Transfer learning-based PET/CT three-dimensional convolutional neural network fusion of image and clinical information for prediction of EGFR mutation in lung adenocarcinoma.

BACKGROUND: To introduce a three-dimensional convolutional neural network (3D CNN) leveraging transfer learning for fusing PET/CT images and clinical data to predict EGFR mutation status in lung adenocarcinoma (LADC). METHODS: Retrospective data from 516 LADC patients, encompassing preoperative PET/CT images, clinical information, and EGFR mutation status, were divided into training (n = 404) and test sets (n = 112). Several deep learning models were developed utilizing transfer learning, involving CT-only and PET-only models. A dual-stream model fusing PET and CT and a three-stream transfer learning model (TS_TL) integrating clinical data were also developed. Image preprocessing includes semi-automatic segmentation, resampling, and image cropping. Considering the impact of class imbalance, the performance of the model was evaluated using ROC curves and AUC values. RESULTS: TS_TL model demonstrated promising performance in predicting the EGFR mutation status, with an AUC of 0.883 (95%CI = 0.849-0.917) in the training set and 0.730 (95%CI = 0.629-0.830) in the independent test set. Particularly in advanced LADC, the model achieved an AUC of 0.871 (95%CI = 0.823-0.919) in the training set and 0.760 (95%CI = 0.638-0.881) in the test set. The model identified distinct activation areas in solid or subsolid lesions associated with wild and mutant types. Additionally, the patterns captured by the model were significantly altered by effective tyrosine kinase inhibitors treatment, leading to notable changes in predicted mutation probabilities. CONCLUSION: PET/CT deep learning model can act as a tool for predicting EGFR mutation in LADC. Additionally, it offers clinicians insights for treatment decisions through evaluations both before and after treatment.

New research progress on 18F-FDG PET/CT radiomics for EGFR mutation prediction in lung adenocarcinoma: a review.

Lung cancer, the most frequently diagnosed cancer worldwide, is the leading cause of cancer-associated deaths. In recent years, significant progress has been achieved in basic and clinical research concerning the epidermal growth factor receptor (EGFR), and the treatment of lung adenocarcinoma has also entered a new era of individualized, targeted therapies. However, the detection of lung adenocarcinoma is usually invasive. 18F-FDG PET/CT can be used as a noninvasive molecular imaging approach, and radiomics can acquire high-throughput data from standard images. These methods play an increasingly prominent role in diagnosing and treating cancers. Herein, we reviewed the progress in applying 18F-FDG PET/CT and radiomics in lung adenocarcinoma clinical research and how these data are analyzed via traditional statistics, machine learning, and deep learning to predict EGFR mutation status, all of which achieved satisfactory results. Traditional statistics extract features effectively, machine learning achieves higher accuracy with complex algorithms, and deep learning obtains significant results through end-to-end methods. Future research should combine these methods to achieve more accurate predictions, providing reliable evidence for the precision treatment of lung adenocarcinoma. At the same time, facing challenges such as data insufficiency and high algorithm complexity, future researchers must continuously explore and optimize to better apply to clinical practice.

18F-FDG PET-CT for the prediction of mortality in patients with dermatomyositis and without malignant tumors: a pilot study.

Background: 18F-fluorodeoxyglucose positron emission tomography-computed tomography (18F-FDG PET-CT) is typically used to screen malignancy in patients with dermatomyositis (DM). The aim of this study was to investigate the value of using PET-CT in assessing the prognosis of patients with DM and without malignant tumors. Methods: A total of 62 patients with DM who underwent 18F-FDG PET-CT were enrolled in the retrospective cohort study. Clinical data and laboratory indicators were obtained. The muscle max standardized uptake value (SUVmax), splenic SUVmax, target-to-background ratio (TBR) of the aorta, pulmonary highest value (hv)/SUVmax, epicardial fat volume (EFV), and coronary artery calcium (CAC) were measured using 18F-FDG PET-CT. The follow-up was conducted until March 2021, and the endpoint was death from any cause. Univariable and multivariable Cox regression analyses were used to analyze prognostic factors. The survival curves were produced with the Kaplan-Meier method. Results: The median duration of follow-up was 36 [interquartile range (IQR), 14-53] months. The survival rates were 85.2% and 73.4% for 1 and 5 years, respectively. A total of 13 (21.0%) patients died during a median follow-up of 7 (IQR, 4-15.5) months. Compared with the survival group, the death group had significantly higher levels of C-reactive protein [CRP; median (IQR), 4.2 (3.0, 6.0) vs. 6.30 (3.7, 22.8)], hypertension [7 (14.3%) vs. 6 (46.2%)], interstitial lung disease [ILD; 26 (53.1%) vs. 12 (92.3%)], positive anti-Ro52 antibody [19 (38.8%) vs. 10 (76.9%)], pulmonary FDG uptake [median (IQR), 1.8 (1.5, 2.9) vs. 3.5 (2.0, 5.8)], CAC [1 (2.0%) vs. 4 (30.8%)], and EFV [median (IQR), 74.1 (44.8, 92.1) vs. 106.5 (75.0, 128.5)] (all P values <0.001). Univariable and multivariable Cox analyses identified high pulmonary FDG uptake [hazard ratio (HR), 7.59; 95% confidence interval (CI), 2.08-27.76; P=0.002] and high EFV (HR, 5.86; 95% CI, 1.77-19.42; P=0.004) as independent risk factors for mortality. The survival rate was significantly lower in patients with the concurrent presence of high pulmonary FDG uptake and high EFV. Conclusions: Pulmonary FDG uptake and EFV detected with PET-CT were independent risk factors for death in patients with DM and without malignant tumors. Patients with the concurrent presence of high pulmonary FDG uptake and high EFV had a worse prognosis compared with patients with 1 or neither of these two risk factors. Early treatment should be applied in patients with concurrent presence of high pulmonary FDG uptake and high EFV to improve the survival rate.

Predicting the negative conversion time of nonsevere COVID-19 patients using machine learning methods.

Based on the patient's clinical characteristics and laboratory indicators, different machine-learning methods were used to develop models for predicting the negative conversion time of nonsevere coronavirus disease 2019 (COVID-19) patients. A retrospective analysis was performed on 376 nonsevere COVID-19 patients admitted to Wuxi Fifth People's Hospital from May 2, 2022, to May 14, 2022. The patients were divided into training set (n = 309) and test set (n = 67). The clinical features and laboratory parameters of the patients were collected. In the training set, the least absolute shrinkage and selection operator (LASSO) was used to select predictive features and train six machine learning models: multiple linear regression (MLR), K-Nearest Neighbors Regression (KNNR), random forest regression (RFR), support vector machine regression (SVR), XGBoost regression (XGBR), and multilayer perceptron regression (MLPR). Seven best predictive features selected by LASSO included: age, gender, vaccination status, IgG, lymphocyte ratio, monocyte ratio, and lymphocyte count. The predictive performance of the models in the test set was MLPR > SVR > MLR > KNNR > XGBR > RFR, and MLPR had the strongest generalization performance, which is significantly better than SVR and MLR. In the MLPR model, vaccination status, IgG, lymphocyte count, and lymphocyte ratio were protective factors for negative conversion time; male gender, age, and monocyte ratio were risk factors. The top three features with the highest weights were vaccination status, gender, and IgG. Machine learning methods (especially MLPR) can effectively predict the negative conversion time of non-severe COVID-19 patients. It can help to rationally allocate limited medical resources and prevent disease transmission, especially during the Omicron pandemic.

Quantitative evaluation of acute myocardial infarction by feature-tracking cardiac magnetic resonance imaging.

Objective: To assess the value of feature-tracking cardiac magnetic resonance (FT-CMR) imaging in the quantitative evaluation of acute myocardial infarction (AMI). Methods: 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. Results: 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). Conclusion: 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.

The predictive value of [18F]FDG PET/CT radiomics combined with clinical features for EGFR mutation status in different clinical staging of lung adenocarcinoma.

BACKGROUND: This study aims to construct radiomics models based on [18F]FDG PET/CT using multiple machine learning methods to predict the EGFR mutation status of lung adenocarcinoma and evaluate whether incorporating clinical parameters can improve the performance of radiomics models. METHODS: A total of 515 patients were retrospectively collected and divided into a training set (n = 404) and an independent testing set (n = 111) according to their examination time. After semi-automatic segmentation of PET/CT images, the radiomics features were extracted, and the best feature sets of CT, PET, and PET/CT modalities were screened out. Nine radiomics models were constructed using logistic regression (LR), random forest (RF), and support vector machine (SVM) methods. According to the performance in the testing set, the best model of the three modalities was kept, and its radiomics score (Rad-score) was calculated. Furthermore, combined with the valuable clinical parameters (gender, smoking history, nodule type, CEA, SCC-Ag), a joint radiomics model was built. RESULTS: Compared with LR and SVM, the RF Rad-score showed the best performance among the three radiomics models of CT, PET, and PET/CT (training and testing sets AUC: 0.688, 0.666, and 0.698 vs. 0.726, 0.678, and 0.704). Among the three joint models, the PET/CT joint model performed the best (training and testing sets AUC: 0.760 vs. 0.730). The further stratified analysis found that CT_RF had the best prediction effect for stage I-II lesions (training set and testing set AUC: 0.791 vs. 0.797), while PET/CT joint model had the best prediction effect for stage III-IV lesions (training and testing sets AUC: 0.722 vs. 0.723). CONCLUSIONS: Combining with clinical parameters can improve the predictive performance of PET/CT radiomics model, especially for patients with advanced lung adenocarcinoma.

Association Analysis of Maximum Standardized Uptake Values Based on 18F-FDG PET/CT and EGFR Mutation Status in Lung Adenocarcinoma.

(1) Background: To investigate the association between maximum standardized uptake value (SUVmax) based on 18F-FDG PET/CT and EGFR mutation status in lung adenocarcinoma. (2) Methods: A total of 366 patients were retrospectively collected and divided into the EGFR mutation group (n = 228) and EGFR wild-type group (n = 138) according to their EGFR mutation status. The two groups' general information and PET/CT imaging parameters were compared. A hierarchical binary logistic regression model was used to assess the interaction effect on the relationship between SUVmax and EGFR mutation in different subgroups. Univariate and multivariate logistic regression was used to analyze the association between SUVmax and EGFR mutation. After adjusting for confounding factors, a generalized additive model and smooth curve fitting were applied to address possible non-linearities. (3) Results: Smoking status significantly affected the relationship between SUVmax and EGFR mutation (p for interaction = 0.012), with an interaction effect. After adjusting for age, gender, nodule type, bronchial sign, and CEA grouping, in the smoking subgroup, curve fitting results showed that the relationship between SUVmax and EGFR mutation was approximately linear (df = 1.000, c2 = 3.897, p = 0.048); with the increase in SUVmax, the probability of EGFR mutation gradually decreased, and the OR value was 0.952 (95%CI: 0.908-0.999; p = 0.045). (4) Conclusions: Smoking status can affect the relationship between SUVmax and EGFR mutation status in lung adenocarcinoma, especially in the positive smoking history subgroup. Fully understanding the effect of smoking status will help to improve the accuracy of SUVmax in predicting EGFR mutations.

Association between squamous cell carcinoma antigen level and EGFR mutation status in Chinese lung adenocarcinoma patients.

BACKGROUND: To investigate the association between squamous cell carcinoma antigen (SCCAg) level and epidermal growth factor receptor (EGFR) mutation status in Chinese lung adenocarcinoma patients. METHODS: We retrospectively analyzed 293 patients with lung adenocarcinoma, divided into EGFR mutant group (n = 178) and EGFR wild-type group (n = 115). The general data and laboratory parameters of the two groups were compared. We used univariable and multivariable logistic regression to analyze the association between SCCAg level and EGFR mutation. Generalized additive model was used for curve fitting, and a hierarchical binary logistic regression model was used for interaction analysis. RESULTS: Squamous cell carcinoma antigen level in the EGFR wild-type group was significantly higher than that in the mutant group (p < 0.001). After adjusting for confounding factors, we found that elevated SCCAg was associated with a lower probability of EGFR mutation, with an OR of 0.717 (95% CI: 0.543-0.947, p = 0.019). For the tripartite SCCAg groups, the increasing trend of SCCAg was significantly associated with the decreasing probability of EGFR mutation (p for trend = 0.015), especially for Tertile 3 versus Tertile 1 (OR = 0.505; 95% CI: 0.258-0.986; p = 0.045). Curve fitting showed that there was an approximate linear negative relationship between continuous SCCAg and EGFR mutation probability (p = 0.020), which was first flattened and then decreased (p < 0.001). The association between the two was consistent among different subgroups, suggesting no interaction (all p > 0.05). CONCLUSION: There is a negative association between SCCAg level and EGFR mutation probability in Chinese lung adenocarcinoma patients.

Risk analysis in peripheral clinical T1 non-small cell lung cancer correlations between tumor-to-blood standardized uptake ratio on 18F-FDG PET-CT and primary tumor pathological invasiveness: a real-world observational study.

BACKGROUND: Sublobar resection is not suitable for patients with pathological invasiveness [including lymph node metastasis (LNM), visceral pleural invasion (VPI), and lymphovascular invasion (LVI)] of peripheral clinical T1 (cT1) non-small cell lung cancer (NSCLC), while primary tumor maximum standardized uptake value (SUVmax) on 18F-FDG PET-CT is related to pathological invasiveness, the significance differed among different institutions is still challenging. This study explored the relationship between the tumor-to-blood standardized uptake ratio (SUR) of 18F-FDG PET-CT and primary tumor pathological invasiveness in peripheral cT1 NSCLC patients. METHODS: This retrospective study included 174 patients with suspected lung neoplasms who underwent preoperative 18F-FDG PET-CT. We compared the differences of the clinicopathological variables, metabolic and morphological parameters in the pathological invasiveness and less-invasiveness group. We performed a trend test for these parameters based on the tertiles of SUR. The relationship between SUR and pathological invasiveness was evaluated by univariate and multivariate logistics regression models (included unadjusted, simple adjusted, and fully adjusted models), odds ratios (ORs), and 95% confidence intervals (95% CIs) were calculated. A smooth fitting curve between SUR and pathological invasiveness was produced by the generalized additive model (GAM). RESULTS: Thirty-eight point five percent of patients had pathological invasiveness and tended to have a higher SUR value than the less-invasiveness group [6.50 (4.82-11.16) vs. 4.12 (2.04-6.61), P<0.001]. The trend of SUVmax, mean standardized uptake value (SUVmean), metabolic tumor volume (MTV), total lesion glycolysis (TLG), mean CT value (CTmean), size of the primary tumor, neuron-specific enolase (NSE), the incidence of LNM, adenocarcinoma (AC), and poor differentiation in the tertiles of SUR value were statistically significant (P were <0.001, <0.001, 0.010, <0.001, <0.001, 0.002, 0.033, <0.001, 0.002, and <0.001, respectively). Univariate analysis showed that the risk of pathological invasiveness increased significantly with increasing SUR [OR: 1.13 (95% CI: 1.06-1.21), P<0.001], and multivariate analysis demonstrated SUR, as a continuous variable, was still significantly related to pathological invasiveness [OR: 1.09 (95% CI: 1.01-1.18), P=0.032] after adjusting for confounding covariates. GAM revealed that SUR tended to be linearly and positively associated with pathological invasiveness and E-value analysis suggested robustness to unmeasured confounding. CONCLUSIONS: SUR is linearly and positively associated with primary tumor pathological invasiveness independent of confounding covariates in peripheral cT1 NSCLC patients and could be used as a supplementary risk maker to assess the risk of pathological invasiveness.

A negative association between low-density lipoprotein cholesterol level and infection risk in elderly stage 5 chronic kidney disease patients.

BACKGROUND: To explore the relationship between low-density lipoprotein cholesterol (LDL-C) level and infection risk in elderly stage 5 kidney disease (CKD) patients. METHODS: This study retrospectively analyzed all 378 patients with grade 5 CKD over 60 years old treated in the Nephrology Department of our hospital from February 2014 to July 2019, including 286 cases with infection and 92 cases without. According to LDL-C levels, the patients were divided into three groups (Tertile 1-Tertile 3). Basic patient data and laboratory test results were collected for all three groups for analysis. RESULTS: The incidence of infection showed a gradually decreasing trend in the three groups (from 80.2, 78.6 to 68.3%), along with increasing LDL-C levels from Tertile 1 to Tertile 3, although the differences were not statistically significant (p = 0.075). After fully adjusting for confounding factors, the risk of infection was significantly reduced (OR = 0.646, 95% CI 0.420-0.993, p = 0.046) with increasing LDL-C levels. For the LDL-C levels of the three groups, the rising trend of LDL-C was significantly associated with the reduction in infection risk (OR = 0.545, 95% CI 0.317-0.937, p = 0.028). Curve fitting revealed that LDL-C levels were linearly negatively associated with the risk of infection, and the relationship between the two was not affected by the other factors (p for interaction: 0.567-1.000). CONCLUSIONS: LDL-C level is linearly negatively associated with the risk of infection in elderly patients with stage 5 CKD.

The relationship between brain glucose metabolism and insulin resistance in subjects with normal cognition - a study based on 18F-FDG PET.

OBJECTIVE: Insulin resistance can increase the risk of cognitive dysfunction and dementia. Our purpose is to use 18F-FDG PET imaging to explore the effect of insulin resistance on brain glucose metabolism in cognitively normal subjects. METHODS: A total of 189 cognitively normal subjects who underwent PET examinations were enrolled. The homeostasis model assessment of insulin resistance (HOMA-IR) was used to evaluate the presence of insulin resistance. Multivariate linear regression and generalized additive models were used to analyze the association between HOMA-IR and glucose metabolism in the whole brain and evaluate the effects of various covariates. The SPM12 software was used to evaluate the regional effect of insulin resistance on brain glucose metabolism. RESULTS: After being fully adjusted for confounding factors, HOMA-IR showed an approximately linear negative correlation with brain glucose metabolism (ß = -0.219, T = -3.331, P = 0.021). Compared with normal subjects, insulin-resistant subjects had reduced glucose metabolism in bilateral middle temporal gyrus, bilateral middle frontal gyrus, right precentral gyrus, right inferior frontal gyrus, right cuneiform lobe and bilateral cerebellar regions. In cognitively normal subjects, systemic insulin resistance has a significant effect on brain glucose metabolism. CONCLUSIONS: 18F-FDG brain PET imaging could be helpful for the early diagnosis and treatment of changes in brain glucose metabolism caused by insulin resistance.

Application of dual-stream 3D convolutional neural network based on 18F-FDG PET/CT in distinguishing benign and invasive adenocarcinoma in ground-glass lung nodules.

PURPOSE: This work aims to train, validate, and test a dual-stream three-dimensional convolutional neural network (3D-CNN) based on fluorine 18 (18F)-fluorodeoxyglucose (FDG) PET/CT to distinguish benign lesions and invasive adenocarcinoma (IAC) in ground-glass nodules (GGNs). METHODS: We retrospectively analyzed patients with suspicious GGNs who underwent 18F-FDG PET/CT in our hospital from November 2011 to November 2020. The patients with benign lesions or IAC were selected for this study. According to the ratio of 7:3, the data were randomly divided into training data and testing data. Partial image feature extraction software was used to segment PET and CT images, and the training data after using the data augmentation were used for the training and validation (fivefold cross-validation) of the three CNNs (PET, CT, and PET/CT networks). RESULTS: A total of 23 benign nodules and 92 IAC nodules from 106 patients were included in this study. In the training set, the performance of PET network (accuracy, sensitivity, and specificity of 0.92 ± 0.02, 0.97 ± 0.03, and 0.76 ± 0.15) was better than the CT network (accuracy, sensitivity, and specificity of 0.84 ± 0.03, 0.90 ± 0.07, and 0.62 ± 0.16) (especially accuracy was significant, P-value was 0.001); in the testing set, the performance of both networks declined. However, the accuracy and sensitivity of PET network were still higher than that of CT network (0.76 vs. 0.67; 0.85 vs. 0.70). For dual-stream PET/CT network, its performance was almost the same as PET network in the training set (P-value was 0.372-1.000), while in the testing set, although its performance decreased, the accuracy and sensitivity (0.85 and 0.96) were still higher than both CT and PET networks. Moreover, the accuracy of PET/CT network was higher than two nuclear medicine physicians [physician 1 (3-year experience): 0.70 and physician 2 (10-year experience): 0.73]. CONCLUSION: The 3D-CNN based on 18F-FDG PET/CT can be used to distinguish benign lesions and IAC in GGNs, and the performance is better when both CT and PET images are used together.

Association Analysis of Insulin Resistance and Osteoporosis Risk in Chinese Patients with T2DM.

BACKGROUND: To explore the relationship between insulin resistance and osteoporosis risk in Chinese patients with type 2 diabetes mellitus (T2DM). METHODS: A total of 234 T2DM patients were retrospectively analyzed, and their lumbar bone mineral density (BMD) and insulin resistance using C-peptide-based homeostasis model of insulin resistance [HOMA-IR (CP)] were assessed. Univariate and multivariable logistic regression methods were used to evaluate the association between HOMA-IR (CP) and osteoporosis, and subgroup analysis was performed on female and male patients. RESULTS: After fully adjusting the covariates, the association between HOMA-IR (CP) and osteoporosis was only significant in female patients (P = 0.022); the interaction effect with gender was significant (P for interaction <0.05). Curve fitting showed that the relationship between HOMA-IR (CP) and osteoporosis in women was nonlinear. When HOMA-IR (CP) is <4.00, its effect on osteoporosis was not significant (P = 0.474); when HOMA-IR (CP) is >4.00, the risk of osteoporosis increased significantly, with OR = 26.88 (95% CI: 2.75-262.69, P = 0.005). The relationship between insulin resistance and osteoporosis risk in T2DM patients is significantly affected by gender. CONCLUSION: The higher the degree of insulin resistance in female patients, the greater the risk of osteoporosis, but the two are not linearly associated.

Investigating the association between ground-glass nodules glucose metabolism and the invasive growth pattern of early lung adenocarcinoma.

BACKGROUND: To explore the association between the glucose metabolism level of lung ground-glass nodules (GGNs), as revealed by 18F-flurodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) imaging, and the invasive pathological growth pattern of early lung adenocarcinoma. METHODS: We retrospectively analyzed patients who underwent PET/CT examination and surgical resection due to persistent GGNs, which were confirmed to be early lung adenocarcinoma by postoperative pathology examination. After adjusting for confounding factors and performing stratified analysis, we explored the association between the maximum standard uptake value of PET (SUVmax) and the invasive pathological growth pattern of early stage lung adenocarcinoma. RESULTS: The proportions of invasive adenocarcinoma (INV) in the SUVmax of Tertile 1, Tertile 2, and Tertile 3 were 52.7%, 73.3%, and 87.1%, respectively. After adjusting for potential confounding factors, the risk of INV gradually increased as the GGN SUVmax increased [odds ratio (OR): 1.520, 95% confidence interval (CI): 1.044-2.213, P=0.029]. This trend was statistically significant (OR: 1.678, 95% CI: 1.064-2.647, P=0.026), especially in Tertile 3 vs. Tertile 1 (OR: 4.879, 95% CI: 1.349-17.648, P=0.016). Curve fitting showed that the SUVmax and INV risk were linearly and positively associated. The association was consistent in different subgroups based on GGN number, type, shape, edge, bronchial sign, vacuole sign, pleural depression sign, diameters, and consolidation-to-tumor ratio, suggesting that there was no significant interaction between different grouping parameters and the association (P for interaction range = 0.129-0.909). CONCLUSIONS: In FDG PET, the glucose metabolism level (SUVmax) of lung GGNs is independently associated with INV risk, and this association is linear and positive.

Preoperative 18F-FDG SUVmax >6.3 or Size >2.3 cm of primary lesions predict lymph nodes metastasis with higher negative predictive value in peripheral cT1 non-small-cell lung cancer.

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 SUVmax ≥2.5) for predicting lymph nodes metastasis (LNM) has unsatisfactory performance. OBJECTIVE: We explore the clinical role of preoperative SUVmax and the size of the primary lesions for predicting peripheral cT1 NSCLC LNM. METHODS: We retrospectively analyzed 174 peripheral cT1 NSCLC patients underwent preoperative 18F-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 SUVmax >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 SUVmax >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 SUVmax >6.3 or size >2.3 cm of the primary lesions based on 18F-FDG PET-CT might identify the patients at low-risk LNM in peripheral cT1 NSCLC.

[The value of circulating microRNA-1 in the early diagnosis of coronary atherosclerotic plaque rupture in patients with stable coronary heart disease].

OBJECTIVE: To evaluate the diagnostic value of circulating microRNA-1 (miR-1) in early coronary artery plaque rupture in patients with stable coronary artery disease (SCAD). METHODS: A prospective cohort study was conducted. Sixty-seven patients with SCAD admitted to the department of cardiology of the Third Affiliated Hospital of Soochow University from January to June in 2019 were enrolled. All patients had completed coronary angiography (CAG), percutaneous coronary intervention (PCI) single stent implantation or only CAG was performed according to the CAG results. Blood samples were collected before (0 hour) and 3 hours after the procedure. The expression of plasma miR-1 was detected by real-time quantitative reverse transcription-polymerase chain reaction (RT-PCR), and electrocardiogram was used to detect cardiac troponin I (cTnI) levels. The difference of miR-1 and cTnI levels in PCI or CAG patients before and after procedure were compared, and the value for early diagnosis of coronary artery plaque rupture in SCAD patients was evaluated. The diagnostic efficacy was evaluated by the receiver operating characteristic curve (ROC curve). RESULTS: There were 38 CAG patients and 29 PCI patients. There were no significant differences in gender, age, previous history (without hypertension history) and baseline data of cardiac function between the two groups. The expression of miR-1 after PCI was significantly higher than that before PCI [2-ΔΔCt: 2.11 (1.56, 2.73) vs. 1.26 (1.07, 1.92), P < 0.01], and there was no significant difference in cTnI level before and after PCI [µg/L: 0.00 (0.00, 0.02) vs. 0.00 (0.00, 0.02), P > 0.05]. There were no significant differences in miR-1 and cTnI levels before and after procedure in the CAG group [miR-1 (2-ΔΔCt): 1.09 (1.00, 1.40) vs. 1.21 (1.00, 1.71), cTnI (µg/L): 0.00 (0.00, 0.02) vs. 0.00 (0.00, 0.02), both P > 0.05]. ROC curve analysis showed that the area under ROC curve (AUC) and 95% confidence interval (95%CI) of miR-1 in the diagnosis of coronary plaque rupture were 0.794 (0.687-0.900), P < 0.01, the sensitivity was 82.8%, the specificity was 68.4%, and the optimal cut-off value was 1.51. The AUC and 95%CI of the difference of miR-1 before and after operation (ΔmiR-1) were 0.704 (0.567-0.842), P = 0.004, the sensitivity was 62.1%, the specificity was 84.2%, and the optimal cut-off value was 0.39. The efficancy of miR-1 and ΔmiR-1 after procedure to diagnose coronary plaque rupture in patients with SCAD was similar (Z = 1.287, P = 0.198). However, baseline miR-1 might not predict whether patients with SCAD need PCI or not (AUC = 0.630, P > 0.05). Multivariate binary Logistic regression analysis showed that increased postoperative miR-1 expression was an independent risk factor for coronary plaque rupture in SCAD patients [odds ratios (OR) = 2.887, 95%CI was 1.044-7.978, P = 0.041]. CONCLUSIONS: Circulating miR-1 might have the value for early diagnosis of coronary artery plaque rupture in SCAD patients.

Establishment and verification of a prediction model based on clinical characteristics and positron emission tomography/computed tomography (PET/CT) parameters for distinguishing malignant from benign ground-glass nodules.

BACKGROUND: To develop and verify a prediction model for distinguishing malignant from benign ground-glass nodules (GGNs) combined with clinical characteristics and 18F-fluorodeoxyglucose (FDG) positron emission tomography-computed tomography (PET/CT) parameters. METHODS: We retrospectively analyzed 170 patients (56 males and 114 females) with GGNs who underwent PET/CT and high-resolution CT examination in our hospital from November 2011 to December 2019. The clinical and imaging data of all patients were collected, and the nodules were randomly divided into a derivation set and a validation set. For the derivation set, we used multivariate logistic regression to develop a prediction model for distinguishing benign from malignant GGNs. A receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficacy of the model, and the data in the validation set were used to verify the prediction model. RESULTS: Among the 170 patients, 197 GGNs were confirmed via postoperative pathological examination or clinical follow-up. There were 21 patients with 27 GGNs in the benign group and 149 patients with 170 GGNs in the adenocarcinoma group. A total of five parameters, including the patient's sex, nodule location, margin, pleural indentation, and standardized uptake value (SUV) index (the ratio of nodule SUVmax to liver SUVmean), were selected to develop a prediction model for distinguishing benign from malignant GGNs. The area under the curve (AUC) of the model was 0.875 in the derivation set, with a sensitivity of 0.702 and a specificity of 0.923. The positive likelihood ratio was 9.131, and the negative likelihood ratio was 0.322. In the validation set, the AUC of the model was 0.874, which was not significantly different from the derivation set (P=0.989). CONCLUSIONS: This study developed and validated a prediction model based on 18F-FDG PET/CT imaging and clinical characteristics for distinguishing malignant from benign GGNs. The model showed good diagnostic efficacy and high specificity, which can improve the preoperative diagnosis of high-risk GGNs.

Association Between 18F-FDG PET/CT-Based SUV Index and Malignant Status of Persistent Ground-Glass Nodules.

To explore the association between 18F-FDG PET/CT-based SUV index and malignant risk of persistent ground-glass nodules (GGNs). We retrospectively analyzed a total of 166 patients with GGN who underwent PET/CT examination from January 2012 to October 2019. There were 113 women and 53 men, with an average age of 60.8 ± 9.1 years old. A total of 192 GGNs were resected and confirmed by pathology, including 22 in benign group and 170 in adenocarcinoma group. They were divided into three groups according to SUV index tertiles: Tertile 1 (0.14-0.54), Tertile 2 (0.55-1.17), and Tertile 3 (1.19-6.78), with 64 GGNs in each group. The clinical and imaging data of all patients were collected and analyzed. After adjusting for the potential confounding factors, we found that the malignancy risk of GGN significantly decreased as the SUV index increased (OR, 0.245; 95%CI, 0.119-0.504; P <0.001), the average probability of malignant GGN was 89.1% (95% CI, 53.1-98.3%), 80.5% (95% CI, 36.7-96.7%), and 34.3% (95%CI, 9.5-72.2%) for Tertile 1 to Tertile 3. And the increasing trend of SUV index was significantly correlated with the reduction of malignant risk (OR, 0.099; 95%CI, 0.025-0.394; P = 0.001), especially between Tertile 3 versus Tertile 1 (OR, 0.064; 95%CI, 0.012-0.356; P = 0.002). Curve fitting showed that the SUV index was linearly and negatively correlated with the malignant risk of GGN. SUV index is an independent correlation factor for malignancy risk of GGN, the higher the SUV index, the lower the probability of GGN malignancy.

FGD5­AS1 promotes cisplatin resistance of human lung adenocarcinoma cell via the miR­142­5p/PD­L1 axis.

Previous studies have reported that long non­coding (lnc) RNA FGD5­antisense 1 (FGD5­AS1) promotes tumor proliferation, migration and invasion. Therefore, the present study aimed to elucidate the biological role and underlying molecular mechanisms of FGD5­AS1 in cisplatin (DDP) resistance of lung adenocarcinoma (LAD) cells. The results demonstrated that FGD5­AS1 was highly expressed in DDP­resistant LAD tissues and cells. Knockdown of FGD5­AS1 decreased the proliferative, migratory and invasive abilities of DDP­resistant LAD cells. Moreover, it was identified that FGD5­AS1 acted as a molecular sponge for microRNA (miR)­142, and FGD5­AS1 enhanced the resistance of A549/DDP cells to DDP by directly interacting with miR­142. Programmed cell death 1 ligand 1 (PD­L1) was also found to be a key effector of the FGD5­AS1/miR­142 axis to regulate the chemoresistance of DDP­resistant LAD cells. In conclusion, the present study demonstrated that FGD5­AS1 increased DDP resistance of LAD via the miR­142/PD­L1 axis, which may offer a novel treatment strategy for patients with DDP­resistant LAD.