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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.

Purpose: In this study, a non-invasive radiomics method was used to extract a large amount of valuable information from patient CT images (including pre-treatment and follow-up images), and the image features were effectively combined with treatment effect evaluation, aiming to solve the problem of lung adenocarcinoma in patients with EGFR mutation positive. The early efficacy evaluation of cancer EGFR-TKI targeted drugs provides a scientific basis for clinical drug selection and adjustment of treatment plans. Methods: It is planned to include 106 patients with EGFR-TKI targeted therapy for lung adenocarcinoma in Shaoxing People's Hospital (pathologically confirmed EGFR mutation positive), and randomly divide them into a training group and a validation group in a ratio of 7:3. The CT plain scan images of patients before and 2 months after treatment were collected, and the images were segmented by ITK-SNAP software. Perform feature screening and modeling, and draw nomograms, calibration curves, and decision curves. Results: After Lasso dimensionality reduction, three features with non-zero coefficients are extracted between groups, sumEntropy,LongRunLowGreyLevelEmphasis_angle0_offset1, LongRunLowGreyLevelEmphasis_angle0_offset4. The average area under the curve (AUC) of the logistic regression model in the training group and the validation group was 0.778 and 0.773, respectively; the Nomogram chart showed that after the three radiomics parameters were scored, the total score was 0-130, and the corresponding effective The risk was 0.1-0.9; the calibration curve showed that the predicted value and the actual clinical observation value approached a 45° slope, indicating that the model was well calibrated; while the decision curve radiomics model was used to evaluate EGFR-TKI in lung adenocarcinoma Targeted drugs have better clinical benefits when they have early efficacy. Conclusions: The radiomics model established based on the radiomics features of CT unenhanced images has good performance in evaluating the efficacy of EGFR-TKI targeted drugs in the treatment of lung adenocarcinoma, and the final Nomogram map can quantitatively evaluate the curative effect, providing individual patient information.

Objective: To investigate the correlations between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) perfusion histogram parameters and vascular endothelial growth factor (VEGF) and epidermal growth factor receptor (EGFR) expressions in advanced gastric cancer (AGC).Methods: This retrospective study included 80 pathologically confirmed patients with AGC who underwent DCE-MRI before surgery from February 2017 to May 2021. The DCE-MRI perfusion histogram parameters were calculated by Omni Kinetics software in four quantitative parameter maps. Immunohistochemical methods were used to detect VEGF and EGFR expressions and calculate the immunohistochemical score.Results: VEGF expression was relatively lower in patients with intestinal-type AGC than those with diffuse-type AGC (p < 0.05). For VEGF, Receiver operating characteristics (ROC) curve analysis revealed that Quantile 90 of Ktrans, Meanvalue of Kep and Quantile 50 of Ve provided the perfect combination of sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for distinguishing high and low VEGF expression, For EGFR, Skewness of Ktrans, Energy of Kep and Entropy of Vp provided the perfect combination of sensitivity, specificity, PPV and NPV for distinguishing high and low EGFR expression. Ktrans (Quantile 90, Entropy) showed the strongest correlation with VEGF and EGFR in patients with intestinal-type AGC (r = 0.854 and r = 0.627, respectively); Ktrans (Mean value, Entropy) had the strongest correlation with VEGF and EGFR in patients with diffuse-type AGC (r = 0.635 and 0.656, respectively).Conclusion: DCE-MRI perfusion histogram parameters can serve as imaging biomarkers to reflect VEGF and EGFR expressions and estimate their difference in different Lauren classifications of AGC.

Cerebral small vessel disease (SVD) related brain changes have been found associated with various clinical symptoms of Parkinson disease (PD). On the other hand, PD pathology and treatment may also accelerate SVD progression.The aim of this study is to explore the interplay between SVD and PD pathology using longitudinal dataset.We screened 66 healthy controls (HCs) and 114 patients from the Parkinson Progression Markers Initiative (PPMI) database. The peak width of skeletonized mean diffusivity (PSMD) was quantified from diffusion tensor images to reflect vascular pathologies at baseline and 24 months follow-up, and dopamine transporter (DAT) imaging data was used to represent the extent of dopaminergic neuronal degeneration at the same point time. We compared the PSMD between PD patients and HCs, and analyzed whether PSMD and DAT availability could predict each other's progression using multiple regression analyses in PD patients.PSMD at baseline had no significant difference between the HCs and patients with PD (P = 0.169). Higher baseline PSMD was associated with less DAT reduction in the caudate (β = 0.216, P = 0.029), but not the putamen (β = 0.058, P = 0.552) in PD patients. Baseline caudate and putamen DAT availability had no significant association with PSMD progression (β = -0.006, P = 0.950; β = 0.017, P = 0.860, respectively).Mild SVD might slow down PD pathology progression, while the effect of PD pathology on the progression of SVD was not significant.

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