Nonalcoholic fatty liver disease (NAFLD) is a multisystem disease that affects the liver and a variety of extra-hepatic organ systems. This study aimed to investigate the relationship between hepatic steatosis and glucose metabolism in liver and extra-hepatic tissues and organs.
The prevalence of dyslipidemia in China was increased over the last several years. Studies have shown that the activity of aBAT is related to the lipid metabolism. In this study, we analyzed blood lipid level in tumor-free healthy Chinese adults in order to determine the role of aBAT in lipid metabolism.We retrospectively analyzed the factors that affect the blood lipid level in 717 tumor-free healthy adults who received blood lipid measurement and PET/CT scan by multivariate regression analysis. We also determined the role of aBAT on lipid profile by case-control study.(1) Our results showed that 411 (57.3 %) subjects had dyslipidemia. The prevalence of the subjects with hypercholesteremia, hypertriglyceridemia, low high-density lipoprotein cholesterol and high low-density lipoprotein cholesterol was 9.5 %, 44.4 %, 30.8 % and 1.4 %, respectively. Multivariate logistic regression analysis with dyslipidemia as the dependent variable showed that body mass index (BMI) and smoking are independent risk factors for dyslipidemia (OR > 1, P < 0.05), while the presence of aBAT is the independent protective factor for dyslipidemia (OR < 1, P < 0.05). (2) The incidence of aBAT was 1.81 %. Subjects with aBAT had significantly lower serum triglyceride and higher serum high-density lipoprotein cholesterol than the subjects without aBAT. The serum total cholesterol and low-density lipoprotein cholesterol were not significantly different between the subjects with aBAT and those without aBAT.Dyslipidemia is caused by multiple factors and the presence of aBAT is a protective factor for dyslipidemia in healthy Chinese adults.
Rationale: Uterine fibroids are the most common pelvic solid tumors and common to 25% of women. 18F-fluorodexyglucose (18F-FDG) is an energy metabolism tracer. Although FDG is generally concentrated in malignant lesions with high glucose metabolism, it can also accumulate in normal tissues, benign lesions, and inflammatory sites. The exact mechanism of FDG uptake by uterine fibroids is not clear. Here, we report a case of uterine fibroids with positive 18F-FDG positron emission tomography/computed tomography (PET/CT) imaging and significantly increased CA19-9. Patients concerns: The patient was a 43-year-old woman and admitted to our hospital because of “1-year-extended menstrual periods.” At admission, she had normal CA125, AFP, and CEA level and CA19-9>1000.00 U/mL. Gynecological transvaginal ultrasound found enlarged uterus with an anterior hypoechoic area of 3.9 × 4.2 cm. CT and contrast-enhanced CT showed significantly enhanced mass shadow on the left anterior wall of uterus. 18F-FDG PET/CT showed increased FDG metabolism of tumor in the anterior wall of the uterus. Interventions: Laparoscopic hysterectomy was performed. Diagnosis: Pathological examination demonstrated subserosal leiomyoma. Outcomes: Her CA19-9 level dropped to 91.50 U/mL 1 day after surgery. Lessons: Significantly elevated CA19-9 was positioned in the uterus by PET/CT imaging, which not only avoided unnecessary gastrointestinal endoscopy and reduced the suffering of patients, but also strengthened the operation confidence in gynecologists.
BACKGROUND:This study aimed to establish a prediction model based on the maternal laboratory index score (Lab-score) for histologic chorioamnionitis (HCA) in patients with prelabor rupture of membranes (PROM) during late pregnancy. MATERIAL AND METHODS:Sixty-nine cases of pregnant women with PROM were retrospectively analyzed. The general information and laboratory indicators were compared between the HCA (n=22) and non-HCA (n=47) groups. A multivariate logistic regression method was used to establish the prediction model. We plotted the receiver operating characteristic curve and calculated the area under the curve (AUC). The clinical effectiveness of each model was compared by decision curve analysis. RESULTS:Only C-reactive protein (CRP) in the laboratory index predicted HCA, but its diagnostic efficacy was not ideal (AUC=0.651). Then, we added CRP to the platelet/white blood cell count ratio and triglyceride level to construct the Lab-score. Based on the Lab-score, important clinical parameters, including body mass index, diastolic blood pressure, and preterm birth, were introduced to construct a complex joint prediction model. The AUC of this model was significantly larger than that of CRP (0.828 vs. 0.651, P=0.035), but not significantly different from that of Lab-score (0.828 vs. 0.724, P=0.120). Considering the purpose of HCA screening, the net benefit of the complex model was better than that of Lab-score and CRP. CONCLUSIONS:The complex model based on Lab-score is useful in the clinical screening of high-risk populations with PROM and HCA during late pregnancy.
Background Sublobar resection is suitable for peripheral cT1N0M0 non-small-cell lung cancer (NSCLC). The traditional PET-CT criterion (lymph node size ≥1.0 cm or SUV max ≥2.5) for predicting lymph nodes metastasis (LNM) has unsatisfactory performance. Objective We explore the clinical role of preoperative SUV max and the size of the primary lesions for predicting peripheral cT1 NSCLC LNM. Methods We retrospectively analyzed 174 peripheral cT1 NSCLC patients underwent preoperative 18 F-FDG PET-CT and divided into the LNM and non-LNM group by pathology. We compared the differences of primary lesions’ baseline characteristics between the two groups. The risk factors of LNM were determined by univariate and multivariate analysis, and we assessed the diagnostic efficacy with the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value (NPV). Results Of the enrolled cases, the incidence of LNM was 24.7%. The preoperative SUV max >6.3 or size >2.3 cm of the primary lesions were independent risk factors of peripheral cT1 NSCLC LNM (ORs, 95% CIs were 6.18 (2.40–15.92) and 3.03 (1.35–6.81). The sensitivity, NPV of SUV max >6.3 or size >2.3 cm of the primary lesions were higher than the traditional PET-CT criterion for predicting LNM (100.0 vs. 86.0%, 100.0 vs. 89.7%). A Hosmer–Lemeshow test showed a goodness-of-fit ( P = 0.479). Conclusions The excellent sensitivity and NPV of preoperative of the SUV max >6.3 or size >2.3 cm of the primary lesions based on 18 F-FDG PET-CT might identify the patients at low-risk LNM in peripheral cT1 NSCLC.
1328 Objectives: To establish a novel model for predicting and validating the invasiveness of early lung adenocarcinoma by 18F-FDG PET combined with HRCT.
Methods: 149 patients with early stage lung adenocarcinoma who underwent preoperative PET/CT and HRCT examination in our hospital from October 2011 to October 2019 were enrolled. 457 ground-glass nodules (GGN) were detected by PET/CT, 37.2% (170/457) of GGNs were surgically removed. In order to establish a new prediction model and verify its accuracy, we randomly divided the 170 GGNs resected into a modeling group (89) and a verification group (81), the two groups were divided into pre-invasive/minimally invasive adenocarcinoma (MIA) subgroup and invasive adenocarcinoma (IAC) subgroup according to pathological subtypes. The stepAIC (Akaike Information Criterion) method was used to build the PET/CT model, respectively. Receiver operating characteristic (ROC) curve analysis was used to compare the diagnostic efficacy of different models.
Results: In the modeling group, the proportion of mixed GGNs, irregular shape, lobulation sign, bronchiectasis/distortion/truncation sign, DGGN, DSolid, CTR, CTGGO, and SUV index in the IAC subgroup were significantly higher than those in the pre-invasive/MIA subgroup, the difference was statistically significant (all P < 0.05). Among the quantitative parameters of PET/CT (DGGN, DSolid, CTR, CTGGO, SUVindex), the SUVindex had the best diagnostic efficacy (AUC = 0.854). Use the stepAIC method to build a multi-factor model: logit (P) = 2.56455 + 0.00592 × CTGGO + 3.55028 × SUVindex. In the modeling group, the efficacy of the model in predicting early lung adenocarcinoma invasion was AUC = 0.854, and the sensitivity, specificity, and accuracy were 0.737, 0.923, and 0.764. In the validation group, the model predicted the early lung adenocarcinoma invasion: the efficacy was AUC = 0.802, and the sensitivity, specificity, and accuracy are 0.970, 0.643, and 0.914. The model has similar prediction performance in the modeling group and the validation group, and has good robustness.
Conclusions: The novel model based on the combination of FDG PET and HRCT parameters has a good predictive value for early lung adenocarcinoma invasiveness and can effectively avoid misdiagnosis and missed diagnosis of IAC.
Key words: ground glass nodule; lung adenocarcinoma; prediction model; validation model; HRCT; PET
To assess the value of feature-tracking cardiac magnetic resonance (FT-CMR) imaging in the quantitative evaluation of acute myocardial infarction (AMI).We retrospectively analyzed medical records of patients with acute myocardial infarction (AMI) diagnosed in the Department of Cardiology of Hubei No.3 People's Hospital of Jianghan University from April 2020 to April 2022, who underwent feature-tracking cardiac magnetic resonance (FT-CMR) examination. Based on the electrocardiogram (ECG) findings, patients were divided into ST-elevation myocardial infarction (STEMI) (n=52) and non-STEMI (NSTEMI) (n=48) groups. We compared myocardial strain parameters between the two groups and applied the Pearson's test to reveal any correlations between the left ventricular myocardial strain parameters and the number of late gadolinium enhancement (LGE) positive segments; we assessed the clinical value of FT-CMR for predicting STEMI using a receiver operating characteristic (ROC) curve.The number of LGE-positive segments in the STEMI group was significantly higher than that in the NSTEMI group. The myocardial radial, circumferential and longitudinal strains in the STEMI group were significantly lower than those in the NSTEMI group (p<0.05). The number of LGE-positive segments in patients with AMI negatively correlated with the radial, circumferential and longitudinal strains. The results of the ROC curve analysis showed that radial, circumferential and longitudinal strain values have a diagnostic value for STEMI (p<0.05).FT-CMR, a non-invasive and rapid method for analyzing myocardial strains, has a high diagnostic value for AMI and should be helpful for the prevention and intervention of ventricular remodeling after myocardial infarctions.
Abstract Background: The incidence of ground-glass nodules (GGNs) in epidermal growth factor receptor (EGFR)-mutated lung adenocarcinoma is significantly higher. For multiple GGNs, invasive detection is difficult to achieve. Therefore, there is an urgent need for an ideal non-invasive examination method to predict the EGFR mutation status in GGNs patients. Radiomics-based machine learning (ML) has been applied in various tumors. However, no predictive models based on 18 F-FDG PET/CT radiomics features have been used to identify the EGFR mutation status in lung adenocarcinoma manifesting as GGNs. We amied to explore the predictive value of combining 18 F-FDG PET/CT based radiomics and ML methods in distinguishing the mutation status of EGFR in lung adenocarcinoma manifesting as GGNs. Results: Among the 106 nodules, 81 had EGFR mutations (76.4%). There were no significant differences in general data, morphological characteristics, and PET/CT parameters between the EGFR mutation group and the wild group (P>0.05). Among the four models in the test set, XGBoost showed the best performance (AUC=0.798, 95%CI: 0.627-0.904) and was significantly better than Random Forest (AUC=0.680, 95%CI: 0.509-0.822) (Z=2.122, P=0.034). Conclusion: The combination of 18 F-FDG PET/CT radiomics and machine learning methods is a potential non-invasive method for predicting the EGFR mutation status of GGNs lung adenocarcinoma.
Purpose Thermogenesis of brown adipose tissue (BAT) is controlled by central modulating mechanisms, although changes in brain metabolism of BAT-positive subjects with different genders are still unclear. We hypothesized that changes in regional cerebral glucose metabolic activity were associated with BAT activities, and this association differed in different genders. Methods Brain glucose metabolism of 26 BAT-positive and 26 BAT-negative healthy subjects was compared using a brain fluorodeoxyglucose (FDG)-PET scan, and gender differences in BAT-related brain functional networks and effect of sex hormones were assessed by comparing the brain PET images of BAT-positive and BAT-negative subjects of different genders and postmenopausal female subjects. Results Compared with controls, BAT-positive male subjects had a significant hypermetabolic area in the right extranuclear and significant hypometabolic areas in the right inferior parietal lobule and right inferior frontal gyrus; while at the same threshold, BAT-positive female subjects had richer hypermetabolic regions, including bilateral limbic lobes, bilateral frontal lobes, right cerebellum, left sublobar, and right parietal lobe. However, BAT-positive postmenopause female subjects only showed significant hypometabolic regions in left lingual gyrus. Conclusions BAT-related brain functional networks are different between male and female subjects. Female networks are more significant and more concentrated while male networks are smaller and more dispersed, and these gender differences may be related to sex hormones.
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