Sarcopenia, defined as decreased skeletal muscle mass, is prevalent and associated with poor prognosis in various solid tumors. This study aimed to determine the prognostic role of sarcopenia in patients with advanced gastric cancer (AGC). This retrospective study consisted of 140 consecutive patients who underwent palliative chemotherapy for AGC. A cross-sectional area of muscle at the level of the third lumbar vertebra (L3) was measured using baseline computed tomography (CT) scans. Sarcopenia was defined as a L3 skeletal muscle index of ≤ 49 cm²/m² for men and ≤ 31 cm²/m² for women using Korean-specific cutoffs. We compared the overall survival (OS) and clinical characteristics of patients with and without sarcopenia. The median age was 67 years, and 133 (95%) patients had metastatic disease. Sarcopenia was present in 67 patients (47.9%) and was significantly related to male sex (p < 0.001) and low body mass index (p = 0.002). Patients with sarcopenia had a significantly shorter OS than those without sarcopenia (median, 6.8 months vs. 10.3 months, respectively; p = 0.033). In the multivariable analysis, sarcopenia was an independent prognostic factor of poor OS (hazard ratio, 1.51, p = 0.029); no response to chemotherapy (p < 0.001), no second-line chemotherapy (p < 0.001), metastatic sites ≥ 3 (p < 0.001), and low serum albumin level (p = 0.033) were also independent prognostic factors of poor OS. Sarcopenia, as determined by baseline CT, can be used to predict poor prognosis in AGC patients treated with palliative chemotherapy.

Suicide in adolescents is a major problem worldwide and previous history of suicide ideation and attempt represents the strongest predictors of future suicidal behavior. The aim of this study was to develop prediction model to identify Korean adolescents of high risk suicide (= who have history of suicide ideation/attempt in previous year) using machine learning techniques. A nationally representative dataset of Korea Youth Risk Behavior Web-based Survey (KYRBWS) was used (n = 59,984 of middle and high school students in 2017). The classification process was performed using machine learning techniques such as logistic regression (LR), random forest (RF), support vector machine (SVM), artificial neural network (ANN), and extreme gradient boosting (XGB). A total of 7,443 adolescents (12.4%) had a previous history of suicidal ideation/attempt. In the multivariable analysis, sadness (odds ratio [OR], 6.41; 95% confidence interval [95% CI], 6.08–6.87), violence (OR, 2.32; 95% CI, 2.01–2.67), substance use (OR, 1.93; 95% CI, 1.52–2.45), and stress (OR, 1.63; 95% CI, 1.40–1.86) were associated factors. Taking into account 26 variables as predictors, the accuracy of models of machine learning techniques to predict the high-risk suicidal was comparable with that of LR; the accuracy was best in XGB (79.0%), followed by SVM (78.7%), LR (77.9%), RF (77.8%), and ANN (77.5%). The machine leaning techniques showed comparable performance with LR to classify adolescents who have previous history of suicidal ideation/attempt. This model will hopefully serve as a foundation for decreasing future suicides as it enables early identification of adolescents at risk of suicide and modification of risk factors.

Skeletal muscle mass (SMM) plays a crucial role in systemic glucose metabolism. To obtain reference data on absolute and relative values of SMM for Korean children and adolescents. Cross-sectional results from 1919 children and adolescents (1024 boys) aged 10–18 years that underwent dual-energy X-ray absorptiometry (DXA) during the Korean National Health and Nutrition Examination Survey 2009–2011 were analyzed. SMMs were evaluated as follows; absolute SMM (appendicular skeletal muscle mass [ASM]) and relative SMMs, namely, height-adjusted skeletal muscle index (SMI; ASM/height²), %SMM (ASM/weight x 100), and skeletal muscle-to-body fat ratio (MFR; ASM/body fat mass). Percentile curves illustrated the developmental patterns of the SMMs of Korean children and adolescents. ASM and SMI increased with age in both genders, and increased from age 10 throughout adolescence in boys, whereas in girls, they increased until age 13 and then stabilized. In boys, %SMM and MFR were highest at age 15 and then slowly stabilized or decreased, whereas in girls, they peaked at age 10 to 11 and then decreased through adolescence. Cut-off values for low MFR were identified and a significant association was found between a low MFR and high risk of metabolic syndrome. However, this association was found to be dependent on gender and the level of BMI. This study provides reference values of absolute and relative SMM for Korean children and adolescents. Detailed body composition analyses including skeletal muscle and fat mass might provide improved measures of metabolic risk.

Objective: The chest X-ray (CXR) is the most readily available and common imaging modality for the assessment of pneumonia. However, detecting pneumonia from chest radiography is a challenging task, even for experienced radiologists. An artificial intelligence (AI) model might help to diagnose pneumonia from CXR more quickly and accurately. We aim to develop an AI model for pneumonia from CXR images and to evaluate diagnostic performance with external dataset. Methods: To train the pneumonia model, a total of 157,016 CXR images from the National Institutes of Health (NIH) and the Korean National Tuberculosis Association (KNTA) were used (normal vs. pneumonia = 120,722 vs.36,294). An ensemble model of two neural networks with DenseNet classifies each CXR image into pneumonia or not. To test the accuracy of the models, a separate external dataset of pneumonia CXR images (n = 212) from a tertiary university hospital (Gachon University Gil Medical Center GUGMC, Incheon, South Korea) was used; the diagnosis of pneumonia was based on both the chest CT findings and clinical information, and the performance evaluated using the area under the receiver operating characteristic curve (AUC). Moreover, we tested the change of the AI probability score for pneumonia using the follow-up CXR images (7 days after the diagnosis of pneumonia, n = 100). Results: When the probability scores of the models that have a threshold of 0.5 for pneumonia, two models (models 1 and 4) having different pre-processing parameters on the histogram equalization distribution showed best AUC performances of 0.973 and 0.960, respectively. As expected, the ensemble model of these two models performed better than each of the classification models with 0.983 AUC. Furthermore, the AI probability score change for pneumonia showed a significant difference between improved cases and aggravated cases (Δ = -0.06 ± 0.14 vs. 0.06 ± 0.09, for 85 improved cases and 15 aggravated cases, respectively, P = 0.001) for CXR taken as a 7-day follow-up. Conclusions: The ensemble model combined two different classification models for pneumonia that performed at 0.983 AUC for an external test dataset from a completely different data source. Furthermore, AI probability scores showed significant changes between cases of different clinical prognosis, which suggest the possibility of increased efficiency and performance of the CXR reading at the diagnosis and follow-up evaluation for pneumonia.

Background: Although emphysema is a known independent risk factor of lung cancer, no study has addressed the prognostic impact of computed tomography (CT)-emphysema score in advanced stage lung cancer. Methods: For 84 consecutive patients with stage IIIB and IV squamous cell lung cancer that underwent palliative chemotherapy, severity of emphysema was semi-quantitatively scored using baseline chest CT images according to the Goddard scoring system (possible scores range, 0–24). The cutoff of high CT-emphysema score was determined using the maximum chi-squared test and the prognostic significance of the high CT-emphysema score was evaluated using Kaplan-Meier analysis and Cox proportional hazards analysis. Results: The median CT-emphysema score was 5 (range, 0–22). Patients with a high CT-emphysema score (≥4) tended to have poorer overall survival (OS) (median: 6.3 vs. 13.7 months) than those with a score of <4 (P=0.071). Multivariable analysis revealed that a higher CT-emphysema score was a significant independent prognostic factor for poor OS [hazard ratio (HR) =2.06; 95% confidence interval (CI), 1.24–3.41; P=0.005), along with no response to first-line therapy (P=0.009) and no second-line therapy (P<0.001). Conclusions: CT-emphysema score is significantly associated with poor prognosis in patients with advanced squamous cell lung cancer.