Single-Cell RNA-Sequencing Reveals the Active Involvement of Macrophage Polarizations in Pulmonary Hypertension.

Background: Sustained hypoxia can trigger a progressive rise in pulmonary artery pressure and cause serious pulmonary diseases. Macrophages play important roles along the progression of pulmonary hypertension. However, the state of macrophage polarization during the early stage of pulmonary hypertension is unclear. Methods: Unlike traditional sequencing method, single-cell sequencing can accurately distinguish among cell types and better understand cell-to-cell relationships. In this study, we investigated the polarization of macrophages in pulmonary hypertension via single-cell RNA-sequencing in a mice hypoxia model, which was then validated in patients with pulmonary hypertension. Results: We identified that the intermittent exposure to hypoxic conditions could lead to the production of more M2-type macrophages than M1-type macrophages in a mouse model. Further validation analysis was performed by analyzing lung tissue of patients with pulmonary hypertension, revealing that the number of disease-associated M2 macrophages was substantially increased. Conclusions: In this study, the active anti-inflammatory response of macrophage involved in pulmonary hypertension has been identified, suggesting that intervention against the polarization of macrophages to the M2 type may be a potential way to reduce chronic pulmonary inflammation, pulmonary vascular remodeling, and artery pressure. Thus, investigation of macrophage polarization associated with hypoxia could help us better understand disease mechanism and craft effective prevention strategies and approaches.

Deep Learning Analysis Using 18F-FDG PET/CT to Predict Occult Lymph Node Metastasis in Patients With Clinical N0 Lung Adenocarcinoma.

Introduction: The aim of this work was to determine the feasibility of using a deep learning approach to predict occult lymph node metastasis (OLM) based on preoperative FDG-PET/CT images in patients with clinical node-negative (cN0) lung adenocarcinoma. Materials and Methods: Dataset 1 (for training and internal validation) included 376 consecutive patients with cN0 lung adenocarcinoma from our hospital between May 2012 and May 2021. Dataset 2 (for prospective test) used 58 consecutive patients with cN0 lung adenocarcinoma from June 2021 to February 2022 at the same center. Three deep learning models: PET alone, CT alone, and combined model, were developed for the prediction of OLM. The performance of the models was evaluated on internal validation and prospective test in terms of accuracy, sensitivity, specificity, and areas under the receiver operating characteristic curve (AUCs). Results: The combined model incorporating PET and CT showed the best performance, achieved an AUC of 0.81 [95% confidence interval (CI): 0.61, 1.00] in the prediction of OLM in internal validation set (n = 60) and an AUC of 0.87 (95% CI: 0.75, 0.99) in the prospective test set (n = 58). The model achieved 87.50% sensitivity, 80.00% specificity, and 81.00% accuracy in the internal validation set and achieved 75.00% sensitivity, 88.46% specificity, and 86.60% accuracy in the prospective test set. Conclusion: This study presented a deep learning approach to enable the prediction of occult nodal involvement based on the PET/CT images before surgery in cN0 lung adenocarcinoma, which would help clinicians select patients who would be suitable for sublobar resection.

A Preliminary Study on the Relationship Between High-Resolution Computed Tomography and Pulmonary Function in People at Risk of Developing Chronic Obstructive Pulmonary Disease.

Objectives: Patients with chronic obstructive pulmonary disease (COPD) have high morbidity and mortality, the opportunity to carry out a thoracic high-resolution CT (HRCT) scan may increase the possibility to identify the group at risk of disease. The aim of our study was to explore the differences in HRCT emphysema parameters, air trapping parameters, and lung density parameters between high and low-risk patients of COPD and evaluate their correlation with pulmonary function parameters. Methods: In this retrospective, single-center cohort study, we enrolled outpatients from the Physical Examination Center and Respiratory Medicine of The First Affiliated Hospital of Wenzhou Medical University. The patients who were ≥ 40 years-old, had chronic cough or sputum production, and/or had exposure to risk factors for the disease and had not reached the diagnostic criteria is considered people at risk of COPD. They were divided into low-risk group and high-risk group according to FEV1/FVC ≥ 80% and 80%>FEV1/FVC ≥ 70%. Data on clinical characteristics, clinical symptom score, pulmonary function, and HRCT were recorded. Results: 72 COPD high-risk patients and 86 COPD low-risk patients were enrolled in the study, and the air trapping index of left, right, and bilateral lungs of the high-risk group were higher than those of the low-risk group. However, the result of mean expiratory lung density was opposite. The emphysema index of left, right, and bilateral lungs were negatively correlated with FEV1/FVC (correlation coefficients were -0.33, -0.22, -0.26). Consistently, the air trapping index of left and right lungs and bilateral lungs were negatively correlated with FEV1/FVC (correlation coefficients were -0.33, -0.23, -0.28). Additionally, the mean expiratory lung density of left and right lungs and bilateral lungs were positively correlated with FEV1/FVC (correlation coefficients were 0.31, 0.25, 0.29). Conclusion: The emphysema index, air trapping index and the mean expiratory lung density shows significantly positive correlation with FEV1/FVC which can be used to assess the pulmonary function status of people at risk of COPD and provide a useful supplement for the early and comprehensive assessment of the disease.

Development and Validation of a 18F-FDG PET-Based Radiomic Model for Evaluating Hypermetabolic Mediastinal-Hilar Lymph Nodes in Non-Small-Cell Lung Cancer.

BACKGROUND: Accurate evaluation of lymph node (LN) status is critical for determining the treatment options in patients with non-small cell lung cancer (NSCLC). This study aimed to develop and validate a 18F-FDG PET-based radiomic model for the identification of metastatic LNs from the hypermetabolic mediastinal-hilar LNs in NSCLC. METHODS: We retrospectively reviewed 259 patients with hypermetabolic LNs who underwent pretreatment 18F-FDG PET/CT and were pathologically confirmed as NSCLC from two centers. Two hundred twenty-eight LNs were allocated to a training cohort (LN = 159) and an internal validation cohort (LN = 69) from one center (7:3 ratio), and 60 LNs were enrolled to an external validation cohort from the other. Radiomic features were extracted from LNs of PET images. A PET radiomics signature was constructed by multivariable logistic regression after using the least absolute shrinkage and selection operator (LASSO) method with 10-fold cross-validation. The PET radiomics signature (model 1) and independent predictors from CT image features and clinical data (model 2) were incorporated into a combined model (model 3). A nomogram was plotted for the complex model, and the performance of the nomogram was assessed by its discrimination, calibration, and clinical usefulness. RESULTS: The area under the curve (AUC) values of model 1 were 0.820, 0.785, and 0.808 in the training, internal, and external validation cohorts, respectively, showing good diagnostic efficacy for lymph node metastasis (LNM). Furthermore, model 2 was able to discriminate metastatic LNs in the training (AUC 0.780), internal (AUC 0.794), and external validation cohorts (AUC 0.802), respectively. Model 3 showed optimal diagnostic performance among the three cohorts, with an AUC of 0.874, 0.845, and 0.841, respectively. The nomogram based on the model 3 showed good discrimination and calibration. CONCLUSIONS: Our study revealed that PET radiomics signature, especially when integrated with CT imaging features, showed the ability to identify true and false positives of mediastinal-hilar LNM detected by PET/CT in patients with NSCLC, which would help clinicians to make individual treatment decisions.

18F-FDG PET-based radiomics model for predicting occult lymph node metastasis in clinical N0 solid lung adenocarcinoma.

BACKGROUND: This study aimed to develop a preoperative positron emission tomography (PET)-based radiomics model for predicting occult lymph node metastasis (OLM) in clinical N0 (cN0) solid lung adenocarcinoma. METHODS: The preoperative fluorine-18-fludeoxyglucose (18F-FDG) PET images of 370 patients with cN0 lung adenocarcinoma confirmed by histopathological examination were retrospectively reviewed. Patients were divided into training and validation sets. Radiomics features and relevant data were extracted from PET images. A nomogram was developed in a training set via univariate and multivariate logistic analyses, and its performance was assessed by concordance-index (C-index), calibration curves, and decision curve analysis (DCA) in the training and validation sets. RESULTS: The multivariate logistic regression analysis showed that only carcinoembryonic antigen (CEA), metabolic tumor volume (MTV), and the radiomics signature had statistically significant differences between patients with and without OLM (P<0.05). A nomogram was developed based on the logistic analyses, and its C-index was 0.769 in the training set and 0.768 in the validation set. The calibration curve demonstrated good consistency between the nomogram-predicted probability of OLM and the actual rate. The DCA also confirmed the clinical utility of the nomogram. CONCLUSIONS: A PET/computed tomography (CT)-based radiomics model including CEA, MTV, and the radiomics signature was developed and demonstrated adequate predictive accuracy and clinical net benefit in the present study, and was conveniently used to facilitate the individualized preoperative prediction of OLM.

Prediction of lymph node metastasis by PET/CT metabolic parameters in patients with esophageal squamous cell carcinoma.

OBJECTIVE: The aim of this study was to investigate the capability of F-FDG PET/computed tomography (CT)-related metabolic parameters to predict lymph node metastasis (LNM) and occult lymph node metastasis (OLNM) in patients with esophageal squamous cell carcinoma (ESCC). METHODS: Totally 84 patients undergoing curative esophagectomy with lymph node dissection were enrolled in this study. Metabolic tumor volume (MTV) was measured using threshold-based methods with a threshold of 40% maximum standardized uptake value (SUVmax). The derivative of the volume (V)-threshold (T) function (volume difference/threshold difference) was defined as the heterogeneity factor (HF). In addition, SUVmax, SUVmean, total lesion glycolysis (TLG), maximum tumor-to-blood SUV ratio (SURmax), SURmean and several clinicopathologic parameters were analyzed to identify risk factors of LNM and OLNM. RESULTS: SUVmax, SUVmean, MTV, TLG, SURmax, SURmean and HF were significantly different between LNM (+) and LNM (-). The optimal cut-off values of those parameters were 12.5, 8.34, 15.01, 117.185, 7.885, 4.855 and 0.300, respectively. Logistic regression analysis showed that MTV (OR = 1.127, P = 0.04) and SURmax (OR = 1.446, P = 0.004) were independent predictors of LNM, with sensitivity and specificity were 51.2%, 83.7% vs. 53.7%, 79.1%. In univariate and multivariate analysis, MTV was the sole parameter associated with OLMN (P = 0.024). CONCLUSION: MTV and SURmax were statistically significant predictors of LNM in patients with ESCC, while MTV was a predictor of OLNM. High SURmax and MTV may indicate that the treatment planning should be tailored, which may improve patient prognosis.

Prediction of occult lymph node metastasis using SUV, volumetric parameters and intratumoral heterogeneity of the primary tumor in T1-2N0M0 lung cancer patients staged by PET/CT.

OBJECTIVE: The aim of this study was to identify whether PET/CT-related metabolic parameters of the primary tumor could predict occult lymph node metastasis (OLM) in patients with T1-2N0M0 NSCLC staged by 18F-FDG PET/CT. METHODS: 215 patients with clinical T1-2N0M0 (cT1-2N0M0) NSCLC who underwent both preoperative FDG PET/CT and surgical resection with the systematic lymph node dissection were included in the retrospective study. Heterogeneity factor (HF) was obtained by finding the derivative of the volume-threshold function from 40 to 80% of the maximum standardized uptake value (SUVmax). Univariate and multivariate stepwise logistic regression analyses were used to identify these PET parameters and clinicopathological variables associated with OLM. RESULTS: Statistically significant differences were detected in sex, tumor site, SUVmax, mean SUV (SUVmean), metabolic tumor volume (MTV), total lesion glycolysis and HF between patients with adenocarcinoma (ADC) and squamous cell carcinoma (SQCC). OLM was detected in 36 (16.7%) of 215 patients (ADC, 27/152 = 17.8% vs. SQCC, 9/63 = 14.3%). In multivariate analysis, MTV (OR = 1.671, P = 0.044) in ADC and HF (OR = 8.799, P = 0.023) in SQCC were potent associated factors for the prediction of OLM. The optimal cutoff values of 5.12 cm3 for MTV in ADC, and 0.198 for HF in SQCC were determined using receiver operating characteristic curve analysis. CONCLUSIONS: In conclusion, MTV was an independent predictor of OLM in cT1-2N0M0 ADC patients, while HF might be the most powerful predictor for OLM in SQCC. These findings would be helpful in selecting patients who might be considered as candidates for sublobar resection or new stereotactic ablative radiotherapy.

Prediction of Occult Lymph Node Metastasis Using Tumor-to-Blood Standardized Uptake Ratio and Metabolic Parameters in Clinical N0 Lung Adenocarcinoma.

PURPOSE: We aimed to investigate whether the tumor-to-blood SUV ratio (SUR) and metabolic parameters of F-FDG uptake could predict occult lymph node metastasis (OLM) in clinically node-negative (cN0) lung adenocarcinoma. MATERIALS AND METHODS: We retrospectively reviewed 157 patients with cN0 lung adenocarcinoma who underwent both preoperative F-FDG PET/CT and surgical resection with the systematic lymph node dissection. The SUVmax, SUVmean, MTV, and total lesion glycolysis (TLG) of the primary tumor was measured on the PET/CT workstation. SURmax, SURmean, and TLGsur were derived from each of them divided by descending aorta SUVmean. These PET parameters and clinicopathological variables were analyzed for OLM. RESULTS: In our study, OLM was detected in 31 (19.7%) of 157 patients. Significantly higher values of tumor size, SUVmax, SUVmean, MTV, TLGsuv, SURmax, SURmean, and TLGsur were found in patients with OLM. In receiver operating characteristic curve analysis, the optimal cutoff values of the above parameters were 29.50, 4.38, 2.45, 6.37, 44.13, 5.30, 1.86, and 28.24, respectively. The multivariate analysis showed that TLGsur (odds ratio, 1.024; P = 0.002) was the most potent associated factor for the prediction of OLM in cN0 lung adenocarcinoma. CONCLUSIONS: TLGsur showed the most powerful predictive performance than the other PET parameters for the prediction of OLM in cN0 lung adenocarcinoma. This normalized volumetric parameter would be helpful in selection of sublobar resection or aggressive tailored treatments in patients with cN0 lung adenocarcinoma.