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.

Purpose: While the benefit of neoadjuvant chemotherapy (NAC) has been established in human epidermal growth factor receptor 2 (HER2)-positive and triple-negative breast cancers, its effectiveness in achieving pathological complete response (pCR) and optimal patient selection in estrogen receptor (ER)-positive, HER2-negative breast cancers remain less clearly defined. This study aimed to identify immunohistochemistry (IHC)-based predictors of pCR and to develop a scoring model for ER-strong positive/HER2-negative breast cancer. Methods: Data from a prospective cohort were retrospectively analyzed. We included 522 patients with ER-strong positive/HER2-negative tumors who received NAC and surgery between 2008 and 2021. IHC markers including progesterone receptor (PR), Ki-67, epidermal growth factor receptor (EGFR), cytokeratin 5/6 (CK5/6), and p53 were evaluated to identify predictors of pCR. Independent predictors of pCR from multivariate logistic regression were used to develop a weighted 4-point model. Model performance was assessed using receiver operating characteristic analysis. The prognostic impact of pCR was evaluated using KaplanMeier and Cox regression analyses. Results: Independent predictors of pCR included PR-negative status, positivity for basallike markers (EGFR or CK5/6), and Ki-67 ≥ 50%. The scoring model demonstrated good discrimination for pCR (area under the curve = 0.754). pCR rates increased stepwise, with scores of 4.9% (low), 10.7% (intermediate), and 36.2% (high). In the high-score group, pCR was significantly associated with improved disease-free survival (hazard ratio [HR], 0.09; p = 0.023) and distant metastasis-free survival (HR, 0.11; p = 0.035), whereas no significant survival differences according to pCR status were observed in the low and intermediate score groups. Conclusion This IHC-based model predicts pCR and helps identify subgroups in which pCR is associated with meaningful survival benefit following NAC in ER-positive/HER2-negative breast cancers. High-scoring patients may benefit from NAC, while patients with low- or intermediatescores may be better managed with surgery and endocrine therapy. This model may support personalized treatment decisions regarding NAC.

Purpose: Lobular carcinoma In Situ (LCIS) is a noninvasive lesion associated with an increased risk of invasive cancer. Since its removal from the tumor, node, metastasis classification in the 8th edition of the American Joint Committee on Cancer (AJCC) guidelines, the clinical management of LCIS has shifted from surgery to surveillance. However, studies focusing on the risk and associated factors for invasive cancer development in pure LCIS without ductal carcinoma In Situ (DCIS) or invasive cancer remain limited. Methods: We retrospectively analyzed 106 patients diagnosed with pure LCIS between 2008 and 2018. This study evaluated the effect of tamoxifen use and histologic type on the development of invasive cancer. Results: All 106 patients underwent surgery, and nine (8.5%) developed invasive cancer over a median follow-up of 67.5 months. The incidence of invasive cancer was lower in the tamoxifen group (6.3%, n = 4) than in the non-tamoxifen group (11.9%, n = 5), although this difference was not statistically significant (p = 0.266). Pleomorphic LCIS had a significantly higher incidence of invasive cancer (30.0%, n = 3) than classic LCIS (6.3%, n = 6) (p = 0.045).Multivariable Cox regression analysis showed no significant difference in the risk of invasive cancer according to tamoxifen use (hazard ratio [HR], 2.031; 95% confidence interval [CI], 0.544–7.579; p = 0.292). However, pleomorphic LCIS showed a trend toward an increased risk of invasive cancer compared to classic LCIS (HR, 3.856; 95% CI, 0.922–16.126; p = 0.064). Conclusion Postoperative tamoxifen did not significantly lower invasive cancer development in patients with pure LCIS. Pleomorphic LCIS may carry a higher risk than classic LCIS. These findings require tailored follow-up and treatment strategies based on the histologic subtype of LCIS.

Background/Aims: The kidneys and retina are highly vascularized organs that frequently exhibit shared pathologies, with nephropathy often associated with retinopathy. Previous studies have successfully predicted estimated glomerular filtration rates (eGFRs) using fundus photographs. We evaluated the performance of the Modification of Diet in Renal Disease (MDRD) and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formulas in eGFR prediction. Methods: We enrolled patients with fundus photographs and corresponding creatinine measurements taken on the same date. One photograph per eye was randomly selected, resulting in a final dataset of 45,108 patients (88,260 photographs). Data including sex, age, and blood creatinine levels were collected for eGFR calculation using the MDRD and CKD-EPI formulas. EfficientNet B3 models were used to predict each parameter. Results: Deep neural network models accurately predicted age and sex using fundus photographs. Sex was identified as a confounding variable in creatinine prediction. The MDRD formula was more susceptible to this confounding effect than the CKD-EPI formula. Notably, the CKD-EPI formula demonstrated superior performance compared to the MDRD formula (area under the curve 0.864 vs. 0.802). Conclusions Fundus photographs are a valuable tool for screening renal function using deep neural network models, demonstrating the role of noninvasive imaging in medical diagnostics. However, these models are susceptible to the influence of sex, a potential confounding factor. The CKD-EPI formula, less susceptible to sex bias, is recommended to obtain more reliable results.

Background/Aims: The kidneys and retina are highly vascularized organs that frequently exhibit shared pathologies, with nephropathy often associated with retinopathy. Previous studies have successfully predicted estimated glomerular filtration rates (eGFRs) using fundus photographs. We evaluated the performance of the Modification of Diet in Renal Disease (MDRD) and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formulas in eGFR prediction. Methods: We enrolled patients with fundus photographs and corresponding creatinine measurements taken on the same date. One photograph per eye was randomly selected, resulting in a final dataset of 45,108 patients (88,260 photographs). Data including sex, age, and blood creatinine levels were collected for eGFR calculation using the MDRD and CKD-EPI formulas. EfficientNet B3 models were used to predict each parameter. Results: Deep neural network models accurately predicted age and sex using fundus photographs. Sex was identified as a confounding variable in creatinine prediction. The MDRD formula was more susceptible to this confounding effect than the CKD-EPI formula. Notably, the CKD-EPI formula demonstrated superior performance compared to the MDRD formula (area under the curve 0.864 vs. 0.802). Conclusions Fundus photographs are a valuable tool for screening renal function using deep neural network models, demonstrating the role of noninvasive imaging in medical diagnostics. However, these models are susceptible to the influence of sex, a potential confounding factor. The CKD-EPI formula, less susceptible to sex bias, is recommended to obtain more reliable results.

Background/Aims: The kidneys and retina are highly vascularized organs that frequently exhibit shared pathologies, with nephropathy often associated with retinopathy. Previous studies have successfully predicted estimated glomerular filtration rates (eGFRs) using fundus photographs. We evaluated the performance of the Modification of Diet in Renal Disease (MDRD) and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formulas in eGFR prediction. Methods: We enrolled patients with fundus photographs and corresponding creatinine measurements taken on the same date. One photograph per eye was randomly selected, resulting in a final dataset of 45,108 patients (88,260 photographs). Data including sex, age, and blood creatinine levels were collected for eGFR calculation using the MDRD and CKD-EPI formulas. EfficientNet B3 models were used to predict each parameter. Results: Deep neural network models accurately predicted age and sex using fundus photographs. Sex was identified as a confounding variable in creatinine prediction. The MDRD formula was more susceptible to this confounding effect than the CKD-EPI formula. Notably, the CKD-EPI formula demonstrated superior performance compared to the MDRD formula (area under the curve 0.864 vs. 0.802). Conclusions Fundus photographs are a valuable tool for screening renal function using deep neural network models, demonstrating the role of noninvasive imaging in medical diagnostics. However, these models are susceptible to the influence of sex, a potential confounding factor. The CKD-EPI formula, less susceptible to sex bias, is recommended to obtain more reliable results.

Background/Aims: The kidneys and retina are highly vascularized organs that frequently exhibit shared pathologies, with nephropathy often associated with retinopathy. Previous studies have successfully predicted estimated glomerular filtration rates (eGFRs) using fundus photographs. We evaluated the performance of the Modification of Diet in Renal Disease (MDRD) and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formulas in eGFR prediction. Methods: We enrolled patients with fundus photographs and corresponding creatinine measurements taken on the same date. One photograph per eye was randomly selected, resulting in a final dataset of 45,108 patients (88,260 photographs). Data including sex, age, and blood creatinine levels were collected for eGFR calculation using the MDRD and CKD-EPI formulas. EfficientNet B3 models were used to predict each parameter. Results: Deep neural network models accurately predicted age and sex using fundus photographs. Sex was identified as a confounding variable in creatinine prediction. The MDRD formula was more susceptible to this confounding effect than the CKD-EPI formula. Notably, the CKD-EPI formula demonstrated superior performance compared to the MDRD formula (area under the curve 0.864 vs. 0.802). Conclusions Fundus photographs are a valuable tool for screening renal function using deep neural network models, demonstrating the role of noninvasive imaging in medical diagnostics. However, these models are susceptible to the influence of sex, a potential confounding factor. The CKD-EPI formula, less susceptible to sex bias, is recommended to obtain more reliable results.

Background/Aims: The kidneys and retina are highly vascularized organs that frequently exhibit shared pathologies, with nephropathy often associated with retinopathy. Previous studies have successfully predicted estimated glomerular filtration rates (eGFRs) using fundus photographs. We evaluated the performance of the Modification of Diet in Renal Disease (MDRD) and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formulas in eGFR prediction. Methods: We enrolled patients with fundus photographs and corresponding creatinine measurements taken on the same date. One photograph per eye was randomly selected, resulting in a final dataset of 45,108 patients (88,260 photographs). Data including sex, age, and blood creatinine levels were collected for eGFR calculation using the MDRD and CKD-EPI formulas. EfficientNet B3 models were used to predict each parameter. Results: Deep neural network models accurately predicted age and sex using fundus photographs. Sex was identified as a confounding variable in creatinine prediction. The MDRD formula was more susceptible to this confounding effect than the CKD-EPI formula. Notably, the CKD-EPI formula demonstrated superior performance compared to the MDRD formula (area under the curve 0.864 vs. 0.802). Conclusions Fundus photographs are a valuable tool for screening renal function using deep neural network models, demonstrating the role of noninvasive imaging in medical diagnostics. However, these models are susceptible to the influence of sex, a potential confounding factor. The CKD-EPI formula, less susceptible to sex bias, is recommended to obtain more reliable results.

Background: A healthcare system’s collapse due to a pandemic, such as the coronavirus disease 2019 (COVID-19), can expose healthcare workers (HCWs) to various mental health problems. This study aimed to investigate the impact of the COVID-19 pandemic on the depression and anxiety of HCWs. Methods: A nationwide questionnaire-based survey was conducted on HCWs who worked in healthcare facilities and public health centers in Korea in December 2020. Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) were used to measure depression and anxiety. To investigate factors associated with depression and anxiety, stepwise multiple logistic regression analysis was performed. Results: A total of 1,425 participating HCWs were included. The mean depression score (PHQ-9) of HCWs before and after COVID-19 increased from 2.37 to 5.39, and the mean anxiety score (GAD-7) increased from 1.41 to 3.41. The proportion of HCWs with moderate to severe depression (PHQ-9 ≥ 10) increased from 3.8% before COVID-19 to 19.5% after COVID-19, whereas that of HCWs with moderate to severe anxiety (GAD-7 ≥ 10) increased from 2.0% to 10.1%. In our study, insomnia, chronic fatigue symptoms and physical symptoms after COVID-19, anxiety score (GAD-7) after COVID-19, living alone, and exhaustion were positively correlated with depression. Furthermore, post-traumatic stress symptoms, stress score (Global Assessment of Recent Stress), depression score (PHQ-9) after COVID-19, and exhaustion were positively correlated with anxiety. Conclusion In Korea, during the COVID-19 pandemic, HCWs commonly suffered from mental health problems, including depression and anxiety. Regularly checking the physical and mental health problems of HCWs during the COVID-19 pandemic is crucial, and social support and strategy are needed to reduce the heavy workload and psychological distress of HCWs.

Objective: Recently, evidence has suggested that the pathophysiology and risk factors of intracranial atherosclerosis (ICAS) differs from those of extracranial atherosclerosis (ECAS). In addition, novel parameters reflecting metabolic conditions, such as insulin resistance or atherogenic dyslipidemia, based on triglycerides (TG) and other biomarkers, have emerged. In this study, we evaluated the association between TG-related parameters and symptomatic cerebral atherosclerosis in patients with acute ischemic stroke resulting from large artery atherosclerosis (LAA). Methods: We assessed consecutive acute LAA-stroke patients between January 2010 and December 2020. Based on the radiological findings, we classified the relevant symptomatic arteries that caused the index stroke into LAA-ICAS and LAA-ECAS. As TGrelated parameters, the atherogenic index of plasma (AIP) and TG-glucose (TyG) index were calculated according to the following formulas: AIP = log10 (TG Level/High-density Lipoprotein Cholesterol Level), TyG Index = Ln (TG Level × Glucose Level/2). Results: A total of 519 patients with LAA-stroke were evaluated. In multivariable logistic regression analysis to identify predictors of LAA-ICAS, AIP was significantly associated with LAA-ICAS (adjusted odds ratio [aOR], 3.60; 95% confidence interval [CI], 1.60–8.06). TyG index also showed a statistically significant relationship with LAA-ICAS (aOR, 1.60; 95% CI, 1.11–2.32). However, TG per se did not show a statistical association with LAA-ECAS. Conclusion TG-related parameters were more closely associated with stroke by ICAS than by ECAS. The metabolic conditions reflected by the AIP or TyG index, rather than hypertriglyceridemia itself, may play a greater role in determining the relevant vessel causally involved in a stroke.