Background/Aims: Early colorectal cancer (ECC) is commonly resected endoscopically. Perforation is a devastating complication of endoscopic resection. We aimed to identify the characteristics and predictive risk factors for perforation related to endoscopic resection of ECC. Methods: This nationwide retrospective multicenter study included patients with ECC who underwent endoscopic resection. We investigated the demographics, endoscopic findings at the time of treatment, and histopathological characteristics of the resected specimens. Logistic regression analysis was used to investigate the clinical factors associated with procedure-related perforations. Survival analysis was conducted to assess the impact of perforation on the overall survival of patients with ECC. Results: This study included 965 participants with a mean age of 63.4 years. The most common endoscopic treatment was conventional endoscopic mucosal resection (n=573, 59.4%), followed by conventional endoscopic submucosal dissection (n=259, 26.8%). Thirty-three patients (3.4%) experienced perforations, most of which were managed endoscopically (n=23/33, 69.7%). Patients who undergo endoscopic submucosal dissection-hybrid and precut endoscopic mucosal resection have a higher risk of perforation than those who undergo conventional endoscopic mucosal resection (odds ratio, 78.65 and 39.72, p<0.05). Procedure-related perforations were not associated with patient survival. Conclusions Perforation after endoscopic resection had no significant impact on the prognosis of ECC. The type of endoscopic resection was a crucial predictor of perforation. Large-scale prospective studies are needed to further investigate endoscopic resection of ECC.

Background/Aims: The prognostic value of serial 18F-fluorodeoxyglucose positron emission tomography/computed tomography ( 18F-FDG PET/CT) for patients with borderline resectable or locally advanced pancreatic cancer who undergo conversion surgery or continue chemotherapy without surgery has not been well-established. Methods: A retrospective analysis of patients with pancreatic ductal adenocarcinoma was conducted at Seoul National University Bundang Hospital between March 2013 and February 2022.Patients underwent PET/CT at baseline and subsequent radiologic evaluations following chemotherapy. Changes in the maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), metabolic tumor volume, and total lesion glycolysis were analyzed.Based on their treatment regimens, patients were stratified into the conversion surgery group or nonconversion surgery group. Survival outcomes and various clinical factors were assessed. Results: Among 121 patients, 52 underwent conversion surgery, and 69 continued to receive chemotherapy without surgery. A significant reduction in the SUVmax was correlated with prolonged recurrence-free survival and overall survival in the conversion surgery group. Confirmation of a pathologic response indicated a significant association between reductions in the SUVmax and positive outcomes. Reductions in the metabolic tumor volume and total lesion glycolysis were associated with improved progression-free survival and overall survival in the nonconversion surgery group. Conclusions Serial PET/CT scans demonstrated prognostic value in pancreatic ductal adenocarcinoma patients, revealing distinct correlations in the conversion surgery group and nonconversion surgery group.

Purpose: Cystic duct cancers (CDCs) have been classified as extrahepatic bile duct cancers or gallbladder cancers (GBCs); however, it is unclear whether their clinical behavior is similar to that of distal extrahepatic bile duct cancers (DBDCs) or GBCs. Materials and Methods: T category of the CDCs was classified using current T category scheme of the GBCs and DBDCs, and clinicopathological factors were compared among 38 CDCs, 345 GBCs, and 349 DBDCs. We modified Nakata’s classifications (type 1, confined within cystic duct [CD]; combined types 2-4, extension beyond CD) and compared them. Results: No significant overall survival (OS) difference was observed between the patients with CDC, GBC, and DBDC. The T category of GBC staging was more accurate at distinguishing OS in patients with CDC than the DBDC staging. Patients with T3 CDC and GBC showed a significant OS difference when using the T category for GBC staging, while those with T1-T2 CDC and GBC showed no significant difference. In contrast, the T category of DBDC staging did not show any significant OS difference between patients with T1-T2 CDC and DBDC or T3 CDC and DBDC. Patients with type 1 CDC had significantly better OS than those with combined types. Conclusion Unlike GBCs and DBDCs, CDCs exhibit distinct clinicopathological characteristics. The OS is better when the CDC confines within the CD, compared to when it extends beyond it. Therefore, we propose a new T category scheme (T1, confined to CD; T2, invaded beyond CD) for better classifying CDCs.

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.