Objective To explore the value of magnetic resonance imaging(MRI)in differentiating Duane retraction syndrome(DRS)from congenital fibrosis of the extraocular muscles(CFEOM). Methods A retrospective analysis was conducted on 64 pediatric patients with congenital strabismus who underwent MRI examinations between June 2015 and October 2024, including 35 patients with DRS and 29 patients with CFEOM.All patients underwent cranial nerve and orbital MRI examinations to observe abnormalities in the oculomotor nerve(CN3)and abducens nerve(CN6).The transverse diameters, longitudinal diameters, and cross-sectional areas of the extraocular muscles[medial rectus（MR）, lateral rectus （LR）, superior rectus（SR）, and inferior rectus（IR）]were measured and compared between the two groups. Result sAmong the 35 patients with DRS,34 had CN6 deficiency or dysplasia, and no obvious abnormalities were observed in CN3.Based on the presence of CN6 abnormalities,70 eyes of 35 patients were divided into the affected-side group(44 eyes)and the unaffected-side group(26 eyes).The transverse diameter of LR in the affected-side group was smaller than that in the unaffected-side group(P<0.05).All CFEOM patients had CN3 deficiency or dysplasia,12 of whom had concurrent CN6 deficiency or dysplasia.The transverse diameters, longitudinal diameters, and cross-sectional areas of MR, SR, and IR in the CFEOM group were significantly smaller than those in the DRS group(P<0.01).The longitudinal diameters and cross-sectional areas of LR in the CFEOM group were also significantly smaller than those in the DRS group(P<0.01).However, there was no significant difference in the transverse diameter of LR between the two groups(P>0.05). Conclusion The MRI features of DRS and CFEOM differ significantly.DRS is characterized by CN6 absence or dysplasia with mild atrophy of LR.CFEOM presents more complex manifestations, mainly characterized by CN3 absence or dysplasia and significant atrophy of the extraocular muscles.

Objective To evaluate the diagnostic value of intravoxel incoherent motion diffusion-weighted imaging（IVIM-DWI）in differentiating early-stage nasopharyngeal carcinoma（NPC）from benign nasopharyngeal hyperplasia using meta-analysis. Methods PubMed, the Cochrane Library, EMBASE, Web of Science（WOS）, China Biomedical Literature Database（CBM）, China National Knowledge Infrastructure（CNKI）, VIP Database, and Wanfang Data Knowledge Service Platform were systematically searched to identify relevant studies evaluating the diagnostic utility of IVIM-DWI in distinguishing early-stage NPC from benign nasopharyngeal hyperplasia.Literature selection was performed according to predefined inclusion and exclusion criteria.The methodological quality of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2（QUADAS-2）tool to evaluate the risk of bias.Pooled diagnostic performance of IVIM-DWI for early NPC was analyzed using Meta-DiSc 1.4 software, including sensitivity, specificity, diagnostic odds ratio（DOR）, and area under the curve（AUC）derived from the summary receiver operating characteristic（SROC）curve.Sensitivity analysis and tests for publication bias were performed using Stata/MP 16.0 software. Result sEight primary studies were included in this meta-analysis.Significant heterogeneity due to non-threshold effects was observed among the included studies.Under the random-effects model, the pooled estimates for the D value were as follows：sensitivity 0.84（95%CI：0.80-0.87）, specificity 0.78（95%CI：0.72-0.83）, DOR 24.09（95%CI：8.17-71.04）, and SROC AUC 0.8974.Among the eight included studies, only five reported D^* measurements.For the D^* value, the pooled sensitivity was 0.67（95%CI：0.60-0.74）, specificity 0.89（95%CI：0.82-0.94）, DOR 19.43（95%CI：4.66-81.11）, and SROC AUC 0.9426. Conclusion sIVIM-DWI demonstrates high diagnostic performance for early-stage NPC and shows potential value in differentiating early-stage NPC from benign nasopharyngeal hyperplasia.

Objective To investigate the predictive value of contrast-enhanced computed tomography（CECT）combined with serum interleukin-1β（IL-1β）and interleukin-17（IL-17）levels for axillary lymph node metastasis in breast cancer. Methods A retrospective review was conducted of clinical data from 101 breast cancer patients treated at our hospital between January 2022 and May 2025.Patients were categorized into metastatic and non-metastatic groups based on the presence of cancer cell metastasis in postoperative pathological examination of lymph nodes.Baseline characteristics, contrast-enhanced CT imaging features, and serum cytokine levels（including IL-1β and IL-17）were compared between groups.Multivariate Logistic regression analysis was employed to evaluate the relationship between clinical and pathological indicators and axillary lymph node metastasis.Receiver operating characteristic（ROC）curves were plotted to evaluate predictive performance. Result sPatients in the metastatic group exhibited higher proportions of histological grade Ⅲ tumors, larger maximum lymph node diameters, higher incidence of lymph node hilum structure loss, higher mean CT values in the arterial phase, higher mean CT values in the delayed phase, and elevated serum IL-1β and IL-17 levels compared to the non-metastatic group（P<0.05）.Multivariate Logistic regression analysis revealed that histological grade, maximum lymph node diameter, loss of lymph node hilum structure, mean arterial phase CT value, mean delayed phase CT value, serum IL-1β, and IL-17 were all independent predictors of axillary lymph node metastasis.ROC curve analysis demonstrated that the combined use of contrast-enhanced CT with serum IL-1β and IL-17 showed superior predictive performance, with an AUC of 0.902 for the combined model, which was significantly higher than that of individual markers（P<0.05）. Conclusion Contrast-enhanced CT, serum IL-1β, and IL-17 levels can effectively predict axillary lymph node metastasis in breast cancer patients.Combined assessment of these three markers improves predictive accuracy, providing a multidimensional reference for formulating individualized surgical plans and optimizing adjuvant treatment strategies.

Objective To evaluate the predictive value of a nomogram model based on multiparametric conventional magnetic resonance imaging（MRI）, diffusion-weighted imaging（DWI）, intravoxel incoherent motion（IVIM）, and diffusion kurtosis imaging（DKI）for predicting microvessel density（MVD）expression in breast cancer. Methods A retrospective analysis was conducted on 99 patients with pathologically confirmed breast carcinoma between June 2022 and December 2024, including 52 patients in the high MVD group and 47 patients in the low MVD group.All patients underwent conventional MRI, DWI, IVIM, and DKI examinations.Conventional MRI features and quantitative parameters from DWI, IVIM, and DKI-including apparent diffusion coefficient（ADC）, true diffusion coefficient（D）, perfusion-related diffusion coefficient（D^*）, perfusion fraction（f）, mean kurtosis（MK）, and mean diffusivity（MD）-were analyzed and compared between the two groups.Independent risk factors were identified using multivariate Logistic regression analysis and incorporated into a nomogram model.The diagnostic performance of the model was evaluated using the area under the receiver operating characteristic curve（AUC）. Result sTumor time-intensity curve（TIC）type showed a statistically significant difference between the high and low MVD groups（P<0.05）.Compared with the low MVD group, the high MVD group exhibited lower D and MD values and higher D^* and f values（all P<0.05）.Multivariate Logistic regression analysis identified TIC type, f, and MD as independent risk factors for predicting MVD expression status in breast cancer.The nomogram model constructed based on these three parameters achieved an AUC of 0.858, which was significantly greater than that of TIC type alone（AUC=0.648）, f alone（AUC=0.792）, and MD alone（AUC=0.746）.The diagnostic efficacy of the nomogram was superior to that of any single parameter（Z=4.196,P<0.001;Z=2.159,P=0.031;and Z=2.461,P=0.014, respectively）.The nomogram demonstrated sensitivity, specificity, and accuracy of 91.49%,71.15%, and 76.8%, respectively, all of which were higher than those of the single-parameter models.In addition, internal validation and decision curve analysis confirmed the good stability of the nomogram model. Conclusion The IVIM-derived parameter f may be the most valuable parameter for assessing MVD expression.The multiparametric MRI-based nomogram model can be used to predict MVD expression in breast cancer, potentially serving as a supportive tool for individualized and precise diagnosis and treatment.

Objective To evaluate the diagnostic value of magnetic resonance elastography（MRE）in differentiating Luminal-type from non-Luminal-type breast cancer. Methods From August 2024 to December 2025, patients categorized as BI-RADS 4 or 5（2025 edition）by ultrasound or mammography at the First Affiliated Hospital of Kunming Medical University were prospectively enrolled.Clinical and imaging features were analyzed to develop diagnostic models. Result sA total of 111 patients were enrolled, including 78 with Luminal-type and 33 with non-Luminal-type breast cancer.Single-parameter models, MRE-combined models, and comprehensive models all demonstrated high diagnostic performance. Conclusion The comprehensive model incorporating MRE facilitates differentiation between Luminal-type and non-Luminal-type breast cancer, providing imaging evidence for individualized treatment decision-making.

Objective To explore the value of artificial intelligence（AI）-based quantitative computed tomography（CT）features in predicting the invasiveness of nodular and mass-type lung adenocarcinoma. Methods Preoperative high-resolution chest CT data of 1218 patients with pathologically confirmed nodular and mass-type lung adenocarcinoma were retrospectively analyzed, including 1052 patients in the training cohort（491 in the non-invasive group and 561 in the invasive group）and 166 patients in the validation cohort（98 in the non-invasive group and 68 in the invasive group）.Sixteen quantitative parameters, including histogram features, entropy, and lesion size, were automatically extracted using AI-based pulmonary nodule analysis software.The Mann-Whitney U test was used to compare differences in parameters between the non-invasive and invasive groups.Variables with area under the curve（AUC）>0.750 were selected by receiver operating characteristic（ROC）curve analysis and further entered into binary Logistic regression analysis to identify independent predictors.The predictive performance of single-factor and multi-factor models was also evaluated. Result sIn the training cohort, AI-extracted CT quantitative features showed significant differences between the non-invasive and invasive groups（P<0.05）.ROC curve and Logistic regression analyses identified mean CT attenuation, entropy, and long diameter as independent predictors of invasiveness.When the thresholds were set at mean CT attenuation>-400.8 HU, entropy>8.0, and long diameter>10 mm, the sensitivity and specificity were 63.99% and 79.23%,75.40% and 82.28%, and 81.82% and 74.75%, respectively.The combined model achieved an AUC of 0.918, with sensitivity of 82.35% and specificity of 88.19%, significantly higher than those of single indicators.In the validation cohort, the predicted probability calculated by the Logistic regression model（AUC=0.939）also demonstrated superior performance compared with mean CT attenuation（AUC=0.807）, entropy（AUC=0.874）, and long diameter（AUC=0.882）. Conclusion AI-based quantitative CT feature analysis can effectively assess the invasiveness of nodular and mass-type lung adenocarcinoma.Mean CT attenuation, entropy, and long diameter are major independent predictive indicators.Combined analysis of these three parameters can significantly improve predictive accuracy, providing an important reference for preoperative invasiveness assessment and individualized treatment strategy formulation.

Objective To explore the predictive value of coronary CT angiography（CCTA）for major adverse cardiovascular events（MACE）in patients with type 2 diabetes mellitus（T2DM）. Methods A retrospective analysis was conducted on 150 patients with T2DM admitted to our hospital from June 2021 to June 2024.Patients were followed up for 12 months, and the occurrence of MACE was recorded.Based on follow-up results, patients were divided into two groups：69 patients who experienced MACE were included in the MACE group, and 81 patients who did not experience MACE were included in the non-MACE group.All patients underwent coronary CT angiography.Baseline characteristics and CCTA parameters were compared between the two groups.Receiver operating characteristic（ROC）curve analysis was used to evaluate the predictive value of CCTA parameters for MACE in T2DM patients. Result sThere was no statistically significant difference in baseline characteristics between the two groups（P>0.05）.The MACE group had a significantly higher proportion of patients with severe obstructive stenosis and high-risk spotty calcification.In addition, total plaque volume, plaque length, plaque burden, and plaque stenosis rate were all significantly greater in the MACE group compared to the non-MACE group（P<0.05）.However, the minimum lumen area and CT-derived fractional flow reserve（CT-FFR）were significantly smaller in the MACE group than in the non-MACE group（P<0.05）.Logistic regression analysis revealed that stenosis severity, total plaque volume, plaque length, plaque burden, plaque stenosis rate, and high-risk spotty calcification were all risk factors for MACE in T2DM patients, whereas minimum lumen area and CT-FFR were protective factors.ROC curve analysis showed that the AUC values for stenosis severity, minimum lumen area, total plaque volume, plaque length, plaque burden, plaque stenosis rate, high-risk spotty calcification, and CT-FFR were 0.678,0.692,0.726,0.741,0.758,0.792,0.659, and 0.778, respectively.DeLong test results demonstrated that the combined prediction model had a higher AUC value than any individual parameter（P<0.05）. Conclusion Patients with MACE had a significantly higher proportion of severe obstructive stenosis and high-risk spotty calcification, along with significantly larger total plaque volume, plaque length, plaque burden, and plaque stenosis rate, but significantly smaller minimum lumen area and CT-FFR.The combination of coronary CT angiography parameters demonstrates high predictive value for MACE in patients with type 2 diabetes mellitus.

Objective To develop an intelligent detection method for subsegmental pulmonary embolism（SSPE）on CT pulmonary angiography（CTPA）using the C2D-PENet network to improve diagnostic accuracy. Methods Patients suspected of having acute pulmonary embolism（APE）who underwent CTPA and were diagnosed with APE and SSPE at the First Affiliated Hospital of Henan University of Chinese Medicine were retrospectively enrolled from February 2015 to November 2024.The patients were randomly allocated into training, validation, and test sets in a 7∶2∶1 ratio.The model was trained using the C2D-PENet network framework incorporating Detail-Enhanced Atrous Spatial Pyramid Pooling（DASPP）and Pinwheel Convolution（PConv）modules, and five-fold cross-validation was performed to optimize the network.On the test set, Dice coefficient, Precision, Recall, Specificity, and intersection over union（IoU）were used to evaluate the model's segmentation performance, and the corresponding parameter curves were plotted.The model's performance was compared with that of the established UNet, UNet++, SCUNet++, and DualUNet models. Result sThis study finally included 233 patients with APE and SSPE（mean age 68±18 years,122 males and 111 females）, with 163 in the training set,47 in the validation set, and 23 in the test set.For SSPE segmentation, the model achieved a Dice coefficient of 0.87, Precision of 0.88, Recall of 0.89, Specificity of 1.00, mean IoU（mIoU）of 0.89, and positive IoU（Pos-IoU）of 0.77.The C2D-PENet network demonstrated superior performance across multiple parameters compared with the four existing classical networks. Conclusion The C2D-PENet network demonstrates good performance in detecting SSPE and is expected to facilitate rapid identification of SSPE and improve diagnostic accuracy.

Objective To develop a contrast-enhanced CT-based radiomics nomogram for preoperative noninvasive risk stratification of thymic epithelial tumors（TETs）. Methods Patients with TETs from three centers were retrospectively enrolled. Radiomics features were extracted from venous-phase CT images to construct a radiomics model, and the predictive probability was defined as the radiomics signature（Rad-signature）.Univariate and multivariate Logistic regression analyses were performed to compare clinical parameters.Subsequently, a combined predictive model incorporating significant clinical and imaging features and the Rad-signature was developed, and a nomogram was constructed to visualize the classification results.The predictive performance of the models for TETs risk stratification was evaluated using the area under the receiver operating characteristic curve（AUC）, calibration curves, and decision curve analysis（DCA）. Result sMultivariate Logistic regression analysis showed that tumor contour, degree of enhancement, and invasion of surrounding major vessels（all P<0.05）were independent predictors of TETs.The combined model achieved AUC values of 0.834 in the training set and 0.763 in the test set, both higher than those of the radiomics model and the clinical model.DCA confirmed its clinical benefit, and calibration curves showed good agreement between nomogram predictions and actual outcomes. Conclusion We developed and validated a combined model for accurate preoperative noninvasive risk stratification of TETs, which may provide more precise information for treatment planning and prognosis evaluation.

Objective To identify independent predictors of adverse treatment outcomes in patients with infiltrative pulmonary tuberculosis and to evaluate their predictive performance. Methods A total of 194 patients diagnosed with infiltrative pulmonary tuberculosis at the Affiliated Hospital of Yan'an University and Yan'an Second People's Hospital from January 2023 to June 2025 were retrospectively collected.Patients were divided into a good outcome group（n=117）and an adverse outcome group（n=77）.For external validation,170 patients with infiltrative pulmonary tuberculosis at Xi'an Chest Hospital from January 2024 to June 2025 were enrolled as an independent validation cohort.Quantitative CT parameters were obtained using “Digital Lung” tuberculosis quantitative analysis software.Differences in clinical, laboratory, and quantitative CT indicators between the two groups were compared.Multivariate Cox regression was used to identify independent predictors of adverse outcomes, and predictive performance was assessed using receiver operating characteristic（ROC）curve analysis. Result sSignificant differences were observed between the two groups in quantitative CT parameters and select clinical and laboratory indicators（P<0.05）.Multivariate Cox regression identified the following as independent predictors of adverse outcomes（P<0.05）：presence of ≥3 cavities, platelet count（PLT,×10⁹/L）, and LeV%.ROC analysis indicated that the combination of the three aforementioned indicators yielded the best predictive performance in both the internal dataset and the external validation set（all P<0.001）.Furthermore, LeV%, either alone or in combination with other indicators, significantly enhanced the predictive performance of the models（all models including LeV% showed increased AUC values,P<0.001）. Conclusion LeV% may serve as an imaging biomarker for predicting adverse treatment outcomes in patients with infiltrative pulmonary tuberculosis.The combination of ≥3 cavities, PLT（×10⁹/L）, and LeV% facilitates early identification of high-risk patients and provides a basis for formulating individualized clinical management strategies.

Objective To explore the predictive value of dynamic contrast-enhanced CT for the efficacy of bronchial artery chemoembolization（BACE）in patients with non-small cell lung cancer（NSCLC）. Methods Clinical and imaging data of 87 NSCLC patients who received BACE treatment from January 2022 to December 2024 were collected.All patients underwent chest dynamic contrast-enhanced CT examination before treatment, and the perfusion value, peak enhancement（PH）, and time to peak（T_p）of the lesion were obtained through time-density curves.Three months after treatment, patients were divided into the objective response group and the non-objective response group according to the Response Evaluation Criteria in Solid Tumors（RECIST）.Differences in pre-treatment parameters between the two groups were compared.The predictive efficacy of each parameter was analyzed using receiver operating characteristic（ROC）curves, and the area under the curve（AUC）was calculated.Influencing factors of BACE efficacy were investigated using multivariate Logistic stepwise regression analysis. Result sThe perfusion value and PH in the non-objective response group were higher than those in the objective response group, while T_p was lower（P<0.05）.ROC analysis showed that the AUCs（95%CI）of perfusion value, PH, T_p, and their combination for predicting BACE efficacy were 0.754（0.709-0.804）,0.729（0.679-0.779）,0.761（0.711-0.806）, and 0.914（0.869-0.964）, respectively.The maximum tumor diameter in the non-objective response group was larger, and the proportions of incomplete BACE embolization and smoking history were higher than those in the objective response group（P<0.05）.Multivariate analysis showed that larger maximum tumor diameter（OR=2.280,95%CI：1.276-4.072）, higher perfusion value（OR=2.394,95%CI：1.519-3.772）, higher PH（OR=2.504,95%CI：1.510-4.152）, and shorter T_p（OR=2.596,95%CI：1.560-4.321）were independent risk factors for BACE efficacy in NSCLC patients（P<0.05）. Conclusion Parameters including perfusion value, PH, and T_p measured by dynamic contrast-enhanced CT can effectively predict the therapeutic response of NSCLC patients to BACE.The combination of multiple parameters can further improve predictive efficacy, providing an important basis for early clinical efficacy evaluation and treatment decision optimization.

Objective To investigate the natural evolution of focal nodular hyperplasia（FNH）during long-term follow-up using gadoxetic acid-enhanced MRI（EOB-MRI）and to analyze the association between clinical factors and changes in lesion size. Methods Eighty-five patients with FNH（117 lesions）diagnosed between July 2016 and October 2025 based on typical imaging features or histopathological confirmation were retrospectively included.All patients underwent at least two EOB-MRI examinations with a minimum follow-up interval of 12 months.Lesions were categorized as enlarged, stable, or decreased according to an absolute diameter change（Δ diameter）≥0.5 cm, and hepatobiliary phase（HBP）phenotype transition was recorded.Multilevel mixed-effects models were used to evaluate the association between clinical factors and lesion evolution. Result sThe mean follow-up duration was（39.2±11.6）months（range：12.1-84.8 months）.During follow-up, the majority of lesions remained stable（68/117,58.1%）or decreased in size（36/117,30.8%）, while only 13 lesions（11.1%）showed enlargement, among which five exhibited an HBP imaging pattern transition from homogeneous iso- to hyperintensity to a “donut-like” hyperintense appearance.At the final follow-up, the overall mean maximum lesion diameter was modestly smaller than that at baseline[（3.24±1.70）cm vs.（3.40±1.51）cm,P=0.020], corresponding to a mean relative change of -10.4% and a mean annual change of -0.05 cm/year.Mixed-effects analysis revealed that sex, age, history of oral contraceptive pill（OCP）use, pregnancy history, baseline body mass index（BMI）, moderate-to-severe hepatic steatosis, and follow-up duration were not significantly associated with lesion enlargement（all P>0.05）.Δ diameter was not correlated with follow-up time（β=-0.003,P=0.425）or BMI change（β=-0.021,P=0.273）. Conclusion Long-term EOB-MRI follow-up demonstrates that FNH lesions predominantly exhibit an indolent course characterized by stability or spontaneous regression, with marked enlargement being uncommon and unrelated to hormonal exposure or common clinical factors.Routine imaging surveillance is generally unnecessary for asymptomatic patients with typical imaging features.

Objective To evaluate the diagnostic performance and interobserver agreement of non-contrast magnetic resonance imaging（MRI）based on T₂-weighted imaging（T₂WI）combined with diffusion-weighted imaging（DWI）in the detection of small hepatocellular carcinoma（HCC）. Methods MRI images of 573 patients were retrospectively analyzed.Two radiologists independently assessed the presence of hepatic lesions and graded their diagnostic confidence on a five-point scale（1-5）.Using histopathology or imaging follow-up as the reference standard, the sensitivity and specificity of T₂WI combined with DWI for detecting small HCC were calculated, and stratified analyses were performed according to lesion size.Inter-reader agreement was assessed using Cohen’s Kappa and Gwet’s AC1.Correlation analysis of diagnostic confidence scores was performed using Spearman's rank coefficient. Result sThe sensitivities of the two radiologists for detecting small HCC were 92.7% and 92.4%, respectively, and the specificities were 95.6% and 93.0%, respectively, with no significant differences between the two readers.Overall inter-reader agreement for the presence or absence of lesions was good, with an observed agreement of 90.1%（516/573）, a Cohen’s Kappa of 0.801, and a Gwet’s AC1 of 0.802.In the stratified analysis using 2 cm as the cutoff.Agreement was lower in the ≤2 cm group（Kappa=0.375, AC1=0.859）and highest in the >2 cm group（AC1=1.000）.For lesions ≤1 cm, interobserver agreement was moderate（AC1=0.635）, whereas it was higher for lesions >1 cm（AC1=0.947）.The diagnostic confidence scores between the two readers were highly correlated（Spearman ρ=0.867,P<0.001）. Conclusion Non-contrast MRI combining T₂WI and DWI provides good diagnostic performance and interobserver agreement in detecting small HCC, with diagnostic accuracy increasing as lesion size enlarges.This protocol provides a valuable reference for the imaging evaluation of small hepatocellular carcinoma.

Objective To evaluate the potential of deep learning（DL）-based automated three-dimensional quantitative tumor burden at CT for predicting postoperative early recurrence（ER）of hepatocellular carcinoma（HCC）. Methods Clinical and imaging data of patients with pathologically confirmed HCC who underwent preoperative contrast-enhanced CT of the upper abdomen were retrospectively analyzed.The imaging data were divided into two parts：the first part consisted of 300 cases annotated by radiologists and used for training of a U-Mamba segmentation model, which were randomly divided into a training set（n=210）and a test set（n=90）at a ratio of 7∶3.The Dice similarity coefficient（DSC）and 95th percentile Hausdorff distance（HD₉₅）of the test set were used to evaluate model performance.The second part included 156 cases used for model validation;two radiologists checked the segmentation results and selected 130 cases with satisfactory segmentation.Data processing was performed on the above 300 and 130 cases.Quantitative total tumor volume（cm³）and total tumor burden（TTB,%）were obtained using the voxel counting method.Cox regression analysis was performed to determine the prognostic value of clinicopathological variables and tumor burden-related parameters for ER. Result sA total of 430 patients were included, with 351 and 79 patients assigned to BCLC stage A and B, respectively[2-year ER rates：28.6% vs. 48.1%;hazard ratio（HR）=1.67;P=0.006].TTB was the most important predictor of ER（HR=2.53;P<0.001）.Using 28.0% as the threshold of TTB, two ER risk strata were obtained in overall（P<0.001）, BCLC A（P=0.003）, and BCLC B（P<0.001）patients, respectively.BCLC B low-TTB patients had a similar risk of ER to BCLC A patients and were thus reassigned to BCLC An stage;meanwhile, BCLC B high-TTB patients remained in BCLC Bn stage.The 2-year ER rates were 30.7% for BCLC An patients and 83.3% for BCLC Bn patients（HR=5.08;P<0.001）. Conclusion sTTB determined by DL-based automated segmentation at CT may serve as an imaging biomarker for predicting ER and facilitate refined subclassification of HCC patients within BCLC stages A and B.

Objective To investigate the value of spectral CT-derived arterial enhancement fraction（AEF）and extracellular volume（ECV）in preoperatively predicting perineural invasion（PNI）in resectable gastric cancer. Methods Clinical and imaging data of 79 patients with surgically pathologically confirmed gastric adenocarcinoma were retrospectively collected.Patients were divided into PNI-positive and PNI-negative groups according to postoperative pathology.Differences in clinical and imaging indicators between the two groups were compared to screen for independent predictors of PNI positivity, and their predictive efficacy was evaluated. Result sStatistically significant differences were observed between the PNI-positive and PNI-negative groups in tumor length, thickness, clinical T stage（cT）, CT-reported lymph node status, venous phase AEF（AEF1）, delayed phase AEF（AEF2）, delayed phase ECV, degree of differentiation, vascular invasion, Lauren classification, Borrmann classification, pathological T stage（pT）, and pathological N stage（pN）（all P<0.05）.Multivariate Logistic regression analysis showed that CT-reported lymph node status, AEF1, and ECV were independent predictors of PNI positivity, and a combined parameter was developed.Receiver operating characteristic（ROC）curve analysis showed that the AUCs for CT-reported lymph node status, AEF1, ECV, and the combined parameter were 0.658,0.666,0.736, and 0.805, respectively.The combined parameter outperformed CT-reported lymph node status and AEF1（DeLong test,P<0.05）, but showed no significant difference compared with ECV（P>0.05）. Conclusion sAEF and ECV derived from spectral CT can effectively predict PNI status in resectable gastric cancer.The combined parameter can further improve the predictive efficacy of PNI classification.

Objective To systematically analyze research hotspots, core clusters, and frontier trends of dual-energy CT in the field of urinary calculi using CiteSpace visual bibliometric analysis, providing reference for clinical diagnosis and research direction. Methods Literature related to dual-energy CT and urinary calculi from January 1,2004 to July 11,2025 in the Web of Science Core Collection was searched.CiteSpace 6.4.R1 software was used to analyze the knowledge maps of national, institutional, and author collaboration networks, keyword co-occurrence, clustering, and burst terms. Result sA total of 218 articles were included, with the number of publications in this field increasing year by year.The United States had the highest publication volume and centrality（94 publications, centrality 0.64）, and Mayo Clinic was the institution with the largest number of publications（33）.McCollough CH was the most prolific author（12 publications）.High-frequency keywords covered urinary calculi, dual-energy CT, computed tomography, kidney stones, uric acid, etc.Emerging hotspots in the past five years focused on prediction, machine learning, image quality, and accuracy. Conclusion Research hotspots focus on three aspects：image quality and radiation dose optimization, clinical diagnosis and treatment integration, and functional quantitative analysis.Future trends will focus on artificial intelligence-driven diagnostic intelligence, clinical universalization of ultra-low-dose technology, and full-cycle health management through interdisciplinary collaboration.Progress in this field depends on the deep integration of multidisciplinary innovation and clinical translation.

Objective To investigate the application value of the test-bolus technique(TBT)combined with Smart mA and organ dose modulation(ODM)in computed tomography angiography(CTA)and CT perfusion(CTP)examinations for acute ischemic stroke, aiming to reduce radiation dose and contrast medium volume while ensuring the reliability of perfusion parameter measurements in normal brain tissue. Methods A total of 160 patients were prospectively enrolled and randomly divided into four groups.Groups A, B, and C adopted the test-bolus technique(10-15 ml contrast agent test to determine scan timing), followed by injection of 50 ml contrast agent to simultaneously complete CTA+CTP scans, with a total contrast agent dosage of 60-65 ml.Group D underwent conventional split scanning, with a total contrast agent dosage of 90-95 ml.Group A used Smart mA combined with ODM technology, Group B used Smart mA only, and Group C used fixed tube current.Radiation doses(DLP, ED), contrast agent dosages, image quality, and core cerebral perfusion parameters were compared among the four groups. Result sThere was no statistically significant difference in radiation dose between Group A[DLP:(1500.1±123.7)mGy·cm;ED:(3.19±0.25)mSv]and Group B[DLP:(1507.1±50.0)mGy·cm;ED:(3.21±0.11)mSv](P>0.05).However, both groups were significantly lower than Group C[DLP:(1950.6±10.9)mGy·cm;ED:(4.16±0.25)mSv]and Group D[DLP:(3127.6±33.4)mGy·cm;ED:(6.63±0.07)mSv](P<0.01).The contrast agent dosage in Group A was approximately 31.6% lower than that in Group D(P<0.05).There were no statistically significant differences in image quality or core perfusion parameters(CBF, CBV, MTT)of normal brain tissue among the four groups, and subjective evaluation results were consistent(P>0.05). Conclusion The combined protocol allows for significant reduction in radiation dose and contrast medium volume without compromising image quality.Notably, the ODM technique employed in Group A provides targeted protection for radiosensitive organs such as the ocular lens and thyroid by specifically reducing anterior tube current, which aligns with the ALARA (as low as reasonably achievable) principle.