http://med-sci.cn/cgzcx/en/contents_list.asp?issue=202504 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.1674-8034.2025.04.001 Sun,20 Apr 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.04.002 ObjectiveTo use neuromelanin (NM)-MRI of the substantia nigra (SN) and locus coeruleus (LC) to compare NM loss in patients with Parkinson^,s disease (PD) and multiple system atrophy (MSA). Materials and MethodsA total of 114 subjects (including 38 patients with PD, 38 patients with MSA, and 38 age-matched healthy controls (HCs) were retrospectively collected. In the first part, voxels-of-interests (VOIs) of the bilateral SN were automatically traced based on a brain template detection method. Relative contrast ratio (rCR), volume and corrected volume (SN volume divided by the total intracranial volume) of the SN territory were measured. In the second part, rCR of the LC was measured using manually drawn regions-of-interests (ROIs) within the structure. One-way analysis of variance combined with Bonferroni correction was used to conduct post-hoc multiple comparisons to analyze the intergroup differences of the NM-related parameters. The NM parameters of the SN and LC were combined to generate a model using logistic regression method. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of different single NM parameters and logistic regression model, and the area under the curve (AUC) was calculated. ResultsCompared to HCs, volume, corrected volume and rCR of the SN, and rCR of the LC were significantly reduced in PD and MSA patients (Bonferroni correction, all P < 0.001). Of note, MSA had significantly lower SN volume and LC rCR than PD (Bonferroni correction, P < 0.001, respectively). The AUC of the corrected volume of the SN to distinguish MSA and HC, PD and HC reached as high as 0.981 and 0.950, respectively. The AUC of the logistic regression model combining the NM measurement of the SN and LC to distinguish MSA and PD reached 0.817. ConclusionsThe NM volume and contrast measures of the SN and contrast measures of the LC provided subjective and quantitative information to better facilitate differential diagnosis between PD and MSA. ]]> Sun,20 Apr 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.04.003 ObjectiveTo explore the characteristic changes in the topological properties of the chronic pain-related neural network in patients with Crohn^,s disease (CD) using resting-state functional magnetic resonance imaging (rs-fMRI) combined with graph theory analysis. Materials and MethodsA total of 20 chronic abdominal pain CD patients (APCD), 24 abdominal pain-free CD patients (FAPCD), and 30 healthy controls (HC) were included. Rs-fMRI and 3D-T1 data were collected from all subjects, and relevant clinical scale scores and clinical indicators were recorded. The topological indices of the functional network were compared among the three groups, and the correlations between the differences in brain regions and clinical scale scores were analyzed. ResultsRegarding global metrics, the FAPCD group exhibited lower small-worldness and normalized clustering coefficients compared to the APCD group (P < 0.05); however, neither the APCD group nor the FAPCD group showed significant differences when compared to the HC group (P > 0.05). Regarding nodal metrics, compared with the HC group, the degree centrality (DC) in the right anterior cingulate and paracingulate gyrus was significantly increased in the APCD group (P < 0.05). The DC and nodal efficiency (NE) in the left superior occipital gyrus and right postcentral gyrus were significantly decreased in the FAPCD group compared with the HC group (P < 0.05). Compared with the FAPCD group, the NE in the orbitofrontal region and the DC in the left precentral gyrus were lower in the APCD group (P < 0.05). Additionally, in APCD and FAPCD two combined CD groups, the increase in the visual analogue scale score for pain was associated with an increase in the DC of the left precentral gyrus (r = 0.386, P < 0.05). ConclusionsThe brain network topology characteristics of CD patients with chronic abdominal pain have changed, especially in DC and NE in specific brain regions. These changes may provide new directions for pain management and further research into the underlying neural mechanisms. ]]> Sun,20 Apr 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.04.004 ObjectiveTo develop an interpretable model to predict the risk of hemorrhagic transformation after endovascular treatment in acute stroke, utilizing multiparameter MRI. Materials and MethodsA retrospective analysis was conducted on 274 patients who presented with acute stroke at our hospital. The assessment of hemorrhagic transformation in these patients was performed using CT or MRI 24 hours post-treatment. Utilize the PyRadiomics software to extract 1143 features from diffusion-weighted imaging and an additional 1143 features from perfusion-weighted imaging, and develop a radiomics score (Radscore) based on these extracted features. Utilize SHapley Additive exPlanations (SHAP) to identify the most pertinent features for model development. Develop an interpretable prediction model for assessing the risk of bleeding conversion by employing six distinct machine learning classifiers: gradient boosting classifier, random forest (RF), eXtreme gradient boosting (XGB), adaptive boosting, Gaussian naive Bayes, and logistic regression. Assess the predictive performance of these machine learning models using receiver operating characteristic (ROC) curves and decision curve analysis (DCA). ResultsFollowing feature screening and dimensionality reduction, 15 features demonstrating a strong correlation with the transformation of acute ischemic stroke bleeding were identified. Five clinical variables with statistical differences (age, time from onset to MRI examination, NIHSS score on admission, history of diabetes, and history of atrial fibrillation) and radscore were incorporated into the machine learning model. Among the models evaluated, the RF model exhibited the highest predictive performance, achieving an area under the curve (AUC) of 0.928. When the critical value is set at 0.844, the model demonstrates an accuracy of 85.5%, a sensitivity of 83.0%, and a specificity of 88.2%. DCA indicates that the RF model provides a substantial net benefit in predicting the risk of hemorrhagic transformation in cases of acute stroke. ConclusionsThe interpretable RF model, which integrates multiparameter MRI radiomics with clinical features, enhances the accuracy of predicting the risk of hemorrhagic transformation following mechanical thrombectomy in acute ischemic stroke. This model offers valuable guidance for early clinical intervention and treatment. ]]> Sun,20 Apr 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.04.005 ObjectiveMagnetic resonance vascular wall imaging (MR-VWI) was used to analyze the characteristics of intracranial atherosclerotic plaques in patients with ischemic stroke in the middle cerebral artery region, and to explore the imaging features independently associated with recurrent stroke. Materials and MethodsA total of 185 patients with middle cerebral artery atherosclerosis were retrospectively selected from our hospital. All patients completed MR-VWI examination and were determined to have responsible plaque in the middle cerebral artery in the infarction area. According to the clinical and imaging data, the patients were divided into three groups, namely the first stroke group, the recurrent stroke group and the negative stroke group. Plaque characteristics, China Ischemic Stroke Subclassification (CISS), and other imaging indicators were recorded. The differences of clinical data and imaging features among the three groups were compared, and the risk factors related to ischemic stroke recurrence were further screened by logistic regression analysis. The area under the curve (AUC) corresponding to the receiver operating characteristic curve was calculated for the independent correlation factors obtained from the multi-factor analysis. ResultsA total of 185 patients were included, including 69 patients in the first stroke group, 76 patients in the recurrent stroke group and 40 patients in the negative stroke group. There was no significant difference in gender among the three groups (P > 0.05). Compared with the first stroke group and the stroke negative group, patients in the recurrent stroke group were older (P < 0.05) and had a higher proportion of diabetes (P < 0.05). Compared with the negative stroke group, the proportion of middle-aged and elderly patients over 45 years old in the first stroke group and the recurrent stroke group was higher (P = 0.006, P< 0.001), and the proportion of diabetes in the recurrent stroke group was higher (P = 0.013). Ring-wall plaques were more common in the first stroke group than in the negative stroke group (P = 0.008). In the first stroke group and the recurrent stroke group, the plaque area, plaque load and degree of stenosis were larger, and the number of enhanced plaques was more (P < 0.05). Compared with the recurrent stroke group, the mechanism of stroke in the recurrent stroke group was more inclined to the type of low perfusion/embolus removal disorder in the CISS classification (P = 0.046), and the recurrent stroke group had larger plaque area (P < 0.001), plaque load (P < 0.001), and more obvious stenosis (P < 0.001). Multivariate logistic regression analysis showed that age, plaque area and plaque load were independent risk factors associated with ischemic stroke recurrence in the middle cerebral artery region (P < 0.05). Age [odds ratio, OR = 1.040, 95% confidence interval (CI): 1.007 to 1.074, P = 0.008], plaque area (OR = 1.286, 95% CI: 1.018 to 1.543, P = 0.002), plaque load (OR = 1.586, 95% CI: 1.013 to 2.155, P < 0.001). The AUC of age was 0.658 (95% CI: 0.568 to 0.748), the AUC of plaque area was 0.662 (95% CI: 0.575 to 0.750), and the AUC of plaque load was 0.758 (95% CI: 0.676 to 0.839). The AUC value of the combined detection of the three factors was 0.827 (95% CI: 0.758 to 0.896), which was higher than the AUC value of other independent predictors, and the overall prediction efficiency was the best. ConclusionsAge, plaque area and plaque load are independently associated with the recurrence of middle cerebral artery ischemic stroke. ]]> Sun,20 Apr 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.04.006 ObjectiveTo explore the value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics model combined with blood cell parameters in differentiating Luminal and Non-Luminal breast cancer (BC). Materials and MethodsDCE-MRI of two hundred and twenty-seven patients with pathologically confirmed BC were retrospectively analyzed. The patients were randomly split into a training set (n = 162) and a validation set (n = 65) at a ratio of 7∶3. Patients were divided into the Luminal group (139 cases) and the Non-Luminal group (88 cases) according to the immunohistochemical results. The BC lesions on the pretreatment DCE-MRI served as the basis for the volume of interest for feature extraction. Three models were constructed to differentiate Luminal from Non-Luminal by analyzing radiomic feature, clinical pathological feature, and hematological parameters. These models were Model 1 (radiomics), Model 2 (hematological parameters), and Model 3 (radiomics + hematological parameters), respectively. The discrimination performance of the models was evaluated using the receiver operating characteristic curve. Decision curve analysis was conducted to determine the clinical usefulness of the models by quantifying the net benefits at different threshold probabilities. ResultsThe area under the curve (AUC), sensitivity, and specificity, of Model 3 were 0.840 (0.774 to 0.893), 87.9%, and 71.4% in the training set, and 0.818 (0.703 to 0.903), 87.5%, and 68.0% in the validation set, respectively. The AUC of the Model 1 was better than that of the Model 2 in the both cohorts (0.817 vs. 0.636, 0.838 vs. 0.515, P = 0.001 and P < 0.001), and the AUC of the Model 3 was also better than that of the Model 2 in the both cohorts (0.840 vs. 0.636, 0.818 vs. 0.515, both P < 0.001). The Model 3 and Model 1 were both more beneficial than Model 2 in clinical practice, as illustrated by decision curve analysis. ConclusionsThe model that integrated the hematological parameters with DCE-MRI radiomics can help differentiate Luminal from Non-Luminal BC, which facilitates the accurate treatment planning for BC. ]]> Sun,20 Apr 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.04.007 ObjectiveTo explore the relationship between quantitative parameters of synthetic MRI (SyMRI) before and after enhancement and the expression status of Ki-67 antigen in breast cancer, and to evaluate the predictive efficacy of these parameters combined with apparent diffusion coefficient (ADC) in the expression status of Ki-67. Materials and MethodsThe clinical and imaging data of 163 patients diagnosed with breast cancer at Xiangyang Central Hospital from March 2023 to October 2024 were retrospectively collected. All patients underwent complete MRI examinations and their tumor natures were confirmed by pathological examination. On the GE workstation, quantitative parameter values of SyMRI before and after enhancement were obtained and recorded, including pre-enhancement T1 value (T1-Pre), pre-enhancement T2 value (T2-Pre), pre-enhancement proton density value (PD-Pre), post-enhancement T1 value (T1-Gd), post-enhancement T2 value (T2-Gd), and post-enhancement PD value (PD-Gd). The relative change rates of relaxation time before and after enhancement were calculated and recorded as ΔT1%, ΔT2%, and ΔPD%. According to the expression status of Ki-67, the collected patients were divided into high expression group (≥ 30%) and low expression group (< 30%). Statistical analysis of the data was performed using SPSS 27 software, with P < 0.05 indicating statistical significance. Qualitative data were analyzed using the chi-square test, and quantitative data were analyzed using the Kolmogorov-Smirnov (K-S) test for normal distribution. Independent sample t-test or non-parametric Mann-Whitney U test was used to compare the differences in MRI parameters between the high and low expression groups of Ki-67. Significant variables were included in binary logistic regression analysis, and the DeLong test was used to evaluate the predictive efficacy of the model for the expression status of Ki-67. ResultsThere were statistically significant differences between the two groups in terms of maximum lesion diameter, ADC value, estrogen receptor, progesterone receptor, time-signal intensity curve (TIC), and treatment plan selection (P < 0.05); However, there were no significant differences in age, human epidermal growth factor receptor (HER-2) expression status, lesion margin and shape, enhancement characteristics, breast gland type, and lymph node metastasis. Regarding SyMRI quantitative parameters, there were statistically significant differences between the high and low expression groups in T1-Pre, T2-Pre, T1-Gd, T2-Gd, and ΔT1% (P < 0.05); While PD-Pre, PD-Gd, ΔT2%, and ΔPD% showed no statistically significant differences between the two groups. Further multivariate logistic regression analysis showed that TIC, T1-Gd, and ADC value had significant statistical significance, with AUCs of 0.608, 0.837, and 0.701, respectively; sensitivities of 52%, 89%, and 85%, respectively; and specificities of 68%, 68%, and 46%, respectively. Additionally, a logistic regression prediction model was established by combining T1-Gd, ADC value, and TIC, which achieved an AUC of 0.881 for predicting the expression status of Ki-67, with a sensitivity of 89% and a specificity of 76%. ConclusionsThis study demonstrates that T1-Gd, as a non-invasive imaging biomarker, can effectively predict the expression level of Ki-67 in breast cancer in SyMRI quantitative analysis. By integrating ADC values and TIC, the constructed combined prediction model significantly improves the accuracy and efficacy of predicting Ki-67 expression levels in breast cancer. This finding provides a new approach and method for non-invasive assessment of breast cancer cell proliferation activity and treatment response. ]]> Sun,20 Apr 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.04.008 ObjectiveTo explore the value of three-dimensional amide proton transfer weighted (3D-APTw) imaging in the assessment of the pathological grade of hepatocellular carcinoma (HCC). Materials and MethodsA total of 43 patients with surgical pathology confirmed HCC from October 2020 to April 2023 and underwent Edmondson-Steiner grade (grade Ⅰ to Ⅳ) were prospectively recruited. We classified grade Ⅰ and Ⅱ HCC as low grade HCC and grade Ⅲ and Ⅳ lesions as high grade HCC. A Philips 3.0 T MRI device was scanned before surgery to acquire T1WI, T2WI, diffusion-weighted imaging, amide proton transfer weighted (APTw) and multi-stage enhanced images, respectively. APTw values for HCC and hepatplasm were measured independently by two experienced radiologists. Differences between groups were analyzed by the Mann-Whitney U non-parametric test. The diagnostic efficacy of APTw value was analyzed using the receiver operating characteristic (ROC) curve, and the area under the curve (AUC), threshold, sensitivity and specificity were calculated. A Spearman correlation analysis was used to assess the association between APTw and the histological grade of HCC. ResultsThe APTw value of low grade HCC (2.15% ± 0.13%) was significantly lower than that of high grade HCC (2.63% ± 0.13%), and the difference was statistically significant (P = 0.03). The AUC for identifying high grade and low grade HCC was 0.69 (95% CI: 0.53 to 0.82), and with an optimal threshold of 1.85%, the sensitivity and specificity were 90.00% and 42.31%, respectively. Spearman results of the correlation analysis showed a positive correlation between APTw value and HCC pathological grade, with a correlation coefficient of r = 0.43 (P = 0.003). ConclusionsThe APTw value can be used to identify high grade and low grade HCC, and the 3D-APTw imaging has some value in predicting the preoperative pathological grade of HCC. ]]> Sun,20 Apr 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.04.009 ObjectiveTo evaluate the segmentation performance of a deep learning (DL) model in the analysis of enhanced three dimensional-magnetic resonance angiography (3D-MRA) images of patients with Budd-Chiari syndrome (BCS), and assess the inter-observer agreement among radiologists in the evaluation of the DL model^,s segmentation outcomes. Materials and MethodsA retrospective analysis was conducted on MRA images from 220 BCS patients. Manual segmentation was performed by two radiologists with 8 and 12 years of experience, respectively. The DL model was trained on the features extracted from these manual segmentations to enable automatic segmentation. The performance of the DL model was assessed using the Dice similarity coefficient (DSC), sensitivity, specificity, and accuracy. Consistency comparison between the segmentation results of different radiologists and the DL model was used by the area under the curve (AUC) of receiver operating characteristics (ROC). Inter-observer agreement regarding the DL model^,s segmentation results was evaluated using the intra-class correlation coefficient (ICC) and Wilcoxon paired test. ResultsThe DL model achieved DSC values of 0.93, 0.84, and 0.65 for the liver, inferior vena cava, and hepatic veins, respectively; sensitivity values were 92%, 81%, and 73%; specificity values were 93%, 93%, and 76%; accuracy values were 95%, 94%, and 86%, and AUC values were 0.95, 0.87, 0.71, respectively. There was no statistically significant difference (P > 0.05) in AUC values between two radiologists and DL model in liver and vascular recognition of BCS patients. The subjective assessments of the DL model^,s segmentation results by the two radiologists showed no statistically significant differences (P > 0.05). The overall ICC was 0.94 (95% CI: 0.92 to 0.95). ConclusionsThe DL model exhibited robust segmentation performance in enhanced 3D-MRA images of BCS patients. Furthermore, there was excellent inter-observer agreement among radiologists of the images segmented by the DL model. ]]> Sun,20 Apr 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.04.010 ObjectiveTo assess the research quality of utilizing MRI radiomics for non-invasive prediction of Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations in colorectal cancer and evaluate the diagnostic accuracy of associated prediction models. Materials and MethodsA comprehensive literature search was conducted utilizing databases including PubMed, Embase, The Cochrane Library, Web of Science, Scopus, CNKI, and WanFang Data. This search aimed to identify all relevant studies that satisfied the established inclusion and exclusion criteria regarding the use of MRI for predicting KRAS gene mutations in colorectal cancer, covering the period from January 2015 to October 2024. The methodological quality of the selected studies was evaluated using the quality assessment of diagnostic accuracy studies 2 (QUADAS-2) and radiomics quality score (RQS) tools. Furthermore, heterogeneity among the included studies was assessed, and pooled weighted sensitivity, specificity, and diagnostic odds ratio (DOR) were calculated using Stata 18 software, along with a summary receiver operating characteristic (SROC) analysis. ResultsA total of 17 studies, encompassing 2684 cases, were included in the analysis. The sensitivity, specificity area under the curve (AUC) values of preoperative prediction of KRAS gene status in rectal cancer utilizing MRI radiomics were 79% [95% confidence interval (CI): 75% to 83%], 74% (95% CI: 68% to 80%), and 0.85 (95% CI: 0.81 to 0.88), respectively. Both sensitivity and specificity combined results showed moderate heterogeneity, with I² heterogeneity statistics values of 56.80% (95% CI: 34.08% to 79.53%) and 77.35% (95% CI: 67.22% to 87.48%), respectively; Q values were 39.35 (P < 0.001) and 75.05 (P < 0.001), respectively. The results of subgroup analysis and univariate Meta-analysis indicated that all variables had a certain impact on heterogeneity (P < 0.05). Deek^,s funnel plot was basically symmetrical, and the slope coefficient was not statistically significant (P = 0.11), suggesting that there was no significant publication bias in the studies included in our analysis. ConclusionsMRI radiomics shows strong potential for non-invasive KRAS status prediction in rectal cancer, though study heterogeneity exists. Future research should focus on improving research quality and validating models with multicenter datasets to boost accuracy and reliability. ]]> Sun,20 Apr 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.04.011 ObjectiveTo explore the value of predicting the response of prostate cancer (PCa) to endocrine therapy based on the histogram features of pharmacokinetic parameters of dynamic contrast enhancement magnetic resonance imaging (DCE-MRI). Materials and MethodsRetrospectively collect the clinical and imaging data of PCa patients from Zhangye People^,s Hospital Affiliated to Hexi University from January 2018 to October 2023 and Gansu Provincial People^,s Hospital from February 2020 to February 2023, two weeks before endocrine therapy. A total of 105 cases were collected from Zhangye People^,s Hospital Affiliated to Hexi University, which were divided into a training set (73 cases) and an internal validation set (32 cases) at a ratio of 7∶3. A total of 47 cases were collected from Gansu Provincial People^,s Hospital as an external validation set. Select the original DCE-MRI images, and obtain the pseudo-color maps of pharmacokinetic parameters including volume transfer constant (K^trans), rate constant (K_ep), and extravascular extracellular volume fraction (V_e) through the Siemens Syngovia workstation. In the 3D Slicer software, referring to the axial T2-weighted imaging (T2WI), delineate the region of interest (ROI) of the whole prostate gland layer by layer on the pseudo-color maps of pharmacokinetic parameters, and then extract the histogram features. Through dimensionality reduction by the least absolute shrinkage and selection operator (LASSO), 8 optimal features were screened out and the histogram features was calculated. Univariate and backward multivariate logistic regression were used to analyze the independent predictive factors of the good-response group and the poor-response group of endocrine therapy, and a clinical model, a histogram features model, and a combined model were constructed. The area under the curve (AUC) was calculated using the receiver operating characteristic (ROC) curve, and the calibration curve and decision curve were used to evaluate the performance of the model. The efficacy of each model was evaluated by the DeLong test. Finally, a nomogram was drawn based on the independent predictive factors of the combined model. ResultsIn the training set, internal validation set, and external validation set, there were statistically significant differences in Gleason score, MRI-T stage, and histogram features between the good-response group and the poor-response group (P < 0.001). Backward multivariate logistic regression analysis showed that the Gleason score (OR = 0.925, 95% CI: 0.859 to 0.958, P = 0.038), MRI-T stage (OR = 0.871, 95% CI: 0.800 to 0.949, P = 0.002), and histogram features (OR = 0.096, 95% CI: 0.056 to 0.137, P < 0.001) were independent predictive factors for the response of PCa to endocrine therapy. The AUCs of the clinical model in the training set, internal validation set, and external validation set were 0.857 (95% CI: 0.774 to 0.939), 0.953 (95% CI: 0.888 to 0.996), and 0.808 (95% CI: 0.676 to 0.941), respectively. The AUCs of the histogram features model in the training set, internal validation set, and external validation set were 0.874 (95% CI: 0.769 to 0.951), 0.816 (95% CI: 0.664 to 0.967), and 0.674 (95% CI: 0.517 to 0.831), respectively. The AUCs of the combined model in the training set, internal validation set, and external validation set were 0.951 (95% CI: 0.906 to 0.994), 0.973 (95% CI: 0.922 to 0.995), and 0.830 (95% CI: 0.699 to 0.960), respectively. The analysis of the decision curve and calibration curve showed that the combined model had good clinical application value and stability. The DeLong test and NRI value showed that the predictive efficacy of the combined model was better than that of the clinical model and the histogram features model. ConclusionsThe histogram features of DCE-MRI pharmacokinetic parameters is an independent predictive factor for predicting the response of PCa to endocrine therapy. The combined model has good value in predicting the response of PCa to endocrine therapy, providing new ideas for clinical treatment decisions. ]]> Sun,20 Apr 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.04.012 ObjectiveTo investigate the predictive value of mean apparent diffusion coefficient (ADCmean) combined with prostate specific antigen density (PSAD) for clinically significant prostate cancer (csPCa) with a prostate imaging reporting and data system version (PI-RADS) score ≥ 3. Materials and MethodsClinical data and imaging data of patients with PI-RADS score ≥ 3 on prostate MRI performed at our hospital between February 2022 and August 2024 and with pathologic histology were retrospectively analyzed. The highest PI-RADS score and the largest dimension of the largest lesion were selected for ROI outlining, and the ADCmean and apparent diffusion coefficient min (ADCmin) of the lesion were measured. Univariate and multivariate logistic regression analyses were performed to identify the best clinical and imaging predictors of csPCa. Receiver operating characteristics (ROC) curves and the DeLong test were used to compare the diagnostic efficacy of the best clinical and imaging predictive models and their combined models by calculating the area under the curve (AUC), sensitivity and specificity. ResultsA total of 75 (48.39%) csPCa patients and 80 (51.61%) non-csPCa patients were included in this study. age, total prostate specific antigen (tPSA), free prostate specific antigen (fPSA), and PSAD were greater in the csPCa group than in the non-csPCa group, and prostate volume (PV), fPSA and tPSA ratio (f/t), ADCmin, and ADCmean were smaller in the csPCa group than in the non-csPCa group, and the differences were statistically significant (P < 0.05). Stepwise logistic regression analysis and comparison of ROC curves yielded the best clinical indicator PSAD and imaging indicator ADCmean for predicting csPCa, with an AUC of 0.846 for PSAD and 0.898 for ADCmean, and an optimal cutoff value of 0.307 ng/mL² for PSAD, with a sensitivity of 66.67% and a specificity of 91.25%; ADCmean had an optimal cutoff value of 773.5 mm²/s, a sensitivity of 86.67%, and a specificity of 85.00%; the AUC of the two combined models was as high as 0.925, and the difference in diagnostic efficacy between the combined model and the single model was statistically significant using DeLong^,s test (P < 0.05). The sensitivity and specificity of the combined model for predicting csPCa were 86.67% and 88.75%. ConclusionsThe predictive efficacy of ADCmean for csPCa with PI-RADS ≥ 3 points was better than that of ADCmin, and the combined model with PSAD can further improve the predictive value of csPCa with PI-RADS ≥ 3 points, which is instructive for clinical diagnosis and treatment. ]]> Sun,20 Apr 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.04.013 ObjectiveTo develop a nomogram model using apparent diffusion coefficient (ADC) histogram features to predict clinically significant prostate cancer (CSPCa) in the transition zone. Materials and MethodsA retrospective analysis was conducted on 283 patients with suspicious prostate cancer admitted to the urology department of our hospital from January 2019 to June 2024. The patients were randomly divided into a development set (70%, 198 cases) and an internal validation set (30%, 85 cases). The least absolute shrinkage and selection operator (LASSO) algorithm was applied to screen for key features: ADC_min (apparent diffusion coefficient minimum), ADC_CoeffOfVar (coefficient of variation of apparent diffusion coefficient), ADC_kurtosis (apparent diffusion coefficient kurtosis) and ADC_entropy (apparent diffusion coefficient entropy). Furthermore, univariate and multivariate logistic regression analyses were performed to select variables and construct a predictive model. Diagnostic performance was evaluated using area under the curve (AUC) of the receiver operating characteristic (ROC), sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. Decision curve analysis (DCA) was also employed to assess clinical net benefit. ResultsADC_CoeffOfVar [odds ratio (OR) = 1.01, P = 0.034] and ADC_entropy (OR = 1.00, P < 0.001) were independent predictors of CSPCa. The nomogram model constructed based on these factors demonstrated good predictive performance in both the development set (AUC = 0.844) and the internal validation set (AUC = 0.765). Calibration curve analysis showed a high degree of agreement between model predictions and actual observations, and decision curve analysis further confirmed the net benefit of the model in clinical decision-making. ConclusionsThe nomogram model constructed based on ADC histogram features not only provides a non-invasive tool for preoperative risk assessment but also holds practical clinical application potential. ]]> Sun,20 Apr 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.04.014 ObjectiveTo explore the value of an radiomics model based on MRI transverse axial plain scan in predicting the clinical efficacy of acupuncture and moxibustion rehabilitation therapy for lumbar disc herniation (LDH). Materials and MethodsA retrospective analysis was conducted on 155 LDH patients who had completed one courses of acupuncture and moxibustion rehabilitation treatment in hospital (102 cases were effective and 53 cases were ineffective). In R software, the patients were divided into a training set (n = 108) and a testing set (n = 47) using the sample function at a ratio of 7∶3. For each patient, 851 radiomics features were extracted using 3D-Slicer software based on the pre-treatment magnetic resonance T2WI transverse axial image. After removing redundant features using Pearson or Spearman correlation analysis, the least absolute shrinkage and selection operator (LASSO) regression analysis was used to further reduce the dimensionality of the features and construct the model. The receiver operating characteristic (ROC) curve was applied to the training and testing sets to validate the model and evaluate the efficacy of radiomics features in predicting the clinical efficacy of acupuncture rehabilitation therapy for LDH. ResultsAfter feature selection, 7 radiomics features were used to construct a clinical efficacy prediction model for LDH acupuncture and moxibustion rehabilitation therapy. The area under the ROC curve (AUC) of the prediction model in the training set was 0.862 [95% confidence interval (CI): 0.789 to 0.935], with sensitivity and specificity of 82.5% and 75.0%, respectively; the AUC of the test set was 0.887 (95% CI: 0.773 to 0.989), with sensitivity and specificity of 92.3% and 79.4%, respectively. ConclusionsBased on the MRI axial imaging-based radiomics model, it exhibits excellent predictive performance for the clinical efficacy of acupuncture and rehabilitation therapy for LDH, serving as a robust basis for clinical decision-making. ]]> Sun,20 Apr 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.04.015 ObjectiveTo investigate the clinical value of 16-channel neonatal head coil for neonatal cranial magnetic resonance imaging (MRI). Materials and MethodsUsing the water film detection method, proton density weighted imaging (PDWI) sequence was selected, and the same parameters were set. The neonatal head coil and adult head coil were respectively tested in vitro. The signal-to-noise ratio (SNR) of the two coils was obtained by processing the images for objective quantitative analysis. A total of 44 healthy neonates undergoing brain magnetic resonance examination from August 2022 to December 2023 were prospectively collected and divided into 2 groups by simple random method. To test image SNR and contrast-to-noise ratio (CNR) of gray and white matter. The T2WI sequence images with acceleration factor R = 2 were collected for both groups using neonatal head coil and adult head coil with the same parameters. Then the acceleration factor R = 2, 3, 4, 5, 6 was adjusted respectively to test the parallel acquisition capability of the coil. Neonatal brain images were comprehensively analyzed, image SNR and gray and white matter CNR of the two coils were calculated, and the parallel acquisition ability of the two coils was independently evaluated by two imaging diagnostic physicians, and the subjective score of image SNR and gray and white matter CNR was statistically analyzed by t-test. ResultsIn vitro experiment, the SNR of neonatal head coil was 1.4 times higher than that of adult head coil, and some cortical surface areas were more than 2 times. In clinical experiments, the SNR of neonatal head coil in bilateral thalamus, basal ganglia and frontal lobe region were higher than those of adult head coil, the CNR of gray matter in bilateral thalamus and basal ganglia and white matter in frontal lobe of neonatal head coil is higher than that of adult head coil, and the differences was statistically significant (P < 0.001). After adjusting the acceleration factor, the neonatal head coil showed high parallel acquisition capability. ConclusionsNeonatal head coil can improve the quality of neonatal brain magnetic resonance image. ]]> Sun,20 Apr 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.04.017 ^,s disease (PD) is the second most common neurological illness among middle-aged and elderly persons worldwide. In the early stages of the disease, it may only manifest as a slight tremor in the hands, making it easy to overlook; however, as the disease progresses, the patient^,s walking, thinking, sleeping, and so on will be affected, and eventually, he or she will be unable to care for himself or herself due to the inability to move around and become bedridden for an extended period of time. As a result, correct disease identification at an early stage can lead to better therapy, reduce disease progression, and improve prognosis. Quantitative MRI approaches can give non-invasive information on neuronal activity and metabolite changes in PD brain tissue, as well as the underlying biological characteristics of the disorders. In this article, we will look at how quantitative MRI techniques can aid with early PD diagnosis, lesion evaluation, treatment, and prognosis in clinical practice. ]]> Sun,20 Apr 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.04.018 Sun,20 Apr 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.04.019 ^,s disease and is the key window of early diagnosis, prevention and treatment, and there is no drug radical cure. Acupuncture is widely used for its wide indications, quick response and simple operation, but its clinical treatment mechanism is still unclear. In recent years, with the rapid development of multimodal imaging technology and the continuous deepening of neuroimaging research on acupuncture treatment of MCI, the acupuncture treatment effect of MCI brain is accurately evaluated based on objective information such as structure, function and molecular imaging. It^,s convenient for early/ultra-early clinical treatment and intervention, provides objective evidence for the discussion of disease pathogenesis. This paper reviews the research progress of brain functional imaging technology in treating MCI patients with acupuncture, in order to provide new ideas for further revealing the mechanism of acupuncture on MCI diseases and a new perspective for clinical accurate diagnosis and treatment. ]]> Sun,20 Apr 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.04.020 Sun,20 Apr 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.04.021 ^,s disease (CD) often experience negative emotions such as anxiety and depression, which not only potentially compromise their mental health but also aggravate the disease progression and diminish their quality of life. Currently, clinical recognition of the negative emotions commonly associated with CD is limited. Standard anti-inflammatory treatments are ineffective in alleviating these symptoms, and the diagnostic methods available are subjective and restricted. Therefore, finding objective assessment methods to guide clinical diagnosis and treatment has become a critical issue. An increasing number of studies suggest that the development of negative emotions in CD patients is closely linked to structural and functional changes in specific brain regions. Currently, multimodal imaging techniques, such as structural magnetic resonance imaging, functional magnetic resonance imaging, and near-infrared spectroscopy, have been widely applied in the study of brain function and structural changes, demonstrating significant clinical value. This review will summarize the recent findings based on multimodal imaging techniques that have identified brain structural and functional changes associated with negative emotions such as anxiety and depression in CD patients. These findings provide neuroimaging-based evidence for future assessments and treatments of CD patients with comorbid negative emotions. ]]> Sun,20 Apr 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.04.022 ^,s disease, Parkinson^,s disease, Huntington^,s disease and other diseases, aiming to provide researchers with neurodegenerative disease researchers with theoretical tools for cross-scale research, and provide new perspectives for elucidating disease mechanisms and developing targeted therapy strategies. ]]> Sun,20 Apr 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.04.023 Sun,20 Apr 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.04.024 Sun,20 Apr 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.04.025 Sun,20 Apr 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.04.026 Sun,20 Apr 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.04.027 Sun,20 Apr 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.04.028 Sun,20 Apr 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.04.029 ^, quality of life. Therefore, early detection of myocardial damage caused by anthracyclines is helpful for early clinical intervention and protection of patients^, heart function. Cardiac magnetic resonance (CMR) has potential value in early detection of myocardial damage due to its non-radiative, non-invasive, multi-parameter, multi-sequence imaging advantages. This review reviews the value of CMR in assessing cardiotoxicity in breast cancer patients treated with anthracyclines, in order to enhance the awareness of radiologist and clinicians regarding myocardial injury in such patients and improve the appliaction of CMR in the assement of myocardial injury caused by chemotherapy for breast cancer. ]]> Sun,20 Apr 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.04.030 ^,s health. Among them, non-mass enhancement (NME) lesions of the breast have always been difficult to diagnose and differentiate due to their complex and diverse pathological types and atypical imaging features. In recent years, functional imaging techniques represented by intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI), as well as artificial intelligence (AI) algorithms, have significantly improved the diagnostic efficiency of magnetic resonance imaging (MRI) for NME lesions. Based on this, this paper systematically reviews the research progress of MRI techniques in NME lesions, focuses on discussing the clinical application values of functional imaging, multimodal fusion, and AI models, and proposes future optimization directions in response to technical bottlenecks, aiming to provide references for the clinical practice and scientific research of NME lesions. ]]> Sun,20 Apr 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.04.031 Sun,20 Apr 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.04.032 Sun,20 Apr 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.04.033 Sun,20 Apr 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.04.034 Sun,20 Apr 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.04.035 Sun,20 Apr 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.04.036 ^, quality of life. With advancements in minimally invasive surgical techniques, percutaneous endoscopic lumbar discectomy (PELD) has emerged as a mainstream treatment option due to its advantages of minimal trauma and rapid recovery. However, conventional imaging techniques have limitations in assessing nerve root function, making it difficult to comprehensively evaluate the microstructure and functional status of nerve roots. Multimodal magnetic resonance imaging (MRI) techniques, such as magnetic resonance neurography (MRN), diffusion tensor imaging (DTI), and diffusion tensor tractography (DTT), integrate morphological and functional assessments to more accurately visualize nerve root compression, injury severity, and postoperative recovery. These methods provide critical insights for LDH diagnosis, surgical planning, and treatment efficacy evaluation. In recent years, the application of artificial intelligence (AI) in medical image analysis has brought breakthroughs in automated LDH detection and nerve root assessment. Deep learning-based image reconstruction and segmentation techniques have significantly improved image quality and diagnostic efficiency. Future research should focus on optimizing AI algorithms and exploring their potential in conjunction with multimodal MRI for pre- and postoperative nerve root evaluation in LDH, aiming to enhance diagnostic accuracy and therapeutic outcomes. This article provides a systematic review of the current application status of multimodal MRI in the evaluation of nerve roots before and after minimally invasive LDH surgery, analyzes its advantages and limitations, and explores the future development direction of combining with artificial intelligence. The aim is to provide reference for optimizing clinical diagnosis and treatment decisions and exploring the mechanism of nerve root injury in scientific research. ]]> Sun,20 Apr 2025 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2025.04.037 Sun,20 Apr 2025 00:00:00 GMT