Reproducibility of the principal component analysis (PCA)-based data-driven respiratory gating on texture features in non-small cell lung cancer patients with 18 F-FDG PET/CT.

OBJECTIVE: Texture analysis is one of the lung cancer countermeasures in the field of radiomics. Even though image quality affects texture features, the reproducibility of principal component analysis (PCA)-based data-driven respiratory gating (DDG) on texture features remains poorly understood. Hence, this study aimed to clarify the reproducibility of PCA-based DDG on texture features in non-small cell lung cancer (NSCLC) patients with 18 F-Fluorodeoxyglucose (18 F-FDG) Positron emission tomography/computed tomography (PET/CT). METHODS: Twenty patients with NSCLC who underwent 18 F-FDG PET/CT in routine clinical practice were retrospectively analyzed. Each patient's PET data were reconstructed in two PET groups of no gating (NG-PET) and PCA-based DDG gating (DDG-PET). Forty-six image features were analyzed using LIFEx software. Reproducibility was evaluated using Lin's concordance correlation coefficient ( ρ c ${\rho _c}$ ) and percentage difference (%Diff). Non-reproducibility was defined as having unacceptable strength ( ρ c $({\rho _c}$  < 0.8) and a %Diff of >10%. NG-PET and DDG-PET were compared using the Wilcoxon signed-rank test. RESULTS: A total of 3/46 (6.5%) image features had unacceptable strength, and 9/46 (19.6%) image features had a %Diff of >10%. Significant differences between the NG-PET and DDG-PET groups were confirmed in only 4/46 (8.7%) of the high %Diff image features. CONCLUSION: Although the DDG application affected several texture features, most image features had adequate reproducibility. PCA-based DDG-PET can be routinely used as interchangeable images for texture feature extraction from NSCLC patients.

Two Distinct Groups Are Shown to Be at Risk of Diabetes by Means of a Cluster Analysis of Four Variables.

Recent attempts to classify adult-onset diabetes using only six diabetes-related variables (GAD antibody, age at diagnosis, BMI, HbA1c, and homeostatic model assessment 2 estimates of b-cell function and insulin resistance (HOMA2-B and HOMA2-IR)) showed that diabetes can be classified into five clusters, of which four correspond to type 2 diabetes (T2DM). Here, we classified nondiabetic individuals to identify risk clusters for incident T2DM to facilitate the refinement of prevention strategies. Of the 1167 participants in the population-based Iwaki Health Promotion Project in 2014 (baseline), 868 nondiabetic individuals who attended at least once during 2015-2019 were included in a prospective study. A hierarchical cluster analysis was performed using four variables (BMI, HbA1c, and HOMA2 indices). Of the four clusters identified, cluster 1 (n = 103), labeled as "obese insulin resistant with sufficient compensatory insulin secretion", and cluster 2 (n = 136), labeled as "low insulin secretion", were found to be at risk of diabetes during the 5-year follow-up period: the multiple factor-adjusted HRs for clusters 1 and 2 were 14.7 and 53.1, respectively. Further, individuals in clusters 1and 2 could be accurately identified: the area under the ROC curves for clusters 1and 2 were 0.997 and 0.983, respectively. The risk of diabetes could be better assessed on the basis of the cluster that an individual belongs to.

The Relationship between Serum Adiponectin, Urinary Albumin/Creatinine Ratio and Type 2 Diabetes: A Population-Based Cross-Sectional Study.

The relationship between serum adiponectin concentration (S-Adipo) and various diseases, such as type 2 diabetes (T2D) is conflicting. We hypothesized that the extent of kidney damage in patients with T2D may be responsible for this inconsistency and, thus, examined association between S-Adipo and T2D after consideration for the extent of kidney damage present. Of the 1816 participants in the population-based Iwaki study of Japanese people, 1751 participants with a complete dataset were included. Multivariate logistic regression analyses revealed that low S-Adipo was independently associated with T2D (<0.001), as was high urinary albumin to creatinine ratio (uACR) (<0.001). Principal components analysis showed that the relative value of S-Adipo to uACR (adiponectin relative excess) was significantly associated with T2D (odds ratio: 0.49, p < 0.001). Receiver operating curve analyses revealed that an index of adiponectin relative excess the ratio of S-Adipo to uACR was superior to S-Adipo per se as a marker of T2D (area under the curve: 0.746 vs. 0.579, p < 0.001). This finding indicates that the relationship between S-Adipo and T2D should be evaluated according to the extent of kidney damage present and may warrant similar analyses of the relationships between S-Adipo and other medicalconditions, such as cardiovascular disease.

Interrelations between Gut Microbiota Composition, Nutrient Intake and Diabetes Status in an Adult Japanese Population.

Upon food digestion, the gut microbiota plays a pivotal role in energy metabolism, thus affecting the development of type 2 diabetes (DM). We aimed to examine the influence of the composition of selected nutrients consumed on the association between the gut microbiota and DM. This cross-sectional study of a general population was conducted on 1019 Japanese volunteers. Compared with non-diabetic subjects, diabetic subjects had larger proportions of the genera Bifidobacterium and Streptococcus but smaller proportions of the genera Roseburia and Blautia in their gut microbiotas. The genera Streptococcus and Roseburia were positively correlated with the amounts of energy (p = 0.027) and carbohydrate and fiber (p = 0.007 and p = 0.010, respectively) consumed, respectively. In contrast, the genera Bifidobacterium and Blautia were not correlated with any of the selected nutrients consumed. Cluster analyses of these four genera revealed that the Blautia-dominant cluster was most negatively associated with DM, whereas the Bifidobacterium-dominant cluster was positively associated with DM (vs. the Blautia-dominant cluster; odds ratio 3.97, 95% confidence interval 1.68-9.35). These results indicate the possible involvement of nutrient factors in the association between the gut microbiota and DM. Furthermore, independent of nutrient factors, having a Bifidobacterium-dominant gut microbiota may be a risk factor for DM compared to having a Blautia-dominant gut microbiota in a general Japanese population.

Impact of patient body habitus on image quality and quantitative value in bone SPECT/CT.

OBJECTIVE: The first edition of guidelines for standardization of bone single photon emission computed tomography (SPECT) imaging was published in 2017, and the optimization and standardization are widely promoted. To the purpose, clarification of the factors related to image quality and quantitative values and their influence are required. The present study aimed to clarify and optimize the influence of patient body habitus on image quality and quantitative values in bone SPECT/CT. METHODS: National Electrical Manufacturers Association body phantom (S-size) and custom-made large body phantoms (M-size and L-size) that simulate the abdomens of Japanese patients weighing 60, 80, and 100 kg, were used. Each phantom was filled with 99mTc-solutions of 108 and 18 kBq/mL for the hot spheres and background, respectively. Dynamic SPECT acquisition was performed for 6000 s (150 s /rotation × 40 rotation). The data were divided into six projection data and reconstructed each acquisition time (150, 300, 450, 600, 750, 900 s, and single projection 6000 s). Image quality was evaluated for contrast (QH, 17 mm), background noise (NB, 17 mm), contrast-to-noise ratio (CNR), maximum standardized uptake value (SUVmax, 17 mm), and visual assessment for a 17 mm hot sphere. RESULTS: Image quality in the 300 s acquisition showed that values of QH, 17 mm, CNR, and SUVmax, 17 mm decreased (-16.7%, -11.8%, and -11.3%) for M-size and (-28.2%, -30.1%, and -21.7%) for L-size compared with S-size, respectively. No significant difference was observed in NB, 17 mm values. M-size and L-size required 1.2 and 2.3 times longer acquisition, to achieve same CNR as S-size. In visual assessment, 17 mm hot sphere could not be detected only in the L-size. When the Japanese bone SPECT guidelines criteria were applied in 600 s, the sphere could be detected between all phantoms. CONCLUSIONS: Patient body habitus significantly affects image quality and decreases the quantitative value in bone SPECT/CT. For the optimization, extend acquisition time according to the patient body habitus is effective for image quality. And for the standardization, it is important to achieve imaging conditions that meet the Japanese bone SPECT guidelines criteria to ensure adequate detectability.

Correction: Tamura et al. Interrelations between Gut Microbiota Composition, Nutrient Intake and Diabetes Status in an Adult Japanese Population. J. Clin. Med. 2022, 11, 3216.

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