Ischemia and hypoxia caused by cerebral vascular stenosis or occlusion constitute the core pathological mechanisms of ischemic stroke. As an important compensatory pathway for blood supply in the body, collateral circulation directly influences the survival time of the ischemic penumbra, the rate of infarct expansion, and the long-term prognosis of patients. This article reviews and analyzes the classification of cerebral collateral circulation and its related hemodynamic basis, CT and MRI-based evaluation of collateral circulation, as well as the pathological basis of collateral circulation in ischemic stroke and its correlation with imaging findings. It is anticipated that future imaging evaluation of collateral circulation will develop toward greater precision, multimodality integration, and intelligence, thereby providing stronger support for individualized patient treatment.

Pancreatic ductal adenocarcinoma (PDAC) is characterized by insidious early symptoms, rapid progression, and extremely poor prognosis. The Chinese Guidelines for the Diagnosis and Treatment of Pancreatic Cancer (2022 Edition) emphasize the importance of early screening and precision imaging assessment in high-risk populations, and recommend MRI as an important modality for screening and diagnosis. In conjunction with the epidemiological characteristics of PDAC in China and the key points of domestic and international expert consensus, this article systematically interprets the latest international MRI screening protocols and reporting templates. The aim is to improve the early detection rate of PDAC, improve patient prognosis, and provide evidence-based support for the continuous refinement of domestic guidelines.

Objective To develop a nomogram based on subjective features from contrast-enhanced CT and to evaluate its ability to predict the metastatic risk of enlarged regional lymph nodes after neoadjuvant immunochemotherapy (nICT) in patients with esophageal squamous cell carcinoma (ESCC). Methods This retrospective multicenter study enrolled 60 patients with locally advanced ESCC who underwent nICT followed by surgical resection at three medical centers. A total of 81 radiologically enlarged regional lymph nodes identified on post-nICT CT scans with matched pathological results were analyzed. Long-axis diameter (LAD), short-axis diameter (SAD), and multiple radiologic features of lymph nodes were evaluated. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of nodal metastasis and to construct a nomogram. Model performance was assessed using receiver operating characteristic (ROC) curves and the area under the curve (AUC), with DeLong’s test used for comparisons. Internal validation was conducted using bootstrap resampling. Model discrimination, calibration, and clinical utility were evaluated using the concordance index (C-index), calibration curves, and decision curve analysis (DCA), respectively. Results Multivariate analysis identified a smaller percentage change in LAD (ΔLAD), the presence of radiologic extranodal extension (iENE), and loss of kidney-shaped morphology as independent predictors of lymph node metastasis. The nomogram demonstrated excellent predictive performance, with a C-index of 0.903 and a bootstrap-corrected C-index of 0.893. The AUC of the nomogram was 0.903, with a sensitivity of 90.0% and specificity of 78.7%, significantly outperforming any single imaging feature alone (all P<0.05). Calibration curves showed good agreement between predicted probabilities and observed outcomes. DCA indicated that the combined model provided a higher net benefit across a wide range of threshold probabilities (0.10-0.75). In addition, a ΔLAD cutoff value greater than 27% effectively differentiated reactive nodal enlargement from metastatic nodes, with a sensitivity of 75.0% and specificity of 63.9%. Conclusion The CT-based nomogram developed in this study enables reliable preoperative prediction of metastatic status in enlarged regional lymph nodes after nICT in ESCC patients. This model may serve as a practical imaging tool to distinguish immune-related pseudoprogression from true nodal metastasis and to facilitate individualized treatment planning.

Objective To investigate the predictive value of quantitatively assessing perirenal and abdominal fat using the mDixon-Quant technique for early renal function changes in patients with type 2 diabetes mellitus (T2DM). Methods Seventy-five patients with T2DM (mean age, 49.65±13.45 years) were prospectively enrolled. According to the urinary albumin creatinine ratio, patients were divided into a normal albuminuria (NAU) group (n=44) and a microalbuminuria (MAU) group (n=31). In addition, 41 age- and sex-matched healthy volunteers were enrolled as a control group. All subjects underwent abdominal mDixon-Quant MRI scanning. Perirenal fat thickness (PRFT) of both kidneys, visceral fat area (VFA), and subcutaneous fat area (SFA) were measured. Independent sample t-test and Mann-Whitney U test were used for comparison between the two groups, while one-way analysis of variance, Kruskal-Wallis test, and chi-square test were used for comparison among three groups. Logistic regression analysis was applied to identify factors associated with early diabetic renal function changes. Pearson or Spearman correlation analyses were used to assess the relationships between total PRFT and clinical characteristics. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the performance of total PRFT, and the area under the curve (AUC) was calculated. Results Compared with the control group, body weight, body mass index (BMI), systolic blood pressure, diastolic blood pressure, VFA, SFA, L-PRFT, R-PRFT, and total PRFT were significantly increased in both the NAU group and the MAU group (all P<0.05). Compared with the NAU group, the MAU groups showed significantly lower estimated glomerular filtration rate (eGFR) and diastolic blood pressure (both P<0.05), while the proportion of males, systolic blood pressure, serum creatinine (SCr), and urea-to-creatinine ratio (UCR) were significantly higher (all P<0.05). Multifactorial logistic regression analysis showed that diastolic blood pressure and total PRFT were independent associated with NAU status in patients with T2DM (P<0.05), and total PRFT remained an independent factor after adjusting diastolic blood pressure (P<0.05). Total PRFT was also an independent associated with MAU status in patients with T2DM (P<0.05). Body weight and BMI were positively correlated with total PRFT (r=0.642 and 0.616, respectively, both P<0.001). ROC curve analysis demonstrated that total PRFT had good predictive performance for NAU and MAU status, with AUC values of 0.822 and 0.810, respectively. Conclusions The mDixon-Quant technique enables noninvasive and quantitative assessment of perirenal and abdominal fat. Perirenal fat, in particular, shows predictive value for early renal function changes in patients with type 2 diabetes mellitus.

Objective To investigate the value of arterial spin labeling (ASL) and intravoxel incoherent motion (IVIM) MRI in assessing renal injury and fibrosis in a rat model of adriamycin-induced nephropathy. Methods Forty-two rats were randomly divided into a continuous-scanning group (n=6) and an intermittent-scanning group (n=36). Magnetic resonance imaging (T₂-weighted imaging, ASL, and IVIM) was performed at baseline and at 2, 3, 4, 5, and 6 weeks after adriamycin injection. Rats in the continuous-scanning group underwent repeated MRI examinations at all time points. In the intermittent-scanning group, six rats were randomly selected at each time point and sacrificed after MRI for laboratory and pathological analyses. Laboratory tests included serum creatinine (Scr) and tumor necrosis factor-α. Renal pathology was evaluated using hematoxylin-eosin and Masson staining to assess renal injury and fibrosis, and immunohistochemistry was performed to semi-quantitatively analyze α-smooth muscle actin (α-SMA) expression. A hematoxylin-eosin score >2 was used to differentiate mild from moderate-to-severe renal injury. Renal fibrosis severity was classified into mild, moderate, and severe groups using K-means clustering. Renal blood flow (RBF), true diffusion coefficient (D), pseudo-diffusion coefficient (Dp), and perfusion fraction (f) were measured. One-way analysis of variance was used to compare imaging parameters, laboratory indices, and pathological findings among different time points in the intermittent-scanning group. Repeated-measures ANOVA was applied to assess longitudinal changes in imaging parameters in the continuous-scanning group. Spearman correlation analysis was performed to evaluate correlations between imaging parameters, laboratory indices, and pathological findings. Receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC) were used to assess the diagnostic performance of imaging parameters and Scr in differentiating renal injury and fibrosis severity, with AUCs compared using the DeLong test. Results In the intermittent-scanning group, Scr levels showed a progressive increase, with significant differences observed at weeks 5 and 6 compared with baseline (P<0.05). Cortical RBF values and cortical and medullary D and f values exhibited a decreasing trend over time. Cortical RBF, D, and f values, as well as medullary f values, were significantly correlated with hematoxylin-eosin scores, fibrosis area, and α-SMA expression (|r| = 0.411-0.868, all P<0.05). Cortical RBF demonstrated the highest diagnostic performance for distinguishing mild from moderate-to-severe renal injury and fibrosis (AUC=0.968 and 0.888, respectively; P<0.05). The AUCs of cortical RBF, D, and f values, as well as medullary f values, for differentiating mild from moderate-to-severe renal fibrosis were significantly higher than those of Scr. Similarly, cortical RBF and medullary f values showed significantly better diagnostic performance than Scr in distinguishing mild from moderate-to-severe renal injury (P<0.05). Conclusion ASL and IVIM imaging can dynamically characterize alterations in renal cortical and medullary perfusion and diffusion in an adriamycin nephropathy model. These imaging parameters demonstrate superior performance over conventional serological markers in differentiating the severity of renal injury and fibrosis, providing strong imaging support for early intervention and therapeutic decision-making.

Objective To develop an artificial intelligence (AI) model based on contrast-enhanced T₁-weighted (CE-T₁WI) and T₂ FLAIR MRI, and to validate and evaluate its diagnostic performance and clinical value in differentiating high-grade glioma (HGG) from brain metastasis. Methods A total of 272 patients with brain tumors confirmed by surgical pathology were retrospectively enrolled, including 143 cases of HGG and 129 cases of brain metastasis. Four radiologists with different levels of experience [two junior (2-3 years) and two mid-level (5-8 years)] were randomly assigned to two groups，AI-assisted test group with 2 radiologists +AI，and a non-AI control group with 2 radiologists only. All radiologists independently interpreted the MRI images of all patients in two rounds using a crossover design, including an AI-assisted test group and a non-AI control group, with a 3-week washout period between readings. Using pathology as the reference standard, diagnostic performance was compared using the DBMH method. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were calculated, and the performance of the AI model was compared with that of junior and mid-level radiologists. Results The AI model achieved a significantly higher AUC of 0.975 (95%CI: 0.956-0.993) for classification, with a sensitivity of 97.20%, specificity of 97.67%, and accuracy of 97.43%. For differentiating HGG from brain metastasis, the AUCs of the test group and control group were 0.934 (95%CI: 0.909-0.958) and 0.707 (95%CI: 0.645-0.768), respectively. Sensitivity was 98.08% versus 83.22%, specificity was 69.19% versus 52.13%, and accuracy was 84.38% versus 68.47%, respectively. Diagnostic performance in the AI-assisted test group was superior to that in the control group for both junior and mid-level radiologists. Conclusion The neural network-based AI model demonstrates excellent diagnostic performance in differentiating high-grade glioma from brain metastasis. It can improve the diagnostic accuracy of radiologists and provide valuable support for clinical diagnostic and therapeutic decision-making.

Objective To explore the correlation between CT perfusion (CTP) parameters and MRI total burden score in patients with cerebral small vessel disease (CSVD), and to analyze the association of both with 90-day prognosis. Methods A total of 115 patients with clinically confirmed CSVD were retrospectively enrolled. The modified Rankin Scale was used to evaluate the prognosis. According to 90-day outcomes after treatment, patients were divided into a poor-prognosis group (43 cases) and a good-prognosis group (72 cases). Multivariate logistic regression was used to analyze factors influencing poor prognosis. Pearson correlation coefficient was used to analyze the correlation between CTP parameters [cerebral blood volume (CBV), cerebral blood flow (CBF), mean transit time (MTT), and time to peak (TTP)] and MRI total burden score. Spearman correlation coefficient was used to analyze the correlation of CTP parameters and MRI total burden score with prognostic outcomes. Results Significant differences between the two groups were observed in hypertension, diabetes, MRI total burden score, CBV, CBF, MTT, and TTP (all P<0.05). Multivariate logistic regression indicated that MRI total burden score, CBV, CBF, MTT, and TTP were influencing factors for poor prognosis (P<0.05). CBV showed no correlation with MRI total burden score (P>0.05); CBF was negatively correlated with MRI total burden score (P<0.05); while MTT and TTP were positively correlated with MRI total burden score (P<0.05). CBV and CBF were negatively correlated with poor prognosis (P<0.05), while MTT and TTP were positively correlated with poor prognosis (P<0.05). Conclusion CTP parameters are closely correlated with MRI total burden score, and both are associated with the prognosis of CSVD patients. Combining CTP parameters with MRI total burden score allows for a more comprehensive and accurate evaluation of lesion severity and prognostic risk in CSVD patients.

Objective To evaluate the setup accuracy of surface-guided radiotherapy (SGRT) in hypofractionated radiotherapy for breast cancer, to quantify the relationship between setup errors and target dose distribution, and to determine setup error control thresholds according to different planning target volume (PTV) sizes. Methods Sixty female patients with breast cancer who underwent breast-conserving surgery and completed hypofractionated radiotherapy were retrospectively enrolled. According to the treatment guidance method, patients were divided into a conventional setup group (n=30) and an SGRT group (n=30). Setup errors between the two groups were compared using the Mann-Whitney U test. Spearman correlation analysis was performed to assess the association between SGRT-derived setup errors and cone-beam computed tomography (CBCT) registration errors, followed by linear fitting using ordinary least squares regression. Dose variations caused by simulated setup errors were evaluated using the Eclipse treatment planning system. Pearson correlation analysis was applied to investigate the linear relationship between PTV volume and actual delivered dose under different setup error conditions. Results Setup errors along the X, Y, and Z axes in the SGRT group were significantly smaller than those in the conventional group (all P<0.05), and the three-dimensional setup errors of all SGRT cases were <0.4 cm. SGRT-derived setup errors showed a strong positive correlation with CBCT-based registration results (r=0.81-0.91, all P<0.001), demonstrating high consistency between the two methods. Dosimetric simulation revealed that when the overall setup error exceeded 0.4 cm, target dose deviations increased markedly, with the Z-axis showing the highest sensitivity. Significant axial differences were observed in the linear correlation between PTV volume and delivered dose. For the Z-axis, a moderate correlation was identified at setup errors of 0.4 cm and ±0.5 cm, and a weak correlation at -0.4 cm. For the X-axis, a weak correlation was observed at -0.4 cm and -0.5 cm, whereas no significant linear correlation was found for the Y-axis at any setup error level. Notably, in patients with PTV <400 cm³, the Z-axis setup error should be controlled within 0.2 cm. Conclusion SGRT provides accurate, safe, and non-invasive guidance for hypofractionated radiotherapy in breast cancer, reducing radiation exposure and patient psychological burden while improving workflow efficiency. Setup error thresholds along the Z-axis should be individualized based on PTV volume to ensure optimal dose delivery.

Objective To explore the value of multiparametric MRI (mpMRI) in detecting clinically significant prostate cancer (csPCa) in patients with biopsy Gleason score 6 prostate cancer. Methods A retrospective study was conducted including 110 patients with biopsy-confirmed prostate cancer and a Gleason score of 6. The mean age was 72.3±7.2 years. All patients underwent mpMRI prior to prostate biopsy, and lesions were assessed using the Prostate Imaging Reporting and Data System (PI-RADS) version 2.1. Postoperative pathological findings served as the reference standard for csPCa, defined according to the Epstein criteria. Patients were divided into csPCa and non-csPCa groups. Clinical and imaging variables were compared using the Mann-Whitney U test, chi-square test, or Fisher’s exact test, as appropriate. Variables showing statistical significance were entered into multivariate logistic regression analysis to identify independent predictors of csPCa. Receiver operating characteristic (ROC) curve analysis was performed to evaluate diagnostic performance, and the area under the curve (AUC) was calculated. Results Among the 110 patients, 92 (83.6%) were confirmed to have csPCa on postoperative pathology. Compared with the non-csPCa group, patients in the csPCa group had significantly larger tumor diameters and a higher proportion of tumor stage ≥T₃ and PI-RADS scores ≥4 (all P<0.05). Multivariate logistic regression analysis identified tumor diameter as the only independent predictor of csPCa (P<0.05). ROC analysis demonstrated that tumor diameter showed the highest diagnostic performance (AUC=0.90), followed by PI-RADS v2.1 score (AUC=0.75) and tumor stage ≥T₃ (AUC=0.63). Tumor diameter achieved the highest sensitivity (95.65%), accuracy (92.52%), and negative predictive value (73.33%), whereas tumor stage ≥T₃ demonstrated the highest specificity and positive predictive value (both 100%). Conclusion mpMRI is valuable for detecting potential csPCa in patients with biopsy Gleason score 6 prostate cancer.

Transcranial magnetic stimulation (TMS) is a non-invasive physical therapy technique that has demonstrated clear efficacy in treating various neurological diseases. However, its therapeutic efficacy varies significantly between individuals, and precise localization of personalized stimulation targets is critical to improving efficacy. Structural MRI can guide TMS by accounting for individual differences in brain anatomy. Functional MRI can identify individualized coordinates of brain function and abnormal brain activity, providing personalized stimulation targets for TMS. Resting-state fMRI functional connectivity is believed to provide a “bridge”, enabling magnetic stimulation to propagate from the cortical surface to deep effect targets. This article introduces the progress of MRI in the precise localization of TMS targets.

Carotid atherosclerotic disease is one of the primary causes of ischemic stroke. Artificial intelligence-based quantitative plaque analysis using computed tomography angiography (CTA) enables automated segmentation and quantification of plaque components, facilitating early prevention and management of ischemic stroke. This article reviews the principle and key parameters of CTA-based quantitative plaque analysis, the detection and segmentation of carotid plaques, and focuses on the application value of this technology in the diagnosis and treatment of carotid atherosclerosis, including plaque quantification and comprehensive assessment of hemodynamic environment, identification of vulnerable plaques, prediction of ischemic stroke recurrence risk, and monitoring and evaluation of treatment efficacy. The limitations of CTA-based quantitative plaque analysis and potential future developments are also discussed.

Cardiovascular CT has become an important imaging evaluation modality in cardio-oncology and has been widely applied in recent years to studies of cancer therapy-related coronary atherosclerosis, myocardial fibrosis, and pericardial and valvular lesions. This article reviews the research progress of cardiovascular CT in cardio-oncology and analyzes its advantages and limitations. Furthermore, it introduces the application potential of emerging technologies, such as photon-counting CT and artificial intelligence, in identifying early cardiotoxicity lesions and risk stratification. This review aims to provide a reference for precise diagnosis, individualized management, and clinical translation in cardio-oncology.

Ultrafast real-time MRI is a magnetic resonance imaging technique capable of monitoring dynamic physiological processes in real time, while offering both high image quality and high temporal resolution. This technique can depict the dynamic movement of contents at the esophagogastric junction, enable monitoring of reflux events and hiatal hernia, and improve diagnostic accuracy for gastroesophageal reflux disease when combined with morphological parameters of the esophagogastric junction. Additionally, it shows clinical potential in preoperative planning for hiatal hernia repair and in postoperative follow-up after fundoplication. Ultrafast real-time MRI provides a novel adjunctive diagnostic approach for gastroesophageal reflux disease. This review summarizes the recent advances in the application of ultrafast real-time MRI in gastroesophageal reflux disease.

Gastric cancer is one of the malignancies with high incidence and mortality worldwide. Neoadjuvant immunotherapy has significantly improved the prognosis patients with gastric cancer, and it is of great clinical significance to accurately evaluate the efficacy of neoadjuvant immunotherapy at an early stage. Conventional imaging modalities, such as CT and MRI, have demonstrated value in evaluating the efficacy of neoadjuvant immunotherapy. In addition, artificial intelligence models can be widely applied to predict responses to neoadjuvant immunotherapy in gastric cancer and have shown good performance. This article reviews the research progress in the evaluation of neoadjuvant immunotherapy efficacy using conventional imaging modalities and artificial intelligence models in gastric cancer.

Bladder cancer is a urogenital system malignancy with high incidence and mortality. With the rapid development of artificial intelligence(AI), AI technologies based on machine learning and deep learning have been applied to the imaging diagnosis of bladder cancer, demonstrating potential clinical value particularly in automatic image segmentation, clinical staging, pathological diagnosis, prediction of relevant protein expression, assessment of chemotherapy efficacy, and prognosis evaluation. This article reviews the research progress of AI in the imaging evaluation of bladder cancer.

Rheumatic diseases (RD) are a group of systemic disorders affecting multiple systems, with chronic inflammatory responses in vascular and connective tissues as their main pathological basis. Due to its sensitive detection of metabolic activity and systemic inflammatory burden, ¹⁸F-FDG PET/CT, has become increasingly important for early diagnosis, assessment of disease activity, and monitoring of treatment in RD. This article reviews the research progress of ¹⁸F-FDG PET/CT in various RD, including rheumatoid arthritis, polymyalgia rheumatica, idiopathic inflammatory myopathies, adult-onset Still’s disease, systemic lupus erythematosus, IgG4-related disease, relapsing polychondritis, Sjögren’s syndrome, spondyloarthritis, and Takayasu arteritis,etc.

Objective To explore the injury patterns resulting from different injury mechanisms of the first metatarsophalangeal joint (MTPJ) and to characterize their magnetic resonance imaging (MRI) features, thereby providing accurate imaging evidence for early diagnosis and treatment. Methods This retrospective study included 84 patients (84 feet) with first MTPJ injuries confirmed by surgery or clinical follow-up, with a mean age of 39.8 ± 9.5 years. All patients underwent MR examinations. Injury patterns were analyzed according to the underlying injury mechanisms, and corresponding MRI features were evaluated. Interobserver agreement between two musculoskeletal radiologists for the diagnosis of first MTPJ injuries was assessed using Kappa statistics. Results Based on the injury mechanism, 68 patients sustained plantar plate injuries caused by isolated hyperextension, 6 patients had medial collateral ligament injuries resulting from isolated valgus stress, 7 patients had lateral collateral ligament injuries caused by isolated varus stress, and 3 patients sustained combined valgus and hyperextension injuries. On MRI, injuries involving the central portion of the plantar plate, intersesamoid ligament, metatarsosesamoid ligament, main collateral ligament, accessory sesamoid ligament, and partial-thickness tears of the sesamophalangeal ligament were characterized by irregular morphology and focal hyperintensity on proton density-weighted fat-suppressed images. Full-thickness tears of the sesamophalangeal ligament were manifested as complete disruption of ligament continuity, with the tear extending through the entire ligament. Interobserver agreement for the diagnosis of first MTPJ injuries was good to excellent (all kappa>0.7). Conclusion MRI allows clear visualization of injury patterns and characteristic imaging findings of first MTPJ injuries and enables accurate classification of injury types. It plays an important role in the early diagnosis and precise management of first MTPJ injuries.