Publications by authors named "Seung-Koo Lee"

218 Publications

Identification of magnetic resonance imaging features for the prediction of molecular profiles of newly diagnosed glioblastoma.

J Neurooncol 2021 Jun 30. Epub 2021 Jun 30.

Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA.

Purpose: We predicted molecular profiles in newly diagnosed glioblastoma patients using magnetic resonance (MR) imaging features and explored the associations between imaging features and major molecular alterations.

Methods: This retrospective study included patients with newly diagnosed glioblastoma and available next-generation sequencing results. From preoperative MR imaging, Visually AcceSAble Rembrandt Images (VASARI) features, volumetric parameters, and apparent diffusion coefficient (ADC) values were obtained. First, univariate random forest was performed to identify gene abnormalities that could be predicted by imaging features with high accuracy and stability. Next, multivariate random forest was trained to predict the selected genes in the discovery cohort and was validated in the external cohort. Univariable logistic regression was performed to further explore the associations between imaging features and genes.

Results: Univariate random forest identified nine genes predicted by imaging features, with high accuracy and stability. The multivariate random forest model showed excellent performance in predicting IDH and PTPN11 mutations in the discovery cohort, which were validated in the external validation cohorts (areas under the receiver operator characteristic curve [AUCs] of 0.855 for IDH and 0.88 for PTPN11). ATRX loss and EGFR mutation were predicted with AUCs of 0.753 and 0.739, respectively, whereas PTEN could not be reliably predicted. Based on univariable logistic regression analyses, IDH, ATRX, and TP53 were clustered according to their shared imaging features, whereas EGFR and CDKN2A/B were clustered in the opposite direction.

Conclusions: MR imaging features are related to specific molecular alterations and can be used to predict molecular profiles in patients with newly diagnosed glioblastoma.
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http://dx.doi.org/10.1007/s11060-021-03801-yDOI Listing
June 2021

The effect of body fatness on regional brain imaging markers and cognitive function in healthy elderly mediated by impaired glucose metabolism.

J Psychiatr Res 2021 Aug 11;140:488-495. Epub 2021 Jun 11.

Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, 03722, Republic of Korea; Department of Public Health, Yonsei University Graduate School, Seoul, 03722, Republic of Korea; Institute of Human Complexity and Systems Science, Yonsei University, Incheon, 21983, Republic of Korea. Electronic address:

Brain atrophy is related to vascular risk factors and can increase cognitive dysfunction risk. This community-based, cross-sectional study investigated whether glucose metabolic disorders due to body fatness are linked to regional changes in brain structure and a decline in neuropsychological function in cognitively healthy older adults. From 2016 to 2019, 429 participants underwent measurements for cortical thickness and subcortical volume using 3 T magnetic resonance imaging and for cognitive function using the neuropsychological screening battery. The effects of body fatness mediated by impaired glucose metabolism on neuroimaging markers and cognitive function was investigated using partial least square structural equation modeling. Total grey matter volume (β = -0.020; bias-corrected (BC) 95% confidence interval (CI) = -0.047 to -0.006), frontal (β = -0.029; BC 95% CI = -0.063 to -0.005) and temporal (β = -0.022; BC 95% CI = -0.051 to -0.004) lobe cortical thickness, and hippocampal volume (β = -0.029; BC 95% CI = -0.058 to -0.008) were indirectly related to body fatness. Further, frontal/temporal lobe thinning was associated with recognition memory (β = -0.005; BC 95% CI = -0.012 to -0.001/β = -0.005; BC 95% CI = -0.013 to -0.001) and delayed recall for visual information (β = -0.005; BC 95% CI = -0.013 to -0.001/β = -0.005; BC 95% CI = -0.013 to -0.001). Additionally, the smaller the hippocampal volume, the lower the score in recognition memory (β = -0.005; BC 95% CI = -0.012 to -0.001), delayed recall for visual information (β = -0.005; BC 95% CI = -0.012 to -0.001), and verbal learning (β = -0.008; BC 95% CI = -0.017 to -0.002). Our findings indicate that impaired glucose metabolism caused by excess body fatness affects memory decline as well as regional grey matter atrophy in elderly individuals with no neurological disease.
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http://dx.doi.org/10.1016/j.jpsychires.2021.06.011DOI Listing
August 2021

Small bowel ulcer bleeding due to suspected clopidogrel use in a patient with clopidogrel resistance: A case report.

World J Clin Cases 2021 May;9(15):3689-3695

Department of Hospital Medicine, Kangwon National University Hospital , Chuncheon-si 24289, South Korea.

Background: Clopidogrel is a platelet aggregation inhibitor used for the management of cardiovascular disease. While antiplatelet therapy decreases cardiovascular events after successful coronary drug-eluting stenting, it increases the risk of gastrointestinal (GI) bleeding. About 20% of the patients who take clopidogrel exhibit resistance to the drug.

Case Summary: We report the first case of a small bowel bleeding ulcer in an 86-year-old man with clopidogrel resistance. He had a history of taking clopidogrel due to unstable angina. There was no evidence of bleeding in the stomach, duodenum, or colon through upper and lower GI endoscopies. The abdominal computed tomography showed the extravasation of radiocontrast media at the ileum. Because of unstable vital signs, emergency surgery was performed. Multiple ulcers with inflammation were found in the ileum. The pathologic findings revealed simple inflammation. The VerifyNow P2Y12 test showed clopidogrel resistance. One year after changing to aspirin, capsule endoscopy was performed and the small bowel ulcers were improved.

Conclusion: Small bowel ulcers and bleeding due to clopidogrel are not very common, but the prevalence is expected to increase in older age patients with risk factors despite clopidogrel resistance.
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http://dx.doi.org/10.12998/wjcc.v9.i15.3689DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8130066PMC
May 2021

Cortical Thickness from MRI to Predict Conversion from Mild Cognitive Impairment to Dementia in Parkinson Disease: A Machine Learning-based Model.

Radiology 2021 Aug 25;300(2):390-399. Epub 2021 May 25.

From the Departments of Radiology (N.Y.S., M.B., K.J.A.) and Neurology (S.W.Y., J.S.K.), College of Medicine, The Catholic University of Korea, Seoul, Korea; Department of Radiology, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea (N.Y.S., K.H., S.K.L.); and Department of Biomedical Engineering, College of Bio and Medical Sciences, Daegu Catholic University, Gyeongbuk, Korea (E.Y., U.Y.).

Background Group comparison results associating cortical thinning and Parkinson disease (PD) dementia (PDD) are limited in their application to clinical settings. Purpose To investigate whether cortical thickness from MRI can help predict conversion from mild cognitive impairment (MCI) to dementia in PD at an individual level using a machine learning-based model. Materials and Methods In this retrospective study, patients with PD and MCI who underwent MRI from September 2008 to November 2016 were included. Features were selected from clinical and cortical thickness variables in 10 000 randomly generated training sets. Features selected 5000 times or more were used to train random forest and support vector machine models. Each model was trained and tested in 10 000 randomly resampled data sets, and a median of 10 000 areas under the receiver operating characteristic curve (AUCs) was calculated for each. Model performances were validated in an external test set. Results Forty-two patients progressed to PDD (converters) (mean age, 71 years ± 6 [standard deviation]; 22 women), and 75 patients did not progress to PDD (nonconverters) (mean age, 68 years ± 6; 40 women). Four PDD converters (mean age, 74 years ± 10; four men) and 20 nonconverters (mean age, 67 years ± 7; 11 women) were included in the external test set. Models trained with cortical thickness variables (AUC range, 0.75-0.83) showed fair to good performances similar to those trained with clinical variables (AUC range, 0.70-0.81). Model performances improved when models were trained with both variables (AUC range, 0.80-0.88). In pair-wise comparisons, models trained with both variables more frequently showed better performance than others in all model types. The models trained with both variables were successfully validated in the external test set (AUC range, 0.69-0.84). Conclusion Cortical thickness from MRI helped predict conversion from mild cognitive impairment to dementia in Parkinson disease at an individual level, with improved performance when integrated with clinical variables. © RSNA, 2021 See also the editorial by Port in this issue.
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http://dx.doi.org/10.1148/radiol.2021203383DOI Listing
August 2021

Neural correlates of empathy for babies in postpartum women: A longitudinal study.

Hum Brain Mapp 2021 Jul 3;42(10):3295-3304. Epub 2021 May 3.

Department of Radiology, College of Medicine, The Catholic University of Korea, Seoul, South Korea.

This study investigated the empathic response of postpartum women to babies in pain and the underlying neural mechanism. Postpartum women responded with more empathy and speed to babies over other stimuli compared to controls. Brain scans taken 3 months after birth showed more elevated activation in the Middle cingulate cortex/middle frontal gyrus (MCC/MFG) than the controls regardless of the task condition. When compared to the adult and neutral conditions, the posterior cingulate cortex (PCC) region was consistently more activated when postpartum women saw babies than controls. In addition, higher activation levels in the PCC region for the baby condition significantly correlated with faster and more empathic responses to babies. Considering that PCC is a core region for the theory of mind or mentalizing which requires cognitive reasoning to understand others, these results suggest that PCC might be a pivotal neural locus facilitating cognitive efforts to empathize with babies during the postpartum period. In a follow-up experiment at 12 months after birth, we were still able to observe higher activity in the MCC/MFG of postpartum women. However, previously observed PCC activation patterns disappeared 12 months after birth, despite the women's response patterns to babies still being maintained. These results suggest that the mentalizing process activated to empathize with babies in the early postpartum period becomes less cognitively demanding over time.
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http://dx.doi.org/10.1002/hbm.25435DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8193525PMC
July 2021

Magnetic Resonance Imaging Parameters for Noninvasive Prediction of Epidermal Growth Factor Receptor Amplification in Isocitrate Dehydrogenase-Wild-Type Lower-Grade Gliomas: A Multicenter Study.

Neurosurgery 2021 Jul;89(2):257-265

Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea.

Background: The epidermal growth factor receptor (EGFR) amplification status of isocitrate dehydrogenase-wild-type (IDHwt) lower-grade gliomas (LGGs; grade II/III) is one of the key markers for diagnosing molecular glioblastoma. However, the association between EGFR status and imaging parameters is unclear.

Objective: To identify noninvasive imaging parameters from diffusion-weighted and dynamic susceptibility contrast imaging for predicting the EGFR amplification status of IDHwt LGGs.

Methods: A total of 86 IDHwt LGG patients with known EGFR amplification status (62 nonamplified and 24 amplified) from 3 tertiary institutions were included. Qualitative and quantitative imaging features, including histogram parameters from apparent diffusion coefficient (ADC), normalized cerebral blood volume (nCBV), and normalized cerebral blood flow (nCBF), were assessed. Univariable and multivariable logistic regression models were constructed.

Results: On multivariable analysis, multifocal/multicentric distribution (odds ratio [OR] = 11.77, P = .006), mean ADC (OR = 0.01, P = .044), 5th percentile of ADC (OR = 0.01, P = .046), and 95th percentile of nCBF (OR = 1.24, P = .031) were independent predictors of EGFR amplification. The diagnostic performance of the model with qualitative imaging parameters increased significantly when quantitative imaging parameters were added, with areas under the curves of 0.81 and 0.93, respectively (P = .004).

Conclusion: The presence of multifocal/multicentric distribution patterns, lower mean ADC, lower 5th percentile of ADC, and higher 95th percentile of nCBF may be useful imaging biomarkers for EGFR amplification in IDHwt LGGs. Moreover, quantitative imaging biomarkers may add value to qualitative imaging parameters.
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http://dx.doi.org/10.1093/neuros/nyab136DOI Listing
July 2021

Neural correlates of episodic memory modulated by temporally delayed rewards.

PLoS One 2021 7;16(4):e0249290. Epub 2021 Apr 7.

Department of Psychology, Yonsei University, Seoul, Republic of Korea.

When a stimulus is associated with an external reward, its chance of being consolidated into long-term memory is boosted via dopaminergic facilitation of long-term potentiation in the hippocampus. Given that higher temporal distance (TD) has been found to discount the subjective value of a reward, we hypothesized that memory performance associated with a more immediate reward will result in better memory performance. We tested this hypothesis by measuring both behavioral memory performance and brain activation using functional magnetic resonance imaging (fMRI) during memory encoding and retrieval tasks. Contrary to our hypothesis, both behavioral and fMRI results suggest that the TD of rewards might enhance the chance of the associated stimulus being remembered. The fMRI data demonstrate that the lateral prefrontal cortex, which shows encoding-related activation proportional to the TD, is reactivated when searching for regions that show activation proportional to the TD during retrieval. This is not surprising given that this region is not only activated to discriminate between future vs. immediate rewards, it is also a part of the retrieval-success network. These results provide support for the conclusion that the encoding-retrieval overlap provoked as the rewards are more delayed may lead to better memory performance of the items associated with the rewards.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0249290PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8026031PMC
April 2021

Quality assessment of meningioma radiomics studies: Bridging the gap between exploratory research and clinical applications.

Eur J Radiol 2021 May 20;138:109673. Epub 2021 Mar 20.

Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Republic of Korea.

Purpose: To evaluate the quality of radiomics studies on meningiomas, using a radiomics quality score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD), and the Image Biomarker Standardization Initiative (IBSI).

Methods: PubMed MEDLINE and Embase were searched to identify radiomics studies on meningiomas. Of 138 identified articles, 25 relevant original research articles were included. Studies were scored according to the RQS, TRIPOD guidelines, and items in IBSI.

Results: Only four studies (16 %) performed external validation. The mean RQS was 5.6 out of 36 (15.4 %), and the basic adherence rate was 26.8 %. The adherence rate was low for stating biological correlation (4%), conducting calibration statistics (12 %), multiple segmentation (16 %), and stating potential clinical utility (16 %). None of the studies conducted a test‒retest or phantom study, stated a comparison to a 'gold standard', conducted prospective studies or cost-effectivity analysis, or opened code and data to the public, resulting in low RQS. The overall adherence rate for TRIPOD was 54.1 %, with low scores for reporting the title (4%), abstract (0%), blind assessment of the outcome (8%), and explaining the sample size (0%). According to IBSI items, only 6 (24 %), 6 (24 %), and 3 (12 %) studies performed N4 bias-field correction, isovoxel resampling, and grey-level discretization, respectively. No study performed skull stripping.

Conclusion: The quality of radiomics studies for meningioma is insufficient. Acknowledgement of RQS, TRIPOD, and IBSI reporting guidelines may improve the quality of meningioma radiomics studies and enable their clinical application.
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http://dx.doi.org/10.1016/j.ejrad.2021.109673DOI Listing
May 2021

Dynamic contrast-enhanced MRI may be helpful to predict response and prognosis after bevacizumab treatment in patients with recurrent high-grade glioma: comparison with diffusion tensor and dynamic susceptibility contrast imaging.

Neuroradiology 2021 Mar 23. Epub 2021 Mar 23.

Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea.

Purpose: We aimed to evaluate the utility of diffusion tensor imaging (DTI), dynamic contrast-enhanced (DCE), and dynamic susceptibility contrast (DSC) imaging for stratifying bevacizumab treatment outcomes in patients with recurrent high-grade glioma.

Methods: Fifty-three patients with recurrent high-grade glioma who underwent baseline magnetic resonance imaging including DTI, DCE, and DSC before bevacizumab treatment were included. The mean apparent diffusion coefficient, fractional anisotropy, normalized cerebral blood volume, normalized cerebral blood flow, volume transfer constant, rate transfer coefficient (K), extravascular extracellular volume fraction, and plasma volume fraction were assessed. Predictors of response status, progression-free survival (PFS), and overall survival (OS) were determined using logistic regression and Cox proportional hazard modeling.

Results: Responders (n = 16) showed significantly longer PFS and OS (P < 0.001) compared with nonresponders (n = 37). Multivariable analysis revealed that lower mean K (odds ratio = 0.01, P = 0.008) was the only independent predictor of favorable response after adjustment for age, isocitrate dehydrogenase (IDH) mutation status, and O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status. Multivariable Cox proportional hazard modeling showed that a higher mean K was the only variable associated with shorter PFS (hazard ratio [HR] = 7.90, P = 0.006) and OS (HR = 9.71, P = 0.020) after adjustment for age, IDH mutation status, and MGMT promoter methylation status.

Conclusion: Baseline mean K may be a useful biomarker for predicting response and stratifying patient outcomes following bevacizumab treatment in patients with recurrent high-grade glioma.
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http://dx.doi.org/10.1007/s00234-021-02693-zDOI Listing
March 2021

Robust performance of deep learning for automatic detection and segmentation of brain metastases using three-dimensional black-blood and three-dimensional gradient echo imaging.

Eur Radiol 2021 Mar 18. Epub 2021 Mar 18.

Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, South Korea.

Objectives: To evaluate whether a deep learning (DL) model using both three-dimensional (3D) black-blood (BB) imaging and 3D gradient echo (GRE) imaging may improve the detection and segmentation performance of brain metastases compared to that using only 3D GRE imaging.

Methods: A total of 188 patients with brain metastases (917 lesions) who underwent a brain metastasis MRI protocol including contrast-enhanced 3D BB and 3D GRE were included in the training set. DL models based on 3D U-net were constructed. The models were validated in the test set consisting of 45 patients with brain metastases (203 lesions) and 49 patients without brain metastases.

Results: The combined 3D BB and 3D GRE model yielded better performance than the 3D GRE model (sensitivities of 93.1% vs 76.8%, p < 0.001), and this effect was significantly stronger in subgroups with small metastases (p interaction < 0.001). For metastases < 3 mm, ≥ 3 mm and < 10 mm, and ≥ 10 mm, the sensitivities were 82.4%, 93.2%, and 100%, respectively. The combined 3D BB and 3D GRE model showed a false-positive per case of 0.59 in the test set. The combined 3D BB and 3D GRE model showed a Dice coefficient of 0.822, while 3D GRE model showed a lower Dice coefficient of 0.756.

Conclusions: The combined 3D BB and 3D GRE DL model may improve the detection and segmentation performance of brain metastases, especially in detecting small metastases.

Key Points: • The combined 3D BB and 3D GRE model yielded better performance for the detection of brain metastases than the 3D GRE model (p < 0.001), with sensitivities of 93.1% and 76.8%, respectively. • The combined 3D BB and 3D GRE model showed a false-positive rate per case of 0.59 in the test set. • The combined 3D BB and 3D GRE model showed a Dice coefficient of 0.822, while the 3D GRE model showed a lower Dice coefficient of 0.756.
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http://dx.doi.org/10.1007/s00330-021-07783-3DOI Listing
March 2021

Clinical and diffusion parameters may noninvasively predict TERT promoter mutation status in grade II meningiomas.

J Neuroradiol 2021 Mar 11. Epub 2021 Mar 11.

Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea.

Background And Purpose: Increasing evidence suggests that genomic and molecular markers need to be integrated in grading of meningioma. Telomerase reverse transcriptase promoter (TERTp) mutation is receiving attention due to its clinical relevance in the treatment of meningiomas. The predictive ability of conventional and diffusion MRI parameters for determining the TERTp mutation status in grade II meningiomas has yet been identified.

Material And Methods: In this study, 63 patients with surgically confirmed grade II meningiomas (56 TERTp wildtype, 7 TERTp mutant) were included. Conventional imaging features were qualitatively assessed. The maximum diameter, volume of the tumors and histogram parameters from the apparent diffusion coefficient (ADC) were assessed. Independent clinical and imaging risk factors for TERTp mutation were investigated using multivariable logistic regression. The discriminative value of the prediction models with and without imaging features was evaluated.

Results: In the univariable regression, older age (odds ratio [OR] = 1.13, P = 0.005), larger maximum diameter (OR = 1.09, P = 0.023), larger volume (OR = 1.04, P = 0.014), lower mean ADC (OR = 0.02, P = 0.025), and lower ADC 10th percentile (OR = 0.01, P = 0.014) were predictors of TERTp mutation. In multivariable regression, age (OR = 1.13, P = 0.009) and ADC 10th percentile (OR = 0.01, P = 0.038) were independent predictors of variables for predicting the TERTp mutation status. The performance of the prediction model increased upon inclusion of imaging parameters (area under the curves of 0.86 and 0.91, respectively, without and with imaging parameters).

Conclusion: Older age and lower ADC 10th percentile may be useful parameters to predict TERTp mutation in grade II meningiomas.
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http://dx.doi.org/10.1016/j.neurad.2021.02.007DOI Listing
March 2021

Radiomics With Ensemble Machine Learning Predicts Dopamine Agonist Response in Patients With Prolactinoma.

J Clin Endocrinol Metab 2021 Jul;106(8):e3069-e3077

Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea.

Context: Early identification of the response of prolactinoma patients to dopamine agonists (DA) is crucial in treatment planning.

Objective: To develop a radiomics model using an ensemble machine learning classifier with conventional magnetic resonance images (MRIs) to predict the DA response in prolactinoma patients.

Design: Retrospective study.

Setting: Severance Hospital, Seoul, Korea.

Patients: A total of 177 prolactinoma patients who underwent baseline MRI (109 DA responders and 68 DA nonresponders) were allocated to the training (n = 141) and test (n = 36) sets. Radiomic features (n = 107) were extracted from coronal T2-weighed MRIs. After feature selection, single models (random forest, light gradient boosting machine, extra-trees, quadratic discrimination analysis, and linear discrimination analysis) with oversampling methods were trained to predict the DA response. A soft voting ensemble classifier was used to achieve the final performance. The performance of the classifier was validated in the test set.

Results: The ensemble classifier showed an area under the curve (AUC) of 0.81 [95% confidence interval (CI), 0.74-0.87] in the training set. In the test set, the ensemble classifier showed an AUC, accuracy, sensitivity, and specificity of 0.81 (95% CI, 0.67-0.96), 77.8%, 78.6%, and 77.3%, respectively. The ensemble classifier achieved the highest performance among all the individual models in the test set.

Conclusions: Radiomic features may be useful biomarkers to predict the DA response in prolactinoma patients.
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http://dx.doi.org/10.1210/clinem/dgab159DOI Listing
July 2021

Perivascular Spaces in the Basal Ganglia and Long-term Motor Prognosis in Newly Diagnosed Parkinson Disease.

Neurology 2021 04 2;96(16):e2121-e2131. Epub 2021 Mar 2.

From the Departments of Neurology (S.J.C., H.S.Y., J.-M.H., Y.J.K., P.H.L., Y.H.S.) and Radiology (Y.W.P., S.-K.L.) and Biostatistics Collaboration Unit (H.S.L.), Yonsei University College of Medicine, Seoul; Department of Neurology (S.J.C., J.-M.H., Y.J.K.), Yongin Severance Hospital, Yonsei University Health System, Yongin; and Department of Radiology (N.-Y.S.), Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea.

Objective: To investigate the association between enlarged perivascular spaces (PVS) in the basal ganglia (BG-PVS) and long-term motor outcomes in Parkinson disease (PD).

Methods: We reviewed the medical records of 248 patients with drug-naive early-stage PD (follow-up >3 years, mean age 67.44 ± 8.46 years, 130 female) who underwent brain MRI and dopamine transporter (DAT) scans at initial assessment. The number of baseline enlarged BG-PVS was counted on axial T2-weighted images. Then, patients were divided into 2 groups: a PD group with a low number (0-10) of enlarged PVS (PD-EPVS-; n = 156) and a PD group with a high number (>10) of enlarged PVS (PD-EPVS+; n = 92). We used Cox regression models to compare the levodopa-induced dyskinesia (LID)-, wearing-off-, and freezing of gait (FOG)-free times between groups. We also compared longitudinal increases in levodopa-equivalent dose per body weight between groups using a linear mixed model.

Results: Patients in the PD-EPVS+ group were older (72.28 ± 6.07 years) and had greater small vessel disease burden than those in the PD-EPVS- group (64.58 ± 8.38 years). The PD-EPVS+ group exhibited more severely decreased DAT availability in all striatal subregions except the ventral striatum. The risk of FOG was higher in the PD-EPVS+ group, but the risk of LID or wearing-off was comparable between groups. The PD-EPVS+ group required higher doses of dopaminergic medications for effective symptom control compared to the PD-EPVS- group.

Conclusion: This study suggests that baseline enlarged BG-PVS can be an indicator of the progression of motor disability in PD.
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http://dx.doi.org/10.1212/WNL.0000000000011797DOI Listing
April 2021

Differentiation of recurrent glioblastoma from radiation necrosis using diffusion radiomics with machine learning model development and external validation.

Sci Rep 2021 Feb 3;11(1):2913. Epub 2021 Feb 3.

Department of Radiology and Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, South Korea.

The purpose of this study was to establish a high-performing radiomics strategy with machine learning from conventional and diffusion MRI to differentiate recurrent glioblastoma (GBM) from radiation necrosis (RN) after concurrent chemoradiotherapy (CCRT) or radiotherapy. Eighty-six patients with GBM were enrolled in the training set after they underwent CCRT or radiotherapy and presented with new or enlarging contrast enhancement within the radiation field on follow-up MRI. A diagnosis was established either pathologically or clinicoradiologically (63 recurrent GBM and 23 RN). Another 41 patients (23 recurrent GBM and 18 RN) from a different institution were enrolled in the test set. Conventional MRI sequences (T2-weighted and postcontrast T1-weighted images) and ADC were analyzed to extract 263 radiomic features. After feature selection, various machine learning models with oversampling methods were trained with combinations of MRI sequences and subsequently validated in the test set. In the independent test set, the model using ADC sequence showed the best diagnostic performance, with an AUC, accuracy, sensitivity, specificity of 0.80, 78%, 66.7%, and 87%, respectively. In conclusion, the radiomics models models using other MRI sequences showed AUCs ranging from 0.65 to 0.66 in the test set. The diffusion radiomics may be helpful in differentiating recurrent GBM from RN..
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http://dx.doi.org/10.1038/s41598-021-82467-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7858615PMC
February 2021

Effect of Lumbar Epidural Steroid Injections on Osteoporotic Fracture and Bone Mineral Density in Elderly Women with Diabetes Mellitus.

Pain Res Manag 2020 5;2020:1538029. Epub 2020 Dec 5.

Department of Anatomic Pathology, School of Medicine, Kangwon National University, Kangwon National University Hospital, Chuncheon 24341, Republic of Korea.

The incidence of osteoporosis and diabetes mellitus (DM) is known to increase with aging. DM is associated with osteoporotic fractures and decreased bone mineral metabolism. However, no studies have compared the effects of DM on the changes in bone mineral density (BMD) and osteoporotic fracture after epidural steroid injections (ESIs). The present study aimed to analyze the relationship between ESI and BMD changes in elderly women with and without DM. The medical records of elderly women who underwent ESI were retrospectively analyzed. All patients had radiographic and BMD assessments performed before and after receiving lumbar ESIs. A total of 172 patients were divided into two groups according to the presence of DM. The duration of BMD monitoring was 16.1 and 16.8 months in the non-DM and DM groups, respectively. The mean total number of ESIs was 3.4 and 3.2, and the mean cumulative administered dose of glucocorticoids (dexamethasone) was 17 and 16 mg in the non-DM and DM groups, respectively. There were no significant differences between baseline and posttreatment BMD in the lumbar spine, total femur, and femoral neck region in either group. The incidence of osteoporotic fractures at the hip joint and thoracolumbar spine was not significantly different in both groups. ESIs could be used without concerns regarding osteoporosis and fractures in elderly women with DM if low doses of glucocorticoids are used.
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http://dx.doi.org/10.1155/2020/1538029DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7735849PMC
March 2021

Predicting Amyloid Pathology in Mild Cognitive Impairment Using Radiomics Analysis of Magnetic Resonance Imaging.

J Alzheimers Dis 2021 ;79(2):483-491

Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea.

Background: Noninvasive identification of amyloid-β (Aβ) is important for better clinical management of mild cognitive impairment (MCI) patients.

Objective: To investigate whether radiomics features in the hippocampus in MCI improve the prediction of cerebrospinal fluid (CSF) Aβ42 status when integrated with clinical profiles.

Methods: A total of 407 MCI subjects from the Alzheimer's Disease Neuroimaging Initiative were allocated to training (n = 324) and test (n = 83) sets. Radiomics features (n = 214) from the bilateral hippocampus were extracted from magnetic resonance imaging (MRI). A cut-off of <192 pg/mL was applied to define CSF Aβ42 status. After feature selection, random forest with subsampling methods were utilized to develop three models with which to predict CSF Aβ42: 1) a radiomics model; 2) a clinical model based on clinical profiles; and 3) a combined model based on radiomics and clinical profiles. The prediction performances thereof were validated in the test set. A prediction model using hippocampus volume was also developed and validated.

Results: The best-performing radiomics model showed an area under the curve (AUC) of 0.674 in the test set. The best-performing clinical model showed an AUC of 0.758 in the test set. The best-performing combined model showed an AUC of 0.823 in the test set. The hippocampal volume model showed a lower performance, with an AUC of 0.543 in the test set.

Conclusion: Radiomics models from MRI can help predict CSF Aβ42 status in MCI patients and potentially triage the patients for invasive and costly Aβ tests.
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http://dx.doi.org/10.3233/JAD-200734DOI Listing
January 2021

Long-Term Ambient Air Pollution Exposures and Brain Imaging Markers in Korean Adults: The Environmental Pollution-Induced Neurological EFfects (EPINEF) Study.

Environ Health Perspect 2020 11 20;128(11):117006. Epub 2020 Nov 20.

Institute of Human Complexity and Systems Science, Yonsei University, Incheon, Republic of Korea.

Background: Only a limited number of neuroimaging studies have explored the effects of ambient air pollution in adults. The prior studies have investigated only cortical volume, and they have reported mixed findings, particularly for gray matter. Furthermore, the association between nitrogen dioxide () and neuroimaging markers has been little studied in adults.

Objectives: We investigated the association between long-term exposure to air pollutants (, particulate matter (PM) with aerodynamic diameters of (PM10) and (PM2.5), and neuroimaging markers.

Methods: The study included 427 men and 530 women dwelling in four cities in the Republic of Korea. Long-term concentrations of PM10, , and PM2.5 at residential addresses were estimated. Neuroimaging markers (cortical thickness and subcortical volume) were obtained from brain magnetic resonance images. A generalized linear model was used, adjusting for potential confounders.

Results: A increase in PM10 was associated with reduced thicknesses in the frontal [ (95% CI: , )] and temporal lobes [ (95% CI: , )]. A increase in PM2.5 was associated with a thinner temporal cortex [ (95% CI: , )]. A 10-ppb increase in was associated with reduced thicknesses in the global [ (95% CI: , 0.00)], frontal [ (95% CI: , )], parietal [ (95% CI: , )], temporal [ (95% CI: , )], and insular lobes [ (95% CI: , 0.00)]. The air pollutants were also associated with increased thicknesses in the occipital and cingulate lobes. Subcortical structures associated with the air pollutants included the thalamus, caudate, pallidum, hippocampus, amygdala, and nucleus accumbens.

Discussion: The findings suggest that long-term exposure to high ambient air pollution may lead to cortical thinning and reduced subcortical volume in adults. https://doi.org/10.1289/EHP7133.
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http://dx.doi.org/10.1289/EHP7133DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7678746PMC
November 2020

Radiomics features of hippocampal regions in magnetic resonance imaging can differentiate medial temporal lobe epilepsy patients from healthy controls.

Sci Rep 2020 11 11;10(1):19567. Epub 2020 Nov 11.

Department of Neurology, Epilepsy and Sleep Center, Ewha Womans University School of Medicine and Ewha Medical Research Institute, 1071, Anyangcheon-ro, Yangcheon-gu, Seoul, 07985, Korea.

To investigative whether radiomics features in bilateral hippocampi from MRI can identify temporal lobe epilepsy (TLE). A total of 131 subjects with MRI (66 TLE patients [35 right and 31 left TLE] and 65 healthy controls [HC]) were allocated to training (n = 90) and test (n = 41) sets. Radiomics features (n = 186) from the bilateral hippocampi were extracted from T1-weighted images. After feature selection, machine learning models were trained. The performance of the classifier was validated in the test set to differentiate TLE from HC and ipsilateral TLE from HC. Identical processes were performed to differentiate right TLE from HC (training set, n = 69; test set; n = 31) and left TLE from HC (training set, n = 66; test set, n = 30). The best-performing model for identifying TLE showed an AUC, accuracy, sensitivity, and specificity of 0.848, 84.8%, 76.2%, and 75.0% in the test set, respectively. The best-performing radiomics models for identifying right TLE and left TLE subgroups showed AUCs of 0.845 and 0.840 in the test set, respectively. In addition, multiple radiomics features significantly correlated with neuropsychological test scores (false discovery rate-corrected p-values < 0.05). The radiomics model from hippocampus can be a potential biomarker for identifying TLE.
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http://dx.doi.org/10.1038/s41598-020-76283-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7658973PMC
November 2020

Quality Reporting of Radiomics Analysis in Mild Cognitive Impairment and Alzheimer's Disease: A Roadmap for Moving Forward.

Korean J Radiol 2020 12 30;21(12):1345-1354. Epub 2020 Oct 30.

Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea.

Objective: To evaluate radiomics analysis in studies on mild cognitive impairment (MCI) and Alzheimer's disease (AD) using a radiomics quality score (RQS) system to establish a roadmap for further improvement in clinical use.

Materials And Methods: PubMed MEDLINE and EMBASE were searched using the terms 'cognitive impairment' or 'Alzheimer' or 'dementia' and 'radiomic' or 'texture' or 'radiogenomic' for articles published until March 2020. From 258 articles, 26 relevant original research articles were selected. Two neuroradiologists assessed the quality of the methodology according to the RQS. Adherence rates for the following six key domains were evaluated: image protocol and reproducibility, feature reduction and validation, biologic/clinical utility, performance index, high level of evidence, and open science.

Results: The hippocampus was the most frequently analyzed (46.2%) anatomical structure. Of the 26 studies, 16 (61.5%) used an open source database (14 from Alzheimer's Disease Neuroimaging Initiative and 2 from Open Access Series of Imaging Studies). The mean RQS was 3.6 out of 36 (9.9%), and the basic adherence rate was 27.6%. Only one study (3.8%) performed external validation. The adherence rate was relatively high for reporting the imaging protocol (96.2%), multiple segmentation (76.9%), discrimination statistics (69.2%), and open science and data (65.4%) but low for conducting test-retest analysis (7.7%) and biologic correlation (3.8%). None of the studies stated potential clinical utility, conducted a phantom study, performed cut-off analysis or calibration statistics, was a prospective study, or conducted cost-effectiveness analysis, resulting in a low level of evidence.

Conclusion: The quality of radiomics reporting in MCI and AD studies is suboptimal. Validation is necessary using external dataset, and improvements need to be made to feature reproducibility, feature selection, clinical utility, model performance index, and pursuits of a higher level of evidence.
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http://dx.doi.org/10.3348/kjr.2020.0715DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7689149PMC
December 2020

Machine Learning Based Radiomic HPV Phenotyping of Oropharyngeal SCC: A Feasibility Study Using MRI.

Laryngoscope 2021 03 13;131(3):E851-E856. Epub 2020 Jul 13.

Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, South Korea.

Objectives: To investigate whether a radiomic MRI feature-based prediction model can differentiate oropharyngeal squamous cell carcinoma (SCC) according to the human papillomavirus (HPV) status.

Study Design: Retrospective cohort study.

Methods: Pretreatment MRI data from 62 consecutive patients with oropharyngeal SCC were retrospectively reviewed, and chronologically allocated to training (n = 43) and test sets (n = 19). Enhancing tumors were semi-automatically segmented on each slice of the postcontrast T1WI to span the entire tumor volume, after registration of T2WI to postcontrast T1WI; 170 radiomic features were extracted from the entire tumor volume. Relevant features were selected and radiomics models were trained using least absolute shrinkage and selection operator (LASSO) logistic regression model with 10-fold cross-validation, after subsampling of training sets using synthetic minority over-sampling technique to mitigate data imbalance. The selected features, weighted by their respective coefficients, were combined linearly to yield a radiomics score. The diagnostic performance of the radiomic score was evaluated using the area under the receiver operating characteristic curve (AUC).

Results: Six radiomic features, which revealed strong association with HPV status of oropharyngeal SCC, were selected using LASSO. The radiomics model yielded excellent performance on the training set (AUC, 0.982 [95% CI, 0.942-1.000]) and moderate performance on the test set (AUC, 0.744 [95% CI, 0.496-0.991]) for differentiating oropharyngeal SCC according to HPV status.

Conclusions: Radiomics-based MRI phenotyping differentiates oropharyngeal SCC according to HPV status, and thus, is a potential imaging biomarker.

Level Of Evidence: 3 Laryngoscope, 131:E851-E856, 2021.
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http://dx.doi.org/10.1002/lary.28889DOI Listing
March 2021

Central Nervous System Infection-Related Isolated Hippocampal Atrophy as Another Subtype of Medial Temporal Lobe Epilepsy with Hippocampal Atrophy: A Comparison to Conventional Medial Temporal Lobe Epilepsy with Hippocampal Atrophy.

J Clin Neurol 2020 Oct;16(4):688-695

Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea.

Background And Purpose: Hippocampal atrophy (HA) resulting from a central nervous system (CNS) infection might be a relevant lesion responsible for the clinical characteristics of medial temporal lobe epilepsy.

Methods: The clinical characteristics of 54 patients with CNS infection-related medial temporal lobe epilepsy (MTLE) with isolated HA (CNS infection group) and 155 patients with conventional MTLE with HA (conventional group) were compared retrospectively. CNS infection alone and bilateral involvement of the HA were analyzed as prognostic factors, in addition to the detailed clinical characteristics, such as limbic aura and the presence and proportion of each type of automatism, between the two groups, and both medical and surgical prognoses were separately considered. A logistic regression analysis was performed.

Results: A statistical analysis including all clinical factors, including CNS infection with bilateral HA, did not reveal significant differences between the two groups. An analysis comparing the prognosis of the two groups based on good or poor prognosis among patients who received medical treatment and good or poor outcomes among patients who received surgical treatment did not produce significant differences.

Conclusions: In addition to bilateral HA, CNS infection alone was not a poor prognostic factor for the CNS infection-related epilepsy with HA group compared with the conventional MTLE with HA group. Based on these negative results, HA is a plausible and relevant lesion with similar clinical characteristics to HA in patients with conventional MTLE. Therefore, CNS infection-related MTLE with isolated HA might represent another subtype of MTLE with HA with a different etiology.
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http://dx.doi.org/10.3988/jcn.2020.16.4.688DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7541999PMC
October 2020

The diagnostic potential of multimodal neuroimaging measures in Parkinson's disease and atypical parkinsonism.

Brain Behav 2020 11 7;10(11):e01808. Epub 2020 Oct 7.

Department of Radiology, College of Medicine, The Catholic University of Korea, Seoul, Korea.

Introduction: For the diagnosis of Parkinson's disease (PD) and atypical parkinsonism (AP) using neuroimaging, structural measures have been largely employed since structural abnormalities are most noticeable in the diseases. Functional abnormalities have been known as well, though less clearly seen, and thus, the addition of functional measures to structural measures is expected to be more informative for the diagnosis. Here, we aimed to assess whether multimodal neuroimaging measures of structural and functional alterations could have potential for enhancing performance in diverse diagnostic classification problems.

Methods: For 77 patients with PD, 86 patients with AP comprising multiple system atrophy and progressive supranuclear palsy, and 53 healthy controls (HC), structural and functional MRI data were collected. Gray matter (GM) volume was acquired as a structural measure, and GM regional homogeneity and degree centrality were acquired as functional measures. The measures were used as predictors individually or in combination in support vector machine classifiers for different problems of distinguishing between HC and each diagnostic type and between different diagnostic types.

Results: In statistical comparisons of the measures, structural alterations were extensively seen in all diagnostic types, whereas functional alterations were limited to specific diagnostic types. The addition of functional measures to the structural measure generally yielded statistically significant improvements to classification accuracy, compared to the use of the structural measure alone.

Conclusion: We suggest the fusion of multimodal neuroimaging measures as an effective strategy that could generally cope with diverse prediction problems of clinical concerns.
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http://dx.doi.org/10.1002/brb3.1808DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7667347PMC
November 2020

Differentiating patients with schizophrenia from healthy controls by hippocampal subfields using radiomics.

Schizophr Res 2020 09 26;223:337-344. Epub 2020 Sep 26.

Department of Psychiatry, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam, Republic of Korea. Electronic address:

Background: Accurately diagnosing schizophrenia is still challenging due to the lack of validated biomarkers. Here, we aimed to investigate whether radiomic features in bilateral hippocampal subfields from magnetic resonance images (MRIs) can differentiate patients with schizophrenia from healthy controls (HCs).

Methods: A total of 152 participants with MRI (86 schizophrenia and 66 HCs) were allocated to training (n = 106) and test (n = 46) sets. Radiomic features (n = 642) from the bilateral hippocampal subfields processed with automatic segmentation techniques were extracted from T1-weighted MRIs. After feature selection, various combinations of classifiers (logistic regression, extra-trees, AdaBoost, XGBoost, or support vector machine) and subsampling were trained. The performance of the classifier was validated in the test set by determining the area under the curve (AUC). Furthermore, the association between selected radiomic features and clinical symptoms in schizophrenia was assessed.

Results: Thirty radiomic features were identified to differentiate participants with schizophrenia from HCs. In the training set, the AUC exhibited poor to good performance (range: 0.683-0.861). The best performing radiomics model in the test set was achieved by the mutual information feature selection and logistic regression with an AUC, accuracy, sensitivity, and specificity of 0.821 (95% confidence interval 0.681-0.961), 82.1%, 76.9%, and 70%, respectively. Greater maximum values in the left cornu ammonis 1-3 subfield were associated with a higher severity of positive symptoms and general psychopathology in participants with schizophrenia.

Conclusion: Radiomic features from hippocampal subfields may be useful biomarkers for identifying schizophrenia.
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http://dx.doi.org/10.1016/j.schres.2020.09.009DOI Listing
September 2020

Squamous Cell Carcinoma and Lymphoma of the Oropharynx: Differentiation Using a Radiomics Approach.

Yonsei Med J 2020 Oct;61(10):895-900

Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea.

The purpose of this study was to evaluate the diagnostic performance of magnetic resonance (MR) radiomics-based machine learning algorithms in differentiating squamous cell carcinoma (SCC) from lymphoma in the oropharynx. MR images from 87 patients with oropharyngeal SCC (n=68) and lymphoma (n=19) were reviewed retrospectively. Tumors were semi-automatically segmented on contrast-enhanced T1-weighted images registered to T2-weighted images, and radiomic features (n=202) were extracted from contrast-enhanced T1- and T2-weighted images. The radiomics classifier was built using elastic-net regularized generalized linear model analyses with nested five-fold cross-validation. The diagnostic abilities of the radiomics classifier and visual assessment by two head and neck radiologists were evaluated using receiver operating characteristic (ROC) analyses for distinguishing SCC from lymphoma. Nineteen radiomics features were selected at least twice during the five-fold cross-validation. The mean area under the ROC curve (AUC) of the radiomics classifier was 0.750 [95% confidence interval (CI), 0.613-0.887], with a sensitivity of 84.2%, specificity of 60.3%, and an accuracy of 65.5%. Two human readers yielded AUCs of 0.613 (95% CI, 0.467-0.759) and 0.663 (95% CI, 0.531-0.795), respectively. The radiomics-based machine learning model can be useful for differentiating SCC from lymphoma of the oropharynx.
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http://dx.doi.org/10.3349/ymj.2020.61.10.895DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515782PMC
October 2020

Serrated Polyposis Syndrome with a Synchronous Colon Adenocarcinoma Treated by an Endoscopic Mucosal Resection.

Korean J Gastroenterol 2020 09;76(3):159-163

Department of Anatomic Pathology, Kangwon National University School of Medicine, Chuncheon, Korea.

Serrated polyposis syndrome (SPS) can transform to malignant lesions through the sessile serrated pathway and traditional serrated pathway. These pathways may cause rapid neoplastic progression compared to the adenoma-carcinoma sequence, which may cause interval colorectal cancer. The authors experienced a case of SPS with a synchronous colon adenocarcinoma that was treated with an endoscopic mucosal resection. In pathology reviews, other parts of the adenocarcinoma showed sessile serrated adenoma. Therefore, patients with SPS have a potential for malignant transformation, highlighting the need for strict colonoscopy surveillance starting at the time of SPS diagnosis.
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http://dx.doi.org/10.4166/kjg.2020.76.3.159DOI Listing
September 2020

Correction to: Diffusion and perfusion MRI may predict EGFR amplification and the TERT promoter mutation status of IDH-wildtype lower-grade gliomas.

Eur Radiol 2021 Mar;31(3):1782

Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, South Korea.

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http://dx.doi.org/10.1007/s00330-020-07257-yDOI Listing
March 2021

Differentiation of recurrent diffuse glioma from treatment-induced change using amide proton transfer imaging: incremental value to diffusion and perfusion parameters.

Neuroradiology 2021 Mar 2;63(3):363-372. Epub 2020 Sep 2.

Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea.

Purpose: To evaluate the incremental value of amide proton transfer (APT) imaging to diffusion tensor imaging (DTI), dynamic susceptibility contrast (DSC) imaging, and dynamic contrast-enhanced (DCE) imaging in differentiating recurrent diffuse gliomas (World Health Organization grade II-IV) from treatment-induced change after concurrent chemoradiotherapy or radiotherapy.

Methods: This study included 36 patients (25 patients with recurrent gliomas and 11 with treatment-induced changes) with post-treatment gliomas. The mean values of apparent diffusion coefficient (ADC), fractional anisotropy (FA), normalized cerebral blood volume (nCBV), normalized cerebral blood flow, volume transfer constant, rate transfer coefficient, extravascular extracellular volume fraction, plasma volume fraction, and APT asymmetry index were assessed. Independent quantitative parameters were investigated to predict recurrent glioma using multivariable logistic regression. The incremental value of APT signal to other parameters was assessed by the increase of the area under the curve, net reclassification index, and integrated discrimination improvement.

Results: Univariable analysis showed that lower ADC (p = 0.018), higher FA (p = 0.031), higher nCBV (p = 0.021), and higher APT signal (p = 0.009) were associated with recurrent gliomas. In multivariable logistic regression, the diagnostic performance of the model with ADC, FA, and nCBV significantly increased when APT signal was added, with areas under the curve of 0.87 and 0.92, respectively (net reclassification index of 0.77 and integrated discrimination improvement of 0.13).

Conclusion: APT imaging may be a useful imaging biomarker that adds value to DTI, DCE, and DSC parameters for distinguishing between recurrent gliomas and treatment-induced changes.
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http://dx.doi.org/10.1007/s00234-020-02542-5DOI Listing
March 2021

Radiomics model predicts granulation pattern in growth hormone-secreting pituitary adenomas.

Pituitary 2020 Dec;23(6):691-700

Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea.

Purpose: To investigate whether radiomic features from magnetic resonance image (MRI) can predict the granulation pattern of growth hormone (GH)-secreting pituitary adenoma patients.

Methods: Sixty-nine pathologically proven acromegaly patients (densely granulated [DG] = 50, sparsely granulated [SG] = 19) were included. Radiomic features (n = 214) were extracted from contrast-enhancing and total tumor portions from T2-weighted (T2) MRIs. Imaging features were selected using a least absolute shrinkage and selection operator (LASSO) logistic regression model with fivefold cross-validation. Diagnostic performance for predicting granulation pattern was compared with that for qualitative T2 signal intensity assessment and T2 relative signal intensity (rSI) using the area under the receiver operating characteristics curve (AUC).

Results: Four significant radiomic features from the contrast-enhancing tumor (1 from shape, 1 from first order feature, and 2 from second order features) were selected by LASSO for model construction. The radiomics model showed an AUC, accuracy, sensitivity, and specificity of 0.834 (95% confidence interval [CI] 0.738-0.930), 73.7%, 74.0%, and 73.9%, respectively. The radiomics model showed significantly better performance than the model using qualitative T2 signal intensity assessment (AUC 0.597 [95% CI 0.447-0.747], P = 0.009) and T2 rSI (AUC 0.647 [95% CI 0.523-0.759], P = 0.037).

Conclusion: Radiomic features may be useful biomarkers to differentiate granulation pattern of GH-secreting pituitary adenoma patients, and showed better performance than qualitative assessment or rSI evaluation.
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http://dx.doi.org/10.1007/s11102-020-01077-5DOI Listing
December 2020

Diffusion tensor and postcontrast T1-weighted imaging radiomics to differentiate the epidermal growth factor receptor mutation status of brain metastases from non-small cell lung cancer.

Neuroradiology 2021 Mar 21;63(3):343-352. Epub 2020 Aug 21.

Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.

Purpose: To assess whether the radiomic features of diffusion tensor imaging (DTI) and conventional postcontrast T1-weighted (T1C) images can differentiate the epidermal growth factor receptor (EGFR) mutation status in brain metastases from non-small cell lung cancer (NSCLC).

Methods: A total of 99 brain metastases in 51 patients who underwent surgery or biopsy with underlying NSCLC and known EGFR mutation statuses (57 from EGFR wild type, 42 from EGFR mutant) were allocated to the training (57 lesions in 31 patients) and test (42 lesions in 20 patients) sets. Radiomic features (n = 526) were extracted from preoperative MR images including T1C and DTI. Radiomics classifiers were constructed by combinations of five feature selectors and four machine learning algorithms. The trained classifiers were validated on the test set, and the classifier performance was assessed by determining the area under the curve (AUC).

Results: EGFR mutation status showed an overall discordance rate of 12% between the primary tumors and corresponding brain metastases. The best performing classifier was a combination of the tree-based feature selection and linear discriminant algorithm and 5 features were selected (1 from ADC, 2 from fractional anisotropy, and 2 from T1C images), resulting in an AUC, accuracy, sensitivity, and specificity of 0.73, 78.6%, 81.3%, and 76.9% in the test set, respectively.

Conclusions: Radiomics classifiers integrating multiparametric MRI parameters may have potential in differentiating the EGFR mutation status in brain metastases from NSCLC.
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http://dx.doi.org/10.1007/s00234-020-02529-2DOI Listing
March 2021

Diffusion and perfusion MRI may predict EGFR amplification and the TERT promoter mutation status of IDH-wildtype lower-grade gliomas.

Eur Radiol 2020 Dec 12;30(12):6475-6484. Epub 2020 Aug 12.

Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science , Yonsei University College of Medicine , 50-1 Yonsei-ro, Seodaemun-gu, 120-752, Seoul, South Korea.

Objectives: Epidermal growth factor receptor (EGFR) amplification and telomerase reverse transcriptase promoter (TERTp) mutation status of isocitrate dehydrogenase-wildtype (IDHwt) lower-grade gliomas (LGGs; grade II/III) are crucial for identifying IDHwt LGG with an aggressive clinical course. The purpose of this study was to assess whether parameters from diffusion tensor imaging, dynamic susceptibility contrast (DSC), and diffusion tensor imaging, dynamic contrast-enhanced imaging can predict the EGFR amplification and TERTp mutation status of IDHwt LGGs.

Methods: A total of 49 patients with IDHwt LGGs with either known EGFR amplification (39 non-amplified, 10 amplified) or TERTp mutation (19 wildtype, 21 mutant) statuses underwent MRI. The mean ADC, fractional anisotropy (FA), normalized cerebral blood volume (nCBV), normalized cerebral blood flow (nCBF), volume transfer constant (K), rate transfer coefficient (K), extravascular extracellular volume fraction (V), and plasma volume fraction (V) values were assessed. Univariate and multivariate logistic regression models were constructed.

Results: EGFR-amplified tumors showed lower mean ADC values than EGFR-non-amplified tumors (p = 0.019). Mean ADC was an independent predictor of EGFR amplification, with an AUC of 0.75. TERTp mutant tumors showed higher mean nCBV (p = 0.020), higher mean nCBF (p = 0.017), and higher mean V (p = 0.002) than TERTp wildtype tumors. With multivariate logistic regression, mean V was the independent predictor of TERTp mutation status, with an AUC of 0.85.

Conclusion: This exploratory pilot study shows that lower ADC values may be useful for prediction of EGFR amplification, whereas higher V values may be useful for prediction of the TERTp mutation status of IDHwt LGGs.

Key Points: • EGFR amplification and TERTp mutation are key molecular markers that predict an aggressive clinical course of IDHwt LGGs. • EGFR-amplified tumors showed lower ADC values than EGFR-non-amplified tumors, suggesting higher cellularity. • TERTp mutant tumors showed a higher plasma volume fraction than TERTp wildtype tumors, suggesting higher vascular proliferation and tumor angiogenesis.
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http://dx.doi.org/10.1007/s00330-020-07090-3DOI Listing
December 2020
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