Background: and Purpose: Brain atrophy, characterized by sulcal widening and ventricular enlargement, is a hallmark of neurodegenerative diseases such as Alzheimer’s disease. Visual assessments are subjective and variable, while automated methods struggle with subtle intensity differences and standardization, highlighting limitations in both approaches. This study aimed to develop and evaluate a novel method focusing on cerebrospinal fluid (CSF) regions by assessing segmentation accuracy, detecting stage-specific atrophy patterns, and testing generalizability to unstandardized datasets. Methods: We utilized T1-weighted magnetic resonance imaging data from 3,315 participants from Samsung Medical Center and 1,439 participants from other hospitals. Segmentation accuracy was evaluated using the Dice similarity coefficient (DSC), and W-scores were calculated for each region of interest (ROI) to assess stage-specific atrophy patterns. Results: The segmentation demonstrated high accuracy, with average DSC values exceeding 0.9 for ventricular and hippocampal regions and above 0.8 for cortical regions. Significant differences in W-scores were observed across cognitive stages (cognitively unimpaired, mild cognitive impairment, dementia of Alzheimer’s type) for all ROIs (all, p<0.05). Similar trends were observed in the images from other hospitals, confirming the algorithm’s generalizability to datasets without prior standardization. Conclusions This study demonstrates the robustness and clinical applicability of a novel CSF-focused segmentation method for assessing brain atrophy. The method provides a scalable and objective framework for evaluating structural changes across cognitive stages and holds potential for broader application in neurodegenerative disease research and clinical practice.

Background/Aims: Inaccurate prediction of lymph node metastasis (LNM) may lead to unnecessary surgery following endoscopic resection of T1 colorectal cancer (CRC). We aimed to validate the usefulness of artificial intelligence (AI) models for predicting LNM in patients with T1 CRC. Methods: We analyzed the clinical data, laboratory results, pathological reports, and endoscopic findings of patients who underwent radical surgery for T1 CRC. We developed AI models to predict LNM using four algorithms: regularized logistic regression classifier (RLRC), random forest classifier (RFC), CatBoost classifier (CBC), and the voting classifier (VC). Four histological factors and four endoscopic findings were included to develop AI models. Areas under the receiver operating characteristics curves (AUROCs) were measured to distinguish AI model performance in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines. Results: Among 1,386 patients with T1 CRC, 173 patients (12.5%) had LNM. The AUROC values of the RLRC, RFC, CBC, and VC models for LNM prediction were significantly higher (0.673, 0.640, 0.679, and 0.677, respectively) than the 0.525 suggested in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines (vs RLRC, p<0.001; vs RFC, p=0.001; vs CBC, p<0.001; vs VC, p<0.001). The AUROC value was similar between T1 colon versus T1 rectal cancers (0.718 vs 0.615, p=0.700). The AUROC value was also similar between the initial endoscopic resection and initial surgery groups (0.581 vs 0.746, p=0.845). Conclusions AI models trained on the basis of endoscopic findings and pathological features performed well in predicting LNM in patients with T1 CRC regardless of tumor location and initial treatment method.

Background: and Purpose The National Brain Biobank of Korea (NBBK) is a brain bank consortium supported by the Korea Disease Control and Prevention Agency and the Korea National Institute of Health, and was launched in 2015 to support research into neurodegenerative disease dementia (NDD). This study aimed to introduce the NBBK and describes clinicopathological correlations based on analyses of data collected from the NBBK. Methods: Four hospital-based brain banks have been established in South Korea: Samsung Medical Center Brain Bank (SMCBB), Seoul National University Hospital Brain Bank (SNUHBB), Pusan National University Hospital Brain Bank (PNUHBB), and Myongji Hospital Brain Bank (MJHBB). Clinical and pathological data were collected from these brain banks using standardized protocols. The prevalence rates of clinical and pathological diagnoses were analyzed in order to characterize the clinicopathological correlations. Results: Between August 2016 and December 2023, 185 brain specimens were collected and pathologically evaluated (SNUHBB: 117; PNUHBB: 27; SMCBB: 34; MJHBB: 7). The age at consent was 70.8±12.6 years, and the age at autopsy was 71.7±12.4 years. The four-most-common clinical diagnoses were Alzheimer’s disease (AD) dementia (20.0%), idiopathic Parkinson’s disease (15.1%), unspecified dementia (11.9%), and cognitively unimpaired (CU) (11.4%).Most cases of unspecified dementia had a pathological diagnosis of central nervous system (CNS) vasculopathy (31.8%) or AD (31.8%). Remarkably, only 14.2% of CU cases had normal pathological findings. The three-most-common pathological diagnoses were AD (26.5%), CNS vasculopathy (14.1%), and Lewy body disease (13.5%). Conclusions These clinical and neuropathological findings provide a deeper understanding of the mechanisms underlying NDD in South Korea.

Background: and Purpose: Brain atrophy, characterized by sulcal widening and ventricular enlargement, is a hallmark of neurodegenerative diseases such as Alzheimer’s disease. Visual assessments are subjective and variable, while automated methods struggle with subtle intensity differences and standardization, highlighting limitations in both approaches. This study aimed to develop and evaluate a novel method focusing on cerebrospinal fluid (CSF) regions by assessing segmentation accuracy, detecting stage-specific atrophy patterns, and testing generalizability to unstandardized datasets. Methods: We utilized T1-weighted magnetic resonance imaging data from 3,315 participants from Samsung Medical Center and 1,439 participants from other hospitals. Segmentation accuracy was evaluated using the Dice similarity coefficient (DSC), and W-scores were calculated for each region of interest (ROI) to assess stage-specific atrophy patterns. Results: The segmentation demonstrated high accuracy, with average DSC values exceeding 0.9 for ventricular and hippocampal regions and above 0.8 for cortical regions. Significant differences in W-scores were observed across cognitive stages (cognitively unimpaired, mild cognitive impairment, dementia of Alzheimer’s type) for all ROIs (all, p<0.05). Similar trends were observed in the images from other hospitals, confirming the algorithm’s generalizability to datasets without prior standardization. Conclusions This study demonstrates the robustness and clinical applicability of a novel CSF-focused segmentation method for assessing brain atrophy. The method provides a scalable and objective framework for evaluating structural changes across cognitive stages and holds potential for broader application in neurodegenerative disease research and clinical practice.

Background/Aims: Inaccurate prediction of lymph node metastasis (LNM) may lead to unnecessary surgery following endoscopic resection of T1 colorectal cancer (CRC). We aimed to validate the usefulness of artificial intelligence (AI) models for predicting LNM in patients with T1 CRC. Methods: We analyzed the clinical data, laboratory results, pathological reports, and endoscopic findings of patients who underwent radical surgery for T1 CRC. We developed AI models to predict LNM using four algorithms: regularized logistic regression classifier (RLRC), random forest classifier (RFC), CatBoost classifier (CBC), and the voting classifier (VC). Four histological factors and four endoscopic findings were included to develop AI models. Areas under the receiver operating characteristics curves (AUROCs) were measured to distinguish AI model performance in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines. Results: Among 1,386 patients with T1 CRC, 173 patients (12.5%) had LNM. The AUROC values of the RLRC, RFC, CBC, and VC models for LNM prediction were significantly higher (0.673, 0.640, 0.679, and 0.677, respectively) than the 0.525 suggested in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines (vs RLRC, p<0.001; vs RFC, p=0.001; vs CBC, p<0.001; vs VC, p<0.001). The AUROC value was similar between T1 colon versus T1 rectal cancers (0.718 vs 0.615, p=0.700). The AUROC value was also similar between the initial endoscopic resection and initial surgery groups (0.581 vs 0.746, p=0.845). Conclusions AI models trained on the basis of endoscopic findings and pathological features performed well in predicting LNM in patients with T1 CRC regardless of tumor location and initial treatment method.

Background: and Purpose The National Brain Biobank of Korea (NBBK) is a brain bank consortium supported by the Korea Disease Control and Prevention Agency and the Korea National Institute of Health, and was launched in 2015 to support research into neurodegenerative disease dementia (NDD). This study aimed to introduce the NBBK and describes clinicopathological correlations based on analyses of data collected from the NBBK. Methods: Four hospital-based brain banks have been established in South Korea: Samsung Medical Center Brain Bank (SMCBB), Seoul National University Hospital Brain Bank (SNUHBB), Pusan National University Hospital Brain Bank (PNUHBB), and Myongji Hospital Brain Bank (MJHBB). Clinical and pathological data were collected from these brain banks using standardized protocols. The prevalence rates of clinical and pathological diagnoses were analyzed in order to characterize the clinicopathological correlations. Results: Between August 2016 and December 2023, 185 brain specimens were collected and pathologically evaluated (SNUHBB: 117; PNUHBB: 27; SMCBB: 34; MJHBB: 7). The age at consent was 70.8±12.6 years, and the age at autopsy was 71.7±12.4 years. The four-most-common clinical diagnoses were Alzheimer’s disease (AD) dementia (20.0%), idiopathic Parkinson’s disease (15.1%), unspecified dementia (11.9%), and cognitively unimpaired (CU) (11.4%).Most cases of unspecified dementia had a pathological diagnosis of central nervous system (CNS) vasculopathy (31.8%) or AD (31.8%). Remarkably, only 14.2% of CU cases had normal pathological findings. The three-most-common pathological diagnoses were AD (26.5%), CNS vasculopathy (14.1%), and Lewy body disease (13.5%). Conclusions These clinical and neuropathological findings provide a deeper understanding of the mechanisms underlying NDD in South Korea.

Background: and Purpose: Brain atrophy, characterized by sulcal widening and ventricular enlargement, is a hallmark of neurodegenerative diseases such as Alzheimer’s disease. Visual assessments are subjective and variable, while automated methods struggle with subtle intensity differences and standardization, highlighting limitations in both approaches. This study aimed to develop and evaluate a novel method focusing on cerebrospinal fluid (CSF) regions by assessing segmentation accuracy, detecting stage-specific atrophy patterns, and testing generalizability to unstandardized datasets. Methods: We utilized T1-weighted magnetic resonance imaging data from 3,315 participants from Samsung Medical Center and 1,439 participants from other hospitals. Segmentation accuracy was evaluated using the Dice similarity coefficient (DSC), and W-scores were calculated for each region of interest (ROI) to assess stage-specific atrophy patterns. Results: The segmentation demonstrated high accuracy, with average DSC values exceeding 0.9 for ventricular and hippocampal regions and above 0.8 for cortical regions. Significant differences in W-scores were observed across cognitive stages (cognitively unimpaired, mild cognitive impairment, dementia of Alzheimer’s type) for all ROIs (all, p<0.05). Similar trends were observed in the images from other hospitals, confirming the algorithm’s generalizability to datasets without prior standardization. Conclusions This study demonstrates the robustness and clinical applicability of a novel CSF-focused segmentation method for assessing brain atrophy. The method provides a scalable and objective framework for evaluating structural changes across cognitive stages and holds potential for broader application in neurodegenerative disease research and clinical practice.

Background/Aims: Inaccurate prediction of lymph node metastasis (LNM) may lead to unnecessary surgery following endoscopic resection of T1 colorectal cancer (CRC). We aimed to validate the usefulness of artificial intelligence (AI) models for predicting LNM in patients with T1 CRC. Methods: We analyzed the clinical data, laboratory results, pathological reports, and endoscopic findings of patients who underwent radical surgery for T1 CRC. We developed AI models to predict LNM using four algorithms: regularized logistic regression classifier (RLRC), random forest classifier (RFC), CatBoost classifier (CBC), and the voting classifier (VC). Four histological factors and four endoscopic findings were included to develop AI models. Areas under the receiver operating characteristics curves (AUROCs) were measured to distinguish AI model performance in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines. Results: Among 1,386 patients with T1 CRC, 173 patients (12.5%) had LNM. The AUROC values of the RLRC, RFC, CBC, and VC models for LNM prediction were significantly higher (0.673, 0.640, 0.679, and 0.677, respectively) than the 0.525 suggested in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines (vs RLRC, p<0.001; vs RFC, p=0.001; vs CBC, p<0.001; vs VC, p<0.001). The AUROC value was similar between T1 colon versus T1 rectal cancers (0.718 vs 0.615, p=0.700). The AUROC value was also similar between the initial endoscopic resection and initial surgery groups (0.581 vs 0.746, p=0.845). Conclusions AI models trained on the basis of endoscopic findings and pathological features performed well in predicting LNM in patients with T1 CRC regardless of tumor location and initial treatment method.

Background: and Purpose: Brain atrophy, characterized by sulcal widening and ventricular enlargement, is a hallmark of neurodegenerative diseases such as Alzheimer’s disease. Visual assessments are subjective and variable, while automated methods struggle with subtle intensity differences and standardization, highlighting limitations in both approaches. This study aimed to develop and evaluate a novel method focusing on cerebrospinal fluid (CSF) regions by assessing segmentation accuracy, detecting stage-specific atrophy patterns, and testing generalizability to unstandardized datasets. Methods: We utilized T1-weighted magnetic resonance imaging data from 3,315 participants from Samsung Medical Center and 1,439 participants from other hospitals. Segmentation accuracy was evaluated using the Dice similarity coefficient (DSC), and W-scores were calculated for each region of interest (ROI) to assess stage-specific atrophy patterns. Results: The segmentation demonstrated high accuracy, with average DSC values exceeding 0.9 for ventricular and hippocampal regions and above 0.8 for cortical regions. Significant differences in W-scores were observed across cognitive stages (cognitively unimpaired, mild cognitive impairment, dementia of Alzheimer’s type) for all ROIs (all, p<0.05). Similar trends were observed in the images from other hospitals, confirming the algorithm’s generalizability to datasets without prior standardization. Conclusions This study demonstrates the robustness and clinical applicability of a novel CSF-focused segmentation method for assessing brain atrophy. The method provides a scalable and objective framework for evaluating structural changes across cognitive stages and holds potential for broader application in neurodegenerative disease research and clinical practice.

Background/Aims: Inaccurate prediction of lymph node metastasis (LNM) may lead to unnecessary surgery following endoscopic resection of T1 colorectal cancer (CRC). We aimed to validate the usefulness of artificial intelligence (AI) models for predicting LNM in patients with T1 CRC. Methods: We analyzed the clinical data, laboratory results, pathological reports, and endoscopic findings of patients who underwent radical surgery for T1 CRC. We developed AI models to predict LNM using four algorithms: regularized logistic regression classifier (RLRC), random forest classifier (RFC), CatBoost classifier (CBC), and the voting classifier (VC). Four histological factors and four endoscopic findings were included to develop AI models. Areas under the receiver operating characteristics curves (AUROCs) were measured to distinguish AI model performance in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines. Results: Among 1,386 patients with T1 CRC, 173 patients (12.5%) had LNM. The AUROC values of the RLRC, RFC, CBC, and VC models for LNM prediction were significantly higher (0.673, 0.640, 0.679, and 0.677, respectively) than the 0.525 suggested in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines (vs RLRC, p<0.001; vs RFC, p=0.001; vs CBC, p<0.001; vs VC, p<0.001). The AUROC value was similar between T1 colon versus T1 rectal cancers (0.718 vs 0.615, p=0.700). The AUROC value was also similar between the initial endoscopic resection and initial surgery groups (0.581 vs 0.746, p=0.845). Conclusions AI models trained on the basis of endoscopic findings and pathological features performed well in predicting LNM in patients with T1 CRC regardless of tumor location and initial treatment method.