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.

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.

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.

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.