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Background To explore if the quantitative perfusion histogram parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) correlates with the expression of PTEN, P-Akt and m-TOR protein in lung cancer. Methods Thirty‐three patients with 33 lesions who had been diagnosed with lung cancer were enrolled in this study. They were divided into three groups: squamous cell carcinoma (SCC, 15 cases), adenocarcinoma (AC, 12 cases) and small cell lung cancer (SCLC, 6 cases). Preoperative imaging (conventional imaging and DCE-MRI) was performed on all patients. The Exchange model was used to measure the phar- macokinetic parameters, including Ktrans, Vp, Kep, Ve and Fp, and then the histogram parameters meanvalue, skewness, kurtosis, uniformity, energy, entropy, quantile of above five parameters were analyzed. The expression of PTEN, P-Akt and m-TOR were assessed by immunohistochemistry. Spearman correlation analysis was used to compare the correlation between the quantitative perfusion histogram parameters and the expression of PTEN, P-Akt and m-TOR in different pathological subtypes of lung cancer. Results The expression of m-TOR (P = 0.013) and P-Akt (P = 0.002) in AC was significantly higher than those in SCC. Vp (uniformity) in SCC group, Ktrans (uniformity), Ve (kurtosis, Q10, Q25) in AC group, Fp (skewness, kurtosis, energy), Ve (Q75, Q90, Q95) in SCLC group was positively correlated with PTEN, and Fp (entropy) in the SCLC group was negatively correlated with PTEN (P ep (Q5, Q10) in the SCLC group was positively correlated with P-Akt, and Kep (energy) in the SCLC group was negatively correlated with P-Akt (P ep (Q5) in SCC group and Vp (meanvalue, Q75, Q90, Q95) in SCLC group was positively correlated with m-TOR, and Ve (meanvalue) in SCC group was negatively correlated with m-TOR (P Conclusions The quantitative perfusion histogram parameters of DCE-MRI was correlated with the expression of PTEN, P-Akt and m-TOR in different pathological types of lung cancer, which may be used to indirectly evaluate the activation status of PI3K/Akt/mTOR signal pathway gene in lung cancer, and provide important reference for clinical treatment.

Background To investigate value of the quantitative perfusion parameters of dynamic triple-phase enhanced CT in differential diagnosis of pulmonary lesions, and explore the correlation between perfusion parameters of lung cancer with microvessel density (MVD) and vascular endothelial growth factor (VEGF). Methods 73 consecutive patients with lung lesions who successfully underwent pre-operative CT perfusion examination with dynamic triple-phase enhanced CT and received a final diagnosis by postoperative pathology or a clinical follow-up. The cases were divided into malignant and benign groups according to the pathological results. CT perfusion parameters, such as Median, Mean, Standard deviation (Std), Q10, Q25, Q50, Q75, Q90 of pulmonary artery perfusion (PAP), bronchial artery perfusion (BAP), perfusion index (PI) and arterial enhancement fraction (AEF) were obtained by performing computed tomography perfusion imaging (CTPI). Computed tomography perfusion (CTP) parameters were compared between malignant and benign lesions. The receiver operating characteristic (ROC) curve was used to assess the diagnostic efficiency of CTP parameters in diagnosing malignant lesions. The correlations between CTP parameters with MVD and VEGF were analysed in 36 lung cancer patients who had extra sections be used for immunohistochemistry staining of CD34 and VEGF. Results BAP (Mean, Std, Q90) and PI Std of benign lesions were higher than malignant lesions (p p p value range is p value range is 0.001–0.041,ρvalue range is − 0.523–− 0.343). Partial perfusion parameters of BAP and AEF Q10 were positively correlated with VEGF (p value range is 0.001–0.016, ρvalue range is 0.398–0.570), meanwhile some perfusion parameters of PAP and PI were negatively correlated with VEGF (p value range is 0.001–0.040, ρ value range is − 0.657–0.343). Conclusions Quantitative parameters of dynamic triple-phase enhanced CT can provide diagnostic basis for the differentiation of lung lesions, and there were connection with tumor angiogenesis and vascular endothelial growth factor expression.

BackgroundWe conduct a study in developing and validating four MRI-based radiomics models to preoperatively predict the risk classification of gastrointestinal stromal tumors (GISTs).MethodsForty-one patients (low-risk = 17, intermediate-risk = 13, high-risk = 11) underwent MRI before surgery between September 2013 and March 2019 in this retrospective study. The Kruskal–Wallis test with Bonferonni correction and variance threshold was used to select appropriate features, and the Random Forest model (three classification model) was used to select features among the high-risk, intermediate-risk, and low-risk of GISTs. The predictive performance of the models built by the Random Forest was estimated by a 5-fold cross validation (5FCV). Their performance was estimated using the receiver operating characteristic (ROC) curve, summarized as the area under the ROC curve (AUC). Area under the curve (AUC), accuracy, sensitivity, and specificity for risk classification were reported. Linear discriminant analysis (LDA) was used to assess the discriminative ability of these radiomics models.ResultsThe high-risk, intermediate-risk, and low-risk of GISTs were well classified by radiomics models, the micro-average of ROC curves was 0.85, 0.81, 0.87 and 0.94 for T1WI, T2WI, ADC and combined three MR sequences. And ROC curves achieved excellent AUCs for T1WI (0.85, 0.75 and 0.82), T2WI (0.69, 0.78 and 0.78), ADC (0.85, 0.77 and 0.80) and combined three MR sequences (0.96, 0.92, 0.81) for the diagnosis of high-risk, intermediate-risk, and low-risk of GISTs, respectively. In addition, LDA demonstrated the different risk of GISTs were correctly classified by radiomics analysis (61.0% for T1WI, 70.7% for T2WI, 83.3% for ADC, and 78.9% for the combined three MR sequences).ConclusionsRadiomics models based on a single sequence and combined three MR sequences can be a noninvasive method to evaluate the risk classification of GISTs, which may help the treatment of GISTs patients in the future.

Purpose To explore the dynamic changes and correlation between CT imaging manifestations and cellular immunity of COVID-19. Materials and methods This retrospective review analyzed 23 patients with COVID-19, including 13 males and 10 females aged 27–70 years, with an average age of 48 years. Patients were divided into two groups: group A with 11 critical-severe patients, and group B with 12 common-mild patients. Clinical, laboratory, and radiological data were collected and analyzed. Results LYM, LYM (%), CD3+, CD4+, and CD8+ decreased, while NEU (%), CRP, and CT scores increased in all patients, WBC in group A increased. In group A, on day 10–12 after disease onset, CT scores and CRP reached the highest point, and day 13–15 LYM, LYM (%) reached the lowest but NEU (%) and WBC reached the highest, CD3+, CD4+ and CD8+ were at the lowest on day 10–15. In group B, on day 7–9, CT scores, NEU (%) and CRP reached the peak, but LYM, LYM (%), CD3+, CD4+ and CD8+ reached the lowest. In all patients, CT scores had a significantly negative correlation with CD3+, CD4+, CD8+, LYM (%), and LYM (p = 0.001, r = − 0.797; p = 0.008, r = − 0.698; p = 0.002, r = − 0.775; p