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: Malnutrition exacerbates the prognosis of numerous diseases; however, its specific impact on severe coronavirus disease 2019 (COVID-19) outcomes remains insufficiently explored. Methods: This multicenter study in Korea evaluated the nutritional status of 1,088 adults with severe COVID-19 using the Geriatric Nutritional Risk Index (GNRI) based on serum albumin levels and body weight. The patients were categorized into two groups: GNRI >98 (no-risk) and GNRI ≤98 (risk). Propensity score matching, adjusted for demographic and clinical variables, was conducted. Results: Of the 1,088 patients, 642 (59%) were classified as at risk of malnutrition. Propensity score matching revealed significant disparities in hospital (34.3% vs. 19.4%, p<0.001) and intensive care unit (ICU) mortality (31.5% vs. 18.9%, p<0.001) between the groups. The risk group was associated with a higher hospital mortality rate in the multivariate Cox regression analyses following propensity score adjustment (hazard ratio [HR], 1.64; p=0.001). Among the 670 elderly patients, 450 were at risk of malnutrition. Furthermore, the risk group demonstrated significantly higher hospital (52.1% vs. 29.5%, p<0.001) and ICU mortality rates (47.2% vs. 29.1%, p<0.001). The risk group was significantly associated with increased hospital mortality rates in the multivariate analyses following propensity score adjustment (HR, 1.66; p=0.001). Conclusion Malnutrition, as indicated by a low GNRI, was associated with increased mortality in patients with severe COVID-19. This effect was also observed in the elderly population. These findings underscore the critical importance of nutritional assessment and effective interventions for patients with severe COVID-19.

Background: Small cell lung cancer (SCLC) is called ‘smoker’s disease’ because it is strongly associated with smoking and most cases occur in smokers. However, it can also occur in never smokers. We investigated the clinical features of never smokers with SCLC and compared their treatment outcomes with those of smokers with SCLC. Methods: We retrospectively reviewed the clinical data of patients who had proven SCLC and had received chemotherapy at a single cancer center between July 2002 and April 2021. Results: Of 1,643 patients, 1,416 (86.2%) were enrolled in this study. A total of 162 (11.4%) and 1,254 (88.6%) patients were never smokers and smokers, respectively. There were more female never smokers than smokers (n=130; 80.2% vs. 79, 6.3%, p=0.000), and the incidence of ischemic heart disease was lower among never smokers than among smokers (4/1,416, [2.5%] vs. 83/1,416 [6.6%], p=0.036). Never smokers showed less symptoms at diagnosis than smokers (80.9% vs. 87.2%, p=0.037); however, they showed more toxicity after first-line treatment (61.7% vs. 47.8%, p=0.001). The objective response rate (ORR) was significantly higher in never smokers (74.1% vs. 59.6%, p=0.000). In the multivariate analysis, never smoking and second-line treatment were associated with a better ORR. However, progression-free survival and overall survival were not significantly different between never smokers and smokers. Conclusion In conclusion, never smokers accounted for 11.4% of patients with SCLC. They had distinguishing clinical characteristics and showed better chemotherapeutic responses than smokers.

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: Malnutrition exacerbates the prognosis of numerous diseases; however, its specific impact on severe coronavirus disease 2019 (COVID-19) outcomes remains insufficiently explored. Methods: This multicenter study in Korea evaluated the nutritional status of 1,088 adults with severe COVID-19 using the Geriatric Nutritional Risk Index (GNRI) based on serum albumin levels and body weight. The patients were categorized into two groups: GNRI >98 (no-risk) and GNRI ≤98 (risk). Propensity score matching, adjusted for demographic and clinical variables, was conducted. Results: Of the 1,088 patients, 642 (59%) were classified as at risk of malnutrition. Propensity score matching revealed significant disparities in hospital (34.3% vs. 19.4%, p<0.001) and intensive care unit (ICU) mortality (31.5% vs. 18.9%, p<0.001) between the groups. The risk group was associated with a higher hospital mortality rate in the multivariate Cox regression analyses following propensity score adjustment (hazard ratio [HR], 1.64; p=0.001). Among the 670 elderly patients, 450 were at risk of malnutrition. Furthermore, the risk group demonstrated significantly higher hospital (52.1% vs. 29.5%, p<0.001) and ICU mortality rates (47.2% vs. 29.1%, p<0.001). The risk group was significantly associated with increased hospital mortality rates in the multivariate analyses following propensity score adjustment (HR, 1.66; p=0.001). Conclusion Malnutrition, as indicated by a low GNRI, was associated with increased mortality in patients with severe COVID-19. This effect was also observed in the elderly population. These findings underscore the critical importance of nutritional assessment and effective interventions for patients with severe COVID-19.

Background: Small cell lung cancer (SCLC) is called ‘smoker’s disease’ because it is strongly associated with smoking and most cases occur in smokers. However, it can also occur in never smokers. We investigated the clinical features of never smokers with SCLC and compared their treatment outcomes with those of smokers with SCLC. Methods: We retrospectively reviewed the clinical data of patients who had proven SCLC and had received chemotherapy at a single cancer center between July 2002 and April 2021. Results: Of 1,643 patients, 1,416 (86.2%) were enrolled in this study. A total of 162 (11.4%) and 1,254 (88.6%) patients were never smokers and smokers, respectively. There were more female never smokers than smokers (n=130; 80.2% vs. 79, 6.3%, p=0.000), and the incidence of ischemic heart disease was lower among never smokers than among smokers (4/1,416, [2.5%] vs. 83/1,416 [6.6%], p=0.036). Never smokers showed less symptoms at diagnosis than smokers (80.9% vs. 87.2%, p=0.037); however, they showed more toxicity after first-line treatment (61.7% vs. 47.8%, p=0.001). The objective response rate (ORR) was significantly higher in never smokers (74.1% vs. 59.6%, p=0.000). In the multivariate analysis, never smoking and second-line treatment were associated with a better ORR. However, progression-free survival and overall survival were not significantly different between never smokers and smokers. Conclusion In conclusion, never smokers accounted for 11.4% of patients with SCLC. They had distinguishing clinical characteristics and showed better chemotherapeutic responses than smokers.

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: Malnutrition exacerbates the prognosis of numerous diseases; however, its specific impact on severe coronavirus disease 2019 (COVID-19) outcomes remains insufficiently explored. Methods: This multicenter study in Korea evaluated the nutritional status of 1,088 adults with severe COVID-19 using the Geriatric Nutritional Risk Index (GNRI) based on serum albumin levels and body weight. The patients were categorized into two groups: GNRI >98 (no-risk) and GNRI ≤98 (risk). Propensity score matching, adjusted for demographic and clinical variables, was conducted. Results: Of the 1,088 patients, 642 (59%) were classified as at risk of malnutrition. Propensity score matching revealed significant disparities in hospital (34.3% vs. 19.4%, p<0.001) and intensive care unit (ICU) mortality (31.5% vs. 18.9%, p<0.001) between the groups. The risk group was associated with a higher hospital mortality rate in the multivariate Cox regression analyses following propensity score adjustment (hazard ratio [HR], 1.64; p=0.001). Among the 670 elderly patients, 450 were at risk of malnutrition. Furthermore, the risk group demonstrated significantly higher hospital (52.1% vs. 29.5%, p<0.001) and ICU mortality rates (47.2% vs. 29.1%, p<0.001). The risk group was significantly associated with increased hospital mortality rates in the multivariate analyses following propensity score adjustment (HR, 1.66; p=0.001). Conclusion Malnutrition, as indicated by a low GNRI, was associated with increased mortality in patients with severe COVID-19. This effect was also observed in the elderly population. These findings underscore the critical importance of nutritional assessment and effective interventions for patients with severe COVID-19.

Background: Small cell lung cancer (SCLC) is called ‘smoker’s disease’ because it is strongly associated with smoking and most cases occur in smokers. However, it can also occur in never smokers. We investigated the clinical features of never smokers with SCLC and compared their treatment outcomes with those of smokers with SCLC. Methods: We retrospectively reviewed the clinical data of patients who had proven SCLC and had received chemotherapy at a single cancer center between July 2002 and April 2021. Results: Of 1,643 patients, 1,416 (86.2%) were enrolled in this study. A total of 162 (11.4%) and 1,254 (88.6%) patients were never smokers and smokers, respectively. There were more female never smokers than smokers (n=130; 80.2% vs. 79, 6.3%, p=0.000), and the incidence of ischemic heart disease was lower among never smokers than among smokers (4/1,416, [2.5%] vs. 83/1,416 [6.6%], p=0.036). Never smokers showed less symptoms at diagnosis than smokers (80.9% vs. 87.2%, p=0.037); however, they showed more toxicity after first-line treatment (61.7% vs. 47.8%, p=0.001). The objective response rate (ORR) was significantly higher in never smokers (74.1% vs. 59.6%, p=0.000). In the multivariate analysis, never smoking and second-line treatment were associated with a better ORR. However, progression-free survival and overall survival were not significantly different between never smokers and smokers. Conclusion In conclusion, never smokers accounted for 11.4% of patients with SCLC. They had distinguishing clinical characteristics and showed better chemotherapeutic responses than smokers.