Flow cytometric lymphocyte subset analysis (FCLSA) is essential for assessing immune status across various diseases and clinical settings. We surveyed current clinical laboratory practices related to FCLSA to establish a baseline reference for future standardization in Korea. Nine university hospitals actively performing FCLSA responded to the 22-question survey, which covered seven categories of laboratory practice. These hospitals used commercial reagent antibody kits from either Beckton Dickinson Biosciences (N = 4) or Beckman Coulter Diagnostics (N = 5). Most hospitals performed daily instrument setup and scheduled maintenance every 2–6 months. Two levels of commercial quality control materials were routinely used each day. Sample and reagent antibody volumes varied across hospitals, even when the same reagent kit was used. Acquired cell counts ranged from 5 × 10 3 to 5 × 10 4 cells, with two hospitals adjusting counts based on the cell type analyzed. Most laboratories reported percentages and general opinions; some additionally reported white blood cell and lymphocyte counts, along with lymphocyte percentages. This is the first comprehensive survey on the clinical laboratory practice of FCLSA in Korea.Standardization of FCLSA should be accelerated to ensure reliable and reproducible results.

Background: The associations between thyroid cancer and skeletal outcomes have not been thoroughly investigated. We aimed to investigate the risk of osteoporotic fractures in patients with thyroid cancer compared to that in a matched control group. Methods: This retrospective cohort study included 2,514 patients with thyroid cancer and 75,420 matched controls from the Korean National Health Insurance Service-National Sample Cohort (NHIS-NSC, 2006–2019). The rates of osteoporotic fractures were analyzed, and associations with the levothyroxine dose were evaluated. Results: Patients with thyroid cancer had a significantly lower risk of fracture than did the control group (hazard ratio [HR], 0.81; 95% confidence interval [CI], 0.69 to 0.94; P=0.006). Patients diagnosed with thyroid cancer after the age of 50 years (older cancer group) had a significantly lower risk of fracture than did those in the control group (HR, 0.72; 95% CI, 0.6 to 0.85; P<0.001), especially those diagnosed with spinal fractures (HR, 0.66; 95% CI, 0.51 to 0.85; P=0.001). Patients in the older cancer group started osteoporosis treatment earlier than did those in the control group (65.5±7.5 years vs. 67.3±7.6 years, P<0.001). Additionally, a lower dose of levothyroxine was associated with a reduced risk of fractures. Conclusion In the clinical setting, the risk of fracture in women diagnosed with thyroid cancer after the age of 50 years was lower than that in the control group, which was caused by more proactive osteoporosis treatment in postmenopausal women with thyroid cancer.

Background: The associations between thyroid cancer and skeletal outcomes have not been thoroughly investigated. We aimed to investigate the risk of osteoporotic fractures in patients with thyroid cancer compared to that in a matched control group. Methods: This retrospective cohort study included 2,514 patients with thyroid cancer and 75,420 matched controls from the Korean National Health Insurance Service-National Sample Cohort (NHIS-NSC, 2006–2019). The rates of osteoporotic fractures were analyzed, and associations with the levothyroxine dose were evaluated. Results: Patients with thyroid cancer had a significantly lower risk of fracture than did the control group (hazard ratio [HR], 0.81; 95% confidence interval [CI], 0.69 to 0.94; P=0.006). Patients diagnosed with thyroid cancer after the age of 50 years (older cancer group) had a significantly lower risk of fracture than did those in the control group (HR, 0.72; 95% CI, 0.6 to 0.85; P<0.001), especially those diagnosed with spinal fractures (HR, 0.66; 95% CI, 0.51 to 0.85; P=0.001). Patients in the older cancer group started osteoporosis treatment earlier than did those in the control group (65.5±7.5 years vs. 67.3±7.6 years, P<0.001). Additionally, a lower dose of levothyroxine was associated with a reduced risk of fractures. Conclusion In the clinical setting, the risk of fracture in women diagnosed with thyroid cancer after the age of 50 years was lower than that in the control group, which was caused by more proactive osteoporosis treatment in postmenopausal women with thyroid cancer.

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.

Background: The associations between thyroid cancer and skeletal outcomes have not been thoroughly investigated. We aimed to investigate the risk of osteoporotic fractures in patients with thyroid cancer compared to that in a matched control group. Methods: This retrospective cohort study included 2,514 patients with thyroid cancer and 75,420 matched controls from the Korean National Health Insurance Service-National Sample Cohort (NHIS-NSC, 2006–2019). The rates of osteoporotic fractures were analyzed, and associations with the levothyroxine dose were evaluated. Results: Patients with thyroid cancer had a significantly lower risk of fracture than did the control group (hazard ratio [HR], 0.81; 95% confidence interval [CI], 0.69 to 0.94; P=0.006). Patients diagnosed with thyroid cancer after the age of 50 years (older cancer group) had a significantly lower risk of fracture than did those in the control group (HR, 0.72; 95% CI, 0.6 to 0.85; P<0.001), especially those diagnosed with spinal fractures (HR, 0.66; 95% CI, 0.51 to 0.85; P=0.001). Patients in the older cancer group started osteoporosis treatment earlier than did those in the control group (65.5±7.5 years vs. 67.3±7.6 years, P<0.001). Additionally, a lower dose of levothyroxine was associated with a reduced risk of fractures. Conclusion In the clinical setting, the risk of fracture in women diagnosed with thyroid cancer after the age of 50 years was lower than that in the control group, which was caused by more proactive osteoporosis treatment in postmenopausal women with thyroid cancer.

Background: The associations between thyroid cancer and skeletal outcomes have not been thoroughly investigated. We aimed to investigate the risk of osteoporotic fractures in patients with thyroid cancer compared to that in a matched control group. Methods: This retrospective cohort study included 2,514 patients with thyroid cancer and 75,420 matched controls from the Korean National Health Insurance Service-National Sample Cohort (NHIS-NSC, 2006–2019). The rates of osteoporotic fractures were analyzed, and associations with the levothyroxine dose were evaluated. Results: Patients with thyroid cancer had a significantly lower risk of fracture than did the control group (hazard ratio [HR], 0.81; 95% confidence interval [CI], 0.69 to 0.94; P=0.006). Patients diagnosed with thyroid cancer after the age of 50 years (older cancer group) had a significantly lower risk of fracture than did those in the control group (HR, 0.72; 95% CI, 0.6 to 0.85; P<0.001), especially those diagnosed with spinal fractures (HR, 0.66; 95% CI, 0.51 to 0.85; P=0.001). Patients in the older cancer group started osteoporosis treatment earlier than did those in the control group (65.5±7.5 years vs. 67.3±7.6 years, P<0.001). Additionally, a lower dose of levothyroxine was associated with a reduced risk of fractures. Conclusion In the clinical setting, the risk of fracture in women diagnosed with thyroid cancer after the age of 50 years was lower than that in the control group, which was caused by more proactive osteoporosis treatment in postmenopausal women with thyroid cancer.

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.

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.