Chiheon,Kang, Koung Mi,Byun, Min Soo,Yi, Dahyun,Song, Huijin,Lee, Ji Ye,Hwang, Inpyeong,Yoo, Roh-Eul,Yun, Tae Jin,Choi, Seung Hong,Kim, Ji-hoon,Sohn, Chul-Ho,Lee, Dong Young Korean Society of Magnetic Resonance in Medicine 2021 Investigative Magnetic Resonance Imaging Vol.25 No.3

Purpose: Mild cognitive impairment (MCI) is a prodromal stage of Alzheimer's disease (AD). Brain atrophy in this disease spectrum begins in the medial temporal lobe structure, which can be recognized by magnetic resonance imaging. To overcome the unsatisfactory inter-observer reliability of visual evaluation, quantitative brain volumetry has been developed and widely investigated for the diagnosis of MCI and AD. The aim of this study was to assess the prediction accuracy of quantitative brain volumetry using a fully automated segmentation software package, NeuroQuant^®, for the diagnosis of MCI. Materials and Methods: A total of 418 subjects from the Korean Brain Aging Study for Early Diagnosis and Prediction of Alzheimer's Disease cohort were included in our study. Each participant was allocated to either a cognitively normal old group (n = 285) or an MCI group (n = 133). Brain volumetric data were obtained from T1-weighted images using the NeuroQuant software package. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed to investigate relevant brain regions and their prediction accuracies. Results: Multivariate logistic regression analysis revealed that normative percentiles of the hippocampus (P < 0.001), amygdala (P = 0.003), frontal lobe (P = 0.049), medial parietal lobe (P = 0.023), and third ventricle (P = 0.012) were independent predictive factors for MCI. In ROC analysis, normative percentiles of the hippocampus and amygdala showed fair accuracies in the diagnosis of MCI (area under the curve: 0.739 and 0.727, respectively). Conclusion: Normative percentiles of the hippocampus and amygdala provided by the fully automated segmentation software could be used for screening MCI with a reasonable post-processing time. This information might help us interpret structural MRI in patients with cognitive impairment.

Added Value of Contrast Leakage Information over the CBV Value of DSC Perfusion MRI to Differentiate between Pseudoprogression and True Progression after Concurrent Chemoradiotherapy in Glioblastoma Patients

Elena Pak,최승홍,박철기,Tae Min Kim,박성혜,Jae-Kyung Won,Joo Ho Lee,Soon-Tae Lee,Inpyeong Hwang,Roh-Eul Yoo,Koung Mi Kang,Tae Jin Yun 대한자기공명의과학회 2022 Investigative Magnetic Resonance Imaging Vol.26 No.1

Purpose: To evaluate whether the added value of contrast leakage information from dynamic susceptibility contrast magnetic resonance imaging (DSC MRI) is a better prognostic imaging biomarker than the cerebral blood volume (CBV) value in distinguishing true progression from pseudoprogression in glioblastoma patients. Materials and Methods: Forty-nine glioblastoma patients who had undergone MRI after concurrent chemoradiotherapy with temozolomide were enrolled in this retrospective study. Twenty features were extracted from the normalized relative CBV (nCBV) and extraction fraction (EF) map of the contrast-enhancing region in each patient. After univariable analysis, we used multivariable stepwise logistic regression analysis to identify significant predictors for differentiating between pseudoprogression and true progression. Receiver operating characteristic (ROC) analysis was employed to determine the best cutoff values for the nCBV and EF features. Finally, leave-one-out cross-validation was used to validate the best predictor in differentiating between true progression and pseudoprogression. Results: Multivariable stepwise logistic regression analysis showed that MGMT (O6- methylguanine-DNA methyltransferase) and EF max were independent differentiating variables (P = 0.004 and P = 0.02, respectively). ROC analysis yielded the best cutoff value of 95.75 for the EF max value for differentiating the two groups (sensitivity, 61%; specificity, 84.6%; AUC, 0.681 ± 0.08; 95% CI, 0.524-0.837; P = 0.03). In the leave-one-out cross-validation of the EF max value, the cross-validated values for predicting true progression and pseudoprogression accuracies were 69.4% and 71.4%, respectively. Conclusion: We demonstrated that contrast leakage information parameter from DSC MRI showed significance in differentiating true progression from pseudoprogression in glioblastoma patients.

Validation of Ultrasound and Computed Tomography-Based Risk Stratification System and Biopsy Criteria for Cervical Lymph Nodes in Preoperative Patients With Thyroid Cancer

Jeon Young Hun,Lee Ji Ye,Yoo Roh-Eul,Rhim Jung Hyo,Lee Kyung Hoon,Choi Kyu Sung,Hwang Inpyeong,Kang Koung Mi,Kim Ji-hoon 대한영상의학회 2023 Korean Journal of Radiology Vol.24 No.9

Objective: This study aimed to validate the risk stratification system (RSS) and biopsy criteria for cervical lymph nodes (LNs) proposed by the Korean Society of Thyroid Radiology (KSThR). Materials and Methods: This retrospective study included a consecutive series of preoperative patients with thyroid cancer who underwent LN biopsy, ultrasound (US), and computed tomography (CT) between December 2006 and June 2015. LNs were categorized as probably benign, indeterminate, or suspicious according to the current US- and CT-based RSS and the size thresholds for cervical LN biopsy as suggested by the KSThR. The diagnostic performance and unnecessary biopsy rates were calculated. Results: A total of 277 LNs (53.1% metastatic) in 228 patients (mean age ± standard deviation, 47.4 years ± 14) were analyzed. In US, the malignancy risks were significantly different among the three categories (all P < 0.001); however, CTdetected probably benign and indeterminate LNs showed similarly low malignancy risks (P = 0.468). The combined US + CT criteria stratified the malignancy risks among the three categories (all P < 0.001) and reduced the proportion of indeterminate LNs (from 20.6% to 14.4%) and the malignancy risk in the indeterminate LNs (from 31.6% to 12.5%) compared with US alone. In all image-based classifications, nodal size did not affect the malignancy risks (short diameter [SD] ≤ 5 mm LNs vs. SD > 5 mm LNs, P ≥ 0.177). The criteria covering only suspicious LNs showed higher specificity and lower unnecessary biopsy rates than the current criteria, while maintaining sensitivity in all imaging modalities. Conclusion: Integrative evaluation of US and CT helps in reducing the proportion of indeterminate LNs and the malignancy risk among them. Nodal size did not affect the malignancy risk of LNs, and the addition of indeterminate LNs to biopsy candidates did not have an advantage in detecting LN metastases in all imaging modalities.

경동맥 3D TOF 자기공명 혈관조영 영상을 활용한 딥러닝 전인학습 기반 뇌백질변성 부피 예측

장혜정(Hei-Jeung Jang),최규성(Kyu Sung Choi),최승홍(Seung-hong Choi),황인평(Inpyeong Hwang),신태훈(Taehoon Shin) 대한전자공학회 2023 대한전자공학회 학술대회 Vol.2023 No.6

It has been suggested that the association between white matter hyperintensity (WMH) and carotid arterial features includes carotid plaques and carotid intima-media thickness, arterial stiffness, or remodeling. Under the hypothesis that white, matter abnormalities correlate with the configuration of the feeding carotid arteries, we use deep learning to estimate WMH volumes based solely on time-of-flight (TOF) carotid MRA images. To improve performance, we use transfer learning from a pre-trained V-Net for carotid artery segmentation. We evaluate the WMH volume prediction performance by applying Grad-CAM and confirm that our proposed transfer learning model estimates the WMH volume derived from carotid arterial features.

Prediction of Prognosis in Glioblastoma Using Radiomics Features of Dynamic Contrast-Enhanced MRI

Pak Elena,Choi Kyu Sung,Choi Seung Hong,Park Chul-Kee,김태민,Park Sung-Hye,Lee Joo Ho,Lee Soon-Tae,Hwang Inpyeong,Yoo Roh-Eul,Kang Koung Mi,Yun Tae Jin,Kim Ji-hoon,Sohn Chul-Ho 대한영상의학회 2021 Korean Journal of Radiology Vol.22 No.9

Objective: To develop a radiomics risk score based on dynamic contrast-enhanced (DCE) MRI for prognosis prediction in patients with glioblastoma. Materials and Methods: One hundred and fifty patients (92 male [61.3%]; mean age ± standard deviation, 60.5 ± 13.5 years) with glioblastoma who underwent preoperative MRI were enrolled in the study. Six hundred and forty-two radiomic features were extracted from volume transfer constant (Ktrans), fractional volume of vascular plasma space (Vp), and fractional volume of extravascular extracellular space (Ve) maps of DCE MRI, wherein the regions of interest were based on both T1- weighted contrast-enhancing areas and non-enhancing T2 hyperintense areas. Using feature selection algorithms, salient radiomic features were selected from the 642 features. Next, a radiomics risk score was developed using a weighted combination of the selected features in the discovery set (n = 105); the risk score was validated in the validation set (n = 45) by investigating the difference in prognosis between the “radiomics risk score” groups. Finally, multivariable Cox regression analysis for progression-free survival was performed using the radiomics risk score and clinical variables as covariates. Results: 16 radiomic features obtained from non-enhancing T2 hyperintense areas were selected among the 642 features identified. The radiomics risk score was used to stratify high- and low-risk groups in both the discovery and validation sets (both p < 0.001 by the log-rank test). The radiomics risk score and presence of isocitrate dehydrogenase (IDH) mutation showed independent associations with progression-free survival in opposite directions (hazard ratio, 3.56; p = 0.004 and hazard ratio, 0.34; p = 0.022, respectively). Conclusion: We developed and validated the “radiomics risk score” from the features of DCE MRI based on non-enhancing T2 hyperintense areas for risk stratification of patients with glioblastoma. It was associated with progression-free survival independently of IDH mutation status.