Objective: To investigate the agreement and reliability of estimating the volumes and normative percentiles (N%) of segmented brain regions among NeuroQuant (NQ), DeepBrain (DB), and FreeSurfer (FS) software programs, focusing on the comparison between NQ and DB. Materials and Methods: Three-dimensional T1-weighted images of 145 participants (48 healthy participants, 50 patients with mild cognitive impairment, and 47 patients with Alzheimer’s disease) from a single medical center (SMC) dataset and 130 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset were included in this retrospective study. All images were analyzed with DB, NQ, and FS software to obtain volume estimates and N% of various segmented brain regions. We used Bland–Altman analysis, repeated measures ANOVA, reproducibility coefficient, effect size, and intraclass correlation coefficient (ICC) to evaluate inter-method agreement and reliability. Results: Among the three software programs, the Bland–Altman plot showed a substantial bias, the ICC showed a broad range of reliability (0.004–0.97), and repeated-measures ANOVA revealed significant mean volume differences in all brain regions.Similarly, the volume differences of the three software programs had large effect sizes in most regions (0.73–5.51). The effect size was largest in the pallidum in both datasets and smallest in the thalamus and cerebral white matter in the SMC and ADNI datasets, respectively. N% of NQ and DB showed an unacceptably broad Bland–Altman limit of agreement in all brain regions and a very wide range of ICC values (-0.142–0.844) in most brain regions. Conclusion NQ and DB showed significant differences in the measured volume and N%, with limited agreement and reliability for most brain regions. Therefore, users should be aware of the lack of interchangeability between these software programs when they are applied in clinical practice.

PURPOSE: Few studies investigated roles of body mass index (BMI) on gastric cancer (GC) risk according to Helicobacter pylori infection status. This study was conducted to evaluate associations between BMI and GC risk with consideration of H. pylori infection information. MATERIALS AND METHODS: We performed a case-cohort study (n=2,458) that consists of a subcohort, (n=2,193 including 67 GC incident cases) randomly selected from the Korean Multicenter Cancer Cohort (KMCC) and 265 incident GC cases outside of the subcohort. H. pylori infection was assessed using an immunoblot assay. GC risk according to BMI was evaluated by calculating hazard ratios (HRs) and their 95% confidence intervals (95% CIs) using weighted Cox hazard regression model. RESULTS: Increased GC risk in lower BMI group (< 23 kg/m²) with marginal significance, (HR, 1.32; 95% CI, 0.98 to 1.77) compared to the reference group (BMI of 23-24.9 kg/m²) was observed. In the H. pylori non-infection, both lower (< 23 kg/m²) and higher BMI (≥ 25 kg/m²) showed non-significantly increased GC risk (HR, 10.82; 95% CI, 1.25 to 93.60 and HR, 11.33; 95% CI, 1.13 to 113.66, respectively). However, these U-shaped associations between BMI and GC risk were not observed in the group who had ever been infected by H. pylori. CONCLUSION: This study suggests the U-shaped associations between BMI and GC risk, especially in subjects who had never been infected by H. pylori.
Body Mass Index ; Cohort Studies ; Helicobacter pylori ; Helicobacter ; Stomach Neoplasms