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 define an optimal age cutoff for early-onset gastric cancer (EOGC) and compare its characteristics with those of late-onset gastric cancer (LOGC) using nationwide survey data. Methods: Using data from a nationwide survey, this comprehensive population-based study analyzed data spanning 3 years (2009, 2014, and 2019). The joinpoint analysis and interrupted time series (ITS) methodology were employed to identify age cutoffs for EOGC based on the sex ratio and tumor histology. Clinicopathologic characteristics and surgical outcomes were compared between the EOGC and LOGC groups. Results: The age cutoff for defining EOGC was suggested to be 50 years, supported by joinpoint and ITS analyses. Early gastric cancer was predominantly present in the EOGC and LOGC groups. Patients with EOGC comprised 20.3% of the total study cohort and demonstrated a more advanced disease stage compared to patients with LOGC. However, patients with EOGC underwent more minimally invasive surgeries, experienced shorter hospital stays, and had lower postoperative morbidity and mortality rates. Conclusion This study proposes an age of ≤50 years as a criterion for defining EOGC and highlights its features compared to LOGC. Further research using this criterion should guide tailored treatment strategies and improve outcomes for young patients with gastric cancer.

Purpose: Multidisciplinary treatment (MDT) in gastric cancer is an effective approach for establishing treatment plans.However, the appropriateness of using “ratio of MDT” as an item for evaluating the adequacy of gastric cancer treatment in Korea has not been previously researched. The purpose of this study is to verify whether the “ratio of MDT” is appropriate as an item for gastric cancer adequacy evaluation from the surgeon’s perspective. Methods: This study involved 142 patients who received MDT at our hospital between December 2015 and January 2023.Patients were divided into 2 groups based on the date when gastric cancer adequacy evaluation was implemented; there were 71 patients before and after the evaluation was conducted, respectively. Based on electronic medical records, the initial plan prepared before the MDT clinic and the final plan prepared after the clinic were compared to determine whether the plan was changed. Results: The average age of patients who received MDT before and after the evaluation was 64.8 and 62.2 years, respectively. Overall, 50 and 21 patients were male (70.4%) and female (29.6%), respectively, in both groups. Before the evaluation, 26 patients (36.6%) who received MDT changed their treatment plans after visiting the clinic, and 15 patients (21.1%) who received MDT after the evaluation had their treatment plans modified. Groups who received MDT and changes in treatment plans were significantly correlated (P = 0.042). Conclusion Our findings suggest that including the “ratio of MDT” as an item of gastric cancer adequacy evaluation needs reassessment.

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 define an optimal age cutoff for early-onset gastric cancer (EOGC) and compare its characteristics with those of late-onset gastric cancer (LOGC) using nationwide survey data. Methods: Using data from a nationwide survey, this comprehensive population-based study analyzed data spanning 3 years (2009, 2014, and 2019). The joinpoint analysis and interrupted time series (ITS) methodology were employed to identify age cutoffs for EOGC based on the sex ratio and tumor histology. Clinicopathologic characteristics and surgical outcomes were compared between the EOGC and LOGC groups. Results: The age cutoff for defining EOGC was suggested to be 50 years, supported by joinpoint and ITS analyses. Early gastric cancer was predominantly present in the EOGC and LOGC groups. Patients with EOGC comprised 20.3% of the total study cohort and demonstrated a more advanced disease stage compared to patients with LOGC. However, patients with EOGC underwent more minimally invasive surgeries, experienced shorter hospital stays, and had lower postoperative morbidity and mortality rates. Conclusion This study proposes an age of ≤50 years as a criterion for defining EOGC and highlights its features compared to LOGC. Further research using this criterion should guide tailored treatment strategies and improve outcomes for young patients with gastric cancer.

Purpose: Multidisciplinary treatment (MDT) in gastric cancer is an effective approach for establishing treatment plans.However, the appropriateness of using “ratio of MDT” as an item for evaluating the adequacy of gastric cancer treatment in Korea has not been previously researched. The purpose of this study is to verify whether the “ratio of MDT” is appropriate as an item for gastric cancer adequacy evaluation from the surgeon’s perspective. Methods: This study involved 142 patients who received MDT at our hospital between December 2015 and January 2023.Patients were divided into 2 groups based on the date when gastric cancer adequacy evaluation was implemented; there were 71 patients before and after the evaluation was conducted, respectively. Based on electronic medical records, the initial plan prepared before the MDT clinic and the final plan prepared after the clinic were compared to determine whether the plan was changed. Results: The average age of patients who received MDT before and after the evaluation was 64.8 and 62.2 years, respectively. Overall, 50 and 21 patients were male (70.4%) and female (29.6%), respectively, in both groups. Before the evaluation, 26 patients (36.6%) who received MDT changed their treatment plans after visiting the clinic, and 15 patients (21.1%) who received MDT after the evaluation had their treatment plans modified. Groups who received MDT and changes in treatment plans were significantly correlated (P = 0.042). Conclusion Our findings suggest that including the “ratio of MDT” as an item of gastric cancer adequacy evaluation needs reassessment.

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 define an optimal age cutoff for early-onset gastric cancer (EOGC) and compare its characteristics with those of late-onset gastric cancer (LOGC) using nationwide survey data. Methods: Using data from a nationwide survey, this comprehensive population-based study analyzed data spanning 3 years (2009, 2014, and 2019). The joinpoint analysis and interrupted time series (ITS) methodology were employed to identify age cutoffs for EOGC based on the sex ratio and tumor histology. Clinicopathologic characteristics and surgical outcomes were compared between the EOGC and LOGC groups. Results: The age cutoff for defining EOGC was suggested to be 50 years, supported by joinpoint and ITS analyses. Early gastric cancer was predominantly present in the EOGC and LOGC groups. Patients with EOGC comprised 20.3% of the total study cohort and demonstrated a more advanced disease stage compared to patients with LOGC. However, patients with EOGC underwent more minimally invasive surgeries, experienced shorter hospital stays, and had lower postoperative morbidity and mortality rates. Conclusion This study proposes an age of ≤50 years as a criterion for defining EOGC and highlights its features compared to LOGC. Further research using this criterion should guide tailored treatment strategies and improve outcomes for young patients with gastric cancer.