Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Purpose: This study aimed to evaluate the long-term clinical outcomes based on the ligation level of the inferior mesenteric artery (IMA) in patients with rectal cancer. Methods: This was a retrospective analysis of a prospectively collected database that included all patients who underwent elective low anterior resection for rectal cancer between January 2013 and December 2019. The clinical outcomes included oncological outcomes, postoperative complications, and functional outcomes. The oncological outcomes included overall survival (OS) and relapse-free survival (RFS). The functional outcomes, including defecatory and urogenital functions, were analyzed using the Fecal Incontinence Severity Index, International Prostate Symptom Score, and International Index of Erectile Function questionnaires. Results: In total, 545 patients were included in the analysis. Of these, 244 patients underwent high ligation (HL), whereas 301 underwent low ligation (LL). The tumor size was larger in the HL group than in the LL group. The number of harvested lymph nodes (LNs) was higher in the HL group than in the LL group. There were no significant differences in complication rates and recurrence patterns between the groups. There were no significant differences in 5-year RFS and OS between the groups. Cox regression analysis revealed that the ligation level (HL vs. LL) was not a significant risk factor for oncological outcomes. Regarding functional outcomes, the LL group showed a significant recovery in defecatory function 1 year postoperatively compared with the HL group. Conclusion LL with LNs dissection around the root of the IMA might not affect the oncologic outcomes comparing to HL; however, it has minimal benefit for defecatory function.

Purpose: Whether to perform surgery or conservatively manage appendicitis in immunosuppressed patients is a concern for clinicians. This study aimed to compare the outcomes of these 2 treatment options for appendicitis in patients with cancer undergoing chemotherapy. Methods: This retrospective study included 206 patients with cancer who were diagnosed with acute appendicitis between August 2001 and December 2021. Among them, patients who received chemotherapy within 1 month were divided into surgical and conservative groups. We evaluated the outcomes, including treatment success within 1 year, 1-year recurrence, and the number of days from the diagnosis of appendicitis to chemotherapy restart, between the 2 groups. Results: Among the 206 patients with cancer who were diagnosed with acute appendicitis, 78 received chemotherapy within 1 month. The patients were divided into surgery (n = 63) and conservative (n = 15) groups. In the surgery group, the duration of antibiotic therapy (7.0 days vs. 16.0 days, P < 0.001) and length of hospital stay (8.0 days vs. 27.5 days, P = 0.002) were significantly shorter than conservative groups. The duration from the diagnosis of appendicitis to the restart of chemotherapy was shorter in the surgery group (20.8 ± 15.1 days vs. 35.2 ± 28.2 days, P = 0.028). The treatment success rate within 1 year was higher in the surgery group (100% vs. 33.3%, P < 0.001). Conclusion Surgical treatment showed a significantly higher success rate than conservative treatment for appendicitis in patients less than 1 month after chemotherapy. Further prospective studies will be needed to clinically determine treatment options.

OBJECTIVES: We explored whether the association between vitamin B2 and colorectal cancer (CRC) risk could be modified by the MTRR rs1801394 and MTR rs1805087 genetic polymorphisms and examined whether the interaction effects are sex-specific. METHODS: We performed a case-control study involving 1,420 CRC patients and 2,840 controls from the Korea National Cancer Center. Dietary vitamin B2 intake was assessed using a semiquantitative food frequency questionnaire, and the association with CRC was evaluated. Genotyping was performed using an Illumina MEGA-Expanded Array. For gene-nutrient interaction analysis, pre-matched (1,081 patients and 2,025 controls) and matched (1,081 patients and 1,081 controls) subsets were included. Unconditional and conditional logistic regression models were used to calculate odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: A higher intake of vitamin B2 was associated with a significantly lower CRC risk (OR, 0.65; 95% CI, 0.51 to 0.82; p<0.001). Carriers of at least 1 minor allele of MTRR rs1801394 showed a significantly higher CRC risk (OR, 1.43; 95% CI, 1.12 to 1.83). Males homozygous for the major allele (A) of MTRR rs1801394 and who had a higher intake of vitamin B2 had a significantly lower CRC risk (OR, 0.31; 95% CI, 0.18 to 0.54; p-interaction=0.02). In MTR rs1805087, males homozygous for the major allele (A) and who had a higher vitamin B2 intake had a significantly lower CRC risk (OR, 0.38; 95% CI, 0.25 to 0.60; p-interaction<0.001). CONCLUSIONS The MTRR rs1801394 and MTR rs1805087 genetic polymorphisms may modify the association between vitamin B2 and CRC risk, particularly in males. However, further studies are warranted to confirm these interaction results.