http://med-sci.cn/cgzcx/en/contents_list.asp?issue=202508 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.1674-8034.2025.08.001 ObjectiveSudden sensorineural hearing loss is one of the otologic emergency diseases which often induces anxiety-depression like emotional impairments. But how sudden sensorineural hearing loss promotes the occurrence of emotional abnormalities is unclear. This study used brain functional gradient technique to explore the relationship between sudden sensorineural hearing loss and emotional impairments. Materials and MethodsWe evaluated hearing, multi-dimensional neural scales, and resting-state brain function gradients in 44 patients with sudden sensorineural hearing loss and 40 healthy controls. Functional connectivity gradients were used to identify alterations in cortical connectivity gradients induced by sudden bilateral sensorineural hearing loss. Statistical parametric mapping 12 (SPM12) was used to process functional magnetic resonance imaging (fMRI) data, and Pearson^,s correlation was used to calculate the correlation between fMRI data and anxiety-depression like emotional impairments based on statistical package for the social sciences 22.0 (SPSS 22.0) software. ResultsPatients with sudden sensorineural hearing loss and healthy controls were well matched for age, gender and education level. The mean hearing thresholds of both ears in patients were significantly higher than healthy controls. The scores of anxiety and depression scales were significantly higher in patients with sudden sensorineural hearing loss. The results showed no significant difference in the primary gradient between the two groups. At the network level, patients with sudden sensorineural hearing loss showed no significant difference in the primary gradient. However, at the nodal level, increased gradient was observed in the left precuneus, while decreased gradients were observed in the left, right calcarine fissure and surrounding cortex, right parahippocampal gyrus and left medial superior frontal gyrus (P < 0.001). Moreover, there was a negative correlation between anxiety and the gradient of calcarine fissure and surrounding cortex in patients with sudden sensorineural hearing loss (r = -0.413, P = 0.005). ConclusionsThe functional gradient changes of brain regions in patients with sudden sensorineural hearing loss may help to clarify the neuropathological basis of emotional impairments in patients with sudden sensorineural hearing loss. ]]> Wed,20 Aug 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.08.002 ObjectiveTo investigate the changes in hippocampal subregion volumes in nasopharyngeal carcinoma (NPC) patients during radiotherapy and assess their correlation with radiation dose. Materials and MethodsThis study was a prospective longitudinal study that enrolled 41 patients with newly diagnosed NPC between March 2019 and April 2025. Brain MRI scans were performed using a 3.0 T scanner at three time points: pre-radiotherapy (1 to 2 days before treatment), mid-radiotherapy (19 to 20 days after treatment initiation), and post-radiotherapy (1 to 2 days after treatment completion). Hippocampal subregions were automatically segmented using FreeSurfer 7.3 software, and volume changes were assessed using a linear mixed model. Pearson correlation analysis was then conducted to explore the relationship between hippocampal volume changes and radiation dose. ResultsCompared to pre-radiotherapy, during the mid-radiotherapy period, the volumes of bilateral whole hippocampus, presubiculum, cornu ammonis 1 (CA1), CA3, CA4, granule cell-molecular layer-dentate gyrus (GC-ML-DG), molecular layer, hippocampal amygdala transition area (HATA), and right subiculum were significantly reduced (family-wise error, FWE correction, P < 0.05). After radiotherapy, except for the left HATA and right presubiculum, the volumes of other hippocampal subregions continued to decrease significantly, and the volume of the right fimbria hippocampi also decreased (FWE correction, P < 0.05). The volume change in the left hippocampal tail was significantly negatively correlated with the left hippocampal radiation dose (r = -0.555). All results were corrected for FWE using the Bonferroni method, with the significance level set at a corrected p-value of <0.05. ConclusionsDuring radiotherapy, the volumes of several hippocampal subregions in NPC patients significantly decreased. Furthermore, after radiotherapy, the volume change in the left hippocampal tail was significantly negatively correlated with the radiation dose to the left hippocampus, indicating that the radiation dose may influence the volume change in this region. ]]> Wed,20 Aug 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.08.003 ObjectiveDiffusion tensor imaging (DTI) technique was used to investigate the changes of brain microstructure of substantia nigra and insula in patients with frozen gait of Parkinson^,s disease patients with frozen gait (PD-FOG). Materials and MethodsThirty-five cases of PD-FOG patients were included as the case group, and twenty-four healthy control (HC) were included as the control group. All patients underwent scans of DTI, and fractional anisotropy (FA) images were obtained. The FA values in the substantia nigra and insula regions were measured separately, and the differences between the case group and the control group were compared. The results showed that the bilateral substantia nigra and insula FA values of the PD-FOG group were significantly different from those of the HC group (P < 0.05). The area under the curve (AUC) of the receiver operating characteristic (ROC) curve of the left insula FA value in the PD-FOG group was the largest, followed by the right substantia nigra FA value, the left substantia nigra A value, the right insula A value, with AUC values of 0.849, 0.812, 0.789, and 0.657, respectively. The correlation analysis between the FA values of the substantia nigra and insula and the Unified Parkinson^,s Disease Rating Scale (UPDRS) score showed that the right substantia nigra FA value (r = -0.693, P = 0.005), the left substantia nigra FA value (r = -0.638, P = 0.014), and the left insula FA value (r = -0.516, P = 0.014) were negatively correlated with the UPDRS score, while the right insula FA value was not significantly correlated with the UPDRS score. ConclusionsDTI not only reflects the brain microstructural variation between the insula regions and substantia nigra of patients with PD and freezing gait, but also it can reveal the correlation between FA values and the UPDRS score scale, providing clinical assistance for the prevention and intervention of freezing gait in PD patients. ]]> Wed,20 Aug 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.08.004 ObjectiveThis study employed quantitative susceptibility mapping (QSM) and diffusion tensor imaging (DTI) to measure alterations in iron content and microstructural integrity within the gray matter nuclei following ischemia in the middle cerebral artery (MCA) territory. Furthermore, the potential correlations between these changes were analyzed. Materials and MethodsThirty-one patients with severe unilateral MCA stenosis or occlusion were selected for QSM, DTI, magnetic resonance arterial spin labeling (ASL) and routine MRI scans. In patients with ASL prompted ischemia, the magnetic susceptibility value (MSV), mean diffusion (MD) values and fractionalisotropy (FA) values of the affected and healthy caudate nucleus, globus pallidus, putamen, red nucleus and substantia nigra were measured respectively. Paired sample t-tests were utilized to compare the differences in these parameters between the gray matter nuclei on the two sides. Additionally, the correlations between the susceptibility values and DTI parameter values in the affected nuclei were analyzed. ResultsThe nucleus MSV on the affected side of the 31 patients were all higher than the contralateral side, and there was no statistically significant difference in MSV on both sides of the red nucleus (P > 0.05), and the MSV differences on the other nucleus (P < 0.05). Both the MD and FA values of the affected nucleus were statistically significant compared with the opposite side (P < 0.05). The FA values of the caudate nucleus, putamen and substantia nigra were positively correlated with MSV (r = 0.438, 0.710 and 0.394, P values​were all < 0.05); the MD values of the caudate nucleus and putamen were negatively correlated with MSV (r = -0.417 and -0.593, P < 0.05). ConclusionsFollowing unilateral MCA territory ischemia, abnormal iron deposition and microstructural alterations may occur in multiple gray matter nuclei of the brain. Notably, a strong correlation exists between abnormal iron deposition and microstructural changes in the putamen. ]]> Wed,20 Aug 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.08.005 ObjectiveBased on high-resolution magnetic resonance vascular wall imaging analysis of Willis ring integrity, responsible plaque characteristics, infarct foci, and plaque numbers in patients with anterior circulation acute ischemic stroke, to explore the correlation between Willis ring integrity and plaque vulnerability, multiple infarctions, and plaque numbers. Materials and MethodsRetrospective analysis of 85 patients with acute ischemic stroke in the anterior circulation who visited Our hospital from January 2022 to December 2023, all of whom underwent high-resolution magnetic resonance angiography within 14 days of symptom onset. And based on the integrity of the Willis ring, it was divided into two groups. Compare the clinical data and intracranial responsible plaque characteristics between two groups of anterior circulation and posterior circulation, respectively. Use Single factor and multiple factor binary logistic regression analysis to evaluate the correlation between Willis ring integrity and plaque coexistence, as well as multiple infarctions. Spearman and multiple linear regression analysis were used to evaluate the correlation between Willis loop integrity and plaque number, as well as enhancement level. ResultsThe group with incomplete Willis anterior circulation had higher plaque burden (P = 0.001), enhancement rate (P = 0.043), greater stenosis of responsible vessels (P = 0.001), and more irregular plaque surfaces (P = 0.001). Compared with the intact symptom side group of the Willis loop, the incomplete symptom side group of the Willis loop had a higher plaque burden (P = 0.002), greater degree of responsible vessel stenosis (P = 0.001), and more irregular plaque surfaces (P = 0.013). Univariate logistic regression analysis showed that the incomplete anterior circulation of Willis ring was independently associated with multiple infarcts (OR = 2.94, 95% CI: 1.153 to 7.478, P = 0.024). Spearman analysis showed that the incomplete anterior circulation of Willis ring was positively correlated with the enhancement level (r = 0.321, P < 0.05); Another incomplete group was positively correlated with the number of plaques and the enhancement level (r = 0.358, 0.302; P < 0.05). Age, gender and clinical risk factors were adjusted, including smoking history, drinking history, hyperlipidemia, hypertension, and diabetes. The results of multiple logistic regression analysis showed that incomplete anterior circulation of Willis was independently associated with multiple infarctions (OR = 3.42, 95% CI: 1.208 to 9.700, P = 0.021). Multiple linear regression analysis showed that incomplete anterior circulation of Willis was a factor affecting the enhancement level (β = 0.571, P < 0.05), while incomplete posterior circulation of Willis was a factor affecting the number of plaques and enhancement level (β = 0.791, 0.341; P < 0.05). ConclusionsIncomplete Willis ring will promote plaque vulnerability, multiple infarcts and atherosclerosis in multiple vascular beds. This indicates that the loss of integrity of the Willis ring may affect the hemodynamics of the extracranial and extracranial great arteries, thereby affecting the blood supply of brain tissue and atherosclerosis process. ]]> Wed,20 Aug 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.08.006 ObjectiveTo investigate the predictive value of MRI-based radiomics models and clinical factor models for hemorrhagic transformation (HT) risk after thrombolysis in acute ischemic stroke (AIS). Materials and MethodsClinical and imaging data were retrospectively collected from 730 AIS patients at first presentation across two Centers. Data from Center 1 were randomly divided into a training set (436 cases) and an internal validation set (188 cases) in a 7:3 ratio. Univariate and multivariate logistic regression analyses identified independent clinical predictors of HT. Three models were constructed: (1) a clinical factor model, (2) a MRI radiomics model, and (3) a combined model integrating both features. External validation was performed using data from 106 patients from Center 2. Receiver operating characteristic (ROC) curves and area under the curve (AUC) values evaluated the predictive performance of the models, while DeLong^,s test was applied to compare statistical differences between AUCs. ResultsIn the training set, the AUCs for the clinical factor model, radiomics model, and combined model were 0.810 (95% CI: 0.756 to 0.864), 0.896 (95% CI: 0.865 to 0.928), and 0.928 (95% CI: 0.899 to 0.958), respectively. In the internal validation set, the corresponding AUCs were 0.757 (95% CI: 0.671 to 0.843), 0.852 (95% CI: 0.791 to 0.913), and 0.872 (95% CI: 0.809 to 0.935). In the external validation set, the AUCs were 0.720 (95% CI: 0.602 to 0.839), 0.804 (95% CI: 0.711 to 0.897), and 0.828 (95% CI: 0.751 to 0.905), respectively. Decision curve analysis indicated that the combined model provided the highest net benefit. ConclusionsBoth MRI-based radiomics models and clinical factor models demonstrated predictive value for HT risk after thrombolysis in AIS. The integration of these two approaches achieved the best performance, offering potential clinical utility for individualized risk stratification. ]]> Wed,20 Aug 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.08.007 ObjectiveTo overcome the limitations of conventional imaging in differentiating glioblastoma (GBM) from primary central nervous system lymphoma (PCNSL), we propose and validate a clinically integrated radiomics model for the preoperative, non-invasive stratification of these two oncological entities. Materials and MethodsA retrospective cohort of 173 patients with intracranial masses (118 GBM, 55 PCNSL), confirmed by histopathology or diagnostic radiotherapy, was randomly divided into training (n = 121) and validation (n = 52) sets in a 7∶3 ratio. Preoperative clinical parameters (serological indices, imaging manifestations) and multimodal MRI sequences [CE-T1WI, T2-FLAIR, DWI (b=1000 s/mm²), and ADC] were acquired. Tumor core regions (excluding peritumoral edema) were delineated as regions of interest (ROIs). Following Z-score normalization, key features were selected using the Mann-Whitney U test, Spearman correlation analysis, and the least absolute shrinkage and selection operator (LASSO) algorithm. An XGBoost classifier with 10-fold cross-validation was employed for model construction. A comparative analysis of five models was performed: the clinical model, four single-modality radiomics models, the multimodal radiomics model, and the integrated clinical-radiomics model. The diagnostic performance was evaluated using receiver operating characteristic (ROC) curves, with the area under the curve (AUC), sensitivity, specificity, and accuracy calculated. The statistical validation included the DeLong test for AUC comparison, calibration curve assessment, and decision curve analysis (DCA) to quantify clinical utility. ResultsThe clinical model demonstrated AUC values of 0.83 (95% CI: 0.76 to 0.90) in the training set and 0.74 (95% CI: 0.61 to 0.87) in the validation set. Among radiomics models, the multimodal radiomics model (T1+ADC+T2+DWI) achieved optimal performance with training/validation AUCs of 0.93 (95% CI: 0.88 to 0.98)/0.84 (95% CI: 0.72 to 0.96). The integrated clinical-radiomics model demonstrated superior diagnostic performance, achieving a training AUC of 0.94 (95% CI: 0.90 to 0.98) (accuracy 90.2%, sensitivity 96.7%) and a validation AUC of 0.85 (95% CI: 0.74 to 0.96) (accuracy 88.6%, sensitivity 83.3%). This combined model significantly outperformed individual models in predictive accuracy (DeLong test, P < 0.05) and clinical net benefit across threshold probability ranges (decision curve analysis). ConclusionsThe combined model, constructed by integrating clinical features and multimodal radiomics, can non-invasively and stably distinguish GBM from PCNSL, providing reliable references for the precise preoperative diagnosis of patients. It helps reduce the need for invasive tests and optimizes the clinical decision-making process. ]]> Wed,20 Aug 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.08.008 ObjectiveTo investigate the clinical value of T2WI-weighted imaging (T2WI), contrast-enhanced T1-weighted imaging (CE-T1WI) of the tumour body and peritumour in combination with conventional factors in identifyingfibrous and non-fibrous meningiomas. Materials and MethodsA total of 108 patients with pathologically confirmed meningiomas, including 30 fibrous meningiomas and 78 non-fibrous meningiomas, were enrolled and divided into a training set (n = 76) and a validation set (n = 32) in a ratio of 7 : 3. In the training set, 1132 radiomics features were extracted from the tumour body and peri-tumour of T2WI and CE-T1WI sequences, respectively. The optimal subset of radiomics features was identified through the maximal correlation minimal redundancy method (mRMR) and the least absolute shrinkage and selection operator (LASSO). Logistic regression (LR) machine learning method to construct imaging genomics models: T2WI tumour, T2WI peritumour, CE-T1WI tumour, CE-T1WI peritumour, (T2WI+CE-T1WI) tumour, (T2WI+CE-T1WI) peritumour and (T2WI+CE-T1WI) tumour+peritumour. The conventional factors with significance (P < 0.05) were screened by single-factor and multifactor logistic regression analysis methods. Then, the radiomics model with the best discriminatory efficacy was combined with the conventional factors to generate nomograms, and the diagnostic efficacy of the nomograms was evaluated by AUC, and the clinical efficacy of the model was assessed by the net benefit value of the decision curve analysis (DCA). the efficacy of this model was validated in the test set. ResultsThe AUC values for the T2WI tumour, T2WI peritumour, CE-T1WI tumour, CE-T1WI peritumour, (T2WI+CE-T1WI) tumour, (T2WI+CE-T1WI) peritumour and (T2WI+CE-T1WI) tumour+peritumour radiomics models in the training set were 0.925, 0.803, 0.837, 0.872, 0.902, 0.894, 0.908, respectively. In the test set, the corresponding values were 0.652, 0.812, 0.700, 0.725, 0.700, 0.816, 0.729. The AUC of the T2WI tumour radiomics model for identifying fibrous and non-fibrous meningiomas was 0.92 in the training set and 0.65 in the test set. This appeared to be an overfitting. The (T2WI+CE-T1WI) peritumour radiomics model had the highest AUC value in the test set, and the model demonstrated the best diagnostic efficacy. The discriminatory efficacy of the established (T2WI+CE-T1WI) peri-tumour radiomics model was improved from the combined model with conventional factors (T2WI signal intensity and peri-tumour oedema), and its AUCs in the training set and test set were 0.89 and 0.82, respectively. The calibration curves showed good agreement between the predicted and actual probabilities of the model^,s preoperative identification of fibrous and non-fibrous meningiomas, DCA results show good clinical efficacy for this model. ConclusionsMultimodal MRI radiomics models can effectively identify fibrous and non-fibrous meningiomas, and their discriminatory efficacy can be futher improved when combined with conventional factors. ]]> Wed,20 Aug 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.08.009 ObjectiveTo explore the value of synthetic MRI (SyMRI) based histogram analysis combined with short axis in diagnosing cervical lymph nodes metastasis (LNM) of nasopharyngeal carcinoma (NPC). Materials and MethodsThis study retrospectively analyzed 53 newly diagnosed NPC patients, and 377 cervical lymph nodes (LNs) with a short axis ≥ 4 mm (metastatic LNs: 297, non-metastatic LNs: 80). The nodes were randomly stratified into training (metastatic LNs: 208, non-metastatic LNs: 56) and test groups (metastatic LNs: 89, non-metastatic LNs: 24) at a 7∶3 ratio. Histogram parameters were extracted from T1, T2, and proton density (PD) maps of SyMRI and short axis was recorded for each LN. The areas under the curve (AUCs) of all histogram parameters were compared, and Spearman correlation coefficients (SCCs) between parameters were calculated. Parameters with higher diagnostic efficiency (AUC ≥ 0.617) and lower correlation (SCC < 0.8) were incorporated into logistic regression analysis for model construction. Receiver operating characteristic curve (ROC), area under the curve (AUC) and DeLong test were used to evaluate the performance of SyMRI model, size model and combined model in the diagnosis of cervical LNs. Then the nomogram and calibration curves were constructed. ResultsThe SyMRI model, constructed using the T1-10th percentile, T1-variance, PD-10th percentile, and PD-minimum, achieved AUCs of 0.895 (training group) and 0.903 (test group), which were significantly higher than those of the short-axis model (AUCs: 0.824 and 0.797, respectively; both P < 0.05). The combined model demonstrated the highest diagnostic efficiency, with AUCs of 0.941 (training group) and 0.938 (test group), significantly outperforming both individual models (both P < 0.05). ConclusionsSyMRI model based on histogram parameters can effectively differentiate metastatic from non-metastatic LNs, and the diagnostic performance improved further when combined with the short axis of nodes. ]]> Wed,20 Aug 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.08.010 ObjectiveThis study investigates the predictive value of cardiac magnetic resonance (CMR) myocardial strain, hospital admission electrocardiogram Q waves, and their combination for microvascular obstruction (MVO) following percutaneous coronary intervention (PCI) in patients with acute ST-segment elevation myocardial infarction (STEMI). Materials and MethodsA retrospective analysis was conducted on the clinical and imaging data of 40 control cases and 133 acute STEMI patients who underwent direct PCI for the first time and underwent CMR examination 3 to 7 days post-treatment between September 2021 and September 2024. Acute STEMI patients were divided into the NQ group and Q group based on the presence or absence of pathological Q waves on the admission electrocardiogram, and a comparative analysis was performed to identify differences in clinical and imaging data between the two groups. Based on CMR examination results, acute STEMI patients were divided into MVO and non-MVO groups. Logistic regression analysis was used to assess the independent association of each parameter with MVO, and receiver operating characteristic (ROC) curves were plotted to evaluate predictive performance. A combined predictive model was established to analyse the predictive efficacy for MVO. ResultsThe acute STEMI group had significantly higher Q-wave width, Q-wave depth, total cholesterol, triglycerides, fasting blood glucose, glycated haemoglobin, preoperative troponin, preoperative creatine kinase, preoperative creatine kinase isoenzymes, left ventricular end-diastolic volume (LVEDV), left ventricular end-systolic volume (LVESV) were all higher than those in the control group, with statistically significant differences (P < 0.05). In the acute STEMI group, left ventricular ejection fraction (LVEF), global circumferential strain (GCS), global radial strain on the short axis (GRSSAX), global radial strain on the long axis (GRSLAX), and global longitudinal strain (GLS) were all lower than those in the control group. In the Q group, the Q wave width at admission, Q wave depth at admission, preoperative troponin, preoperative creatine kinase, preoperative creatine kinase isoenzymes, infarct size (IS), transmural infarction patients, patients with MVO, the proportion of MVO in the left ventricular myocardium, and LVESV were all higher than those in the NQ group, with statistically significant differences (P < 0.05). The Q group had lower LVEF, GCS, GRSSAX, GRSLAX, and GLS than the NQ group, with statistically significant differences (P < 0.05). The Q wave depth at admission was significantly higher in the MVO group than in the non-MVO group (P < 0.05). Logistic regression analysis showed that GLS, GCS, and GRSSAX were independent predictors of MVO (P < 0.05). GLS, GCS, and GRSSAX were significantly reduced in the MVO group (P < 0.05), with GRSSAX exhibiting the highest predictive efficacy for MVO, with an area under the curve of 0.791. The combination of admission Q-wave depth and GRSSAX demonstrated superior predictive performance for MVO compared to the use of admission Q-wave depth or CMR myocardial strain parameters alone, with an area under the curve of 0.824. ConclusionsThe depth of the Q wave at admission and CMR myocardial strain parameters have predictive value for MVO after PCI in patients with acute STEMI. In addition, the combination of Q wave depth at admission and GRSSAX can further improve the accuracy of predicting the risk of MVO. ]]> Wed,20 Aug 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.08.011 ObjectiveTo investigate the value of imaging omics model based on multimodal magnetic resonance imaging (MRI) in the differential diagnosis of non mass breast cancer and non lactating mastitis (NLM). Materials and MethodsThe MRI data of 193 patients with non mass breast cancer and NLM confirmed by pathology in the First Affiliated Hospital of Traditional Chinese Medicine, Xinjiang Medical University from June 2020 to June 2024 were retrospectively collected, including 100 cases of non mass breast cancer and 93 cases of NLM. The total number of lesions in the two groups was 225, including 110 breast cancer (48.89%) and 115 NLM (51.11%). It is randomly divided into training set (157 cases) and test set (68 cases) according to 7∶3. The support vector machines (SVM) learning algorithm was used to construct single sequence models and multi parameter MRI models for the first, fourth, and seventh phases of dynamic contrast-enhanced magnetic resonance imaging (CE1, CE4, CE7), T2 weighted imaging (T2WI) and diffusion weighted imaging (DWI). The fusion model was established by combining the data of five sequences and clinical characteristics. The performance of different models was evaluated by receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA), and the model was interpreted and visualized using shap graphics. ResultsThe area under the curve (AUC) of CE1, CE4, CE7, T2WI and DWI sequences in the test set were 0.768, 0.804, 0.746, 0.769 and 0.812, respectively. The AUC of DWI in the test set was the highest, followed by CE4; the AUC of the multi parameter MRI model in the test set was 0.840 (95% confidence interval was 0.749 to 0.932), while the AUC of the fusion model in the test set was 0.866 (95% confidence interval was 0.783 to 0.948), which was significantly different from CE1, CE4, CE7 and T2WI single-mode models (P < 0.01). The results showed that the accuracy of the integrated model was the highest (77.94%); the sensitivity of the integrated model was the highest (90.00%); and the specificity of the integrated model and the CE4 sequence was the highest (both at 68.42%). ConclusionsThe fusion model of multi parameter MRI combined with clinical features has higher accuracy, sensitivity and specificity, and better prediction performance than the single sequence model and multi-parametric MRI models, which can provide higher value for the differential diagnosis of non mass breast cancer and NLM. ]]> Wed,20 Aug 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.08.012 ObjectiveTo explore the value of multiphase magnetic resonance imaging radiomics combined with different machine learning models in predicting risk stratification of hepatocellular carcinoma (HCC). Materials and MethodsWe retrospectively analyzed clinical and imaging data from ​​a cohort of 120 patients​​ with pathologically confirmed HCC who underwent surgery between January 2020 and December 2024, ​​all meeting predefined inclusion/exclusion criteria. Based on the Edmondson-Steiner (ES) grading system, patients were stratified into two groups: the low-grade group (ES grade Ⅰ and Ⅰ/Ⅱ; n=29) and the high-grade group (ES grade Ⅱ, Ⅱ/Ⅲ, Ⅲ, Ⅲ/Ⅳ, and Ⅳ; n=91). The cohort was subsequently randomly divided in a 7∶3 ratio into a training set (84 cases: 60 high-grade and 24 low-grade) and a validation set (36 cases: 31 high-grade and 5 low-grade). Arterial-phase MRI images were used to delineate the whole-tumor region of interest (ROI) using ITK-SNAP software. ROIs were propagated to portal venous and delayed phases via registration. A total of 3396 radiomic features were extracted using PyRadiomics. Feature selection was performed using Spearman correlation analysis, maximum relevance-minimum redundancy (mRMR), and least absolute shrinkage and selection operator (LASSO) regression. Radiomics models were constructed using five machine learning algorithms: logistic regression (LR), support vector machine (SVM), random forest (RF), naive Bayes (NB), and multilayer perceptron (MLP). The optimal radiomics model was combined with clinical imaging features to develop a combined model. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), calibration curves, and decision curve analysis (DCA). ResultsA total of 1132 radiomics features were extracted from three contrast-enhanced phases (arterial, portal venous, and delayed). Following dimensionality reduction and feature selection, 8 radiomics features (2 from arterial phase, 3 from portal venous phase, and 3 from delayed phase) were selected to construct radiomics models. Five machine learning algorithms LR, SVM, RF, NB, and MLP demonstrated training and validation sets AUC values of 0.899, 0.897, 0.893, 0.814, 0.876 and 0.865, 0.845, 0.590, 0.723, 0.735, respectively, for predicting HCC pathological grades, indicating that the LR model exhibited the best performance and stability. Univariate and multivariate logistic regression analyses of clinical-radiological features identified age (P = 0.046) and alpha-fetoprotein (AFP) (P = 0.031) as independent predictors of HCC pathological grading. These predictors were subsequently integrated with the radiomics model to develop a combined model, achieving AUC of 0.929 (training set) and 0.884 (validation set). DeLong test revealed significant differences between the clinical model versus the radiomics model and combined model in the training set (P < 0.05), while no statistical distinction was observed between the radiomics and combined models (P > 0.05). In the validation set, no significant differences were found among the three models (P > 0.05). Calibration curves demonstrated closer alignment between predicted and actual probabilities for the combined model in both sets. DCA indicated enhanced net clinical benefit within clinically relevant threshold probabilities. Ultimately, the combined model integrating clinical and radiomics features provided a more accurate prediction of HCC pathological grading. ConclusionsThe integration of multiphase dynamic contrast-enhanced MRI radiomics with clinical imaging features enables accurate prediction of HCC risk stratification. ]]> Wed,20 Aug 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.08.013 ObjectiveTo investigate the efficacy of MRI multiple b-value diffusion weighted imaging (DWI) quantitative parameters in predicting lymphovascular invasion of gastric cancer. Materials and MethodsTwo hundred and thirty gastric cancer patients who underwent radical gastrectomy and gastric MRI examination before the operation. The patients were divided into positive group and negative group according to postoperative pathological results for lymphorascular invasion. The preoperative image T-staging and image N-staging were evaluated, and the lesion thickness, lesion volume and quantitative parameters of mono-exponential mode (MEM), intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI) and stretched exponential model (SEM) of the patients were measured. Logistic regression analysis was used to screen out independent risk factors with positive lymphovascular invasion, receiver operating characteristic (ROC) curve was used to evaluate the efficacy of each parameter in identifying lymphovascular invasion status, and DeLong test was used to compare the efficacy of each parameter. ResultsThere were statistical differences in image T-staging, image N-staging, lesion thickness, lesion volume, apparent diffusion coefficient (ADC) of MEM, mean kurtosis (MK) of DKI, diffusion coefficient (D) and pseudodiffusion coefficient (D^*) of IVIM and α of SEM between two groups (all P < 0.05). The area under the curve (AUC) values of DKI_MK, image N-staging and combined models were 0.809 [95% confidence interval (CI): 0.752 to 0.866], 0.666 (0.596 to 0.736) and 0.828 (0.776 to 0.879), respectively. There was no significant difference between DKI_MK and combined model (P > 0.05). ConclusionsMRI multiple b-value DWI quantitative parameters can predict lymphovascular invasion in gastric cancer effectively before operation. ]]> Wed,20 Aug 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.08.014 ObjectiveTo explore the diagnostic value of prostate imaging reporting and data system version 2.1 (PI-RADS v2.1) score based on biparametric magnetic resonance imaging (bpMRI) combined with prostate specific antigen density (PSAD) for transitional zone prostate cancer (TZPCa). Materials and MethodsA retrospective analysis was conducted on 115 patients with prostate diseases confirmed by pathology, and patients were divided into TZPCa group and benign prostatic hyperplasia (BPH) groups. The MRI images were scored according to PI-RADS v2.1, univariate and multivariate logistic regression analyses were performed on the patients^, age, prostate volume (PV), total prostate specific antigen (tPSA), the ratio of free PSA to tPSA (fPSA/tPSA), PSAD and PI-RADS v2.1 score. The diagnostic efficacy of PI-RADS V2.1, PSAD and combined diagnosis for TZPCa was analyzed by receiver operating characteristic (ROC) curve , and the area under the curve (AUC) was calculated. ResultstPSA, fPSA/tPSA, PSAD, and PI-RADS v2.1 scores were statistically significant between TZPCa group and BPH group (P < 0.05); PI-RADS v2.1 score and PSAD were independent predictors of TZPCa; the AUC values of PI-RADS v2.1 score, PSAD and combined model for diagnosing TZPCa are 0.916 [95% confidence interval (CI): 0.864 to 1.000], 0.812 (95% CI: 0.702 to 0.921), and 0.952 (95% CI: 0.903 to 1.000) respectively. The combined model have the best diagnostic performance. ConclusionsThe combination of PI-RADS v2.1 score and PSAD improves the diagnostic value for TZPCa and reduces unnecessary biopsy. ]]> Wed,20 Aug 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.08.015 ObjectiveTo investigate the correlation between bone marrow fat fraction (FF) of human lumbar spine and aging. Materials and MethodsA prospective cohort of 135 volunteers, ranging in age from 30 to 79 years, were recruited to measure the lumbar FF using IDEAL-IQ technology based on magnetic resonance imaging. The subjects were divided into 30+ to 70+ groups according to the interval of 10 years old. The differences and trends of L3 and average FF of lumbar spine (FF_L3 & FF_A) in different age groups were analyzed. The correlation between bone marrow fat content of lumbar vertebrae and age was analyzed. ResultsFor all subjects, there was no significant difference in age, L1-L5 and average FF between males and females (P > 0.05). After age segmentation, both FF_L3 and FF_A showed an increasing trend with age in both males and females (P < 0.05). The differences between male and female groups were not statistically significant in the 50+ and 70+ groups (P > 0.05). In 30+ group and 40+ group, FF_L3 and FF_A of the male were significantly higher than female; while in 60+ group, the male was significantly lower (P < 0.05). Compared to the previous age group, FF_L3 and FF_A of male subjects increased rapidly after 70 years of age (P < 0.05). Women, on the other hand, grew rapidly after the age of 50 (P < 0.05) and slowed down after the age of 70 (P > 0.05). Both FF_L3 and FF_A were moderately linearly positively correlated with aging in males (r = 0.516, 0.553, respectively, P < 0.05) and strongly correlated with aging in females (r = 0.777, 0.780, respectively, P < 0.001). ConclusionsThe bone marrow fat content of male and female lumbar spine show different trends with aging, but both are closely related to it, with rapid growth observed in females after the age of 50 and males after the age of 70. ]]> Wed,20 Aug 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.08.016 ObjectiveTo explore the correlation between the vertebral bone quality score based on MRI (VBQ) derived from multi-sequence optimization of non-contrast-enhanced MRI scans of thoracolumbar vertebrae (including T11-L2, L1-2 and individual vertebrae) and the volume bone density (vBMD) measured by quantitative computed tomography (QCT), as well as the diagnostic value for osteoporosis (OP). It also aims to clarify the optimal "single vertebra window" and sequences for screening and diagnosis grouping. Materials and MethodsSelect healthy individuals aged over 18 years old who underwent both 1.5 T MRI plain scan and CT plain scan of thoracolumbar spine segments simultaneously (with a scan time interval less than 3 days) in the Department of Medical Imaging of Kaifeng Central Hospital (69 cases). Measure the average signal intensity (signal intensity, SI) of the vertebral bodies of thoracolumbar spine segments and the cerebrospinal fluid (CSF) region of interest (ROI) at the posterior side of L3 respectively. Further calculate the multi-sequence and optimized VBQs (including T11, T12, L1, L2, T11-L2 and L1-L2), where the VBQ of T11-L2 and L1-L2 vertebral bodies are averaged (multi-sequence and optimized VBQ, including VBQT1, VBQT2, VBQT2-STIR, VBQFLAIR, VBQ_{T2-STIR optimization}, VBQ_{FLAIR optimization}, VBQ_{common optimization}). Measured the vBMD of T11, T12, L1 and L2, and further calculated the vBMD of T11-L2 and L1-L2 vertebral bodies by averaging. Divide them into two groups twice: (1) screening group, divided into normal bone mass group and non-normal bone mass group (OP + bone mass reduction) with vBMD = 120 mg/cm³ as the boundary value. (2) diagnosis group, divided into OP group and non OP group (normal bone mass + bone mass reduction) with vBMD = 80 mg/cm³ as the boundary value. Explore the correlation between multi-sequence and optimized VBQs of thoracolumbar spine segments and QCT-vBMD. Exploring the diagnostic value of multi-sequence and optimized VBQ for OP using receiver operating characteristic (ROC) curves. Results(1) Correlation analysis revealed that among the individual vertebral body VBQ, the correlation between VBQ_{(L2) T2-STIR optimization} and vBMD was the strongest (r = -0.531, P < 0.001). In the average VBQ of L1-L2 vertebral bodies, the correlation between VBQ_{(L1-2) T2-STIR optimization} was the strongest (r = -0.561, P < 0.001). In the average VBQ of T11-L2 vertebral bodies, the correlation between VBQ_{(overall) T2-STIR optimization} and vBMD was the strongest (r = -0.562, P < 0.001), all of which were fat suppression optimization sequences. It is notable that in the VBQ of the L1 vertebral body, VBQ_{(L1) FLAIR optimization} had the strongest correlation with vBMD (r = -0.463, P < 0.001), which was a FLAIR optimization sequence. (2) From T11 to L2, the average vBMD values of individual vertebral bodies decreased successively, and the VBQ values optimized by T2-STIR sequence increased successively. (3) ROC curve analysis showed that in a single vertebral body, the diagnostic VBQ_{(L2) T2-STIR optimization} with the highest area under the curve (AUC) was 0.928, the optimal Cut-off value was 8.765, the sensitivity was 88.9%, and the specificity was 80.8%. (4) Screening group: in the VBQ at the T11-L2 level, the VBQ_{(overall) T2-STIR optimization} with the highest AUC (0.791), the optimal Cut-off value was 7.829; in the VBQs of L1-L2, the VBQ_{(L1-2) T2-STIR optimization} with the highest AUC was the highest (0.756), the maximum Youden index was 0.492, the sensitivity was 63.2%, the specificity was 86.0%, and the optimal Cut-off value was 8.406. (5) Diagnostic group: in the VBQ at the T11-L2 level, the VBQ_{(overall) T2-STIR optimization} with the highest AUC (0.791), the optimal Cut-off value was 7.829; in the VBQ of L1-L2, the VBQ_{(L1-2) T2-STIR optimization} with the highest AUC was the highest (0.912), the maximum Youden index was 0.680, the sensitivity was 86.0%, the specificity was 82.0%, and the optimal Cut-off value was 8.406. Conclusions(1) The VBQ of multiple sequences are negatively correlated with vBMD. (2) From T11 to L2, the bone density of individual vertebrae gradually decreases while the fat content gradually increases. A single vertebra can predict the occurrence of OP. In the population undergoing physical examination, the predictive value of a single vertebra VBQ is higher than that of the average values of T11-L2 or L1-L2. The VBQ of a single vertebra can accurately evaluate the bone quality of the vertebra without radiation, with the aim of providing a prompt for clinical practice. (3) Regardless of whether the screening criteria or diagnostic criteria are used for grouping, and regardless of whether it is T11-L2 or L1-L2, the diagnostic efficacy of the optimized VBQ with fat reduction is the highest. ]]> Wed,20 Aug 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.08.017 ObjectiveTo evaluate a combined MRI radiomics and semantic features for distinguishing benign from malignant vertebral compression fractures (VCFs), and compare machine learning algorithms^, performance. Materials and MethodRetrospectively analyzed 449 VCFs patients (550 vertebrae) from First Affiliated Hospital of Chongqing Medical University (center 1) and Chongqing Yubei District Traditional Chinese Medicine Hospital (center 2). The patients of center 1 (229 patients: 103 benign, 126 malignant) were split 7∶3 into training set and internal validation set; The patients of center 2 (220 patients: 163 benign, 57 malignant) served as external validation set. Radiomics features from MRI sagittal sequences [T1WI, T2WI, T2WI- fat saturation (FS)] and semantic features were integrated. Models (clinical, radiomics, combined) were built using logistic regression (LR), random forest (RF), and support vector machine (SVM). The area under the curve (AUC) values of receiver operating characteristic (ROC) curves were compared across datasets. ResultThe combined model integrated two semantic features and four radiomics features. The AUC of the combined model constructed using LR algorithm in the training set, internal validation set, and external validation set were 0.957 [95% confidence interval (CI): 0.921 to 0.983], 0.936 (95% CI: 0.871 to 0.975) and 0.921 (95% CI: 0.872 to 0.960), respectively, which were significantly better than the clinical model and single sequence radiomics model (DeLong test P < 0.05). The combined model demonstrated good calibration performance and showed higher clinical net benefits in decision curve analysis. ConclusionsThe combined radiomics-semantic model with LR significantly enhances MRI diagnostic accuracy for VCFs, offering a reliable clinical tool. ]]> Wed,20 Aug 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.08.018 ObjectiveTo investigate whether the dose of ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) used in chest positron emission tomography/magnetic resonance (PET/MR) examinations can be reduced while ensuring image quality and diagnostic accuracy. Materials and MethodsA total of 118 patients with abnormal radionuclide accumulation lesions who underwent ¹⁸F-FDG chest PET/MR examination (injected dose of 3.70 MBq/kg) using SIGNA PET / MR in the General Hospital of Eastern Theater Command between March 2022 and August 2023 were retrospectively analyzed. Five different PET acquisition times (20 min, 10 min, 5 min, 2 min, 1 min) were used to retrospectively reconstruct the list-mode (list) data, which were used to simulate 100%, 50%, 25%, 10%, and 5% ¹⁸F-FDG injection dose, named as G100, G50, G25, G10, and G5 groups. The overall image quality of the five groups was subjectively scored on a 5-point Likert scale, and the Friedman test was used to compare the differences between the groups. Objective analysis metrics included maximum standardized uptake value (SUV) (L-SUV_max), mean SUV (L-SUV_mean), and standard deviation (L-SUV_sd) of the lesions SUV, standard deviation of background SUV (B-SUV_sd), signal-to-noise ratio of the lesions (L-SNR), image noise ratio (IN), and L-SUV_max relative to background noise ratio (LBR), and the differences of the metrics between groups were compared using the One-Way Repeated Measures ANOVA, with post hoc inter-subgroups analyses using Bonferroni correction. G100 served as the reference for the other 4 groups to assess their lesion detectability. ResultsHigher ¹⁸F-FDG doses also resulted in higher subjective scores in all 5 groups (P < 0.05). G25, G50, and G100 had high image quality to satisfy clinical diagnostic needs (all scores > 4). The L-SUV_max, L-SUV_mean, L-SUV_sd, IN and LBR decreased with the increase of ¹⁸F-FDG dose in each dose group, and the difference was statistically significant (all P < 0.05), and L-SNR decreased with the ¹⁸F-FDG dose increased, and the difference was also statistically significant (P < 0.05). In post hoc inter-subgroups analyses, there was no significant difference in G25, G50 and G100 between any two groups at L-SUV_max and L-SUV_sd (all P > 0.05), but there was significant difference between any other two groups (all P < 0.05). There was no significant difference in L-SUV_mean and L-SNR between G5 and G10 groups or between G25, G50 and G100 groups (all P > 0.05), and there was significant difference between the other two groups (all P < 0.05). There were significant differences between any two groups in IN (all P < 0.05). There was no significant difference between G5 and G10, G25 and G100, G50 and G100 on LBR (all P > 0.05), and there was significant difference between any other two groups (all P < 0.05). With G100 as the reference, the missed detection rates in G50, G25, G10 and G5 groups were 1.4%, 2.4%, 4.4% and 6.8%, respectively. ConclusionsUsing SIGNA PET/MR, if the PET scan time of the chest ¹⁸F-FDG PET/MR examination is 20 min, the ¹⁸F-FDG injection dose can be reduced from 3.70 MBq/kg to 0.93 MBq/kg, and the dosage is reduced by 75%, which will not affect PET image quality and quantitative assessment results. ]]> Wed,20 Aug 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.08.019 ObjectiveTo investigate the image quality, morphological evaluation, and diagnostic accuracy of 3.0 T non-contrast magnetic resonance coronary angiography (MRCA) using Fast 3D and R-wave correction techniques in patients with coronary artery disease (CAD) for detecting ≥ 50% coronary stenosis. Materials and MethodsForty-six CAD patients were prospectively enrolled and underwent 3.0 T MRCA within 48 to 72 hours after coronary computed tomography angiography (CCTA). The MRCA protocol incorporated R-wave correction (trigger interval ± 15%) and Fast 3D. Two radiologists independently assessed MRCA image quality (5-point scale) and coronary stenosis. Generalized Estimating Equations (GEE) analyzed image quality across eight coronary segments. Bland-Altman analysis evaluated vessel length consistency between MRCA and CCTA, while receiver operating characteristic curves assessed diagnostic performance for ≥ 50% stenosis. ResultsThe MRCA examination success rate was 89.13% (41/46), with an excellent image rate of 65.85%. Image quality scores for proximal right coronary artery (RCA) and left anterior descending coronary artery (LAD) segments were superior to distal segments (P < 0.05). Higher heart rate (≥ 70 bpm) and body mass index (BMI) (≥ 25 kg/m²) significantly reduced image quality (P < 0.05). MRCA and CCTA showed good consistency in vessel length (95% limits of agreement: -5.94 to -‍0.68; outlier rate < 5%), except for left circumflex branch (LCX) (9.76% outlier rate). For ≥ 50% stenosis, MRCA demonstrated a sensitivity of 81.08%, specificity of 95.29%, and area under the curve of 0.889 (95% confidence interval: 0.819 to 0.938). Conclusions3.0 T MRCA with Fast 3D and R-wave correction techniques demonstrated high examination success rate and excellent image quality in CAD patients. Morphologically, the display of vessel length is in good agreement with CCTA in RCA and LAD. It can serve as an effective non-invasive screening tool for coronary artery assessment, particularly suitable for patients with contrast agent contraindications or requiring radiation avoidance. ]]> Wed,20 Aug 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.08.021 Wed,20 Aug 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.08.022 Wed,20 Aug 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.08.023 Wed,20 Aug 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.08.024 Wed,20 Aug 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.08.025 Wed,20 Aug 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.08.026 Wed,20 Aug 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.08.027 Wed,20 Aug 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.08.028 Wed,20 Aug 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.08.029 Wed,20 Aug 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.08.030 Wed,20 Aug 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.08.031 Wed,20 Aug 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.08.032 Wed,20 Aug 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.08.033 ^, health and survival. Tumor budding (TB) serves as a crucial pathological indicator for evaluating prognosis in rectal cancer patients. However, traditional pathological assessment relies on invasive biopsies and suffers from limitations including strong subjectivity and inability to obtain preoperative evaluation. Multimodal MRI technology offers potential for noninvasive TB assessment by analyzing microenvironmental characteristics through combined anatomical and functional imaging, thereby compensating for the shortcomings of pathological methods. Nevertheless, challenges remain regarding parameter stability and limited image resolution. Research demonstrates that artificial intelligence technologies can overcome imaging analysis bottlenecks. For instance, radiomics improves diagnostic objectivity through high-throughput quantitative feature extraction, while deep learning enhances model performance via cross-modal fusion and adaptive learning mechanisms. However, current studies are still constrained by insufficient technical standardization, weak model generalizability, and lack of clinical validation. To date, there has been no systematic review comprehensively addressing these aspects. This review proposes that future efforts should focus on optimizing multimodal MRI technology, developing higher-performance models, and validating TB assessment systems through prospective multicenter clinical trials to guide individualized treatment decisions, ultimately achieving substantial progress from scientific innovation to routine clinical application. ]]> Wed,20 Aug 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.08.034 ^,s clinical utility across multiple body regions.This review systematically synthesizes technological advancements and MRI clinical progress in ACS, critically evaluating its strengths, limitations, and unresolved challenges in multi-system imaging (head-neck, musculoskeletal, cardiothoracic, abdominal, pelvic). We aim to provide evidence-based guidance for optimizing clinical implementation of ACS and direct future research toward advancing precision, efficiency, and intelligence in MRI. ]]> Wed,20 Aug 2025 00:00:00 GMT