BACKGROUND:Mutations in the telomerase reverse transcriptase promoter (pTERT) are important molecular markers in glioblastoma (GBM). Although several imaging-based approaches have attempted to predict pTERT mutation status preoperatively, the value of quantitative metrics extracted from lesion subregions remains unclear. This study investigated whether quantitative FLAIR metrics derived from contrast-enhanced T1-weighted imaging (CE T1WI)-FLAIR fused images contribute to the differentiation of pTERT subtypes in GBM. METHODS:MRI and clinical data from 135 GBM patients, 94 with pTERT-mutant (pTERTm) and 41 with pTERT-wild-type (pTERTw) tumors, were retrospectively analyzed. Patients were randomly assigned to training and validation cohorts in a 7:3 ratio. Clinical characteristics and conventional MRI variables were compared between pTERTm and pTERTw groups. FLAIR signal intensity (SI) metrics were measured in three subregions on CE T1WI-FLAIR fused images: the enhancement region, the edema region (non-enhancing), and the whole lesion (enhancement + edema). Significant variables identified by logistic regression were incorporated into clinical, MRI, and combined predictive models. Model performance was internally evaluated using leave-one-out cross-validation (LOOCV) in the training cohort and externally assessed in the validation cohort. RESULTS:Significant differences between pTERTm and pTERTw groups were observed in age, FLAIR SI standard deviation (FLAIRSD) and relative FLAIR SI (rFLAIR) of the edema region, and FLAIRSD of the enhancement region (all p < 0.05). Logistic regression identified older age (> 42.5 years; OR = 1.09; p = 0.002), higher FLAIRSD in the enhancement region (> 62.45; OR = 1.01; p = 0.027), and higher rFLAIR in the edema region (> 1.706; OR = 7.49; p = 0.025) as independent predictors of pTERTm. In the training cohort, the combined model achieved an area under the ROC curve (AUC) of 0.833, outperforming the clinical model (0.675) and MRI-based model (enhancement-region FLAIRSD: 0.737; edema-region rFLAIR: 0.699). The combined model achieved the highest predictive performance, with LOOCV in the training cohort yielding a mean AUC of 0.784 (95% CI: 0.672-0.895) and external validation showing an AUC of 0.667 (95% CI: 0.466-0.867). CONCLUSIONS:Quantitative FLAIR metrics extracted from subregions on CE T1WI-FLAIR fused images differ significantly between pTERTm and pTERTw GBM. Subregional quantitative analysis may therefore contribute to noninvasive preoperative prediction of pTERT mutation status.

BACKGROUND:Timely identification of early recurrence (≤ 6 months) may improve prognosis of glioblastoma (GBM), but conventional MRI has shown limited accuracy in this setting. PURPOSE:Risk-assessment models and a nomogram were constructed by integrating SyMRI metrics, clinical-pathological variables, and cMRI features, including contrast-enhanced T1-weighted imaging and fluid-attenuated inversion recovery (FLAIR) findings. STUDY TYPE:Retrospective observational study. POPULATION:Seventy-eight patients with GBM (median age, 59 years; 44 [56.4%] males). FIELD STRENGTH/SEQUENCE:3 T; pre- and post-contrast three-dimensional T1-weighted imaging, FLAIR, and SyMRI. ASSESSMENT:Histogram-based quantitative metrics were extracted from SyMRI maps using subregions defined on fused FLAIR and contrast-enhanced T1-weighted images. All candidate clinical-pathological variables, cMRI features, and SyMRI-derived metrics were entered directly into least absolute shrinkage and selection operator (LASSO) regression for variable selection. Risk-assessment models and a nomogram were constructed. STATISTICAL TESTS:Multivariable logistic regression, multicollinearity assessment using variance inflation factors, receiver operating characteristic curve analysis with DeLong test, stratified 10-fold cross-validation, leave-one-out cross-validation, nested 5-fold cross-validation, calibration curves, and decision curve analysis. A two-sided p < 0.05 was considered statistically significant. RESULTS:Multivariate analysis identified reduced T2 entropy (< 2.113) in enhancement-corresponding regions (odds ratio [OR] = 0.08), thick linear or nodular residual cavity wall enhancement (OR = 5.28), corpus callosum involvement (OR = 5.08), and O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation (OR = 0.19) as variables associated with early recurrence. The integrated model achieved the highest performance (AUC = 0.864). Nested cross-validation showed moderate internal validation performance, with an AUC of 0.724 (0.722-0.726). DATA CONCLUSION:Histogram-based pre-radiotherapy SyMRI metrics, particularly T2 entropy, were associated with early GBM recurrence. The integrated model achieved the highest apparent performance; nested internal validation showed moderate performance. External validation in larger multicenter cohorts is required before clinical implementation. EVIDENCE LEVEL:3. TECHNICAL EFFICACY:Stage 2.

ObjectiveThis study aimed to investigate the dose-response association between magnetic resonance imaging-derived hepatic proton density fat fraction (MRI-derived PDFF) and the type 2 diabetes mellitus (T2DM) prevalence in postmenopausal women with abnormal glucose metabolism, evaluate its utility in risk stratification, while examine the potential mediating role of liver enzymes and to explore the nonlinear relationship between MRI-derived PDFF and insulin resistance assessed by the Triglyceride-Glucose (TyG) index, in this specific population.MethodsIn this cross-sectional study, 96 postmenopausal women with abnormal glucose metabolism (46 with prediabetes, 50 with T2DM) were included. Hepatic and pancreatic fat fractions were measured by MRI, and visceral fat mass (VFM) was quantified by quantitative computed tomography. Inter-observer agreement was assessed using intraclass correlation coefficients. Multivariable logistic regression, with sequential adjustment for age, BMI, total cholesterol, and VFM, was used to assess the association between liver fat fraction and T2DM, treating hepatic proton density fat fraction of the left lobe (FF-LL) as both a continuous variable and in quartiles. The discriminative ability of FF-LL was evaluated by ROC analysis. Mediation analysis explored the potential role of liver enzymes, and restricted cubic splines examined the relationship between FF-LL and the TyG index.ResultsIn postmenopausal women with abnormal glucose metabolism, those with T2DM demonstrated a more unfavorable metabolic profile compared to those with prediabetes. Inter-observer agreement was excellent for liver-related parameters and VFM (ICCs: 0.977–0.984), but moderate for pancreatic fat fraction (ICC range: 0.602–0.637). FF-LL was significantly higher in the T2DM group compared to the prediabetes group. ROC analysis demonstrated that FF-LL had good discriminative ability for T2DM (AUC = 0.816). The optimal cutoff value was 6.3%, yielding a sensitivity of 80.0% and a specificity of 87.0%. As a continuous variable, each unit increase in FF-LL was associated with a 15% to 20% higher prevalence of T2DM across adjusted models. A significant dose-response trend was observed across increasing quartiles of FF-LL. In addition, mediation analysis showed that both alanine aminotransferase (ALT) and the AST (aspartate aminotransferase)/ALT ratio partially mediated the association of FF-LL with T2DM in postmenopausal women, accounting for 22.0% and 24.9% of the total effect, respectively (both P = 0.040). Additionally, restricted cubic spline analysis revealed a significant nonlinear association between FF-LL and the TyG index (overall P < 0.001, P for nonlinearity = 0.007), characterized by a threshold effect with a steep increase in FF-LL when TyG exceeded approximately 8.75.ConclusionIn postmenopausal women with abnormal glucose metabolism, FF-LL is an independent risk factor for T2DM, exhibiting a dose–response relationship partially mediated by liver enzymes. With an optimal cutoff of 6.3%, FF-LL demonstrates good discriminative ability and may serve as an effective biomarker for T2DM risk stratification. Additionally, a significant nonlinear association is observed between FF-LL and the TyG index in this population.

ObjectiveThis study aimed to explore the significant factors of prognosis in patients with Moyamoya disease (MMD) after surgical revascularization and to develop a nomogram model for predicting poor prognosis.Materials and methodsWe retrospectively analyzed magnetic resonance imaging (MRI) and clinical data of 128 patients with MMD. The patients were randomly assigned to training and validation cohorts in a ratio of 7:3. Multivariate logistic regression analysis was applied to identify factors significantly associated with prognosis. The predictive efficiencies of the models were evaluated using receiver operating characteristic (ROC) curves and compared using the Delong test. We then developed a nomogram model for prediction and verified it using a validation cohort.ResultsPreoperative arterial spin labeling (ASL)-Alberta Stroke Program Early computed tomography Score (ASL-ASPECTS), admission modified Rankin scale (mRS) score, ivy sign, and Houkin’s grade >2 were significantly associated with poor prognosis (mRS > 2). The areas under the curves (AUCs) for predicting poor prognosis were 0.772, 0.855, 0.899, and 0.994 for clinical, conventional MRI, ASL-based, and combination models, respectively. The results of the Delong test demonstrated the superior prediction ability of the combination model compared with the clinical, conventional MRI, and ASL models (all p < 0.001). Calibration curve analysis showed that the predictive probability of the nomogram model was highly consistent in the training cohort. The decision curve showed a net predictive benefit in the validation cohort.ConclusionPreoperative ASL-ASPECTS, admission mRS, ivy sign, and Houkin grade >2 were significantly associated with poor prognosis in patients with MMD after surgical revascularization. The nomogram model, including enrolled ASL-ASPECTS and MRI features, may help improve prognosis prediction.

Objectives:This study aimed to investigate whether the body composition metrics extracted from quantitative CT (QCT) are associated with the survival prognosis of acute myeloid leukemia (AML) patients and to evaluate the impact of a nomogram based on QCT and clinical-physical factors in predicting the prognosis of AML. Methods:The clinical factors and QCT metrics of 127 AML patients undergoing initial chest CT were analyzed retrospectively. The AML patients were divided into favorable and poor prognosis groups based on the threshold of median overall survival (OS). A QCT metrics- and clinical factors-derived nomogram was constructed using multivariate Cox regression. The performance of the nomogram was assessed with a receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). Results:Compared to patients in the favorable survival prognosis group, patients with poor prognosis were older (p = 0.027), had higher risk stratification (p = 0.006), more positive minimal residual disease (MRD) (p = 0.014), lower skeletal muscle index (SMI) (p = 0.045), and a higher incidence of volumetric bone mineral density (vBMD) ≤ 120 (p = 0.035). Older age, higher risk stratification, positive MRD, and SMI < 15.74cm2/m2 were independent risk factors for poor prognosis in AML patients. The areas under the ROC curve (AUCs) of the nomogram, which included SMI and independent clinical factors, for predicting 1- and 2-year OS were 0.792 and 0.794, respectively. The calibration curve and DCA demonstrated the good performance of the nomogram prediction model. Conclusions:Sarcopenia revealed by QCT, integrated into a nomogram with age, risk stratification, and MRD, can facilitate individualized prediction of survival prognosis in AML patients.

AIM:To explore the relationship between quantitative CT histogram indices of abdominal muscles and coronary artery disease (CAD) severity. MATERIALS AND METHODS:CAD patients and controls who received chest CT covering the L1-L2 intervertebral disc were enrolled. Abdominal muscles at the L1-L2 level, including the abdominal wall muscles, the psoas major muscles and the paraspinal muscles, were segmented. Six histogram indices, covering area, average and median attenuation, skewness, kurtosis and standard deviation (SD) of attenuation, were measured and compared with controls. Associations between histogram indices and coronary artery calcium score (CACS) and total coronary plaque burden were then assessed using multivariate regression analysis. RESULTS:Two hundred ninety male and 165 female CAD patients were enrolled. Compared with controls, both sexes of CAD patients had a broader, platykurtic and right-skewed distribution in the psoas major muscles and female CAD patients had a leptokurtic and left-skewed distribution in the abdominal wall muscles additionally. After adjusting for cardiovascular risk factors, BMI, liver fat fraction, visceral adipose tissue and subcutaneous adipose tissue, the SD in the psoas major muscles was positively associated with CACS in male CAD patients (β=0.13; 95% CI: 0.02 to 0.23; P=0.02), and kurtosis in the abdominal wall muscles was positively associated with total plaque burden in female CAD patients (β=0.15; 95% CI, 0.01 to 0.29; P=0.03). CONCLUSION:The histogram indices for abdominal wall muscles were independently associated with CAD severity in CAD patients, and the relationships were different between the two sexes.

Leptomeningeal metastasis (LM) is a severe complication of solid malignancies, including lung adenocarcinoma, characterized by poor prognosis and diagnostic challenges. This study assesses whether curvilinear peri-brainstem hyperintense signals on MRI are a characteristic feature of LM in lung adenocarcinoma patients. Methods: This retrospective study analyzed data from multiple centers, encompassing lung adenocarcinoma patients with peri-brainstem curvilinear hyperintense signals on MRI between January 2016 and March 2022. Clinical and radiological data were reviewed, and diagnostic and survival outcomes were analyzed. Results: Eleven patients (45-76 years; 6 males and 5 females) were included. The mean interval from lung adenocarcinoma diagnosis to detection of brainstem signal was 1.59 years. Four patients had elevated carcinoembryonic antigen levels, and 6 showed systemic metastases. MRI revealed curvilinear hyperintense signals along the pons, medulla, and cerebral peduncles on T2-Weighted Imaging (T2WI), Fluid-Attenuated Inversion Recovery (FLAIR), and Diffusion-Weighted Imaging (DWI). Mild linear enhancement was observed in 4 of 6 patients undergoing contrastenhanced MRI, and tumor cells were detected in 4 of 6 Cerebrospinal Fluid (CSF) samples. The mean survival time in 7 patients with follow-up data was 3.42 months. Two patients exhibited peri-brainstem calcifications on CT 4-6 months after MRI and died shortly after. Discussion: These findings suggest that peri-brainstem curvilinear hyperintense signals and mild linear enhancement may serve as radiological markers of LM in lung adenocarcinoma. This pattern may reflect tumor infiltration or secondary changes in the leptomeninges. Conclusion: Peri-brainstem curvilinear hyperintense signals and mild linear enhancement on T2WI, FLAIR, and DWI may be characteristic of LM in lung adenocarcinoma. These findings suggest further evaluation with contrast-enhanced MRI or CSF analysis for confirmation.

To explore morphology and enhancement features of malignant non-mass enhancement (NME) lesions in contrast-enhanced mammography (CEM), and to develop a multivariable model that can accurately predict the probability of malignancy in NME lesions. A total of 162 patients with 206 NME lesions were enrolled. The ratio of 7:3 was randomly divided into a training data set and a test data set. Differences between benign and malignant NME diseases were compared using statistical analysis in the training data set. A logistic regression analysis was used to develop a multivariable model for predicting the probability of malignancy in the training data set. The predictive value of the model was assessed by calculating the area under the curve (AUC) in both training and test data sets. The incidence of malignancy was higher in cases with malignant microcalcification (32.35

Rationale and Objectives The prognosis of ductal carcinoma in situ with microinvasion (DCISM) is more similar to that of small invasive ductal carcinoma (IDC) than to pure ductal carcinoma in situ (DCIS). It is particularly important to accurately distinguish between DCISM and DCIS. The present study aims to compare the clinical and imaging characteristics of contrast-enhanced mammography (CEM) and magnetic resonance imaging (MRI) between DCISM and pure DCIS, and to identify predictive factors of microinvasive carcinoma, which may contribute to a comprehensive understanding of DCISM in clinical diagnosis and support surveillance strategies, such as surgery, radiation, and other treatment decisions. Materials and Methods Forty-seven female patients diagnosed with DCIS were included in the study from May 2019 to August 2023. Patients were further divided into two groups based on pathological diagnosis: DCIS and DCISM. Clinical and imaging characteristics of these two groups were analyzed statistically. The independent clinical risk factors were selected using multivariate logistic regression and used to establish the logistic model [Logit(P)]. The diagnostic performance of independent predictors was assessed and compared using receiver operating characteristic (ROC) analysis and DeLong's test. Results In CEM, the maximum cross-sectional area (CSAmax), the percentage signal difference between the enhancing lesion and background in the craniocaudal and mediolateral oblique projection (%RSCC, and %RSMLO) were found to be significantly higher for DCISM compared to DCIS (p = 0.001; p < 0.001; p = 0.008). Additionally, there were noticeable statistical differences in the patterns of enhancement morphological distribution (EMD) and internal enhancement pattern (IEP) between DCIS and DCISM (p = 0.047; p = 0.008). In MRI, only CSAmax (p = 0.012) and IEP (p = 0.020) showed significant statistical differences. The multivariate regression analysis suggested that CSAmax (in CEM or MR) and %RSCC were independent predictors of DCISM (all p < 0.05). The area under the curve (AUC) of CSAmax (CEM), %RSCC (CEM), Logit(P) (CEM), and CSAmax (MR) were 0.764, 0.795, 0.842, and 0.739, respectively. There were no significant differences in DeLong's test for these values (all p > 0.10). DCISM was significantly associated with high nuclear grade, comedo type, high axillary lymph node (ALN) metastasis, and high Ki-67 positivity compared to DCIS (all p < 0.05). Conclusion The tumor size (CSAmax), enhancement index (%RS), and internal enhancement pattern (IEP) were highly indicative of DCISM. DCISM tends to express more aggressive pathological features, such as high nuclear grade, comedo-type necrosis, ALN metastasis, and Ki-67 overexpression. As with MRI, CEM has the capability to help predict when DCISM is accompanying DCIS.

Rationale and Objectives The Kaiser score (KS) is a simple and intuitive machine-learning derived decision rule for characterizing breast lesions in a clinical setting and screening for breast cancer. The present study aims to investigate the applicability of the KS for contrast-enhanced mammography (CEM) in breast masses, and to compare its diagnostic accuracy with magnetic resonance imaging (MRI). CEM may provide an alternative option for patients with breast masses, especially for those with MRI contraindications. Materials and Methods Two hundred and seventy-five patients with breast enhanced masses were included in the study from May 2019 to September 2022. Patients were further divided into benign and malignant groups based on pathological diagnosis. The CEM and MRI imaging characteristics of these two groups were analyzed statistically. The paired chi-square and Cohen's kappa coefficient (κ) analysis were used to compare imaging characteristics between CEM and MRI. The Breast Imaging Reporting and Data System (BI-RADS) and KS for CEM and MRI were evaluated based on imaging characteristics. The diagnostic performance of BI-RADS and KS for CEM and MRI was assessed and compared using receiver operating characteristic (ROC) analysis and DeLong’s test. Results The imaging characteristics of root sign, time-signal intensity curve (TIC/mTIC), margin, internal enhancement pattern (IEP), edema, apparent diffusion coefficient (ADC) values, and suspicious malignant microcalcifications showed significant differences between benign and malignant lesions (all p ≤ 0.011). The detection rate of root sign and margin showed substantial agreement between CEM and MRI (κ = 0.656, κ = 0.640), but IEP, TIC/mTIC, and edema showed poor agreement (κ = 0.380, κ = 0.320, κ = 0.324). For all lesion analyses, the area under the curves (AUCs) of the KS (0.897 ∼ 0.932) were higher than that of BI-RADS (0.691) in CEM (all p < 0.001). The AUC of KS (calcification)-CEM (0.932) was higher than those of both KS-CEM and KS (edema)-CEM (0.897 and 0.899) (all p < 0.001). For subgroup analyses, the AUCs of the KS (0.875 ∼ 0.876) were higher than that of BI-RADS (0.740) in MRI (all p < 0.001). The AUCs of KS-MRI (0.876) and KS (ADC)-MRI (0.875) were similar to those of KS-CEM (0.878) and KS (edema)-CEM (0.870) (all p > 0.100). The AUC of KS (calcification)-CEM (0.934) was slightly higher than those of both KS-MRI (0.876) and KS (ADC)-MRI (0.875), but no significant difference was observed (p = 0.051; p = 0.071). Conclusion The KS for CEM provided high diagnostic accuracy in distinguishing breast masses, comparable to that of MRI. The application of KS (calcification)-CEM combined with suspicious malignant microcalcifications can improve diagnostic efficiency with an AUC of 0.932 ∼ 0.934. However, edema did not significantly improve performance when using the KS for CEM.

Background This study developed a nomogram using quantitative indices of this subregion before chemoradiotherapy (CRT) to predict early GBM recurrence. Methods Adult patients with GBM diagnosed between October 2018 and October 2022 were retrospectively analyzed and randomly divided into training and validation groups. Using T1-weighted imaging enhancement and FLAIR fusion maps, the CRT extra-residual FLAIR high-signal area was segmented into categories and the signal intensity of each subzone was measured.The study compared clinical, pathological, and imaging indexes between recurrent and non-recurrent groups, identified independent prognostic risk factors, and developed a prediction model using univariate Cox analysis and LASSO Cox regression analysis. The discriminatory ability of the model was assessed using the C-index, and its performance was evaluated through calibration curves and decision curves. Results A study found that 53.4% of 129 patients with GBM experienced postoperative recurrence. Factors such as the subventricular zone involvement, enhanced regional outside the residual cavity (ER) median, enhanced + unenhanced regional outside the residual cavity (ER + UR) rFLAIR, and corpus callosum involvement were identified as independent predictors of recurrence. The model had a C-index of 0.733 in the training group and 0.746 in the validation group for predicting recurrence at 1 year post-surgery. Patients were also stratified based on these factors.Patients were divided into high and low-risk groups based on their nomogram score, showing a significant difference in progression-free survival between the two groups. Conclusions Quantitative assessment of FLAIR high signal areas in GBM after segmenting subregions shows promise for predicting survival prognosis. Emphasizing specific subregions may improve predictive accuracy.

The hyperintensity area surrounding the residual cavity on postoperative fluid-attenuated inversion recovery (FLAIR) image is a potential site for glioblastoma (GBM) recurrence. This study aimed to develop a nomogram using quantitative metrics from subregions of this area, prior to chemoradiotherapy (CRT), to predict early GBM recurrence. Adult patients with GBM diagnosed between October 2018 and October 2022 were retrospectively analyzed. Quantitative metrics, including the mean, maximum, minimum, median values, and standard deviation of FLAIR signal intensity (SI) (measured using 3D-Slicer software), were extracted from the following subregions surrounding the residual cavity on post-contrast T1-weighted (CE-T1WI)-FLAIR fusion images: the enhancing region (ER), non-enhancing region (NER), and combined ER + NER. Independent prognostic factors were identified using Cox regression and least absolute shrinkage and selection operator (LASSO) analyses and were incorporated into the prediction nomogram model. The model’s performance was evaluated using the C-index, calibration curves, and decision curves. A total of 129 adult GBM patients were enrolled and randomly assigned to a training (n = 90) and a validation cohorts (n = 39) in a 7:3 ratio. Sixty-nine patients experienced postoperative recurrence. Cox regression analysis identified subventricular zone involvement, the median FLAIR intensity in the ER, the rFLAIR (relative FLAIR intensity compared to the contralateral normal region) of ER + NER, and corpus callosum involvement as independent prognostic factors. For predicting recurrence within 1 year after surgery, the nomogram model had a C-index of 0.733 in the training cohort and 0.746 in the validation cohort. Based on the nomogram score, post-operative GBM patients could be stratified into high- and low-risk for recurrence. Nomogram models which based on quantitative metrics from FLAIR hyperintensity subregions may serve as potential markers for assessing GBM recurrence risk. This approach could enhance clinical decision-making and provide an alternative method for recurrence estimation in GBM patients.

目的:基于对比增强能谱X线摄影(CESM)影像与病理指标构建预测模型,用于预测乳腺癌的 HER-2、Ki-67 表达水平.方法:回顾性分析 200 例肿块型乳腺癌的 CESM影像及病理资料,按照7:3 比例分为训练集(n=140)和验证集(n=60).根据 HER-2、Ki-67 表达水平,分为阴性组和阳性组.比较阴性组和阳性组间CESM特征及病理指标的差异.利用 logistic 回归筛选出独立危险因素,构建预测模型.采用受试者操作特征(ROC)曲线下面积(AUC)、校准曲线和决策曲线分析评估预测模型的效能.结果:Logistic回归分析示乳腺癌边缘毛刺征、强化均匀性、可疑恶性微钙化、腋窝淋巴结转移是HER-2 阳性的独立预测因子;肿块最大径、边缘毛刺征、强化均匀性、RS%CC、组织学分级是 Ki-67 阳性的独立预测因子.基于上述特征构建的 HER-2 和Ki-67 预测模型均具有较好的预测能力,其 AUC 值分别为0.858～0.899、0.901～0.916.其校准曲线及DCA曲线提示预测结果接近实际结果,该预测模型有良好的临床应用价值.结论:HER-2 和Ki-67 预测模型可作为一种有效和简便的方法用于预测乳腺癌患者 HER-2 和Ki-67 表达水平,对于临床医生决策及患者预后有一定帮助.

MRI是高级别胶质瘤(HGG)诊治决策的重要评估手段.合成MRI定量参数值能够随组织物理特征变化而改变,据此可反映胶质瘤微细结构和功能异常.近年来,合成MRI已被广泛应用于脑胶质瘤的诊治研究.现就合成MRI定量参数对胶质瘤分级、基因表型预测、肿瘤范围界定、术后真性进展和假性进展的鉴别、早期进展评估的研究进展进行综述.

患者 女,68岁.无明显诱因间断性右唇麻木,每日发作1～2次,每次持续约1 min,不伴肢体抽动或无力,无头痛、头晕、视物模糊、意识障碍.常规实验室检查及胸片未见异常.神经系统查体未见明显阳性体征.

Purpose To evaluate the association between osteoporosis and coronary calcification and coronary plaque burden in patients with atherosclerosis and coronary artery disease (CAD). Methods This study included 290 men and 177 postmenopausal women with angiography-confirmed atherosclerosis or CAD who underwent chest multidetector row computed tomography covering L1-L2 between September 2020 and October 2021. Quantitative computed tomography was used to measure the lumbar vertebra's bone mineral density (BMD). The coronary artery calcium score (CACS) and total coronary plaque burden were quantified using the Agatston and modified Gensini scores, respectively. Associations between BMD and CACS and modified Gensini scores were assessed using multivariate regression analysis. Lasso regression was used in model selection. Results In men, BMD was inversely associated with CACS [beta = -0.24; 95% confidence interval (CI), -0.35 to -0.13; P < 0.001) and coronary artery calcification (CAC) presence [odds ratio (OR) = 0.71; 95% CI, 0.52-0.96; P = 0.03) in the unadjusted model. After adjusting for age, modified Gensini score, prior percutaneous coronary intervention and hypertension, BMD was inversely associated with CACS (beta = -0.11; 95% CI, -0.22 to -0.01; P = 0.04). In postmenopausal women, BMD was inversely associated with CACS (beta = -0.24; 95% CI, -0.39 to 0.10; P < 0.001) and CAC presence (OR = 0.66; 95% CI, 0.47-0.92; P = 0.01) in the unadjusted model but no other models (P > 0.05). In both sexes, BMD did not correlate with the modified Gensini score or CAD prevalence (all P > 0.05). Conclusion In patients with coronary atherosclerosis and CAD, BMD of the lumbar vertebra correlated inversely with CACS in men but not postmenopausal women. Additionally, BMD did not correlate with the modified Gensini score in both sexes.

IntroductionThis study was designed to explore the feasibility of semiautomatic measurement of abnormal signal volume (ASV) in glioblastoma (GBM) patients, and the predictive value of ASV evolution for the survival prognosis after chemoradiotherapy (CRT).MethodsThis retrospective trial included 110 consecutive patients with GBM. MRI metrics, including the orthogonal diameter (OD) of the abnormal signal lesions, the pre-radiation enhancement volume (PRRCE), the volume change rate of enhancement (rCE), and fluid attenuated inversion recovery (rFLAIR) before and after CRT were analyzed. Semi-automatic measurements of ASV were done through the Slicer software.ResultsIn logistic regression analysis, age (HR = 2.185, p = 0.012), PRRCE (HR = 0.373, p < 0.001), post CE volume (HR = 4.261, p = 0.001), rCE(1m) (HR = 0.519, p = 0.046) were the significant independent predictors of short overall survival (OS) (< 15.43 months). The areas under the receiver operating characteristic curve (AUCs) for predicting short OS with rFLAIR(3m) and rCE(1m) were 0.646 and 0.771, respectively. The AUCs of Model 1 (clinical), Model 2 (clinical + conventional MRI), Model 3 (volume parameters), Model 4 (volume parameters + conventional MRI), and Model 5 (clinical + conventional MRI + volume parameters) for predicting short OS were 0.690, 0.723, 0.877, 0.879, 0.898, respectively.ConclusionSemi-automatic measurement of ASV in GBM patients is feasible. The early evolution of ASV after CRT was beneficial in improving the survival evaluation after CRT. The efficacy of rCE(1m) was better than that of rFLAIR(3m) in this evaluation.

Recurrence is a significant adverse outcome of ischemic stroke (IS), particularly in cases of intracranial arteriosclerosis (ICAS). In this study, we investigated the impact of imaging features of culprit plaque using high-resolution magnetic resonance vessel wall imaging (HR-MR-VWI) on the prediction of IS recurrence. A total of 86 patients diagnosed with ICAS-related IS within the middle cerebral artery (MCA) territory were included, of which 23.25% experienced recurrent IS within one year. Our findings revealed significant differences between the recurrence and non-recurrence groups in terms of age (p = 0.007), diabetes mellitus (p = 0.031), hyperhomocysteinemia (p = 0.021), artery-artery embolism (AAE) infarction (p = 0.019), prominent enhancement (p = 0.013), and surface irregularity of the culprit plaque (p = 0.009). Age (HR = 1.063, p = 0.005), AAE infarction (HR = 5.708, p = 0.008), and prominent enhancement of the culprit plaque (HR = 4.105, p = 0.025) were identified as independent risk factors for stroke recurrence. The areas under the receiver operating characteristic curve (AUCs) for predicting IS recurrence using clinical factors, conventional imaging findings, HR-MR-VWI plaque features, and a combination of clinical and conventional imaging models were 0.728, 0.645, 0.705, and 0.814, respectively. Notably, the combination model demonstrated superior predictive performance with an AUC of 0.870. Similarly, AUC of combination model for predicting IS recurrence in validation cohort which enrolled another 37 patients was 0.865. In conclusion, the presence of obvious enhancement in culprit plaque on HR-MR-VWI is a valuable factor in predicting IS recurrence in ICAS-related strokes within the MCA territory. Furthermore, our combination model, incorporating plaque features, exhibited improved prediction accuracy.

急性缺血性脑卒中(AIS)患者机械取栓(MT)后早期颅脑 CT 所见的脑实质高密度征(CHD)是其术后常见征象,可能与对比剂外渗(CE)和(或)出血性转化(HT)相关.CHD 具有一定部位和演变特点.非增强CT(NCCT)、双能CT、MRI 等有助于甄别 HT 与 CE,但不同检查方法的鉴别效能不同.CHD对于预测AIS的出血性转化、最终梗死体积以及神经功能预后具有一定价值.本文对CHD病理学特点、影像学检查的性质判别以及其预后评估价值进行综述.