Background: Metabolic Score for Visceral Fat (METS-VF) is a novel indicator for estimating intra-abdominal fat, yet its connection with coronary artery calcification (CAC) remains uncharted. Our research aims to explore the novel METS-VF indicator’s link to CAC while comparing its performance against relevant anthropometric indices. Methods: This study enrolled participants who underwent health checkups and computed tomography scans for categorizing severity of CAC using the coronary artery calcium score. The METS-VF was calculated and compared with anthropometric indices in estimating the presence of CAC and different CAC severity using receiver operating characteristic curves. Results: Overall, 1217 participants (mean age 50.7 ± 9.9, 53.8% male) were included. METS-VF (odds ratio [OR], 1.506; 95% confidence interval [CI], 1.181–1.921; p = 0.001) was positively associated with the presence of CAC, even after accounting for cardiometabolic factors. Notably, METS-VF was positively associated with mild (OR, 1.450; 95% CI, 1.115–1.886; p = 0.006), moderate (OR, 1.865; 95% CI, 1.137–3.062; p = 0.014), and severe (OR, 2.316; 95% CI, 1.090–4.923; p = 0.029) CAC. Moreover, METS-VF yielded the highest area under curve (AUC) value in the estimation of the CAC presence (AUC = 0.710), mild (AUC = 0.682), moderate (AUC = 0.757), and severe (AUC = 0.807) CAC when compared with body mass index, waist circumference, visceral adiposity index, triglyceride–glucose index, and metabolic score for insulin resistance. The optimal METS-VF cut-off value was 6.4 for predicting CAC. Conclusions: METS-VF emerged as a strong independent marker for detecting CAC presence across mild, moderate, and severe CAC categories, outperforming major anthropometric indices in accurately estimating the presence of CAC and different severity of CAC.

Proper bone age assessment is crucial for the clinical diagnosis and evaluation of treatment responses. We investigated the applicability of Greulich and Pyle (GP), and Tanner and Whitehouse 3 (TW3) methods for children in modern Taiwan, using computer‐aided diagnosis. Hand and wrist radiographs were obtained from 611 children (3‐17 years) who came to our emergency department due to trauma. Ages 0 to 2 years old were excluded because of a limited number of cases. Skeletal maturation was assessed using the BoneXpert (version 2.5.4.1 automated software), which determines GP and TW3 bone age. The two scoring systems were evaluated for comparing the chronological ages in each subgroup. In boys, mean GP bone age vs mean chronological ages were delayed for ages 3 to 11 and advanced for age 12 to 17. In girls, mean GP bone age vs mean chronological ages was delayed for ages 4 to 8 and 17, and advanced for ages 3 and 9 to 17. In boys, the mean TW3 bone ages vs mean chronological ages were delayed for ages 5 to 10 except age 8, and advanced for ages 3 to 4, 8, and 11 to 15. In girls, the mean TW3 bone ages vs mean chronological ages were delayed for ages 4 to 12, and advanced for ages 3 and 13 to 14. By using the BoneXpert automatic software, we established bone age reference standards for children in Taiwan. Clinical application of GP and TW3 scoring methods can be adjusted according to our results to better assess bone age.

Non‐small cell lung cancer (NSCLC) is the most common form of lung cancer, which remains a leading cause of cancer mortality worldwide. Early detection, particularly at Stage I, is critical for improving survival outcomes. Low‐dose computed tomography (LDCT) has increased the detection of small asymptomatic tumors, resulting in an earlier diagnosis and facilitating less invasive surgical approaches such as segmentectomy and wedge resection, which preserve lung function while offering survival outcomes comparable to lobectomy. However, the risk factors for recurrence in Stage I NSCLC remain poorly understood. This retrospective study analyzed 1077 Stage I NSCLC patients treated at Kaohsiung Medical University Hospital from 2010 to 2022. Data including AJCC 7th and 8th editions staging, surgical interventions, and pathological features were analyzed. The proportion of Stage I cases increased significantly from 9.3% in 2010 to 33.8% in 2017, with Stage IA cases increasing from 12.1% in 2018 to 38.2% in 2022. Concurrently, the lobectomy rate decreased from 75% in 2010 to 43.6% in 2022, with more sublobar resections performed. Kaplan–Meier analysis found no significant differences in recurrence‐free survival between lobectomy and sublobar resection with regional lymph node dissection. Cox regression identified spread through air spaces (STAS) as an independent risk factor for recurrence (hazard ratio 2.87, p = 0.006), along with male sex, larger tumor size, and visceral pleural invasion. These findings highlight the role of LDCT in early detection and the importance of tailored treatment strategies, particularly addressing STAS, to optimize recurrence‐free survival and improve outcomes of patients with Stage I NSCLC.

Aims: Ejection fraction (EF) and end-systolic volume (ESV) are prognostic markers in cardiovascular disease. While MRI provides accurate assessments, its cost limits widespread use. Non-contrast cardiac CT (NCCT), used for coronary artery disease screening, may offer additional functional information. To evaluate the accuracy of AI-derived ventricular volumes and EF from NCCT compared with contrast cardiac CT (CCT) and MRI. Methods and results: This single center study included 205 patients who underwent cardiac CT for valve planning, divided into retrospective and prospective cohorts. A validated AI algorithm was applied to low-dose NCCT images at end-diastole and end-systole. Right (RV) and left ventricles (LV) volumes and their EFs were compared with CCT and MRI. In the prospective cohort (49 women, 53 men; mean age 73.9 ± 10.3 years), NCCT correlated strongly with CCT for LVEDV (152 mL; –14.2% relative difference; r = 0.91) and LVESV (96 mL; +32.6%; r = 0.84), with similar correlations for RVEDV (163 mL; –8.4%; r = 0.82) and RVESV (121.4 mL; +33.1%; r = 0.85). NCCT predicted LVEF <40% with 98% negative predictive value and 87% accuracy. LVEDV correlated strongly with MRI (n = 16) for CCT (240 mL; +4.2%; r = 0.99) and NCCT (197 mL; –14.3%; r = 0.97), as did LVESV for CCT (115 mL; –5%; r = 0.99) and NCCT (134 mL; +11%; r = 0.97). Conclusion: AI-derived ventricular volumes from NCCT show moderate to strong correlations, but EF is underestimated. The derived EF can be a screening tool to rule out significant ventricular dysfunction.