http://med-sci.cn/cgzcx/en/contents_list.asp?issue=202510 zh-cn An RSS feed for Chinese Journal of Magnetic Resonance Imaging http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.10.001 ObjectiveTo observe the clinical efficacy of point application combined with ear point pressing in the treatment of benign paroxysmal positional vertigo (BPPV) patients with residual dizziness (RD) after successful canalith repositioning procedure (CRP), and explore its influence on the spontaneous brain activities of the patients. Materials and MethodsUsing a prospective randomized controlled trial design, a total of 62 patients with RD who visited from July 2022 to December 2024 were randomly divided into an experimental group (EG) and a control group (CG), with 31 cases in each group. The CG was treated with betahistine, while the EG was given point application combined with ear point pressing therapy on the basis of CG. Data of scale scores and resting-state functional magnetic resonance imaging (fMRI) of the two groups before and after treatment were collected. Dizziness Handicap Inventory (DHI), Visual Analog Scale (VAS), and Hamilton Anxiety/Depression Scale (HAMA/HAMD) were included as clinical assessment indicators, and fractional amplitude of low-frequency fluctuation (fALFF) was included as the imaging assessment indicator. ResultsBefore treatment, there were no significant differences in scores of DHI, VAS, HAMA, and HAMD between the two groups (P > 0.05). After treatment, the EG showed significantly lower scores on the DHI, VAS, HAMA, and HAMD compared to the CG (P < 0.05). Compared to the baseline, the EG showed increased fALFF in the right insular cortex and decreased fALFF in the right superior occipital gyrus after treatment (P < 0.05, FDR correction). Among the patients in the EG after treatment, the fALFF value of the right insular cortex was positively correlated with the DHI score (r = 0.622, P = 0.001), and the fALFF value of the right superior occipital gyrus was negatively correlated with the DHI score (r = -0.418, P = 0.033). ConclusionsPoint application combined with ear point pressing therapy might help improve the RD symptoms in BPPV patients by regulating the spontaneous functional activities of the vestibular-related brain regions and visual cortex. ]]> Mon,20 Oct 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.10.002 ObjectiveTo assess the sustained effect of acupuncture at the Si Guan points on functional connectivity of hippocampal subregions in patients with Alzheimer^,s disease (AD) and normal controls (NC). Materials and MethodsDemographic data, neuropsychological assessments, and resting-state functional magnetic resonance imaging (fMRI) data were collected from 14 AD patients and 14 NCs matched by age, sex, and educational level at baseline. After the baseline MRI scan, acupuncture stimulation on the Si Guan points was performed for 3 minutes. Then, another 10 minutes of fMRI data were acquired after the needle was withdrawn. A dataset that included 100 healthy participants was also included to construct a reliable functional connectivity map of the hippocampal subregions. Regions of interest (ROIs) in the bilateral anterior hippocampus (aHPC) and posterior hippocampus (pHPC) were selected to assess the sustained effect of acupuncture on functional connectivity of hippocampal subregions in AD patients and NCs. ResultsResting-state functional connectivity analysis demonstrated that multiple brain regions, including the orbitofrontal cortex (OFC), parahippocampal gyrus (PHG), superior temporal gyrus (STG) and insula, showed increased functional connectivity with the hippocampal subregions in the AD group (all P < 0.05) and decreased functional connectivity with the hippocampal subregions in the NC group after acupuncture compared to that at baseline (all P < 0.05). However, superior frontal gyrus (SFG) showed decreased functional connectivity with the hippocampal subregions in the AD group (P < 0.05) and increased functional connectivity with the hippocampal subregions in the NC group (P < 0.01) after acupuncture compared to that at baseline. Acupuncture also specifically elicited increased functional connectivity between the aHPC and the medial frontal cortex (P < 0.01) as well as decreased functional connectivity between the pHPC and the PHG (P < 0.05) in the NC group. Additionally, functional connectivity between the aHPC and the OFC was positively correlated with neuropsychological scale scores in the AD group before acupuncture treatment (r = 0.70, P = 0.016). ConclusionsThese findings confirm and extend previous studies suggesting that acupuncture at Si Guan points can exert bidirectional and benign regulatory effects on functional connectivity of hippocampal subregions in AD patients. ]]> Mon,20 Oct 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.10.003 ObjectiveTo investigate consistent alterations in spontaneous brain activity in patients with borderline personality disorder (BPD) using resting-state functional magnetic resonance imaging (rs-fMRI), in order to further explore the potential neurobiological mechanisms underlying BPD. Materials and MethodsRelevant literature published before May 8, 2025, was systematically searched using amplitude of low-frequency fluctuation/fractional ALFF (ALFF/fALFF) and regional homogeneity (ReHo) analytical methods to assess changes in resting-state brain function in BPD patients. Based on strict inclusion and exclusion criteria, the activation likelihood estimation (ALE) method was used to integrate and analyze brain regions exhibiting aberrant spontaneous neural activity in BPD patients compared to healthy controls (HCs), using GingerALE 3.0.2 software. ResultsA total of six studies met the inclusion criteria, involving 293 BPD patients and 197 HCs. By combining ALFF/fALFF and ReHo data, the results showed significantly increased spontaneous activity in the left lentiform nucleus, left parahippocampal gyrus, and bilateral cuneus in BPD patients compared to HCs (P < 0.05). In contrast, decreased activity was observed in the right cuneus, right posterior cingulate cortex, left cingulate gyrus, left precuneus, left middle frontal gyrus, and left superior frontal gyrus (P < 0.05). ConclusionsThis ALE meta-analysis identified abnormal spontaneous brain activity across multiple brain regions in BPD patients, contributing to a deeper neuroimaging-based understanding of BPD and offering valuable insights for future clinical interventions. ]]> Mon,20 Oct 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.10.004 ObjectiveTo explore segmental microstructural alterations of cerebral white matter tracts in patients with migraine without aura (MwoA) in the ictal phase using automatic fiber quantification (AFQ), and to analyze the relationship between these alterations and clinical symptoms. Materials and MethodsA total of 45 MwoA patients in the ictal phase (MwoA group) and 30 matched healthy controls (HC) were enrolled. Clinical data and MRI data were collected. AFQ technology was applied to analyze diffusion tensor imaging (DTI) data at both entire and nodal levels. Twenty white matter tracts across the brain were tracked, and 100 equidistant nodes were defined for each tract to calculate diffusion indices, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD). Two-sample t-tests were used to compare diffusion indices between groups, and partial correlation analysis was performed to assess the correlation between abnormal tract indices in the MwoA group and clinical scale scores. ResultsCompared with the HC group, at the entire level, the MwoA group exhibited increased MD and RD in the forceps minor of the corpus callosum (CF minor) (FDR correction, all P < 0.05). Nodal analysis revealed that the MwoA group exhibited decreased FA in the left inferior fronto-occipital fasciculus (IFOF), increased MD in the left thalamic radiation (TR), the CF minor, and the right IFOF, increased AD in the right IFOF, decreased AD in the left uncinate fasciculus (UF), and increased RD in the left TR and CF minor (FDR correction, all P < 0.05). Additionally, the mean AD and MD values of the abnormal segments in the right IFOF were positively correlated with headache impact test scores (r = 0.351, r = 0.331, all P < 0.05), while the mean AD value of the abnormal segments in the left UF was negatively correlated with migraine-specific quality of life questionnaire scores (r = -0.535, P < 0.001). ConclusionsIn the MwoA ictal period, patients exhibit segmental microstructural damage in multiple white matter fiber tracts. Segmental abnormalities in the right inferior fronto-occipital fasciculus and left uncinate fasciculus may be closely associated with the neuropathological mechanisms of MwoA in the ictal phase. ]]> Mon,20 Oct 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.10.005 ObjectiveTo investigate iron deposition and microstructural damage in gray matter nuclei of patients with cerebral small vessel disease with mild cognitive impairment (CSVD-MCI) using quantitative susceptibility mapping (QSM) and diffusional kurtosis imaging (DKI), and to explore their correlations with cognitive function. Materials and MethodsThe imaging and clinical data of 5 CSVD-MCI patients diagnosed in Guizhou Provincial People^,s Hospital from December 2022 to March 2024 were retrospectively collected. A total of 28 CSVD-MCI patients with comprehensive clinical diagnosis in Guizhou Provincial People^,s Hospital from March 2024 to December 2024 were prospectively enrolled. Finally, 33 CSVD-MCI patients were enrolled. Thirty-two normal controls matched for age, sex and years of education were recruited, and the Montreal Cognitive Assessment (MoCA) scores of the two groups were collected. All participants underwent 3D-T1WI, QSM and DKI sequence scanning on a GE 3.0 T superconducting MRI scanner. Using the uAI Discovery-brain platform and a brain atlas, the whole brain was segmented into 51 subregions. Bilateral gray matter nuclei, including the caudate nucleus, putamen, globus pallidus, and thalamus, were selected for analysis. Susceptibility values, kurtosis fractional anisotropy (KFA), mean kurtosis (MK), axial kurtosis (AK) and radial kurtosis (RK) were extracted for each nucleus using coarse-grained quantitative analysis based on segmentation. Between-group differences in susceptibility and DKI parameters were assessed using independent samples t-tests for normally distributed data and Mann-Whitney U tests for non-normally distributed data. Spearman correlation analysis was used to examine relationships between imaging parameters and MoCA scores. A significance threshold of P < 0.05 was applied. Results(1) No significant differences were observed between groups in age, sex, or education level (all P > 0.05), while MoCA scores differed significantly (P < 0.05). (2) Compared to HCs, the CSVD-MCI group exhibited significantly increased susceptibility in the bilateral globus pallidus (both P < 0.05). Significantly decreased KFA was observed in the bilateral caudate nucleus, putamen, and thalamus (all P < 0.05). MK showed no significant differences (P > 0.05). Significantly decreased AK was found in the bilateral caudate nucleus, right putamen, and bilateral thalamus (all P < 0.05). Significantly increased RK was observed in the bilateral putamen (both P < 0.05). (3) In the CSVD-MCI group, susceptibility in the bilateral putamen (left: r = -0.294, P = 0.017; right: r = -0.328, P = 0.008) correlated negatively with MoCA scores. KFA (r = 0.417, P = 0.016), MK (r = 0.401, P = 0.020), AK (r = 0.395, P = 0.023), and RK (r = 0.351, P = 0.045) in the left globus pallidus correlated positively with MoCA scores. Susceptibility in the bilateral putamen (left: r = -0.356, P = 0.041; right: r = -0.449, P = 0.008) correlated negatively with KFA values. ConclusionsThere are abnormal iron metabolism and microstructural damage in the gray matter nucleus in CSVD-MCI patients. The cognitive ability of CSVD-MCI patients is related to the iron content in bilateral putamen and the microstructural integrity of left globus pallidus. QSM and DKI provide a new perspective for early diagnosis, early intervention and personalized treatment of cerebral small vessel disease. ]]> Mon,20 Oct 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.10.006 ObjectiveTo explore the value of a deep neural network model based on multi-sequence MRI in predicting the methylation status of the O⁶-methylguanine-DNA methyltransferase (MGMT) promoter in patients with glioblastoma. Materials and MethodsT1WI and contrast enhanced T1-weighted imaging (CE-T1WI) data from 262 glioblastoma patients (162 methylation and 100 unmethylation) were retrospectively analyzed. The Mann-Whitney U test, least absolute shrinkage and selection operator (LASSO) regression analysis, combined with the Pearson correlation coefficient method, were used to screen the features. Based on the screened features, a prediction model was constructed by means of the deep neural network algorithm. To evaluate the prediction efficiency of this model, the area under the receiver operating characteristic curve (AUC) was adopted to measure the prediction accuracy and reliability of the model. ResultsThe T1WI model (AUC = 0.752 in the validation set, sensitivity = 68.8%, specificity = 75.0%), the CE-T1WI model (AUC = 0.823 in the validation set, sensitivity = 75.0%, specificity = 75.0%), and the multi sequence combined model (AUC = 0.847 in the validation set, sensitivity = 81.3%, specificity = 80.0%) based on the deep neural network could be used to predict the MGMT promoter methylation of patients with glioblastoma, and the multi sequence combined model had the highest diagnostic efficacy compared with the single sequence models. ConclusionsThe multi sequence MRI radiomics model based on deep neural network can noninvasively predict the MGMT promoter methylation features in glioblastomas. ]]> Mon,20 Oct 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.10.007 ObjectiveTo explore the value of multiparametric MRI radiomics combined with MRI features in non-invasively predicting human epidermal growth factor receptor 2 (HER-2) overexpression and low expression in non-mass enhancement (NME)-type breast cancer. Materials and MethodsA total of 156 breast cancer cases with NME on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and pathologically confirmed were collected from our hospital, and divided into the HER-2 overexpression group (66 cases) and the HER-2 low expression group (90 cases). They were randomly assigned to a training set (124 cases) and a test set (32 cases) at a ratio of 8∶2. Volumes of interest (VOIs) were segmented on the 2nd phase (DCE-2), 8th phase (DCE-8) of DCE-MRI, and diffusion weighted imaging (DWI) sequences, and radiomic features were extracted. The Elastic Net (Enet) algorithm was used to construct models based on DCE-2, DCE-8, DWI, and their combination. Logistic regression analysis was performed to identify independent influencing factors for HER-2 expression. Finally, a fusion model was built by combining the rad-score of the combined model with independent influencing factors. ResultsThe areas under the curve (AUC) of the radiomic models based on DCE-2, DCE-8, DWI, and their combination in the training and test sets were 0.746 and 0.714, 0.768 and 0.714, 0.721 and 0.635, 0.823 and 0.734, respectively. Logistic regression analysis showed that the maximum tumor diameter was an independent factor for distinguishing HER-2 expression (P < 0.05). The fusion model achieved the best predictive performance, with AUCs of 0.844 and 0.808 in the training and test sets, respectively. DeLong^,s test indicated no significant difference between the combined model and the fusion model (P = 0.316). Analysis of SHAP results showed that rad-score contributed the most to the fusion model. ConclusionsMulti-parametric MRI radiomics combined with MRI features can effectively predict HER-2 overexpression and low expression in NME-type breast cancer, and the combination with SHAP algorithm can further improve the interpretability of the model. ]]> Mon,20 Oct 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.10.008 ObjectiveTo investigate the feasibility of using a deep learning-based image classification model for distinguishing liver multi-parameter magnetic resonance imaging (mpMRI) sequences. Materials and MethodsA retrospective dataset of 1744 liver mpMRI examinations from 1676 patients (November 16, 2022 to June 29, 2023) was collected as model development set, yielding 25 365 independent sequences. These were randomly divided into training [number of series (ns) = 20 207], validation (ns = 2664), and test sets (ns = 2494) at an 8∶1∶1 ratio. A 3D-ResNet model was trained to classify liver mpMRI sequences, with input as image and output categories including: T1-weighted in-phase (T1WI_In), T1-weighted opposed-phase (T1WI_Opp), T2-weighted imaging with fat-suppression (T2WI_Fs), high b-value DWI, ADC maps, and dynamic contrast-enhanced MRI (pre-contrast, arterial, portal venous, delayed). The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA_LIHC) dataset was used as the external validation set for the model, comprising a total of 59 mpMRI examinations involving 38 patients. Radiologists^, classifications served as the gold standard. Model performance was evaluated using confusion matrices. ResultsAt the overall classification level, the training, validation and test sets achieved average accuracy, macro-F1, and micro-F1 scores of 97.2% to 99.0%, 0.949 to 0.982 and 0.960 to 0.985, respectively. For individual sequences, the training, validation and test sets demonstrated per-class accuracy (89.6% to 100.0%), sensitivity (81.0% to 100.0%), specificity (98.2% to 100.0%), and F1 scores (0.797 to 1.000). On the external validation set, the model achieved macro-accuracy, macro-F1, and micro-F1 scores of 91.6%, 0.819, and 0.816, respectively. Per-sequence metrics included accuracy (74.1% to 99.4%), sensitivity (55.4% to 100.0%), specificity (92.8% to 100.0%), and F1 score (0.579 to 0.968). ConclusionsThe deep learning-based model demonstrated high accuracy in classifying liver mpMRI sequences, supporting its potential for automated sequence classification in clinical practice. ]]> Mon,20 Oct 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.10.009 ObjectiveTo explore the correlation between quantitative perfusion parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and the pathological grade and angiogenesis of extrahepatic cholangiocarcinoma (ECCA). Materials and MethodsFifty patients with ECCA underwent conventional MRI and DCE-MRI scans before surgery. The microvessel density (MVD) and vascular endothelial growth factor (VEGF) expression in postoperative specimens were detected by immunohistochemistry. The relationships between DCE-MRI quantitative parameters and pathological grade, MVD, and VEGF expression were analyzed. ResultsAmong the 50 patients with ECCA, 15 cases were well differentiated, 23 cases were moderately differentiated, and 12 cases were poorly differentiated. The volume transfer constant (K^trans) and extracellular extravascular volume fraction (V_e) of ECCA patients were not correlated with pathological grade or VEGF expression (P > 0.05). K^trans was positively correlated with MVD (r = 0.524, P < 0.001), while the rate constant (K_ep) and V_e were not related to MVD (P > 0.05). ConclusionsThe DCE-MRI parameter K^trans is positively correlated with MVD, confirming its feasibility in non-invasively reflecting the angiogenesis of ECCA to a certain extent; however, since K_ep and V_e values do not show a similar correlation, their efficacy in evaluating invasiveness and prognosis still needs to be further verified by prospective studies with larger samples. ]]> Mon,20 Oct 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.10.010 ObjectiveTo predict regional lymph node metastasis (LNM) in rectal cancer (RC) using deep learning-based tumor auto-segmentation and radiomics. Materials and MethodsThis single-center research retrospectively analyzed T2WI and DWI of 282 rectal cancers from two MR scanners. The deep learning-based auto-segmentation models were constructed on T2WI and DWI with 3D U-Net, 3D V-Net, and nnU-Net v2 and assessed with the dice similarity coefficient (DSC). Radiomics features on manual-based volume of interest (MbV) and deep learning-based volume of interest (DbV, with the highest DSC) were extracted respectively. After feature normalization and selection, five machine learning algorithms were used for radiomics model building and then for LNM prediction. The optimal model was evaluated with area under the curve (AUC), accuracy, specificity, and sensitivity. ResultsThe DSC of the nnU-Net v2 was significantly higher than that of the 3D U-Net and 3D V-Net (T2WI: 0.886 vs. 0.548 vs. 0.616, P < 0.001; DWI: 0.906 vs. 0.583 vs. 0.433, P < 0.001) in test set. The AUC of DbV based-radiomics models constructed with logistic regression algorithm were comparable to those of the corresponding MbV-based radiomics models (T2WI: 0.700 vs. 0.633, P = 0.638; DWI: 0.667 vs. 0.700, P = 0.544; T2WI + DWI: 0.800 vs. 0.833, P = 0.248) in LNM prediction in validation set. ConclusionsRadiomics features of T2WI and DWI based on nnU-net v2 segmented tumor area showed a reliable performance in predicting LNM in RC. ]]> Mon,20 Oct 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.10.011 ObjectiveThis study aimed to predict the development of castration-resistant prostate cancer (CRPC) in prostate cancer (PCa) patients following androgen deprivation therapy (ADT) by establishing habitat imaging analysis and intra/peri-tumor radiomics models. Materials and MethodsClinical and multiparametric magnetic resonance imaging (mpMRI) data from 195 pathologically confirmed PCa patients treated with ADT were retrospectively analyzed. Patients were randomized into training (n = 138) and validation (n = 57) sets at a 7∶3 ratio. Tumor regions were segmented using habitat imaging, and habitat-specific features representing distinct subregions were extracted. K-means clustering algorithm partitioned tumors into two subclusters based on habitat heterogeneity. Radiomic features were selected from four regions: habitat subregions (17 features), intra-tumor (16 features), peri-tumor (15 features), and combined intra-tumor + 3 mm peri-tumor (ROIintra + 3 mm, 19 features). A logistic regression classifier was trained to construct radiomics models. The optimal habitat model was integrated with clinical features to establish a combined habitat-clinical (H + C) model. A radiomics nomogram (RN) was developed for individualized prediction. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). ResultsThe habitat model demonstrated superior predictive performance (AUC = 0.821) compared to conventional radiomics models. The ROIintra + 3 mm model (AUC = 0.752) outperformed intra-tumor (AUC = 0.697) models and peri-tumor (AUC = 0.725) models. The H + C model achieved the highest predictive efficacy (AUC = 0.828). Calibration curves indicated excellent agreement between predicted and observed outcomes, while DCA curves confirmed greater clinical net benefit for the combined model. ConclusionsHabitat imaging analysis significantly enhances CRPC prediction accuracy in PCa patients by resolving intratumoral heterogeneity. Peri-tumor radiomics provides independent prognostic value for CRPC progression, and integration of peri-tumor features improves model performance. ]]> Mon,20 Oct 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.10.012 ObjectiveTo investigate the correlation between preoperative diffusion tensor imaging (DTI) parameters and the early ablation efficacy of high-intensity focused ultrasound (HIFU) in the treatment of adenomyosis, and to evaluate the predictive value of DTI-derived quantitative parameters. Materials and MethodsA retrospective study was conducted on 40 patients with adenomyosis, involving a total of 42 lesions. All participants underwent contrast-enhanced MRI (CE-MRI) and DTI scans both preoperatively and within one day after HIFU treatment. The following preoperative DTI parameters were measured: fractional anisotropy (FA), apparent diffusion coefficient (ADC), exponential map (EXP), mean fiber tract length, and mean fiber density. Correlation analyses were performed between these parameters and the non-perfused volume ratio (NPVR) as a measure of ablation efficacy. ResultsMean fiber tract length (r = 0.524, P = 0.002) and fiber density (r = 0.603, P = 0.001) were significantly positively correlated with NPVR. In contrast, FA, ADC, and EXP showed no significant correlation. When using NPVR > 50% as the threshold for satisfactory ablation, the area under the ROC curve (AUC) was 0.785 [95% confidence interval (CI): 0.622 to 0.949, P = 0.006] for mean fiber density and 0.711 (95% CI: 0.525 to 0.897, P = 0.042) for mean fiber tract length. ConclusionsDTI parameters are associated with variations in HIFU treatment efficacy for adenomyosis. Specifically, greater mean fiber tract length and fiber density are positively correlated with better ablation outcomes. These preoperative DTI-based quantitative parameters may serve as useful predictors of therapeutic efficacy in HIFU treatment for adenomyosis. ]]> Mon,20 Oct 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.10.013 ObjectiveTo evaluate whether super-resolution reconstruction technology can improve the diagnostic efficacy of deep learning in predicting lymphovascular space invasion (LVSI) in endometrial cancer. Materials and MethodsThis retrospective study enrolled 406 patients randomly split into training (n = 325) and validation (n = 81) sets (8∶2 ratio). Super-resolution reconstruction was performed on conventional T2-weighted imaging (T2WI) to obtain super high-resolution T2WI (SRT2). Deep learning models were developed based on both conventional T2WI and SRT2 images to predict LVSI status in endometrial cancer. The models were subsequently validated in validation set, and their diagnostic performance was compared across the training and validation sets. Using pathological diagnosis as the gold standard, the evaluation metrics included the area under the curve (AUC), sensitivity, and specificity, with model differences compared using DeLong^,s test. ResultsIn both the training and validation sets, the deep learning model based on conventional T2WI demonstrated AUC values (95% confidence interval) of 0.792 (0.733 to 0.851) and 0.759 (0.649 to 0.870), with sensitivities of 77.50% and 68.18%, and specificities of 77.08% and 80.67%, respectively. The model utilizing SRT2 achieved AUCs of 0.897 (0.852 to 0.943) and 0.899 (0.819 to 0.980), sensitivities of 87.80% and 86.40%, and specificities of 88.45% and 89.20%. Statistically significant differences between the two models were observed in both sets (P < 0.05), indicating superior performance of the SRT2-based deep learning model. ConclusionsSuper-resolution reconstruction technology has the potential to enhance the diagnostic efficacyof preoperative prediction of LVSI in endometrial cancer by improving image quality. ]]> Mon,20 Oct 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.10.014 ObjectiveTo investigate the diagnostic value of a radiomic habitat model based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) quantitative parametric maps for predicting the status of lymph-vascular space invasion (LVSI) in patients with cervical cancer before surgery. Materials and MethodsA total of 102 cervical cancer patients who underwent radical hysterectomy at Gansu Provincial People^,s Hospital between May 2015 and October 2024 were retrospectively analyzed. Patients were stratified into LVSI-positive [LVSI (+)] and LVSI-negative [LVSI (-)] groups according to postoperative pathological findings. Clinical parameters and DCE-MRI quantitative metrics were compared between the two groups. Univariate and multivariate regression analyses were performed to identify independent risk factors associated with LVSI status in cervical cancer. On DCE-MRI images, Tissue4D is applied to determine the peak blood flow phase via the time-intensity curve (TIC) of the internal iliac artery. During this phase, the entire tumor contour is contoured as the volume of interest (VOI) to obtain the transport constant (K^trans) parameter map. The K-means method was employed to determine the optimal number of clusters, leveraging the voxels and eigenvalues of the K^trans parametric maps to categorize the VOI into distinct subregions. Intratumoral radiomics features and habitat radiomics features were extracted. Feature dimensionality reduction was performed on each feature dataset of the training set using t-tests, Pearson analysis, and the least absolute shrinkage and selection operator (LASSO) regression. Machine learning algorithms, including the support vector machine (SVM), adaptive boosting (AdaBoost), and multilayer perceptron (MLP), were utilized to construct intratumoral radiomics models and habitat radiomics models. Feature fusion (pre-fusion) and result fusion (post-fusion) methods were adopted to construct a combined model integrating habitat radiomics and intratumoral radiomics for model development. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve were used to evaluate model performance. ResultsAmong the 102 cervical cancer patients, 38 cases were LVSI (+) and 64 cases were LVSI (-). Univariate logistic regression analysis revealed that age, height, body weight, body mass index, K^trans, and rate constant (K_ep) were factors associated with LVSI status in cervical cancer [odds ratios (ORs) = 0.989, 0.997, 0.991, 0.978, 0.045, 0.372; P = 0.011, 0.010, 0.008, 0.010, 0.038, 0.018, respectively]. Multivariate logistic regression analysis of clinical parameters did not identify any independent risk factors associated with LVSI for the construction of a clinical model (P > 0.05). The optimal number of habitat subregion clusters was determined to be three. Intratumoral and habitat radiomics models were constructed using 18 and 8 optimal radiomics features derived from the habitat regions and the entire tumor, respectively. Among these models, the pre-fusion model integrating intratumoral and habitat radiomics features based on the AdaBoost classifier (Pre_AdaBoost model) demonstrated the highest predictive performance compared to the intratumoral model, habitat model, and post-fusion model. In the training and validation sets, the Pre_AdaBoost model achieved the highest diagnostic capabilities, with area under the ROC curve (AUC), sensitivities, and specificities of 0.916 [95% confidence interval (CI): 0.856 to 0.977], 88.5%, 77.8% and 0.831 (95% CI: 0.691 to 0.972), 91.7%, 57.9%, respectively. The AUC values were 0.916 in the training set and 0.831 in the test set, indicating high clinical net benefit. ConclusionsThe combined model integrating habitat radiomics and intratumoral radiomics based on DCE-MRI quantitative parametric maps demonstrated significant value in predicting LVSI in cervical cancer, potentially facilitating personalized treatment decisions. ]]> Mon,20 Oct 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.10.015 ObjectiveTo investigate the changes in T2 values of key knee joint structures in amateur marathon runners before and after a half-marathon, explore their correlations with clinical variables, and identify the influencing factors for patellofemoral joint (PFJ) cartilage injury. Materials and MethodsAmateur runners participating in the Xinpu Half Marathon in Zunyi City in October 2024 were prospectively recruited all participants underwent MRI scans of the same-side knee joint using the same equipment and parameters twice: the first scan was performed within 1 week before the race (with no running exercise during this period), and the second scan was conducted within 24 hours after completing the half-marathon. Additionally, all participants received a weight-bearing knee joint X-ray examination. T2 mapping technique was used to measure the T2 values of the PFJ patellar cartilage (PC), medial meniscus (MM), lateral meniscus (ML), anterior cruciate ligament (ACL), posterior cruciate ligament (PCL), popliteus muscle (PM), and medial gastrocnemius (MG). PFJ cartilage was graded according to the Recht MRI grading standard, and knee osteoarthritis (KOA) was graded based on the Kellgren-Lawrence grading (KLG) system using weight-bearing knee X-ray findings. Wilcoxon non-parametric test and paired t-test were applied to analyze the differences in T2 values of the aforementioned structures before and after the race. Spearman and Pearson correlation analyses were used to examine the correlations between the differences in T2 values and age, body mass index (BMI), PFJ cartilage Recht grade, and KOA KLG. Multiple linear regression analysis was further conducted to screen the influencing factors for PFJ cartilage injury. ResultsAfter the half-marathon, the T2 values of the aforementioned knee joint structures were significantly higher than those before the race (all P < 0.001). The difference in PC T2 value showed a positive correlation with Recht grade (r = 0.84), KLG (r = 0.87), age (r = 0.62), and BMI (r = 0.82) (all P < 0.001). Multiple linear regression analysis indicated that BMI, and the average value of Recht grade and KOA KLG were risk factors for PFJ cartilage injury (regression coefficients: 0.715 and 2.389, respectively; all P < 0.001). ConclusionsAmong the population of amateur marathon runners, higher BMI, higher PFJ cartilage Recht grade, and higher KOA KLG can further increase the risk of PFJ cartilage injury. ]]> Mon,20 Oct 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.10.017 Mon,20 Oct 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.10.018 Mon,20 Oct 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.10.019 Mon,20 Oct 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.10.020 Mon,20 Oct 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.10.021 Mon,20 Oct 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.10.022 Mon,20 Oct 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.10.023 Mon,20 Oct 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.10.024 Mon,20 Oct 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.10.025 ^, long-term prognosis, especially regarding the evolution of left ventricular function and the progression of myocardial fibrosis. In recent years, multimodal cardiac magnetic resonance (CMR) has emerged as a crucial tool for studying the mechanisms and prognosis of left ventricular remodeling after CABG, thanks to its non-invasiveness, high resolution, and ability for multi-parameter quantitative assessment. This article systematically reviews the application value of multimodal CMR techniques in postoperative evaluation, focusing on the latest research progress of quantitative myocardial perfusion, late gadolinium enhancement, cardiac magnetic resonance feature tracking, T1/T2 mapping, and oxygen-sensitive cardiovascular magnetic resonance in assessing postoperative myocardial function recovery, fibrosis degree, and predicting prognosis.It also summarizes the limitations and complementarity of current multimodal CMR technology in the research on post-CABG evaluation, and proposes that future research should focus on the optimization of technical standardization, large-sample multicenter verification, and in-depth integration with clinical treatment decisions. This review aims to provide a reference for the standardized application of multimodal CMR techniques in the clinical evaluation of post-CABG patients, and contribute to improving the individualized diagnosis and treatment as well as long-term prognosis management of post-CABG patients. ]]> Mon,20 Oct 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.10.026 ^, quality of life. Early and precise diagnosis is therefore a critical strategy for improving prognosis. Traditional cardiac magnetic resonance (CMR) analysis methods have limitations in utilizing image information, The emergence of CMR radiomics has introduced a breakthrough in the diagnosis and treatment of cardiac diseases. This advanced technique enables the extraction of high-dimensional features from medical images, demonstrating significant advantages in the phenotyping, severity assessment, and progression evaluation of various cardiac conditions. However, current studies are mostly based on single-center, small-sample cohorts and lack external validation; imaging scan parameters and post-processing procedures have not been standardized, the reproducibility and biological interpretability of radiomics features remain insufficient, and models are still at the offline validation stage, lacking evidence for real-time decision support integrated with clinical workflows, which severely limits the speed and scope of translating CMR radiomics into clinical practice. In view of this,this review aims to review the key technical advances and clinical translations of CMR radiomics in the diagnosis and prognosis prediction of various cardiac conditions. It will also analyze current challenges and future directions, with the goal of providing an evidence-based foundation for clinical practice. Ultimately, this review seeks to foster early disease identification and improved prognosis management, thereby enhancing patient quality of life and clinical outcomes. ]]> Mon,20 Oct 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.10.027 ^, quality of life and long-term outcomes. ]]> Mon,20 Oct 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.10.028 Mon,20 Oct 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.10.029 Mon,20 Oct 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.10.030 Mon,20 Oct 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.10.031 Mon,20 Oct 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.10.032 Mon,20 Oct 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.10.033 Mon,20 Oct 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.10.034 Mon,20 Oct 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.10.035 ^,s disease (AD), Parkinson^,s disease (PD), essential tremor (ET), and other conditions through mechanisms including thermal ablation, mechanical effects, and blood-brain barrier (BBB) opening, while exploring future research directions. Current evidence indicates that MRgFUS exhibits significant advantages in improving motor symptoms, enhancing drug delivery, and neuromodulation. However, further validation is required regarding its long-term efficacy and individualized treatment protocols. This article reviews the clinical applications of MRgFUS in central nervous system disorders, analyzes the limitations of the current study, and proposes future research directions, aiming to provide a comprehensive reference for the clinical application of MRgFUS, to promote its optimization and innovation in the treatment of diseases, and to provide reference and assistance for related clinical research. ]]> Mon,20 Oct 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.10.036 Mon,20 Oct 2025 00:00:00 GMT