Rectal cancer, which accounts for approximately 40% of colorectal cancers, remains a major clinical concern. Recent advances in diagnostic imaging, surgical techniques, radiotherapy, and systemic treatment have steadily improved rectal cancer outcomes. Considering this, the Korean Rectal Cancer Multidisciplinary (KRCM) Committee has aimed to provide clinicians and policymakers with up-to-date, evidence-based clinical practice guidelines to support optimal decision-making, reflecting current evidence, the Korean healthcare context, and patient values and preferences. The Clinical Practice Guidelines for Rectal Cancer version 2.0 were developed through multidisciplinary collaboration with related academic societies, building upon and updating the KRCM Clinical Practice Guidelines version 1.0 (titled “Multidisciplinary guidelines for the management of rectal cancer”). These consensus guidelines of the KRCM were established based on a comprehensive literature review, evidence synthesis, with recommendation development guided by the GRADE (Grading of Recommendations Assessment, Development and Evaluation) methodology, and consideration of applicability in real-world clinical practice under the national health insurance system. Each recommendation has been presented with its strength and level of evidence.

Objective: To evaluate the impact of adding CT to ultrasound (US) for nodal assessment in patients with papillary thyroid microcarcinoma (PTMC), particularly in those with US-node-negative disease. Materials and Methods: This single-center retrospective study included consecutive patients with PTMC (≤1 cm on US) who underwent both US and CT for PTMC staging between August 2016 and January 2020, and subsequently underwent surgery including neck dissection. The number of patients with clinical N1 and pathological N1 disease was assessed. The diagnostic performance of US, CT, and combined US + CT (positive if either was positive) for macroscopic lymph node metastasis (LNM) (i.e., metastatic tumor foci >2 mm) was evaluated. Cases with discordant nodal staging between US and CT were identified.The diagnostic utility of CT was also assessed in a subgroup of patients with node-negative findings on US. Results: Among 982 patients (mean age ± standard deviation, 47.3 ± 11.5 years; 774 female), pathological analysis confirmed cervical LNM in 377 patients, including macroscopic, microscopic, and size-unknown LNM in 187, 175, and 15 patients, respectively. The addition of CT to US improved sensitivity for detecting macroscopic LNM compared to US alone (68.4% [128/187] vs. 26.7% [50/187]; P < 0.001), while maintaining high specificity despite a significant decrease (90.9% [709/780] vs. 97.2% [758/780]; P < 0.001). Discordant nodal staging between US and CT regarding macroscopic LNM was observed in 149 cases (15.2% [149/982]), with 131 patients (87.9% [131/149]) being upstaged by CT. In patients with node-negative US findings, CT detected US-undetected macroscopic LNM in 78 patients (8.7% [78/895]) and exhibited a sensitivity of 56.9% (78/137) and specificity of 93.5% (709/758) for macroscopic LNM. Conclusion The integration of CT with US improved sensitivity for detecting macroscopic LNM in patients with PTMC, identifying those who would otherwise be inappropriately considered candidates for active surveillance based solely on US findings. This may assist in refining patient management.

Background: The conventional magnetic resonance imaging (MRI) grading system for foraminal stenosis (FS), known as the Lee classification, was introduced in 2010 and is widely utilized in clinical practice. Previous studies have reported that the conventional grading system for FS lacks prediction ability for surgical treatment. The purpose of this study was to develop a novel MRI grading system for lumbar FS with improved prediction ability for surgical treatment by incorporating facet effusion to indicate segmental instability. Methods: We retrospectively reviewed patients diagnosed with lumbar FS between 2011 and 2017 who had a follow-up period of at least 5 years. The FS severity was assessed using a conventional MRI grading system developed by Lee et al. We recorded whether the patient underwent surgical treatment for FS during the follow-up period and the time from the initial diagnosis to surgery. Survival analysis using a Kaplan-Meier curve and log-rank test was performed to verify the impact of FS severity on the surgical treatment. We performed additional survival analysis after modifying the grading system by incorporating the presence of excessive facet joint effusion assessed using axial MRI. We also compared the discrimination ability of the modified and conventional grading systems using Uno’s concordance index (C-index). Results: In total, 235 patients with a mean age of 63.7 years were included in this study. During the mean follow-up period of 8.1 years, 63 patients underwent surgical treatment for FS. The conventional grading system revealed no significant difference in survival between the grade 2 and 3 groups (p = 0.104). Conversely, the modified grading system revealed a significant difference in survival between the new grade 2 and 3 groups (p < 0.001). After modification, the discrimination ability, assessed using Uno’s Cindex, significantly improved from 0.69 to 0.73. Conclusions The Facet Effusion-Incorporating Grading System, which adds excessive facet joint effusion to the conventional MRI grading framework, demonstrated improved predictive value for surgical treatment and better discriminatory ability compared with the original system.

Purpose: Overactive bladder (OAB) symptoms may improve following surgical correction of cystocele. This study aimed to evaluate changes in urgency, the primary symptom of OAB, and identify factors associated with improvement in urgency after surgical treatment for cystocele. Methods: We conducted a retrospective analysis of medical records for patients who underwent surgical treatment for cystocele and had preoperative urgency measuring ≥3 on a 5-point urinary sensation scale. Patients were categorized into 2 groups based on their urgency status 3 months postsurgery: those with improved urgency and those without improvement. Improvement was defined as a reduction of 2 or more points on the scale following surgery. We compared preoperative clinical and urodynamic factors between the 2 groups. Results: A total of 137 patients were included in the study, with 98 (71.5%) showing improvement in urgency symptoms after surgery. The improved group had a significantly higher prevalence of preoperative urgency urinary incontinence (UUI) (62.2% vs. 30.8%, P=0.001) and bladder outlet obstruction (43.9% vs. 20.5%, P=0.011) compared to the nonimproved group. Urodynamic evaluations indicated that the detrusor pressure at maximum flow rate (PdetQmax) was significantly higher in the improved group than in the nonimproved group (P=0.004). Multivariate logistic regression analysis identified preoperative UUI, higher PdetQmax, and lower vaginal parity as independent predictors of improvement in urgency postoperatively. Conclusions Preoperative UUI, higher PdetQmax, and lower vaginal parity were significant predictors of postoperative improvement in urgency. Both clinical and urodynamic factors may help identify patients with cystocele and preoperative urgency who are most likely to benefit from anatomical correction.

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.

Background: and Purpose We aimed to determine the proportion of Korean patients with early Alzheimer’s disease (AD) who are eligible to receive lecanemab based on the United States Appropriate Use Recommendations (US AUR), and also identify the barriers to this treatment. Methods: We retrospectively enrolled 6,132 patients with amnestic mild cognitive impairment or mild amnestic dementia at 13 hospitals from June 2023 to May 2024. Among them, 2,058 patients underwent amyloid positron emission tomography (PET) and 1,199 (58.3%) of these patients were amyloid-positive on PET. We excluded 732 patients who did not undergo brain magnetic resonance imaging between June 2023 and May 2024. Finally, 467 patients were included in the present study. Results: When applying the criteria of the US AUR, approximately 50% of patients with early AD were eligible to receive lecanemab treatment. Among the 467 included patients, 36.8% did not meet the inclusion criterion of a Mini-Mental State Examination (MMSE) score of ≥22. Conclusions Eligibility for lecanemab treatment was not restricted to Korean patients with early AD except for those with an MMSE score of ≥22. The MMSE criteria should therefore be reconsidered in areas with a higher proportion of older people, who tend to have lower levels of education.