Prevalence and Radiographic Characteristics of Cerebral Infarction after Surgery in Patients with Glioma: A Retrospective Study.

AIM: The aim of our study was to analyze risk factors for postoperative cerebral infarction in patients with glioma in our hospital, and to compare medical imaging techniques for early diagnosis of postoperative cerebral infarction. METHODS: A retrospective analysis was conducted on 178 patients (male: 78, female: 100) who underwent glioma surgery at our hospital between May 2015 and October 2023. They were divided into two groups based on the presence of postoperative cerebral infarction within 7 days: the cerebral infarction group (n = 85) and the non-cerebral infarction group (n = 93). Magnetic resonance imaging (MRI) was used to assess the location, distribution, and volume of the tumor before surgery. During the perioperative period, patient postoperative time, intraoperative blood loss, and other relevant data were documented. Computed tomography perfusion (CTP) and diffusion-weighted imaging (DWI) imaging techniques were employed to evaluate the occurrence, area, location, and shape of cerebral infarction. The imaging characteristics of postoperative cerebral infarction were noted. Apparent diffusion coefficient values, apparent diffusion coefficient (ADC) of whole-brain CTP parameters, cerebral blood flow (CBF), cerebral blood volume (CBV), time to peak (TTP), mean transit time (MTT), and DWI parameters were measured. The sensitivity and specificity of CTP, DWI, and their combined diagnosis for postoperative cerebral infarction were compared, with consistency assessed using the Kappa value. RESULTS: This study found that 85 patients (47.8%) experienced postoperative cerebral infarction. Significant risk factors included tumor location in the temporal lobe, tumor volume ≥23.57 cm3, number of surgeries >1, World Health Organization (WHO) grade >3, and intraoperative blood loss >79.83 mL (p < 0.05). Imaging examinations revealed that CTP combined with DWI diagnosis detected cerebral infarctions in 84 patients, showing lower CBF and CBV, and higher TTP, and MTT in the infarct group (p < 0.05). The Kappa values for CTP, DWI, and the combined diagnosis were 0.762, 0.833, and 0.937, respectively (p < 0.001). CONCLUSIONS: The prevalence of cerebral infarction in patients with glioma is high and is affected by many factors. Timely imaging examination can detect and predict the occurrence of cerebral infarction in patients after surgery, which is of great significance for improving the prognosis of patients.

Alteration of N 6 -Methyladenosine mRNA Methylation in a Rat Model of Cerebral Ischemia-Reperfusion Injury.

AIM: This study was conducted in order to reveal the alterations in the N 6-methyladenosine (m6A) modification profile of cerebral ischemia-reperfusion injury model rats. MATERIALS AND METHODS: Rats were used to establish the middle cerebral artery occlusion and reperfusion (MCAO/R) model. MeRIP-seq and RNA-seq were performed to identify differences in m6A methylation and gene expression. The expression of m6A methylation regulators was analyzed in three datasets and detected by quantitative real-time polymerase chain reaction, western blot, and immunofluorescence. RESULTS: We identified 1,160 differentially expressed genes with hypermethylated or hypomethylated m6A modifications. The differentially expressed genes with hypermethylated m6A modifications were involved in the pathways associated with inflammation, while hypomethylated differentially expressed genes were related to neurons and nerve synapses. Among the m6A regulators, FTO was specifically localized in neurons and significantly downregulated after MCAO/R. CONCLUSION: Our study provided an m6A transcriptome-wide map of the MACO/R rat samples, which might provide new insights into the mechanisms of cerebral ischemia-reperfusion injury.

Computed tomography-based radiomics model for discriminating the risk stratification of gastrointestinal stromal tumors.

PURPOSE: The pathological risk degree of gastrointestinal stromal tumors (GISTs) has become an issue of great concern. Computed tomography (CT) is beneficial for showing adjacent tissues in detail and determining metastasis or recurrence of GISTs, but its function is still limited. Radiomics has recently shown a great potential in aiding clinical decision-making. The purpose of our study is to develop and validate CT-based radiomics models for GIST risk stratification. METHODS: Three hundred and sixty-six patients clinically suspected of primary GISTs from January 2013 to February 2018 were retrospectively enrolled, among which data from 140 patients were eventually analyzed after exclusion. Data from patient CT images were partitioned based on the National Institutes of Health Consensus Classification, including tumor segmentation, radiomics feature extraction and selection. A radiomics model was then proposed and validated. RESULTS: The radiomics signature demonstrated discriminative performance for advanced and nonadvanced GISTs with an area under the curve (AUC) of 0.935 [95% confidence interval (CI) 0.870-1.000] and an accuracy of 90.2% for validation cohort. The radiomics signature demonstrated favorable performance for the risk stratification of GISTs with an AUC of 0.809 (95% CI 0.777-0.841) and an accuracy of 67.5% for the validation cohort. Radiomics analysis could capture features of the four risk categories of GISTs. Meanwhile, this CT-based radiomics signature showed good diagnostic accuracy to distinguish between nonadvanced and advanced GISTs, as well as the four risk stratifications of GISTs. CONCLUSION: Our findings highlight the potential of a quantitative radiomics analysis as a complementary tool to achieve an accurate diagnosis for GISTs.

MRI Radiomics Analysis for Predicting the Pathologic Classification and TNM Staging of Thymic Epithelial Tumors: A Pilot Study.

OBJECTIVE. The purpose of this study was to explore the performance of MRI radiomics in predicting the pathologic classification and TNM staging of thymic epithelial tumors (TETs). MATERIALS AND METHODS. Clinical and MRI data for 189 patients with TETs were retrospectively collected. A total of 2088 radiomics features were extracted from T2-weighted images and T2-weighted fat-suppressed (FS) images. With the use of a support vector machine with recursive feature elimination, the optimal feature subsets were selected and used to construct two predictive models for pathologic classification and TNM staging. In multivariable logistic regression analysis, we incorporated the radiomics model, conventional MRI findings, and clinical variables to develop a radiomics nomogram for predicting risk stratification of advanced TETs. RESULTS. Of the extracted features, 125 features were selected to construct the radiomics model for predicting pathologic classification, and 69 features were selected to construct the radiomics model for predicting TNM staging. The models achieved AUC values of 0.880 and 0.948 in the training cohort and 0.771 and 0.908 in the test cohort, respectively, for distinguishing among low-risk thymomas, high-risk thymomas, and thymic carcinomas and differentiating between early-stage and advanced-stage TETs. The radiomics model, symptom, and pericardial effusion constituted a radiomics nomogram, with an AUC value of 0.967 (95% CI, 0.891-0.989) in the training cohort and 0.957 (95% CI, 0.842-0.974) in the test cohort. CONCLUSION. MRI radiomics analysis has the potential to differentiate the pathologic classification and TNM staging of TETs. A radiomics nomogram provides a useful tool for in dividualized prediction of the risk of advanced-stage TET before a patient undergoes treatment.

Radiomics analysis of multiparametric MRI for prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer.

OBJECTIVES: To develop and validate a radiomics predictive model based on pre-treatment multiparameter magnetic resonance imaging (MRI) features and clinical features to predict a pathological complete response (pCR) in patients with locally advanced rectal cancer (LARC) after receiving neoadjuvant chemoradiotherapy (CRT). METHODS: One hundred and eighty-six consecutive patients with LARC (training dataset, n = 131; validation dataset, n = 55) were enrolled in our retrospective study. A total of 1,188 imaging features were extracted from pre-CRT T2-weighted (T2-w), contrast-enhanced T1-weighted (cT1-w) and ADC images for each patient. Three steps including least absolute shrinkage and selection operator (LASSO) regression were performed to select key features and build a radiomics signature. Combining clinical risk factors, a radiomics nomogram was constructed. The predictive performance was evaluated by receiver operator characteristic (ROC) curve analysis, and then assessed with respect to its calibration, discrimination and clinical usefulness. RESULTS: Thirty-one of 186 patients (16.7%) achieved pCR. The radiomics signature derived from joint T2-w, ADC, and cT1-w images, comprising 12 selected features, was significantly associated with pCR status and showed better predictive performance than signatures derived from either of them alone in both datasets. The radiomics nomogram, incorporating the radiomics signature and MR-reported T-stages, also showed good discrimination, with areas under the ROC curves (AUCs) of 0.948 (95% CI, 0.907-0.989) and 0.966 (95% CI, 0.924-1.000), as well as good calibration in both datasets. Decision curve analysis confirmed its clinical usefulness. CONCLUSIONS: This study demonstrated that the pre-treatment radiomics nomogram can predict pCR in patients with LARC and potentially guide treatments to select patients for a "wait-and-see" policy. KEY POINTS: • Radiomics analysis of pre-CRT multiparameter MR images could predict pCR in patients with LARC. • Proposed radiomics signature from joint T2-w, ADC and cT1-w images showed better predictive performance than individual signatures. • Most of the clinical characteristics were unable to predict pCR.

Grading of pancreatic neuroendocrine neoplasms using pharmacokinetic parameters derived from dynamic contrast-enhanced MRI.

The present study aimed to evaluate the diagnostic efficacy of pharmacokinetic parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in prospective evaluation of pancreatic neuroendocrine neoplasms (pNENs) grading. A total of 25 histologically proven patients with pNENs (30 lesions in total) who underwent DCE-MRI were enrolled. Lesions were divided into G1, G2 neuroendocrine tumor (NET) and G3 NET/neuroendocrine carcinoma (NEC) groups based on their histological findings according to 2017 World Health Organization Neuroendocrine Tumor Classification Guideline. In addition, the same numbers of tumor-free regions were selected using as normal control group. For each group, pharmacokinetic DCE parameters: volume transfer constant (Ktrans); contrast transfer rate constant (kep); extravascular extracellular space volume fraction (ve); and plasma volume fraction (vp) were calculated with Extended Tofts Linear model. Receiver operator characteristics analysis was conducted to assess the diagnostic efficacy of these parameters in pNENs grading. There were significant differences of Ktrans, kep, ve and vp between tumor-free areas and G1, G2 NET (P<0.001). The Ktrans and kep of G1 NET were significantly lower compared with those of G2 ones (P<0.005). The area under the curve of Ktrans and kep in differentiating G2 from G1 NET were 0.767 and 0.846, respectively. When Ktrans was >0.667 and kep >1.644, the sensitivity of diagnosing G2 NET was the lowest (53.85%), but the specificity was the highest (93.75%). When Ktrans was >0.667 or kep >1.644, the sensitivity of diagnosing G2 NET was 92.31%, but the specificity was 75.00%. Pharmacokinetic parameters of DCE-MRI, particularly the quantitative values of Ktrans and kep, are helpful for differentiating G2 NET from G1 ones.

[An Improved Retrospective Respiratory Navigator Gating Technique].

Abdominal imaging is one of the important clinical applications of magnetic resonance imagining, but image degradation due to respiratory motion remains a major problem. Retrospective respiratory navigator gating technique is an effective approach to alleviate such degradation but is subject to long scan time and low signal-to-noise ratio (SNR) efficiency. In this study, a modified retrospective navigator gating technique with variable over-sampling ratio acquisition and weighted average reconstruction algorithm is presented. Experiments in phantom and the imaging results of seven volunteers demonstrated that the proposed method provided an enhanced SNR and reduced ghost-to-image ratio compared to the conventional method. The proposed method can also be used to reduce imaging time while maintaining comparable image quality.