Purpose: Protective ileostomy and colostomy are performed in patients undergoing low anterior resection with a high leakage risk. We aimed to compare surgical, medical, and daily care complications between these 2 ostomies in order to make individual choice. Methods: Patients who underwent low anterior resection for rectal tumors with protective stomas between January 2011 and September 2018 were enrolled. Stoma-related complications were prospectively recorded by wound, ostomy, and continence nurses. The cancer stage and treatment data were obtained from the Taiwan Cancer Database of our Big Data Center. Other demographic data were collected retrospectively from medical notes. The complications after stoma creation and after the stoma reversal were compared. Results: There were 176 patients with protective colostomy and 234 with protective ileostomy. Protective ileostomy had higher proportions of high output from the stoma for 2 consecutive days than protective colostomy (11.1% vs. 0%, P<0.001). Protective colostomy resulted in more stoma retraction than protective ileostomy (21.6% vs. 9.4%, P=0.001). Female, open operation, ileostomy, and carrying stoma more than 4 months were also significantly associated with a higher risk of stoma-related complications during diversion. For stoma retraction, the multivariate analysis revealed that female (odds ratio [OR], 4.00; 95% confidence interval [CI], 2.13–7.69; P<0.001) and long diversion duration (≥4 months; OR, 2.33; 95% CI, 1.22–4.43; P=0.010) were independent risk factors, but ileostomy was an independent favorable factor (OR, 0.40; 95% CI, 0.22–0.72; P=0.003). The incidence of complication after stoma reversal did not differ between colostomy group and ileostomy group (24.3% vs. 20.9%, P=0.542). Conclusion We suggest avoiding colostomy in patients who are female and potential prolonged diversion when stoma retraction is a concern. Otherwise, ileostomy should be avoided for patients with impaired renal function. Wise selection and flexibility are more important than using one type of stoma routinely.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Purpose: Protective ileostomy and colostomy are performed in patients undergoing low anterior resection with a high leakage risk. We aimed to compare surgical, medical, and daily care complications between these 2 ostomies in order to make individual choice. Methods: Patients who underwent low anterior resection for rectal tumors with protective stomas between January 2011 and September 2018 were enrolled. Stoma-related complications were prospectively recorded by wound, ostomy, and continence nurses. The cancer stage and treatment data were obtained from the Taiwan Cancer Database of our Big Data Center. Other demographic data were collected retrospectively from medical notes. The complications after stoma creation and after the stoma reversal were compared. Results: There were 176 patients with protective colostomy and 234 with protective ileostomy. Protective ileostomy had higher proportions of high output from the stoma for 2 consecutive days than protective colostomy (11.1% vs. 0%, P<0.001). Protective colostomy resulted in more stoma retraction than protective ileostomy (21.6% vs. 9.4%, P=0.001). Female, open operation, ileostomy, and carrying stoma more than 4 months were also significantly associated with a higher risk of stoma-related complications during diversion. For stoma retraction, the multivariate analysis revealed that female (odds ratio [OR], 4.00; 95% confidence interval [CI], 2.13–7.69; P<0.001) and long diversion duration (≥4 months; OR, 2.33; 95% CI, 1.22–4.43; P=0.010) were independent risk factors, but ileostomy was an independent favorable factor (OR, 0.40; 95% CI, 0.22–0.72; P=0.003). The incidence of complication after stoma reversal did not differ between colostomy group and ileostomy group (24.3% vs. 20.9%, P=0.542). Conclusion We suggest avoiding colostomy in patients who are female and potential prolonged diversion when stoma retraction is a concern. Otherwise, ileostomy should be avoided for patients with impaired renal function. Wise selection and flexibility are more important than using one type of stoma routinely.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

Background/Aims: Chronic hepatitis C (CHC) patients who failed antiviral therapy are at increased risk for hepatocellular carcinoma (HCC). This study assessed the potential role of metformin and statins, medications for diabetes mellitus (DM) and hyperlipidemia (HLP), in reducing HCC risk among these patients. Methods: We included CHC patients from the T-COACH study who failed antiviral therapy. We tracked the onset of HCC 1.5 years post-therapy by linking to Taiwan’s cancer registry data from 2003 to 2019. We accounted for death and liver transplantation as competing risks and employed Gray’s cumulative incidence and Cox subdistribution hazards models to analyze HCC development. Results: Out of 2,779 patients, 480 (17.3%) developed HCC post-therapy. DM patients not using metformin had a 51% increased risk of HCC compared to non-DM patients, while HLP patients on statins had a 50% reduced risk compared to those without HLP. The 5-year HCC incidence was significantly higher for metformin non-users (16.5%) versus non-DM patients (11.3%; adjusted sub-distribution hazard ratio [aSHR]=1.51; P=0.007) and metformin users (3.1%; aSHR=1.59; P=0.022). Statin use in HLP patients correlated with a lower HCC risk (3.8%) compared to non-HLP patients (12.5%; aSHR=0.50; P<0.001). Notably, the increased HCC risk associated with non-use of metformin was primarily seen in non-cirrhotic patients, whereas statins decreased HCC risk in both cirrhotic and non-cirrhotic patients. Conclusions Metformin and statins may have a chemopreventive effect against HCC in CHC patients who failed antiviral therapy. These results support the need for personalized preventive strategies in managing HCC risk.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Purpose: Protective ileostomy and colostomy are performed in patients undergoing low anterior resection with a high leakage risk. We aimed to compare surgical, medical, and daily care complications between these 2 ostomies in order to make individual choice. Methods: Patients who underwent low anterior resection for rectal tumors with protective stomas between January 2011 and September 2018 were enrolled. Stoma-related complications were prospectively recorded by wound, ostomy, and continence nurses. The cancer stage and treatment data were obtained from the Taiwan Cancer Database of our Big Data Center. Other demographic data were collected retrospectively from medical notes. The complications after stoma creation and after the stoma reversal were compared. Results: There were 176 patients with protective colostomy and 234 with protective ileostomy. Protective ileostomy had higher proportions of high output from the stoma for 2 consecutive days than protective colostomy (11.1% vs. 0%, P<0.001). Protective colostomy resulted in more stoma retraction than protective ileostomy (21.6% vs. 9.4%, P=0.001). Female, open operation, ileostomy, and carrying stoma more than 4 months were also significantly associated with a higher risk of stoma-related complications during diversion. For stoma retraction, the multivariate analysis revealed that female (odds ratio [OR], 4.00; 95% confidence interval [CI], 2.13–7.69; P<0.001) and long diversion duration (≥4 months; OR, 2.33; 95% CI, 1.22–4.43; P=0.010) were independent risk factors, but ileostomy was an independent favorable factor (OR, 0.40; 95% CI, 0.22–0.72; P=0.003). The incidence of complication after stoma reversal did not differ between colostomy group and ileostomy group (24.3% vs. 20.9%, P=0.542). Conclusion We suggest avoiding colostomy in patients who are female and potential prolonged diversion when stoma retraction is a concern. Otherwise, ileostomy should be avoided for patients with impaired renal function. Wise selection and flexibility are more important than using one type of stoma routinely.

Background/Aims: Ineffective esophageal motility (IEM) is common in patients with gastroesophageal reflux disease (GERD) and can be associated with poor esophageal contraction reserve on multiple rapid swallows. Alterations in the esophageal microbiome have been reported in GERD, but the relationship to presence or absence of contraction reserve in IEM patients has not been evaluated. We aim to investigate whether contraction reserve influences esophageal microbiome alterations in patients with GERD and IEM. Methods: We prospectively enrolled GERD patients with normal endoscopy and evaluated esophageal motility and contraction reserve with multiple rapid swallows during high-resolution manometry. The esophageal mucosa was biopsied for DNA extraction and 16S ribosomal RNA gene V3-V4 (Illumina)/full-length (Pacbio) amplicon sequencing analysis. Results: Among the 56 recruited patients, 20 had normal motility (NM), 19 had IEM with contraction reserve (IEM-R), and 17 had IEM without contraction reserve (IEM-NR). Esophageal microbiome analysis showed a significant decrease in microbial richness in patients with IEM-NR when compared to NM. The beta diversity revealed different microbiome profiles between patients with NM or IEM-R and IEM-NR (P = 0.037). Several esophageal bacterial taxa were characteristic in patients with IEM-NR, including reduced Prevotella spp.and Veillonella dispar, and enriched Fusobacterium nucleatum. In a microbiome-based random forest model for predicting IEM-NR, an area under the receiver operating characteristic curve of 0.81 was yielded. Conclusions In symptomatic GERD patients with normal endoscopic findings, the esophageal microbiome differs based on contraction reserve among IEM. Absent contraction reserve appears to alter the physiology and microbiota of the esophagus.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.