Magnetic resonance technology is safe and radiation-free, and has been widely used in the whole body. Multi-sequence and multi-parameter combined MRI imaging breaks the limitation of single imaging, and can reflect the information of lesion diffusion, perfusion and metabolism through quantitative analysis by DWI, IVIM, DCE-MRI,MRS And other technologies, so as to differentiate and evaluate the benign and malignant of isolated pulmonary nodules from the molecular level. Multi-parameter combined imaging significantly improved the sensitivity and specificity of single imaging, which is superior to single-parameter imaging. Additionally, it demonstrates potential for further analyzing pathological typing of lung cancer. MRI multi-parameter combined imaging is expected to improve the early diagnosis rate and specificity of pulmonary nodules, and provide a more reliable basis for individualized treatment.

Objective To develop a fusion model that integrates multiparametric magnetic resonance imaging (mpMRI)-based radiomics features with clinical and pathological variables for predicting lymph-vascular space invasion (LVSI) in patients with endometrial cancer. Methods This retrospective study included 96 patients with pathologically confirmed endometrial cancer treated at Northwest Women's and Children's Hospital from January 2015 to June 2024. Axial T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) sequences were used to manually delineate both tumor lesions and corresponding uterine body regions. radiomics features were extracted from the delineated regions. clinical and pathological variables were screened using univariate analysis and significant predictors were integrated with imaging features to construct a fusion model. model performance was evaluated using leave-one-out cross-validation. Results The AUC for DWI-based radiomics models reached 0.84 for tumor lesions and 0.87 for the uterine body, while the T2WI-based models yielded AUCs of 0.82 and 0.84, respectively. multivariate logistic regression identified age, CA199 and Ki67 expression as independent predictors of LVSI (P<0.05), with the combined clinical-pathological model achieving an AUC of 0.834. The final fusion model, incorporating both radiomics and clinical-pathological features, achieved an AUC of 0.920, demonstrating superior predictive performance compared to single-modality models. Conclusion The integration of mpMRI-derived radiomics with key clinical and pathological factors significantly enhances the predictive accuracy for LVSI in endometrial cancer. this fusion approach may provide valuable support for accurate preoperative staging and the development of individualized treatment strategies.

The incidence and mortality of dilated cardiomyopathy have risen significantly in recent years, establishing it as the most common cardiomyopathy and a primary indication for heart transplantation. Accurate assessment of viable and fibrotic myocardium is crucial for guiding treatment strategies, evaluating therapeutic efficacy, and predicting clinical outcomes in dilated cardiomyopathy patients. Cardiac magnetic resonance imaging with late gadolinium enhancement (CMR-LGE) has proven to be a reliable modality for identifying viable myocardium, providing critical guidance for early diagnosis and personalized treatment strategies in dilated cardiomyopathy. This review systematically summarizes the non-invasive imaging features for evaluating myocardial viability and fibrosis, highlights their clinical applications and prognostic value in cardiovascular diseases, and explores future technological advancements in the field. By integrating current research findings and emerging trends, this work aims to advance innovative methodologies and frameworks for improving early diagnosis and precision medicine in cardiac care.

Concomitant depression is extremely common among the patients with Parkinson's disease and greatly affects the quality of life and prognosis of Parkinson, at the same time,the importance of recognising and diagnosing depression in Parkinson's remains grossly underestimated in clinical practice. Abnormal structural and functional alterations of the nucleus accumbens, as a key component of the limbic system and reward circuits in the brain, are often regarded as a potential physiological mechanism leading to depression and other psychiatric disorders. In this paper, we would not only take the nucleus accumbens as an entry point, but also review from both structural and functional MRI technology aspects,and discusse the significance of abnormalities in the nucleus accumbens of patients of depression with Parkinson's disease,with the aim of helping clinicians better diagnose and pave new perspectives and ideas for future research work.

Objective To evaluate coronary microcirculation obstruction (MVO) in hypertensive patients using multimodal magnetic resonance imaging. Methods A total of 145 patients diagnosed with hypertension in our hospital from October 2023 to October 2024 were randomly included. Multimodal magnetic resonance imaging included conventional cardiac cine sequence, late-gadolinium enhanced (LGE) delayed phase scan, and feature tracking technique. MVO was diagnosed based on delayed phase scan and the patients were divided into observation group (n=68) and control group (n=77). The measurement parameters included global longitudinal strain (GLS), global radial strain (GRS), global circumferential strain (GCS) of left ventricle, as well as LS, RS, and CS of the apical, middle, and basal segments, left ventricular mass index (LVMI), and so on. Results Compared with control group, the observation group showed significant increases in average systolic blood pressure, duration of hypertension, LVMI, GLS, and basal LS (P<0.05). Multivariate logistic regression showed that increased GLS was only independent risk factor to MVO in hypertensive patients (OR=3.032, 95% CI: 2.426-4.032, P<0.001). ROC curve showed that area under the curve of GLS for MVO diagnosis was 0.856 (95% CI: 0.756-0.923, P<0.001). Conclusion Magnetic resonance imaging based on feature tracking technology measuring left ventricular GLS has good accuracy in hypertensive patients for the diagnosis of MVO, which can be used as an alternative technique for LGE delayed phase scan, improving the safety of examination.

Abdominal CT examinations, characterized by broad anatomical coverage and multiphase scanning protocols, pose significant challenges in radiation dose control. Merely adjusting hardware parameters often fails to balance radiation reduction with diagnostic image quality. As a core technology that breaks through traditional hardware limitations, reconstruction algorithms have shown great potential in the field of low-dose abdominal CT. This review systematically examines the developmental trajectory, clinical applications, and limitations of conventional filtered back projection (FBP), iterative reconstruction (IR) and deep learning image reconstruction algorithms in low-dose abdominal CT. A focused analysis is provided on the technical principles, clinical application value, and potential shortcomings of mainstream algorithms such as ASIR-V and TrueFidelity^TM, aims to establish a theoretical foundation for selecting optimal reconstruction protocols in clinical practice and proposes future directions for algorithm refinement (such as integration with spectral CT and photon-counting detector technologies).

Objective To evaluate the effectiveness of an artificial intelligence (AI) image-assisted diagnostic system in the prediction of pulmonary nodules and its clinical application value. Methods A total of 212 patients with definitive pathologic diagnoses of pulmonary nodules underwent analysis of their preoperative chest CT images, which were provided in DICOM format, using the AI-assisted diagnostic system. The diagnostic effectiveness of the AI model and Lung-RADS were compared in predicting of benign and malignant pulmonary nodules with different clinical and imaging characteristics. Results The AI model demonstrated higher diagnostic accuracy than Lung-RADS in distinguishing between benign and malignant pulmonary nodules (70.75% vs 60.85%, P<0.05). Results of the stratified analysis were as follows. By age: The AI model showed higher accuracy than Lung-RADS for patients aged 50-59 years (70.31% vs 53.13%, P<0.05). By nodule position: There were no significant differences between he AI model and Lung-RADS (P>0.05). By nodule density: The AI model showed higher accuracy than Lung-RADS for the mixed-ground glass nodules (74.51% vs 49.02%, P<0.05). By nodule size: The AI model showed higher accuracy than Lung-RADS for the nodules measuring 10-19 mm in diameter (74.75% vs 66.67%, P<0.05). By malignant pathology: The AI model exhibited higher accuracy in predicting adenocarcinoma nodules compared to Lung-RADS (77.52% vs 62.79%, P<0.05). Conclusion The AI image-assisted diagnostic system surpasses Lung-RADS in assessing the benign and malignant pulmonary nodules. With ongoing technological advancements, it has the potential to provide a reliable foundation for the early, non-invasive diagnosis of pulmonary nodules.

Objective To explore the value of a nomogram based on ultrasound features combined with clinical and pathological indicators in predicting sentinel lymph node metastasis (SLNM) risk in T1 breast cancer. Methods A retrospective analysis was conducted on 306 breast cancer patients pathologically confirmed at The First Affiliated Hospital of Xinjiang Medical University from January 2021 to December 2023. Patients were randomly divided into training and validation sets in a 7:3 ratio. Multivariate logistic regression was used to identify independent predictors of SLNM. A predictive model was established and visualized as a Nomogram. The model was validated using the validation set, calibration curves, ROC curves and decision curve analysis. Results Multivariate Logistic regression identified four independent predictors of SLNM: tumor aspect ratio, margin characteristics, axillary lymph node status, and Ki-67 expression status. The nomogram incorporating these indicators demonstrated good predictive performance, with AUC of 0.79 (95% CI: 0.72-0.86) in the training set and 0.83 (95% CI: 0.74-0.93) in the validation set. Calibration curves confirmed the model's accuracy. Conclusion The developed nomogram effectively predicts SLNM risk in T1 breast cancer patients. It may serve as a tool to identify patients who do not require sentinel lymph node biopsy and guide decisions on axillary lymph node dissection and adjuvant therapy.

Objective To explore the metabolic parameters of ¹⁸F-FDG PET/CT in lung cancer, and analyze their differences and correlations with various histologic types, different histologic grades, and Ki-67 indices. Methods A retrospective analysis was conducted on the clinical data of 178 lung cancer patients (119 males and 59 females, 35-79 years old) who underwent ¹⁸F-FDG PET/CT examinations prior to surgical resection or pathological biopsy at our institution from January 2022 to October 2024. The SUV_max, SUV_mean, MTV and TLG were extracted from the imaging data. One-way analysis of variance was used to assess the differences in metabolic parameters across different pathological types and differentiation grades, while Spearman rank correlation analysis was employed to evaluate the correlation between metabolic parameters and the Ki-67 index. Results Significant differences in SUV_max, SUV_mean, MTV, TLG, and Ki-67 index were observed among squamous cell carcinoma, adenocarcinoma, and small cell lung cancer (P<0.001). Small cell lung cancer exhibited the highest values for these parameters, followed by squamous cell carcinoma. In overall analysis, SUV_max, SUV_mean, MTV, TLG, and Ki-67 in lung cancer all exhibited moderate to strong positive correlations (P<0.05). Among them, SUV_max had the strongest positive correlation with Ki-67 (r=0.522, P<0.05), followed by TLG (r=0.520, P<0.05). According to lung cancer subtypes,adenocarcinoma's SUV_max, SUV_mean, MTV and TLG demonstrated varying degrees of significant positive correlation with the Ki-67 index (P<0.001), with correlation coefficients (r) of 0.460, 0.414, 0.396, 0.408, respectively, where SUV_max showed the strongest correlation, followed by SUV_mean. In contrast, the metabolic parameters in squamous cell carcinoma and small cell carcinoma do not show a significant correlation with Ki-67 (P>0.05). The Ki-67 index in squamous cell carcinoma revealed significant differences among different differentiation grades (P<0.05), while no significant differences were noted for SUV_max, SUV_mean, MTV and TLG (P>0.05). In adenocarcinoma, significant differences were found in SUV_max, SUV_mean and Ki-67 index across various differentiation grades (P<0.05), whereas MTV and TLG showed no significant differences (P>0.05). Conclusion There were significant differences in ¹⁸F-FDG PET/CT metabolic parameters and Ki-67 among different pathological types of lung cancer, and there was a certain correlation between metabolic parameters and Ki-67. Significant differences in Ki-67 were observed between squamous cell carcinoma and adenocarcinoma. Additionally, the SUV_max and SUV_mean of adenocarcinoma showed significant variations across different differentiation levels.

Objective To analyze the effects of contrast media with different iodine concentrations on image quality and radiation dose of coronary computed tomography angiography (CCTA). Methods The data of 144 patients with coronary heart disease who were admitted to the Sichuan Science City Hospital from January 2020 to January 2024 were retrospectively analyzed. All patients underwent dual-source CCTA after admission, and the fixed iodine flow rate was controlled within the same weight range. The patients were divided into group A (n=48), group B (n=48) and group C (n=48) according to different iodine concentrations in contrast media. The iodine concentrations of contrast media used in group A, B and C were 270 mgI/mL, 320 mgI/mL, 350 mgI/mL, respectively. The subjective image quality, CT values of the aortic root, right coronary artery and proximal left anterior descending artery, image noise, signal-to-noise ratios (SNRs) and radiation doses under contrast media with different iodine concentrations were compared. Results There were no statistically significant differences among the three groups in terms of the distribution of excellent, good and moderate quality of subjective coronary artery display, CT values of the aortic root, right coronary artery and left anterior descending artery, SD value, SNR, CTDI, DLP, ED (P>0.05). Conclusion Under a fixed iodine flow rate, there is no significant difference in image quality and radiation dose of CCTA using contrast media with different iodine concentrations (270, 320, 350 mgI/mL). For patients without obesity or severe renal dysfunction prioritize, contrast media with low iodine concentration is recommended to reduce the risk of nephrotoxicity.

Objective To investigate the predictive value of diverse expression parameters of ¹⁸F-FDG and ¹⁸F-PSMA-1007 PET/CT imaging for pathological grading of prostate cancer. Methods A retrospective analysis was carried out on the clinical characteristics, as well as ¹⁸F-FDG and ¹⁸F-PSMA-1007 PET/CT imaging data of 235 patients who were initially diagnosed with prostate cancer at the People's Hospital of Xinjiang Uygur Autonomous Region from July 2022 to May 2024. The enrolled patients were classified into the low-grade group and high-grade group according to the Gleason grading system. Mann-Whitney U test was employed to compare the differences in total prostate specific antigen (TPSA) and each expression parameter of PET/CT before treatment between the two groups. Spearman's rank correlation coefficient was utilized to analyze the correlation between different expression parameters and the International Society of Urological Pathology (ISUP) grouping, as well as pre-treatment TPSA values. A binary multifactorial logistic regression model was adopted to identify the independent predictors of prostate cancer pathological grading. Additionally, ROC curves were used to evaluate the predictive efficacy of different expression parameters for high-grade prostate cancer. Results Statistically significant differences was observed in the different expression parameters between the two groups (P<0.001). Spearman's rank correlation coefficient analysis indicated that each expression parameter of both imaging modalities was positively correlated with ISUP grading and pre-examination TPSA values. Binary multifactorial logistic regression analysis revealed that only SUV_max2 of ¹⁸F-PSMA-1007 PET/CT imaging was an independent predictor of ISUP grading, with an OR of 0.54(0.293-0.993, P=0.047). PSMA-TV and TL-PSMA had the largest areas under the ROC curve (0.950 and 0.928, respectively, P<0.001). Conclusion ¹⁸F-PSMA-1007 PET/CT expression parameters, namely SUV_max2, PSMA-TV, and TL-PSMA, exhibit greater advantages in the early prediction of prostate cancer pathologic grading.

Objective To analyze the two-dimensional sonographic characteristics and blood flow distribution patterns of endometrial cancer (EC) using the consensus terminology of the International Endometrial Tumor Analysis (IETA), and explore the diagnostic value of combining IETA ultrasound features with tumor markers in predicting the pathological grading of EC. Methods A retrospective analysis was conducted on the ultrasound images and serum tumor marker levels of 147 EC patients at Affiliated Hospital of Qiaodao University from January 2020 to August 2024. According to the 2023 staging system of the International Federation of Gynecology and Obstetrics, the enrolled cases were divided into a low-grade group (n=104) and a high-grade group (n=43) based on histological types. Univariate and multivariate logistic regression analyses were performed to assess the diagnostic efficacy of individual indicators and combined multi-factor indicators. Diagnostic performance was evaluated using ROC curves, and the AUC was calculated. Results Univariate analysis showed statistically significant differences between the low-grade and high-grade groups in CA125, HE4, endometrium-myometrium junction morphology, endometrial echogenicity, blood flow scores, and lesion diameter (P<0.05). Multivariate logistic regression analysis identified CA125, endometrium-myometrium junction morphology, blood flow scores, and lesion diameter as independent risk factors for predicting high-grade EC (P<0.05). The combined multi-factor indicators showed a sensitivity of 69.80%, specificity of 82.70%, accuracy of 81.13%, and an AUC of 0.81, which was significantly higher than that of individual indicators. Conclusion The combination of IETA ultrasound features and CA125 demonstrates high diagnostic value in predicting the pathological grades of EC. It provides critical evidence for the early identification of high-grade EC patients.

Objective To investigate the predictive potential of building a nomogram model for testicular torsion (TT) by combining clinical characteristics and ultrasound signals. Methods The clinical data of 272 patients with scrotal pain in Inner Mongolia People's Hospital from March 2011 to April 2024 were retrospectively analyzed. According to the 7:3 random machine, they were divided into training set (n=190) and validation set (n=82). The training set consisted of 97 non-TT patients and 93 TT patients, based on the findings of surgical exploration and follow-up. The validation set consisted of 41 non-TT patients and 41 TT patients. The clinical information and several ultrasonic characteristics of the patients were compared using the training set. R software was used to create the risk prediction model of TT visual nomogram in scrotal discomfort after univariate analysis and multivariate logistic regression were used to check for independent influencing factors. The model's performance was examined and assessed using the ROC curve, calibration, and decision curves. Results Age, internal testicular parenchymal echogenicity, testicular blood flow signal grade, Spermatic cord whirlpool, and neutrophil/lymphocyte ratio were all independent risk variables for TT prediction, according to the results of multifactorial logistic regression analysis (P<0.05). A nomogram prediction model was established using the aforementioned criteria. Both the validation set and the model training set had corresponding area under the curves of 0.980 and 0.995. The calibration curve demonstrated that the model's TT prediction probability in both the training and validation sets tended to match the likelihood of actual occurrence. In the training and validation sets, the average absolute error between the expected and actual outcomes was 0.018 and 0.040, respectively. The decision curve demonstrates that the net benefit of predicting TT in the training set and the verification set is higher when the model's probability thresholds are 1%-100% and 1%-93%, respectively. Conclusion In addition to providing a visual aid for clinical decision-making, the nomogram model based on ultrasound indicators in conjunction with clinical factors can accurately estimate TT risk.

Objective To evaluate the potential of deep learning image reconstruction (DLIR) in improving image quality and reducing radiation dose by comparing the noise power spectrum, task-based transfer function and lesion detection capability. Methods The ACR464 phantom was scanned using GE Revolution APEX CT and eight different noise indices (NI=10, 14, 16, 18, 20, 22, 24, 28) were set. The original data were subjected to image reconstruction using filtered back-projection (FBP), multi-model iterative reconstruction algorithms (ASiR-V) at 40%, ASiR-V at 60%, ASiR-V at 80%, and different levels of deep learning image reconstruction (DLIR-L, DLIR-M, DLIR-H) algorithms. The image quality was evaluated by using imQuest software to calculate the noise power spectrum (NPS), task-based transfer function (TTF), and detection capability index (d') of different reconstruction algorithms. Results Among all the reconstruction algorithms, the NPS peak of DLIR-H was the lowest. With the increase of noise index, both NPS and fav move towards low frequencies. The fav of DLIR-H (0.24-0.27 mm^-1) was only 40% lower than that of ASiR-V (0.26-0.28 mm^-1). The TTF_50% value was not affected by the DLIR level. The TTF_50% value was (37.44±10.85)% and (46.24±15.28)% higher than that of ASiR-V60% and 80%, respectively. The detection ability of both large and small features in deep learning image reconstruction was 40% higher than that of ASiR-V. When comparing the radiation doses with comparable lesions detection capabilities of 40% ASiR-V at NI=10 and DLIR-H, the radiation dose for small features decreased by approximately 76.48%, and that for large features decreased by approximately 72.59%. Conclusion Deep learning image reconstruction can not only reduce noise, improve spatial resolution and lesion detectibility without changing noise texture, but also has more powerful ability to reduce radiation dose than ASiR-V.

Objective To develop and validate a 3D convolutional neural network (3D-CNN)-based CT-assisted diagnostic model for tuberculosis (TB) activity grading, aiming to improve diagnostic efficiency and accuracy. The model incorporates Grad-CAM visualization technology to provide interpretability analysis of its decision-making process. Methods Retrospective data were collected from 300 patients who underwent non-contrast chest CT scans at Jining Public Health Medical Center from January 2020 to December 2024. According to the Diagnostic Criteria for Pulmonary Tuberculosis (WS 288-2017) and comprehensive clinical evaluation (including sputum culture, pathological results, anti-TB treatment response, and follow-up imaging dynamics), patients were stratified into three groups, with 100 cases in each group: normal lung group, active TB group, inactive TB group. The 3D-CNN model was employed to extract spatial features from CT images, with model parameters optimized through cross-validation. Model performance was systematically evaluated. The Grad-CAM algorithm generated heatmaps to identify critical regions of model attention, with results validated against clinical diagnostic standards. Results The model achieved 95.00% of classification accuracy, 95.30% of sensitivity, and 95.60% of specificity on the test set. Grad-CAM visualizations demonstrated high spatial concordance between model-identified regions and expert-annotated lesion areas. Conclusion The 3D-CNN-based CT-assisted diagnostic model shows high performance in TB activity grading and may serve as an effective clinical decision-support tool. The integration of Grad-CAM enhances model transparency and credibility.

Objective To evaluate the value of a radiomics-based machine learning model in predicting the invasiveness of pulmonary pure ground-glass nodules (pGGNs). Methods A retrospective cohort study was conducted on 208 pGGNs identified in our department from August 2022 to August 2024. Based on pathological results, the nodules were classified into non-invasive and invasive groups. CT characteristics of the nodules were recorded, and radiomics features were extracted from CT images. Optimal radiomics features were selected to construct a predictive model. ROC curves were plotted and the area under the curve (AUC) was calculated. The diagnostic performance of the radiomics model was compared with that of radiologists alone and radiologists assisted by the radiomics model. Results After dimensionality reduction, six optimal features were selected to build a logistic regression model. In the training set, the model achieved an AUC, sensitivity, and specificity of 0.786, 0.771 and 0.875, respectively, while in the validation set, these values were 0.776, 0.735 and 0.859. The radiomics model outperformed radiologists in diagnostic accuracy and enhanced radiologists' diagnostic performance when used in combination. Conclusion The CT radiomics-based machine learning model demonstrates high predictive efficacy for determining the invasiveness of pulmonary pGGNs and provides valuable guidance for clinical decision-making.

Acute pulmonary embolism (APE) is a serious health-threatening syndrome characterized by an extremely high mortality and disability rate, therefore, early diagnosis, treatment and prognosis assessment of patients with APE are critical. Inflammatory response and coagulation processes are important links in the formation of embolism and are involved in this pathophysiological process. Pulmonary vascular remodeling is often correlated with the extent of pulmonary artery involvement and the degree of right heart dysfunction. Key imaging parameters, including the longest diameter of the right ventricular short axis to the longest diameter of the left ventricle short axis ratio, the Qanadli index, pulmonary artery diameter, and pulmonary artery dilation, are crucial for predicting poor prognosis in APE. This article aims to review recent advancements in clinical features, imaging parameters, serum biomarkers and their combinations for predicting the prognosis of patients with APE. The goal is to enhance the prognostic accuracy of APE, assist clinicians in better risk stratification and personalized treatment planning and ultimately improve patient outcomes.

The metal artifacts generated by the metal implants in the body will cover the anatomical structure and reduce the diagnostic accuracy in the postoperative review.Dual-energy CT virtual monoenergetic imaging combined with MAR technology can effectively reduce the influence of metal artifacts on CT image quality, but the optimal monoenergetic level for suppressing metal artifacts in different parts is different. Therefore, this article reviews the current research status of the optimal monochromatic energy levels for reducing metal artifacts in different locations, including intracranial aneurysm embolization, oral metal implants, spinal internal fixation, hip replacement, and other implants. This review aims to provide a scientific basis for clinical imaging practice, thereby optimizing imaging parameters and improving the accuracy of postoperative evaluation.

The tumor microenvironment plays an important role in tumorigenesis, development, metastasis, and drug sensitivity. Carbonic anhydrase IX (CAIX) is a metalloenzyme that is highly expressed in certain tumor microenvironments and is closely related to tumor cell genesis, development and drug resistance. Its high expression in tumors and low expression in normal tissues makes it a potential target for the diagnosis and treatment of a variety of tumors. With the in-depth research on antitumor drugs and radionuclide-targeted therapy targeting CAIX, the detection of CAIX expression using noninvasive radiopharmaceuticals has become more urgent in order to meet the strategy of diagnostic and therapeutic integration. In this paper, we summarize and review the research on CAIX-targeting radiopharmaceuticals in recent years, analyze and summarize the advantages and disadvantages of monoclonal antibodies, small molecules and peptides, as well as the prospects of their clinical application, so as to optimize the structure of the drugs, reduce the side effects and meet the clinical needs, and to accelerate the clinical application of these drugs.

Vascular cognitive impairment (VCI) is a clinical syndrome characterized by cognitive impairment in at least one domain, caused by cerebrovascular lesions and their risk factors, with an increasing incidence among middle-aged and elderly populations. In recent years, advancements in MRI have provided new methods for the study of VCI. Among these, diffusion tensor imaging, which is currently the only non-invasive technique that allows visualization of the arrangement of white matter fiber tracts in living brains, has unique advantages in VCI research. This article will review the concept of diffusion tensor imaging and its various applications in VCI, including white matter lesions, diagnosis and classification, risk factor identification, and prevention and treatment, while also discussing its limitations and prospects for future development. This aims to provide new insights for the clinical diagnosis and pathological research of neurological diseases.

Objective To utilize ultrasound radiomics technology combined with clinical indicators and contrast-enhanced ultrasound (CEUS) to construct a combined model, thereby improving the diagnostic accuracy of PCa in the gray zone. Methods The diagnostic accuracy of prostate cancer (PCa) within the prostate-specific antigen gray zone (4-10 ng/mL) is relatively low, and traditional methods struggle to effectively differentiate between benign and malignant lesions. This study aims to utilize ultrasound radiomics technology combined with clinical indicators and contrast-enhanced ultrasound (CEUS) to construct a combined model, thereby improving the diagnostic accuracy of PCa in the gray zone. Results Among the 137 patients, 55 were diagnosed with prostate cancer and 82 had benign lesions. Multivariate logistic regression analysis showed that free prostate-specific antigen and peak intensity were independent predictors of PCa in the gray zone (P<0.05). The combined model achieved AUC values of 0.965 and 0.893 in the training and validation sets, respectively, significantly outperforming the single models. The DCA curve indicated that the combined model had a higher net benefit at low-risk thresholds, and the calibration curve further confirmed its accuracy. Conclusion The combined model constructed using ultrasound radiomics, clinical indicators, and CEUS significantly improves the diagnostic accuracy of PCa in the gray zone, providing an effective auxiliary diagnostic tool for clinical practice.

Objective To explore the value of a Nomogram model based on CT radiomics with clinical parameters in differentiating non-small cell lung cancer from benign pulmonary lesions. Methods A retrospective study was conducted on 177 patients with benign pulmonary lesions and non-small cell lung cancer, confirmed by pathology, at the First Affiliated Hospital of Bengbu Medical Unversity from December 2020 to December 2023. The cases were randomly divided into a training group and a validation group in an 8:2 ratio. Radiomic features were extracted from contrast-enhanced CT images, and a stepwise dimensionality reduction was performed using the Relief-LASSO algorithm, ultimately selecting five optimal features from a total of 2264 radiomic features. Single and multiple factor Logistic regression was employed to screen independent clinical risk factors. Clinical, radiomics, and Nomogram models were constructed respectively. The performance of the Nomogram model was comprehensively evaluated using multiple metrics, including the area under the ROC curve (AUC), calibration curves, and decision curve analysis. Results The results indicated that the Nomogram model exhibited excellent predictive performance, with AUC values of 0.872 (95% CI: 0.817-0.928) in the training set and 0.788 (95% CI: 0.627-0.948) in the validation set. These values were significantly higher than those of the individual imaging model (0.811, 0.722) and the clinical model (0.797, 0.734). Conclusion The established Nomogram model serves as a non-surgical predictive tool for the differential diagnosis of non-small cell lung cancer and benign pulmonary lesions. Validation demonstrated that the Nomogram model exhibited excellent differentiation and calibration abilities, indicating its clinical utility in the early screening of lung cancer and providing important guidance for clinical decision-making prior to surgery.

Objective To construct a machine learning model using multimodal MRI radiomics features and clinical characteristics to predict lymph node metastasis in rectal cancer patients. Methods A retrospective analysis was conducted on clinical data and MRI images of 223 rectal cancer patients treated at Shunde Hospital of Southern Medical University from May 2018 to May 2023. Tumor regions were delineated using 3D Slicer software, and radiomics features were extracted using PyRadiomics software. Univariate logistic regression and LASSO regression were used to screen features, and clinical, radiomics, and clinico-radiomics models were constructed and evaluated in the training cohort (n=157) and validation cohort (n=66) to evaluate their predictive performance for lymph node metastasis. Results In the training cohort, the AUC values for the clinical, radiomics, and clinico-radiomics models were 0.668, 0.725, and 0.771, respectively. The radiomics model and clinico-radiomics model demonstrated good predictive performance in the validation cohort, with the radiomics model achieving an AUC of 0.780. Conclusion Machine learning models based on MRI radiomics and clinical features can effectively predict lymph node metastasis in rectal cancer. Radiomics features provide high predictive value, while clinical features offer limited predictive value.

Objective To explore the clinical value of a combined model constructed based on ultrasound radiomics features and clinicopathological features in predicting pathological complete response after neoadjuvant chemotherapy (NAC) for breast cancer. Methods Retrospective analysis included 160 breast cancer patients receiving NAC before surgery at the First Affiliated Hospital of Bengbu Medical College from August 2021 to July 2024. By analyzing the ultrasound images, the radiomics features were extracted, and the features with statistical significance were screened out for subsequent modeling. Multivariate Logistic regression analysis and the XGBoost algorithm were used to construct joint models based on ultrasound radiomics features, clinicopathological features, and their combination, respectively, and nomograms were drawn. Model performance for NAC response was evaluated using AUC and decision curve analysis, with calibration curves assessing prediction-actuality agreement in the combined model. Results Estrogen receptor (P=0.006), progesterone receptor (P=0.024), and human epidermal growth factor receptor-2 (P=0.034) emerged as independent predictors of NAC response among clinicopathological features. The integrated model combining these three clinical predictors with radiomics features demonstrated optimal predictive performance, achieving a training-set AUC of 0.863, significantly higher than individual models. Calibration curves confirmed robust agreement between predicted and observed outcomes, while decision curve analysis indicated substantial clinical net benefit. Conclusion The combined model constructed based on the characteristics of ultrasound radiomics and clinicopathological features can effectively predict the efficacy of NAC in breast cancer and provide strong support for clinical decision-making. This model has high predictive accuracy and clinical application value, and is expected to become an important tool for individualized treatment of breast cancer.

Objective To develop a novel multifunctional integrated diagnostic and therapeutic nanoprobes targeting breast cancer, MnO₂@DOX@Cancer Cell Membrane (MDC), evaluate its basic properties, and explore its magnetic resonance imaging (MRI) capabilities and chemotherapy effects. Methods MnO₂ with a favorable morphology was synthesized. The cell membrane of 4T1 mouse breast cancer cells was obtained using chemical lysis and ultrasonic fragmentation techniques. A lipid extrusion method was used to synthesize the cell membrane-modified nanoprobes MDC loaded with doxorubicin (DOX). Key properties of MDC, including morphology, surface potential, and particle size, were characterized, and its MRI capability was explored. The cytotoxic effects of MDC at different concentrations on tumor cells were observed to assess its inhibitory effect on breast cancer tumors in chemotherapy. The integrated diagnostic and therapeutic functions at the cellular level were also verified. Results The nanoprobes MDC with a nanoflower-like structure were successfully synthesized, with a Zeta potential of -20±1.5 mV and an average particle size of 151.2 nm. Transmission electron microscopy (TEM) imaging confirmed successful coating of the cell membrane onto the nanoprobes. MRI results showed that MDC exhibited excellent MRI T1 imaging ability in a simulated tumor microenvironment. Laser confocal microscopy experiments further confirmed that MDC had excellent homologous targeting capability. Cytotoxicity experiments indicated that MDC significantly killed cancer cells. Conclusion This study successfully synthesized a multifunctional integrated diagnostic and therapeutic nanoprobes MDC, which exhibited excellent homologous targeting and anti-tumor effects in chemotherapy, significantly enhanced MRI imaging, and demonstrated integrated diagnostic and therapeutic functions at the cellular level.

Objective To explore the diagnostic efficacy of 3.0T MRI diffusion tensor imaging (DTI) in lumbar disc degeneration and the correlation of clinical function. Methods A total of 85 patients with back and leg pain were selected from the First Affiliated Hospital of Bengbu Medical University from January 2024 to June 2024. Routine MRI and DTI sequence scanning were performed on all patients. The Pfirrmann grading scale was used to evaluate the degeneration of intervertebral discs from L3 to S1. The severity of lumbar pain and dysfunction was assessed using the visual analog scale (VAS), Oswestry disability index (ODI) and Japanese Orthopaedic Association (JOA) score. The fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values of the nucleus pulposus region in each intervertebral disc from L3 to S1 were measured. Spearman correlation analysis was performed to investigate the relationship between FA and ADC values and Pfirrmann grading. ROC curves were used to evaluate the diagnostic efficiency of FA and ADC values for different grades of disc degeneration. Differences in VAS ODI, and JOA scores among patients with different Pfirrmann grades were compared. The linear correlation between FA and ADC values and VAS, ODI and JOA scores was analyzed. Results A total of 249 lumbar intervertebral discs were evaluated, with 63, 98, 60 and 28 discs classified as Pfirrmann grades II, III, IV, and V, respectively. There were significant differences in FA and ADC values among all grades (P<0.05). Spearman correlation analysis showed that the Pfirrmann grade of disc degeneration was highly positively correlated with FA values (r=0.858, P<0.001) and highly negatively correlated with ADC values (r=-0.764, P<0.001). ROC curve analysis indicated that the diagnostic efficacy of FA and ADC values for differentiating grades II-III, III-IV and IV-V was 0.829, 0.906, 0.924 for FA and 0.776, 0.854, 0.869 for ADC, respectively. Pearson correlation analysis demonstrated significant correlations between FA and ADC values and VAS, ODI, and JOA scores (P<0.05). Conclusion 3.0 T MRI DTI can effectively and intuitively evaluate Pfirrmann grade and lumbar function of lumbar disc degeneration, which is helpful for clinical qualitative and quantitative diagnosis.

Amide proton transfer-weighted (APTw) imaging, a unique chemical exchange saturation transfer imaging technique, indirectly quantifies the amide proton content in mobile proteins and peptides and pH changes in tissues by detecting the attenuation of free water signals. This provides insights into tissue metabolism, offering rich molecular-level information for the diagnosis and treatment monitoring of breast cancer. In recent years, the integration of APTw imaging with AI has gained attention in breast cancer diagnosis. This review focuses on the applications of APTw imaging combined with AI in differentiating benign and malignant breast lesions, histological grading, analyzing correlations with biomarkers, and evaluating the efficacy of neoadjuvant chemotherapy. It also discusses the challenges and limitations of this technology in practical applications.

Objective To develop a novel PET tracer, ¹⁸F-JR-1002, targeting the cannabinoid type 2 receptor (CB2R), to enhance molecular imaging capabilities for CB2R-related diseases. Methods The synthesis of a PET tracer targeting cannabinoid type 2 receptor (N-(4-(diethylamino)benzyl)-4-(2-(fluoro-¹⁸F) ethoxy)-N-(p-tolyl) benzenesulfonamide) was designed and prepared stably and efficiently on the automatic synthesizer, denoted as ¹⁸F-JR-1002. Cell affinity and specificity experiments, in vitro stability experiments, whole-body dynamic and static scanning of mice, and spleen autoradiography were carried out. Results The decay-corrected radiochemical yield was 34.7%±8.1% (n=12), corresponding to a molar activity Am of 264.5±41.2 GBq/μmol. Stability tests showed that ¹⁸F-JR-1002 had excellent in vitro stability. The cellular uptake assays and spleen tissue autoradiography showed that ¹⁸F-JR-1002 exhibits high binding affinity and specific imaging capability for CB2R. In mice, most of the ¹⁸F-JR-1002 probes were concentrated in the small intestine, followed by obvious uptake in the liver, kidneys, and bladder. The spleen and lungs had relatively low uptake, and the bones basically did not take up the tracer. According to dynamic scanning data, the PET tracer accumulated in the liver and kidneys in the early stage. Over time, the uptake of the tracer in the small intestine and bladder increased significantly and then stabilized. Conclusion This study developed a high-affinity, specific and metabolically stable CB2R probe ¹⁸F-JR-1002, which improved the accuracy and reliability of CB2R imaging and provided more accurate imaging support for clinical practice.

Pulmonary thromboembolism is an umbrella term for a group of diseases or clinical syndromes caused by thrombus blockage of the pulmonary artery and its branches, which can lead to circulatory instability or sudden death without timely treatment, with a mortality rate as high as 30%. Energy-spectrum CT can make qualitative and quantitative judgment on tissues through its attenuation characteristics under different X-ray energies, and through post-processing technology, it can generate images such as base matter map, single-energy image, effective atomic number, and energy-spectrum curve at one time, which overcomes the limitations of traditional CT in characterizing tissues and allows for a more comprehensive analysis of the disease. In recent years, energy spectrum CT has been widely used in the diagnosis and risk stratification assessment of pulmonary embolism disease. Its multiparameter quantitative analysis has shown significant advantages in improving the accuracy of PE diagnosis and optimizing image quality. The purpose of this paper is to review the imaging principle of energy spectrum CT and its application value in pulmonary embolism disease.

Objective To investigate the application value of cardiac magnetic resonance imaging with late gadolinium enhancement (CMR-LGE) in the diagnosis of dilated cardiomyopathy (DCM), and to compare the diagnostic results of CMR-LGE with those of echocardiogram (UCG). Methods A retrospective study was conducted on 36 patients diagnosed with DCM upon discharge from Anhui Second Provincial Hospital from January 2023 to March 2025. All patients underwent both CMR-LGE (observation group) and UCG (control group) examinations. Within the observation group, 29 patients were CMR-LGE positive for DCM (observation subgroup 1), and 7 were CMR-LGE negative (observation subgroup 2). Within the control group, 20 patients were UCG positive for DCM (control subgroup 1), and 16 were UCG negative (control subgroup 2). Differences in diagnostic results, CMR-derived parameters, and clinical specificity parameters were analyzed between the groups. Results CMR-LGE confirmed the diagnosis in 29 cases, which was significantly higher than the 20 cases diagnosed by UCG (P=0.014). In observation subgroup 1 (CMR-LGE positive), the left ventricular end-diastolic volume index, left ventricular end-systolic volume index, and myocardial mass index were significantly higher than those in observation subgroup 2 (CMR-LGE negative). Conversely, body surface area, left ventricular ejection fraction, and stroke volume index were significantly lower in observation subgroup 1 compared to observation subgroup 2 (P0.05). ROC curve analysis demonstrated that SVI had the highest diagnostic efficacy for DCM, with an AUC of 0.990. Conclusion CMR-LGE demonstrates higher sensitivity in diagnosing DCM compared to UCG. Its quantitative parameters provide valuable reference for clinical diagnosis and treatment.

The incidence of papillary thyroid cancer (PTC) is increasing year by year, and accurate preoperative diagnosis plays a crucial role in clinical management and prognosis. Contrast-enhanced ultrasound is a non-invasive, real-time imaging technique that enhances tissue contrast through intravenous injection of ultrasound contrast agents, dynamically displaying microcirculation perfusion information of lesions and enabling qualitative and quantitative analysis. The integration of Contrast-enhanced ultrasound with novel technologies such as radiomics and deep learning holds promising potential in early disease diagnosis and treatment evaluation. This article reviews the principles of thyroid contrast-enhanced ultrasound, the application of contrast-enhanced ultrasound in the diagnosis of PTC, the evaluation of PTC treatment effects, and the research on related new technologies in contrast-enhanced ultrasound. It aims to provide guidance for the precise diagnosis and treatment practice of PTC.

Objective To explore the predictive value of imaging features, serological immune indicators, and clinical features for interstitial lung disease in Sj?gren's syndrome (SS-ILD). Methods A total of 161 patients with Sj?gren's Syndrome (SS) who were treated at the First Affiliated Hospital of Henan Medical University from July 2018 to February 2025 were retrospectively enrolled; through propensity score matching (PSM), the patients were divided into the SS without interstitial lung disease (ILD) group (n=56) and the SS with ILD group (n=29). Univariate analysis was used to screen for meaningful variables, the Spearman correlation coefficient was applied to analyze the correlation between these variables and SS-ILD, binary logistic regression was performed to construct a combined prediction model, the area under the receiver operating characteristic curve (AUC) and Delong test were used to verify the predictive performance of the combined model, a calibration curve was plotted to validate the value of imaging indicators incorporated into the combined model. Results Statistically significant differences were observed between the two groups in mean spleen density, percentage of CD4?T cells, complement C4, Dry mouth, Dry eyes, and oral symptoms, and these variables were all negatively correlated with the occurrence of SS-ILD (all P<0.05); Logistic regression analysis revealed that the mean splenic density (AUC=0.698, sensitivity=0.759, specificity=0.607), percentage of CD4⁺ T lymphocytes (AUC=0.677, sensitivity=0.982, specificity=0.310), and oral symptoms (AUC=0.635, sensitivity=0.339, specificity=0.931) all had independent predictive value. The three-factor combined model (AUC=0.781, sensitivity 0.862, specificity 0.554) was significantly superior to any single indicator (P<0.05). Conclusion Decreased mean splenic density, reduced percentage of CD4? T lymphocytes, and absence of oral symptoms are independent predictive factors for the development of SS-ILD. The combination of these three factors can effectively improve the predictive efficacy with high sensitivity, which is helpful for the early clinical identification of high-risk patients with SS-ILD.

Objective To evaluate the clinical, pathological, and imaging characteristics of multiple renal hemangiomas in different renal functions, namely end-stage renal disease (ESRD) and non-ESRD. Methods A retrospective analysis was performed on 1 case of ESRD-related multiple renal hemangioma confirmed by surgery in Shanxi Bethune Hospital (Shanxi Academy of Medical Sciences) on April 2017. Additionally, 23 cases of pathologically confirmed multiple renal hemangiomas patients from 1980 to 2023 were collected from the PubMed database. They were divided into the ESRD group and non-ESRD group according to renal function status. The clinical basic conditions, imaging features, treatment methods, and prognosis of the two groups were analyzed. Results The ESRD group included 19 cases (79.2%) , and the non-ESRD group included 5 cases (20.8%). Males were more common. There were no statistically significant differences between the two groups in terms of age, gender, clinical symptoms, tumor side, location, and concomitant renal epithelial tumors (P0.05). Extramedullary hematopoiesis was only observed in the ESRD group, with a statistically significant difference (P0.05). Except for the 1 case in our hospital, 5 patients were followed up for an average of 16 months, and no signs of tumor recurrence or metastasis were found. Conclusion Multiple renal hemangiomas are rare and mostly occur in the kidneys with ESRD. There were no significant differences in gender, age, clinical features, and imaging manifestations between multiple renal hemangiomas in ESRD and non-ESRD, but there were differences in extramedullary hematopoiesis.

Objective To explore the application value of combining low tube voltage and low contrast agent dose ("dual-low" technique) with a deep learning image reconstruction algorithm in coronary computed tomography angiography (CCTA). Methods A prospective study was conducted on 100 patients who underwent CCTA at the Affiliated Hospital of Shaanxi University of Chinese Medicine from June to September 2024. The patients were randomly divided into group A and group B, with 50 patients in each group. All patients were scanned using a GE Revolution APEX-CT. Group A was scanned with a tube voltage of 120 kV and a contrast agent dose of 0.8 mL/kg, while group B was scanned with a tube voltage of 70 kV and a contrast agent dose of 0.5 mL/kg. After scanning, the radiation dose was calculated for both groups. Group A utilized 50% adaptive statistical iterative reconstruction-V, while group B employed high-strength deep learning image reconstruction. CT values and noise values were measured in the aortic root, proximal left anterior descending artery, proximal left circumflex artery, proximal right coronary artery, and pericardial fat on images from both groups. The signal-to-noise ratio and contrast-to-noise ratio were calculated. Image quality was subjectively scored using a 5-point scale under a double-blind method. Results The radiation dose and contrast agent dose in group B were significantly lower than those in group A (P<0.001). The noise values in group B were lower than those in group A (P<0.05), while the CT values, signal-to-noise ratio, contrast-to-noise ratio, and subjective scores in group B were higher than those in group A (P<0.001). The subjective scores between the two physicians showed good consistency (Kappa value=0.829). Conclusion The application of the "dual-low" technique combined with high-strength deep learning reconstruction in CCTA can significantly reduce radiation dose and contrast agent dose while achieving better image quality.

Objective To construct and validate a machine learning model based on enhanced CT radiomics features to predict the expression status of immunohistochemical index P504S in renal cancer. Methods Clinical, pathological and imaging data of 117 patients with pathologically confirmed renal carcinoma and defined P504S expression status in the First Affiliated Hospital of Bengbu Medical University from January 2019 to September 2024 were collected and retrospectively analyzed; Three-dimensional radiomics features from contrast-enhanced CT of the lesions were extracted to establish a predictive model for distinguishing between P504S-negative and P504S-positive cases. All cases were randomly divided into a training set and a test set at a ratio of 7:3. 5-fold cross-validation was performed on the training set to select the optimal hyperparameters for establishing the predictive model, and the model using was validated the test set and the diagnostic performance of the model was analyzed using the ROC curve, calibration curve, and decision curve analysis. The region of interest was delineated based on the arterial and venous phases of CT scans. Data were normalized using Min-max normalization, and dimensionality reduction was performed through principal component analysis and Pearson similarity. The Relief algorithm was used for feature selection, and support vector machine and Naive Bayes were used as classifiers to construct the radiomics models for the arterial and venous phases, respectively. Results The radiomics model for the arterial phase achieved an AUC and accuracy of 0.801 and 0.805 on the training set and 0.833 and 0.743 on the test set, respectively. The radiomics model for the venous phase achieved an AUC and accuracy of 0.791 and 0.683 on the training set and 0.808 and 0.714 on the test set, respectively. The combined model for the arterial and venous phases achieved an AUC of 0.846 for all cases (95% CI: 0.768-0.906), slightly higher than the radiomics model for the arterial phase (0.804, 95% CI: 0.720-0.871) and the radiomics model for the venous phase (0.823, 95% CI: 0.742-0.887), but there was no statistically significant difference (P>0.05). Conclusion A machine learning model based on enhanced CT radiomics features of renal cancer can predict the expression status of immunohistochemical indicator P504S.

Alzheimer's disease (AD) is a neurodegenerative disorder that severely threatens human health, with a highly complex pathogenesis. It is generally believed that extracellular deposition of Aβ and intracellular aggregation of tau protein, which form NFTs, are key factors influencing the onset of AD. Medical imaging techniques, especially MRI, are considered essential tools in the study of brain diseases, particularly AD. However, conventional MRI techniques have certain limitations in the early diagnosis of AD, such as low sensitivity and difficulty in specifically targeting biomarkers associated with AD and other related diseases.Nanomaterials, when used as MRI contrast agents, can enhance the imaging signals of affected brain regions, enabling accurate diagnosis and imaging of AD. In addition, nanomaterials have demonstrated controlled-release properties, targeting capabilities, and the ability to help drugs cross the blood-brain barrier in MRI-guided drug delivery systems. These features offer new possibilities for AD treatment, even enabling synergistic effects between the nanomaterials, thus achieving both diagnostic and therapeutic benefits.This paper systematically reviews recent advancements in the combination of MRI and nanomaterials for the early diagnosis and treatment of AD. It also provides an in-depth outlook on the future development of MRI-nanomaterial integration in AD diagnosis and therapy.

Objective To construct a dual-phase integrated enhanced CT radiomics model for predicting PD-L1 expression in non-small cell lung cancer (NSCLC) patients. Methods A retrospective study was conducted on 150 NSCLC patients who were pathologically confirmed at the Affiliated Hospital of Jining Medical University from November 2019 to July 2023. These patients were randomly assigned to a training cohort (105 cases) and a testing cohort (45 cases) at a ratio of 7:3. Radiomics features were extracted from both the arterial and venous phases of CT images. Dimensionality reduction and key feature selection were performed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. Eight machine learning algorithms, including logistic regression, were employed to construct radiomics models. The best predictive model was identified through ROC curve analysis, and a dual-phase integrated radiomics model was developed by combining radiomics features from both phases. Univariate and multivariate logistic regression analyses were conducted to evaluate clinical features and to identify independent predictors for constructing a clinical model. A Combine model was then established by integrating radiomics and clinical features. The performance of the models was assessed using ROC curves, and their clinical utility was evaluated using decision curve analysis. Results A total of 1835 radiomics features were extracted from both the arterial and venous phase CT images. After dimensionality reduction and selection, 9 radiomics features were ultimately chosen from each phase. Among the radiomics models, the logistic regression model exhibited higher predictive efficiency and robustness. The dual-phase integrated enhanced CT radiomics model demonstrated superior performance compared to single-phase models. The radiomics-clinical model showed the best discriminative ability, with AUC values of 0.822 in the training cohort and 0.681 in the testing cohort. Decision curve analysis indicated the best clinical effectiveness. Conclusion The diagnostic model combining radiomics and clinical features of NSCLC has a good ability to predict PD-L1 expression and can provide a non-invasive and effective diagnostic method for clinical practice.

Objective To analyze the imaging features of pediatric diffuse midline glioma H3K27M variant (DMG-A). Methods The data of 22 children with DMG-A admitted to our hospital from February 2019 to October 2023 were retrospectively analyzed. Preoperative CT scan was performed in 12 cases (11 cases simultaneously with MRI). The images were independently read by two experienced pediatric imaging diagnostic doctors with over 10 years of working experience. Observed the lesion location, shape, size, density/signal characteristics of the solid part, signal intensity, restricted diffusion and MRS Manifestations, intratumoral calcification, hemorrhage and necrosis/cystic changes, enhancement features, whether there are blood vessels passing through the tumor, peritumoral edema, adjacent and secondary changes of the tumor, whether there is distant metastasis, and the metastatic site, etc. Results CT examinations were conducted in 10 cases. The brainstem lesions were mostly low/slightly low density shadows (9 cases), followed by equal or slightly high density shadows (1 case each). It is manifested as thickening of the brainstem, mostly centered on the pons, and partially involving the medulla oblongata, midbrain, cerebellar peduncle and cerebellar hemispheres. The thalamic lesion was a mass-like, uneven, slightly high-density shadow (1 case), extending towards the cisterns and the right side of the midbrain. MRI examination was conducted in 21 cases. The brainstem lesions were characterized by significant thickening and enlargement of the brainstem, with the pons being the most prominent. The main signal shadows were low on T1WI and slightly high on T2WI/FLAIR (15 cases). DWI could have diffuse limitation (9 cases), or no diffuse limitation (5 cases), and another case had no DWI examination. The lesions mostly surrounded the basilar artery (14 cases). Thalamic lesions were manifested as an increase in thalamic volume (6 cases), mainly low signal shadows on T1WI (5 cases), followed by isosignal shadows (1 case), and slightly high signal shadows on T2WI/FLAIR. DWI may have diffuse limitation (4 cases) or no limitation (2 cases). It partially affects the cerebellum, midbrain and other parts downward. After enhancement, most cases presented with obvious nodular, flower-shaped and patchy enhancement (13 cases), and could also show mild heterogeneous enhancement and no obvious enhancement (3 cases each). Additionally, 2 cases had no MRI enhancement examination. Nine cases underwent MRS Examination. The main manifestations were elevated Cho and Cr peaks, decreased NAA peak, and elevated Cho+Cr/NAA. Conclusion Although the imaging manifestations of DMG-A in children are varied, they still have certain characteristics. A comprehensive analysis of the age of occurrence, location of onset, whether the diffusion is limited, and the way and extent of intensification is helpful to improve the imaging diagnosis and differential diagnosis of the disease.

Objective To explore the construction of different peritumoral prediction models based on enhanced MRI radiomics, and to predict the 2-year PFS survival rate of hepatocellular carcinoma patients before transarterial chemoembolization. Methods A total of 201 patients who received transarterial chemoembolization (TACE) treatment in interventional radiology center from the First Affiliated Hospital of Xinjiang Medical University from January 2021 to January 2023 were retrospectively collected. Patients included in the study underwent 3.0T enhanced MRI scan of upper abdomen one week before TACE. Based on the images of arterial phase, portal vein phase and delayed phase of enhanced MRI, the tumor region of interest (ROI) was delineated, and the tumor was expanded by 2, 4, 6 mm equidistantly, and the histological model of intra-tumor and different tumor weeks was constructed: intratumoral+peritumoral 2 mm model, intratumoral+peritumoral 4 mm model, intratumoral+peritumoral 6 mm model, Nomogram model. The effectiveness of the above model to understand the independent risk factors affecting prognosis and the prediction of survival rate were evaluated. Results In the training corhort and test corhort, the AUC efficiency of intra-tumor+peritumoral 6mm model was 0.997 and 0.966, respectively, and its intra-tumor+peritumoral 6mm model was better than intra-tumor+peritumoral 2 mm and 4 mm models. Combined with Nomogram model, intra-tumor+peritumoral 6 mm model showed good clinical predictive value; COX survival analysis showed that the predicted probability of intra-tumor+peritumoral 6 mm was an independent risk factor for PFS. Kaplan-Meier curve survival analysis showed that the prediction probability of intra-tumor+peritumoral 6 mm model was a risk factor affecting the prognosis (P<0.05), the median PFS in the high-risk group was 14.8 months (P<0.001) in the training corhort and the median PFS in the risk group was 12 months (P=0.001) in the test corhort. Conclusion The intra-tumor+peritumoral 6mm model based on enhanced MRI radiomics has good clinical application value in predicting the 2-year PFS survival rate before TACE, and it can become a new imaging biomarker in the future.

Objective To explore the dynamic changes in B-line counts at the posterolateral alveolar and/or pleural syndrome point (PLAPS point ) during the early stage of acute cerebral hemorrhage, to assess their diagnostic value in the early identification of neurogenic pulmonary edema (NPE). Methods This retrospective study analyzed 40 patients with acute cerebral hemorrhage who were admitted to Chongqing Seventh People's Hospital from January to October 2024, and they were categorized into the NPE group and the non-pulmonary edema group based on the presence or absence of NPE. All patients underwent bedside lung ultrasound scans at the PLAPS point, and B-line counts were recorded. Results The B-line count was significantly higher in the NPE group than in the non-pulmonary edema group (5.13±1.22 vs 2.32±1.41, P<0.01). ROC curve analysis showed that B-line counts at the PLAPS point had a sensitivity of 92.50% and a specificity of 86.42% for diagnosing NPE, with an optimal cutoff value of 3.5 lines. Additionally, in some patients with NPE, B-line counts at the left PLAPS point were generally higher than those at the right side. Conclusion B-line quantification at the PLAPS point offers high sensitivity and specificity for the diagnosis of NPE, making it particularly valuable for early detection in patients with acute cerebral hemorrhage. Left-sided B-line predominance may indicate greater fluid accumulation in the left lung, which could inform and refine ultrasound assessment strategies. Wider clinical adoption is recommended.