Pulmonary lesions: correlative study of dynamic triple-phase enhanced CT perfusion imaging with tumor angiogenesis and vascular endothelial growth factor expression.

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 < 0.05), and PAP (Q10, Q25), PI (Median, Mean, Q10, Q25, Q50) of malignant lesions were higher than the benign (p < 0.05). The area under the ROC curve of PI Mean, PI Q10 and PI Std was 0.722 (95% CI = [0.595-0.845]), 0.728 (95% CI = [0.612-0.844]) and 0.717 (95% CI = [0.598-0.835]) respectively. Partial perfusion parameters of BAP and AEF Q10 were positively correlated with MVD (p value range is < 0.001-0.037, ρ value range is 0.483-0.683), and partial perfusion parameters of PI were negatively correlated with MVD (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.

Correlation between quantitative perfusion histogram parameters of DCE-MRI and PTEN, P-Akt and m-TOR in different pathological types of lung cancer.

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 < 0.05); Kep (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 < 0.05); Kep (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 < 0.05). 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.

The Diagnostic Accuracy of PET(CT) in Patients With Neuroblastoma: A Meta-Analysis and Systematic Review.

OBJECTIVE: The objective of this study was to evaluate the overall diagnostic value of PET(CT) in patients with neuroblastoma (NB) based on qualified studies. METHODS: PubMed, Cochrane, and Embase database were searched by the index words to identify the qualified studies, and relevant literature sources were also searched. The latest research was performed in April 2019. Heterogeneity of the included studies was tested, which was used to select proper effect model to calculate pooled weighted sensitivity, specificity, and diagnostic odds ratio (DOR). Summary receiver operating characteristic (SROC) analyses were also performed. RESULTS: Eleven studies with 580 patients were involved in the meta-analysis to explore the diagnostic accuracy of PET(CT) for NB. PET(CT) has high diagnostic accuracy of NB: the global sensitivity was 91% (95% confidence interval [CI], 86%-94%), the global specificity was 78% (95% CI, 66%-86%), the global positive likelihood ratio was 4.07 (95% CI, 2.54-6.50), the global negative likelihood ratio was 0.12 (95% CI, 0.08-0.18), the global DOR was 27.43 (95% CI, 14.45-52.07), and the area under the SROC was high (area under the curve, 0.93; 95% CI, 0.90-0.95). Besides this, PET(CT) has high diagnostic accuracy of primary NB: the global sensitivity was 86% (95% CI, 73%-93%), the global specificity was 82% (95% CI, 57%-94%), the global positive likelihood ratio was 4.90 (95% CI, 1.63-14.72), the global negative likelihood ratio was 0.17 (95% CI, 0.07-0.40), the global DOR was 25.427 (95% CI, 3.988-162.098), and the area under the SROC was high (area under the curve, 0.91; 95% CI, 0.88-0.93). However, there has no significant accuracy of PET(CT) in NB with bone marrow. CONCLUSIONS: This study provides a systematic review and meta-analysis of diagnostic accuracy studies of PET(CT) for NB. The results indicated that PET(CT) is a highly accurate diagnostic tool for NB.

Correlation of MRI quantitative perfusion parameters with EGFR, VEGF and EGFR gene mutations in non-small cell cancer.

To explore the relationship between quantitative perfusion histogram parameters of dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) with the expression of tumor tissue epidermal growth factor receptor (EGFR), vascular endothelial growth factor (VEGF) and EGFR gene mutations in non-small cell lung cancer (NSCLC). A total of 44 consecutive patients with known NSCLC were recruited from March 2018 to August 2021. Histogram parameters (mean, uniformity, skewness, energy, kurtosis, entropy, percentile) of each (Ktrans, Kep, Ve, Vp, Fp) were obtained by Omni Kinetics software. Immunohistochemistry staining was used in the detection of the expression of VEGF and EGFR protein, and the mutation of EGFR gene was detected by PCR. Corresponding statistical test was performed to compare the parameters and protein expression between squamous cell carcinoma (SCC) and adenocarcinoma (AC), as well as EGFR mutations and wild-type. Correlation analysis was used to evaluate the correlation between parameters with the expression of VEGF and EGFR protein. Fp (skewness, kurtosis, energy) were statistically significant between SCC and AC, and the area under the ROC curve were 0.733, 0.700 and 0.675, respectively. The expression of VEGF in AC was higher than in SCC. Fp (skewness, kurtosis, energy) were negatively correlated with VEGF (r = - 0.527, - 0.428, - 0.342); Ktrans (Q50) was positively correlated with VEGF (r = 0.32); Kep (energy), Ktrans (skewness, kurtosis) were positively correlated with EGFR (r = 0.622, r = 0.375, 0.358), some histogram parameters of Kep, Ktrans (uniformity, entropy) and Ve (kurtosis) were negatively correlated with EGFR (r = - 0.312 to - 0.644). Some perfusion histogram parameters were statistically significant between EGFR mutations and wild-type, they were higher in wild-type than mutated (P < 0.05). Quantitative perfusion histogram parameters of DCE-MRI have a certain value in the differential diagnosis of NSCLC, which have the potential to non-invasively evaluate the expression of cell signaling pathway-related protein.

Prediction of Microwave Ablation Recurrence in Pulmonary Malignancies Using Preoperative Computed Tomography Radiomics Models.

BACKGROUND: Assessing the early efficacy of microwave ablation (MWA) for pulmonary malignancies is a challenge for interventionalists. However, performing an accurate efficacy assessment at an earlier stage can significantly enhance clinical intervention and improve the patient's prognosis. PURPOSE: This research aimed to create and assess non-invasive diagnostic techniques using pre-operative computed tomography (CT) radiomics models to predict the recurrence of MWA in pulmonary malignancies. MATERIALS AND METHODS: We retrospectively enrolled 116 eligible patients with pulmonary malignancies treated with MWA. we separated the patients into two groups: a recurrence group (n = 28) and a non-recurrence group (n = 88), following the modified Response Evaluation Criteria in Solid Tumors (m-RECIST) criteria. We segmented the preoperative tumor area manually. We expanded outward the tumor boundary 4 times, with a width of 3 mm, using the tumor boundary as the baseline. Five groups of radiomics features were extracted and screened using max-relevance and min-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) regression. Weight coefficients of the aforementioned features were used to calculate the Radscore and construct radiomics models for both tumoral and peritumoral areas. The Radscore from the radiomics model was combined with clinical risk factors to construct a combined model. The performance and clinical usefulness of the combined models were assessed through the evaluation of receiver operating characteristic (ROC) curves, the Delong test, calibration curves, and decision curve analysis (DCA) curves. RESULTS: The clinical risk factor for recurrence after MWA was tumor diameter (P < 0.05). Both tumoral and four peritumoral radiomics models exhibited high diagnostic efficacy. Furthermore, the combined 1 (C1)-RO model and the combined 2 (C2)-RO model showed higher efficacy with area under the curve (AUCs) of 0.89 and 0.89 in the training cohort, and 0.93 and 0.94 in the validation cohort, respectively. Both combined models demonstrated excellent predictive accuracy and clinical benefit. CONCLUSION: Preoperative CT radiomics models for both tumoral and peritumoral regions are capable of accurately predicting the recurrence of pulmonary malignancies after MWA. The combination of both models may lead to better performance and may aid in devising more effective preoperative treatment strategies.

Development and Validation of a Nomogram Based on DCE-MRI Radiomics for Predicting Hypoxia-Inducible Factor 1α Expression in Locally Advanced Rectal Cancer.

RATIONALE AND OBJECTIVES: The expression levels of hypoxia-inducible factor 1 alpha (HIF-1α) have been identified as a pivotal marker, correlating with treatment response in patients with locally advanced rectal cancer (LARC). This study aimed to develop and validate a nomogram based on dynamic contrast-enhanced MRI (DCE-MRI) radiomics and clinical features for predicting the expression of HIF-1α in patients with LARC. MATERIALS AND METHODS: A total of 102 patients diagnosed with locally advanced rectal cancer were divided into training (n = 71) and validation (n = 31) cohorts. The expression statuses of HIF-1α were histopathologically classified, categorizing patients into high and low expression groups. The intraclass correlation coefficient (ICC), minimum redundancy maximum relevance (mRMR), and the least absolute shrinkage and selection operator (LASSO) were employed for feature selection to construct a radiomics signature and calculate the radiomics score (Rad-score). Univariate and multivariate analyses of clinical features and Rad-score were applied, and the clinical model and the nomogram were constructed. The predictive performance of the nomogram incorporating clinical features and Rad-score was assessed using Receiver Operating Characteristics (ROC) curves, decision curve analysis (DCA), and calibration curves. RESULTS: Seven radiomics features from DCE-MRI were used to build the radiomics signature. The nomogram incorporating CEA, Ki-67 and Rad-score had the highest AUC values in the training cohort and in the validation cohort (AUC: 0.918 and 0.920). Decision curve analysis showed that the nomogram outperformed the clinical model and radiomics signature in terms of clinical utility. In addition, the calibration curve for the nomogram demonstrated good agreement between prediction and actual observation. CONCLUSION: The nomogram based on DCE-MRI radiomics and clinical features showed favorable predictive efficacy and might be useful for preoperatively discriminating the expression of HIF-1α.

DCE-MRI radiomics models predicting the expression of radioresistant-related factors of LRP-1 and survivin in locally advanced rectal cancer.

Objective: Low-density lipoprotein receptor-related protein-1 (LRP-1) and survivin are associated with radiotherapy resistance in patients with locally advanced rectal cancer (LARC). This study aimed to evaluate the value of a radiomics model based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for the preoperative assessment of LRP-1 and survivin expressions in these patients. Methods: One hundred patients with pathologically confirmed LARC who underwent DCE-MRI before surgery between February 2017 and September 2021 were included in this retrospective study. DCE-MRI perfusion histogram parameters were calculated for the entire lesion using post-processing software (Omni Kinetics, G.E. Healthcare, China), with three quantitative parameter maps. LRP-1 and survivin expressions were assessed by immunohistochemical methods and patients were classified into low- and high-expression groups. Results: Four radiomics features were selected to construct the LRP-1 discrimination model. The LRP-1 predictive model achieved excellent diagnostic performance, with areas under the receiver operating curve (AUCs) of 0.853 and 0.747 in the training and validation cohorts, respectively. The other four radiomics characteristics were screened to construct the survivin predictive model, with AUCs of 0.780 and 0.800 in the training and validation cohorts, respectively. Decision curve analysis confirmed the clinical usefulness of the radiomics models. Conclusion: DCE-MRI radiomics models are particularly useful for evaluating LRP-1 and survivin expressions in patients with LARC. Our model has significant potential for the preoperative identification of patients with radiotherapy resistance and can serve as an essential reference for treatment planning.

The interplay between small vessel disease and Parkinson disease pathology: A longitudinal study.

BACKGROUND: 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. OBJECTIVE: The aim of this study is to explore the interplay between SVD and PD pathology using longitudinal dataset. METHODS: 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. RESULTS: 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 (ß = 0.216, P = 0.029), but not the putamen (ß = 0.058, P = 0.552) in PD patients. Baseline caudate and putamen DAT availability had no significant association with PSMD progression (ß = -0.006, P = 0.950; ß = 0.017, P = 0.860, respectively). CONCLUSIONS: Mild SVD might slow down PD pathology progression, while the effect of PD pathology on the progression of SVD was not significant.

Imaging Manifestations of Intrahepatic Reactive Lymphoid Hyperplasia: A Case Report and Literature Review.

Reactive lymphoid hyperplasia (RLH) of the liver is a rare benign disease. This article describes a 77-year-old female patient with RLH of the liver. The patient was admitted to the hospital due to atrial fibrillation. A liver tumor was incidentally found during abdominal enhanced CT. Further magnetic resonance imaging (MRI) and PET/CT showed four lesions in the liver. The imaging findings suggested hepatocellular carcinoma (HCC), but it was not consistent that the patient had no history of liver cirrhosis and hepatitis, and a variety of tumor markers were within the normal range. The largest lesion was surgically removed and microscopically diagnosed as RLH of the liver. The pathology included a large number of reactive hyperplastic lymphoid follicles. Immunohistochemical examination showed that the infiltrating lymphocytes were polyclonal. The authors believe that the perinodular enhancement on MRI, the obvious limitation of diffusion on DWI, the insignificant increase of SUVmax on PET-CT delayed phase, and the support of clinical data can help distinguish liver RLH from lymphoma and HCC.

Association Between DCE-MRI Perfusion Histogram Parameters and EGFR and VEGF Expressions in Different Lauren Classifications of Advanced Gastric Cancer.

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.

MRI-Based Radiomics Models for Predicting Risk Classification of Gastrointestinal Stromal Tumors.

BACKGROUND: We conduct a study in developing and validating four MRI-based radiomics models to preoperatively predict the risk classification of gastrointestinal stromal tumors (GISTs). METHODS: Forty-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. RESULTS: The 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). CONCLUSIONS: Radiomics 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.

Study on the dynamic change law and correlation between CT imaging manifestations and cellular immunity of 2019 novel coronavirus disease.

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 < 0.001, r = - 0.785; p = 0.021, r = - 0.571, respectively), and a significantly positive correlation with WBC and NEU (%) (p < 0.001, r = 0.785; p = 0.003, r = 0.691, respectively). CONCLUSION: Dynamic changes of CT manifestations and cellular immunity of patients with COVID-19 were regular and correlation was high between these two parameters.