Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Purpose: This study aimed to investigate whether levetiracetam (LEV), the most used antiepileptic drug, influences survival in patients with glioblastoma (GBM), using a national database. Materials and Methods: This study used data from the Korea Health Insurance Review and Assessment database. Patients diagnosed with GBM between 2007-2018 treated with standard therapy were included. The study population was divided into long-term (≥ 60 days) and short-term (< 30 days) LEV groups. A separate long-term valproic acid (VPA) group (≥ 60 days) was identified for comparison. Demographics, disease characteristics, and treatment parameters were collected. Kaplan-Meier method and Cox regression were used to compare survival outcomes between the groups. Results: Overall, 2,971 patients were included, with 1,319 and 1,652 in the short-term and long-term LEV groups, respectively. The median overall survival (OS) for the entire population was 19.15 months post-surgery. Kaplan-Meier analysis revealed a significantly longer median OS in the long-term LEV group versus the short-term LEV group. After adjusting for confounders, Cox proportional hazard analysis revealed an association of long-term LEV use with improved survival, which was also observed in a subgroup analysis of patients without preoperative seizure history. The long-term LEV group demonstrated longer median OS, compared with the long-term VPA group. Conclusion Our nationwide population-based study found an association between long-term LEV use and improved survival in patients with GBM, regardless of preoperative seizure history. Prospective studies are needed to validate these findings and investigate the potential impact of LEV on the survival outcomes of patients with GBM.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Purpose: This study aimed to investigate whether levetiracetam (LEV), the most used antiepileptic drug, influences survival in patients with glioblastoma (GBM), using a national database. Materials and Methods: This study used data from the Korea Health Insurance Review and Assessment database. Patients diagnosed with GBM between 2007-2018 treated with standard therapy were included. The study population was divided into long-term (≥ 60 days) and short-term (< 30 days) LEV groups. A separate long-term valproic acid (VPA) group (≥ 60 days) was identified for comparison. Demographics, disease characteristics, and treatment parameters were collected. Kaplan-Meier method and Cox regression were used to compare survival outcomes between the groups. Results: Overall, 2,971 patients were included, with 1,319 and 1,652 in the short-term and long-term LEV groups, respectively. The median overall survival (OS) for the entire population was 19.15 months post-surgery. Kaplan-Meier analysis revealed a significantly longer median OS in the long-term LEV group versus the short-term LEV group. After adjusting for confounders, Cox proportional hazard analysis revealed an association of long-term LEV use with improved survival, which was also observed in a subgroup analysis of patients without preoperative seizure history. The long-term LEV group demonstrated longer median OS, compared with the long-term VPA group. Conclusion Our nationwide population-based study found an association between long-term LEV use and improved survival in patients with GBM, regardless of preoperative seizure history. Prospective studies are needed to validate these findings and investigate the potential impact of LEV on the survival outcomes of patients with GBM.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Purpose: This study aimed to investigate whether levetiracetam (LEV), the most used antiepileptic drug, influences survival in patients with glioblastoma (GBM), using a national database. Materials and Methods: This study used data from the Korea Health Insurance Review and Assessment database. Patients diagnosed with GBM between 2007-2018 treated with standard therapy were included. The study population was divided into long-term (≥ 60 days) and short-term (< 30 days) LEV groups. A separate long-term valproic acid (VPA) group (≥ 60 days) was identified for comparison. Demographics, disease characteristics, and treatment parameters were collected. Kaplan-Meier method and Cox regression were used to compare survival outcomes between the groups. Results: Overall, 2,971 patients were included, with 1,319 and 1,652 in the short-term and long-term LEV groups, respectively. The median overall survival (OS) for the entire population was 19.15 months post-surgery. Kaplan-Meier analysis revealed a significantly longer median OS in the long-term LEV group versus the short-term LEV group. After adjusting for confounders, Cox proportional hazard analysis revealed an association of long-term LEV use with improved survival, which was also observed in a subgroup analysis of patients without preoperative seizure history. The long-term LEV group demonstrated longer median OS, compared with the long-term VPA group. Conclusion Our nationwide population-based study found an association between long-term LEV use and improved survival in patients with GBM, regardless of preoperative seizure history. Prospective studies are needed to validate these findings and investigate the potential impact of LEV on the survival outcomes of patients with GBM.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Background: For treating recurrent glioblastoma, for which there is no established treatment, the antiangiogenic antibody, bevacizumab, is used alone or with irinotecan. This study was aimed at comparing the survival of patients with recurrent glioblastoma receiving bevacizumab monotherapy and those receiving bevacizumab plus irinotecan combination therapy (B+I) by using a nationwide population-based dataset. Methods: Patients matching the International Classification of Diseases code C71.x were screened from the Health Insurance Review and Assessment Service database. From January 2008 to November 2021, patients who underwent surgery or biopsy and subsequent standard concurrent chemoradiation with temozolomide were included. Among them, those who received bevacizumab monotherapy or B+I were selected. Demographic characteristics, inpatient stay, prescription frequency, survival outcomes, and steroid prescription duration were compared between these two groups. Results: Eight hundred and forty-six patients who underwent surgery or biopsy and received concurrent chemoradiotherapy with temozolomide were included. Of these, 450 and 396 received bevacizumab monotherapy and B+I, respectively. The corresponding median overall survival from the initial surgery was 22.60 months (95% confidence interval [CI], 20.50– 24.21) and 20.44 months (95% CI, 18.55–22.60; P = 0.508, log-rank test). The B+I group had significantly more bevacizumab prescriptions (median 5 times; BEV group: median 3 times).Cox analysis, based on the postsurgery period, revealed that male sex (hazard ratio [HR], 1.28;P = 0.002), older age (HR, 1.01; P = 0.042), and undergoing biopsy instead of surgery (HR, 1.79; P < 0.0001) were significantly associated with decreased survival. Fewer radiotherapy cycles correlated with improved survival outcomes (HR, 0.63; P = 0.001). Cox analysis, conducted from the start of chemotherapy including bevacizumab, showed that male sex was the only variable significantly associated with decreased survival (HR, 1.18; P = 0.044). Conclusion We found no significant difference in overall survival between the bevacizumab monotherapy and B+I groups. Considering the additional potential toxicity associated with irinotecan, bevacizumab monotherapy could be a suitable treatment option for treating recurrent glioblastoma.

Portal vein tumor thrombosis (PVTT) is an uncommon condition in which tumor cells expand into the vessels, causing blood clot formation in the portal vein. PVTT is mainly associated with hepatocellular carcinoma, leading to an unfavorable prognosis; however, it can also develop in patients with other cancer types. Herein, we report a case of metastatic renal cell carcinoma diagnosed by a blind liver biopsy in a patient with dynamic computed tomography-confirmed portal vein thrombosis and cholangiopathy. This case illustrates the importance of systematic surveillance with routine laboratory tests and contrast-enhanced imaging studies on patients with cancer to detect potential liver infiltration of metastatic cancer.