Objective To build machine learning(ML)models based on genetic variants and demographic factors to predict grey matter volume(GMV)and white matter volume(WMV),and to utilize interpretability methods to identify key predictive features as well as novel genetic loci associated with brain structure. Methods Brain imaging-derived phenotypes(IDPs),genetic data,and demographic information from over 30000 participants in the UK Biobank were leveraged.Six machine learning models,including XGBoost and LightGBM,were employed to investigate the associations between measurements of brain structure and both genetic variants and demographic factors.Key genetic loci were identified using the SHAP(Shapley Additive Explanation)interpretability framework and were subsequently subjected to gene annotation. Results Model performance evaluation demonstrated that the LightGBM and CatBoost models achieved optimal predictive accuracy for GMV and WMV,respectively,with correlation coefficients reaching 0.6874 and 0.4101.SHAP analysis not only recapitulated several known genetic loci associated with brain structure but also identified novel candidate loci not previously reported in conventional genome-wide association studies(GWAS).For instance,the GMV-associated locus rs187620717 is located near the GMNC gene,a region previously implicated in cerebrospinal fluid p-tau levels.Similarly,the WMV-associated locus rs11196180 is proximal to the TCF7L2 gene,which has been linked to smaller amygdala volume and schizophrenia.Furthermore,interactive effects between age and genes(e.g.,FAM3C and PPM1K)were observed associated with brain structure. Conclusion The application of ML models and interpretability techniques to neuroimaging phenotypes enabled the identification of novel brain structure-associated loci and the characterization of complex genetic-demographic interactions,providing new insights into brain disease mechanisms.
Objective To evaluate the value of non-contrast 3D-FLAIR sequence in 5.0 T MRI for the diagnosis of endolymphatic hydrops. Methods 25 patients（12 males and 13 females;mean age 57.42±12.66 years）with clinically confirmed Ménière’s disease were prospectively included,and all underwent 5.0 T gadolinium magnetic resonance imaging of the inner ear.The scanning sequence included:conventional inner ear MRI scan,internal auditory canal water imaging and non-contrast 3D-FLAIR scan;Four hours after double dose contrast injection,enhanced 3D-FLAIR and thin T1WI fat suppression sequences were performed.Two radiologists with 10 years of experience read the non-enhanced 3D-FLAIR and enhanced 3D-FLAIR sequences independently,the presence or absence of endolymphatic hydrops in the cochlea and vestibule was independently evaluated,if there is any difference,they should reach an agreement through negotiation. Results Using the diagnosis of the enhanced 3D-FLAIR sequence as the reference standard,endolymphatic hydrops of varying locations and degrees were detected in 25 patients,4 cases of endolymphatic hydrops combined with vestibular neuritis,and 1 of these 4 cases also had labyrinthitis.Among the 50 ears of the 25 patients,19 left ears had cochlear endolymphatic hydrops,and 18 right ears had cochlear endolymphatic hydrops.Endolymphatic hydrops was present in 9 left vestibules and 6 right vestibules.Vestibular endolymphatic hydrops in 15 ears could be observed with the same imaging findings on non-contrast 3D-FLAIR,and 34 of the 37 cochlear endolymphatic hydrops cases could be observed on the non-contrast sequence.The diagnostic consistency of non-contrast 3D-FLAIR for vestibular hydrops was 100%,for cochlear hydrops was 91.9%（34/37).The McNemar exact test P value of the two methods for the diagnosis of endolymphatic hydrops was greater than 0.05,and the difference was not statistically significant. Conclusion 5.0 T non-enhanced 3D-FLAIR has high accuracy in the diagnosis of endolymphatic hydrops,and can be used in Ménière patients who cannot tolerate enhanced examination.
Objective To investigate the value of a multiphasic CT-based radiomics model combined with clinical risk factors for preoperative prediction of central cervical lymph node metastasis(CLNM)in patients with papillary thyroid carcinoma(PTC). Methods This retrospective study included 181 pathologically confirmed PTC patients, randomly divided into a training set(n=126)and a testing set(n=55)in a 7∶3 ratio.Univariate and multivariate Logistic regression analyses were performed to identify independent predictors of CLNM and establish a clinical model.Tumor volumes of interest(VOIs)were manually segmented using ITK-SNAP,and radiomics features were extracted from intratumoral and peritumoral(1 mm and 2 mm extensions)to construct corresponding radiomics models.Model performance was evaluated using the area under the receiver operating characteristic curve(AUC),sensitivity,specificity,and accuracy.The optimal radiomics model was integrated with the clinical model to develop a combined model.AUC differences were compared using the DeLong test,while calibration curves and decision curve analysis(DCA)were used to assess calibration and clinical utility. Results Multivariate analysis identified serum FT4 level（OR=1.207）and maximum tumor diameter（OR=1.103）as independent predictors of CLNM（P<0.05）.The intratumoral radiomics model demonstrated the highest AUCs in both the training and testing sets（0.804 and 0.817,respectively),outperforming the peritumoral 1mm model（AUCs：0.647 and 0.633）and the peritumoral 2 mm model（AUCs：0.681 and 0.669).The combined clinicalradiomics model achieved further improved AUCs of 0.871（training）and 0.851（testing),which were significantly higher than those of the standalone radiomics model（AUCs：0.804, 0.817）and the clinical model（AUCs：0.768, 0.763)（P<0.05）. Conclusion Intratumoral radiomics features derived from multiphasic CT can effectively predict CLNM in PTC.Integrating with serum FT4 level and maximum tumor diameter further enhances predictive performance,providing a noninvasive tool to aid preoperative risk stratification and support individualized surgical planning for PTC patients.
Objective This study aims to integrate quantitative and qualitative CT parameters with clinical characteristics to construct and compare Logistic regression and decision tree models for predicting the efficacy of immunotherapy combined with chemotherapy in patients with pulmonary fibrosis with lung cancer(PF-LC).This approach provides imaging-based evidence for personalized treatment decision-making. Methods A retrospective review included 120 patients with PF-LC treated.Patients were categorized into the response group(CR+PR,n=59)and the non-response group(PD+SD,n=61)based on the RECIST 1.1 response criteria.Clinical characteristics and imaging parameters were collected.Univariate analysis identified statistically significant variables,which were used to construct Logistic regression and decision tree models.Model predictive performance was comprehensively evaluated using receiver operating characteristic(ROC)curves,area under the curve(AUC),and multiple performance metrics. Results Univariate analysis revealed statistically significant differences between groups in cough with sputum production,Velcro rales,tumor volume,fibrosis proportion,and concomitant emphysema(all P<0.05).Logistic regression indicated that pulmonary emphysema and fibrosis proportion were independent risk factors for poor treatment response.The decision tree model used fibrosis proportion as the first-level split node,forming four terminal nodes with a prediction accuracy of 79.2%.Model comparison showed the decision tree model's AUC of 0.877 significantly exceeded the Logistic regression model's 0.745(P=0.006),with superior accuracy(79.17%),specificity(93.44%),positive predictive value(90.48%),and F1 score（75.25%). Conclusion The decision tree model outperforms traditional Logistic regression models in predicting the efficacy of immunotherapy combined with chemotherapy in PF-LC patients,demonstrating superior discriminatory power and clinical utility.It provides an interpretable quantitative tool for identifying patients likely to benefit,though further validation and optimization through multicenter,prospective studies are required.
Objective This study aimed to explore the value of habitat imaging and peritumoral-containing radiomics models based on dynamic contrast-enhanced MRI in predicting postoperative early recurrence（ER）of hepatocellular carcinoma（HCC). Methods Clinical and imaging data of 282 patients with HCC confirmed by postoperative pathology in the Henan Cancer Hospital were analyzed retrospectively.All patients were randomly divided into a training set（n=199）and a validation set（n=83）at the ratio of 7∶3.The region of interest（ROI）was drawn based on the tumor boundary and extended outwardly by 3 mm,5 mm,and 10 mm to obtain peritumoral information with different ranges;the tumor region was divided into three subregions using habitat imaging technology,radiomics features were extracted and screened. Logistic regression（LR）classifier and 10-fold cross-validation method were used for modeling and verification.Intratumoral,peritumoral-containing,and habitat radiomics models were established.and after univariate and multivariate Logistic regression analysis was used to screen the independent predictors and construct the clinical model.A combined model was developed by incorporating habitat model,optimal peritumoral-containing model and clinical independent predictors,and presented as a nomogram.ROC curves,decision curves,and calibration curves were used to evaluate model performance. Results Maximum tumor diameter（MTD)（P=0.009）and microvascular invasion（MVI)（P<0.001）were identified as independent predictors of early postoperative recurrence in HCC.The clinical model and intratumoral model demonstrated moderate predictive performance,achieving AUCs of 0.723 and 0.717,0.794 and 0.74 in the training and validation set,respectively.The peritumoral-containing radiomic model demonstrated superior performance within an extended range of 5 mm,with AUCs of 0.866 and 0.847 in the training and validation set,respectively.The Habitat model exhibited outstanding predictive performance,achieving AUCs of 0.902 and 0.903 in the training and validation set,respectively.The combined model achieved the highest AUCs（0.924 and 0.915 in the training and validation set,respectively).The combined model showed good predictive performance and clinical utility on both decision curve and calibration curve. Conclusion sHabitat imaging and peritumoral-containing radiomics models based on dynamic contrast-enhanced MRI can effectively predict the early recurrence after HCC resection,the combined model integrating the clinical factor can further enhances predictive performance,demonstrating optimal clinical benefit.
Objective To explore the potential of diffusion-weighted imaging（DWI）and dynamic contrast-enhanced magnetic resonance imaging（DCE-MRI）in predicting regional lymph node metastasis of gastric cancer. Methods A total of 209 gastric cancer patients who underwent gastric MRI examination and radical gastrectomy were prospectively enrolled.Postoperative pathological N-stage was the reference standard,and patients were categorized into LNM-positive and LNM-negative groups.The relevant DWI quantitative parameters of advanced DWI models and the quantitative parameters of DCE-MRI were calculated.Univariate and multivariate Logistic regression models were used to screen for independent predictors.Receiver operating characteristic（ROC）curves were plotted to evaluate the predictive performance of each parameter,and the DeLong test was used to compare the areas under the curves（AUC). Results Statistically significant differences were observed between the two groups in MEM_ADC, DKI_MD, DKI_MK, IVIM_f, SEM_DDC, SEM_α, Ktrans mean, Ktrans median, Ve mean, Ve median, Vp mean, Vp median, Kep mean, and Kep median（all P<0.05). MEM_ADC, DKI_MK, SEM_α,and Ktrans median were identified as independent predictors.The combined predictive model demonstrated the best performance,with an AUC of 0.857（95%CI:0.805-0.910),which significantly outperformed all single parameters（all P<0.05). Conclusion The quantitative parameter model based on DWI and XD-VIBE DCE-MRI exhibits good performance in the preoperative prediction of LNM in gastric cancer,providing a valuable imaging tool for personalized treatment strategies.
Objective This meta-analysis aims to evaluate the diagnostic performance of artificial intelligence（AI）models based on computed tomography（CT）images for predicting microsatellite instability（MSI）in gastric cancer（GC). Methods A comprehensive search was conducted in PubMed,Embase,and Web of Science for relevant studies published until December 2025.Nine studies were included,providing 30 data sets,with the PROBAST+AI tool used to assess the quality of the studies. Results In the internal validation set,the pooled sensitivity was 74%（95%CI:0.66-0.81),specificity was 75%（95%CI:0.70-0.80),and the area under the curve（AUC）was 0.81（95%CI:0.77-0.84).In the external validation set,the sensitivity,specificity,and AUC were 74%（95%CI:0.66-0.80),75%（95%CI:0.66-0.82),and 0.77（95%CI:0.73-0.81),respectively. Conclusion sAI models based on CT images show good potential for predicting MSI in GC,but are limited by retrospective design and single-country data.Current conclusions are mainly applicable to similar clinical backgrounds.Future multi-center prospective studies are still needed.
Objective To develop and validate a magnetic resonance imaging（MRI)-based scoring model for risk stratification of Ovarian-Adnexal Reporting and Data System（O-RADS）category 4 cystic-solid lesions. Methods A retrospective study was conducted on 137 patients with pathologically confirmed O-RADS 4 cystic-solid lesions who underwent pelvic mass resection.Preoperative MRI features were analyzed,including maximum lesion diameter,amount of ascites,morphology of solid tissue,signal intensity on high b-value diffusion-weighted imaging（DWI),and degree of contrast enhancement.Multivariable Logistic regression was used to identify independent predictors of malignancy and to construct a scoring model.The diagnostic performance of the model and individual predictors was assessed using receiver operating characteristic（ROC）curve analysis,with the DeLong test used for AUC comparisons. The Hosmer-Lemeshow test was employed to evaluate the model's calibration. Results The morphology of the solid component,high signal intensity on DWI,and marked contrast enhancement were identified as independent predictors of malignancy,with AUCs of 0.739,0.744,and 0.738,respectively.The scoring model incorporating these three features achieved a significantly higher AUC of 0.867（95%CI:0.796-0.937）for discriminating benign from malignant lesions.The Hosmer-Lemeshow test indicated good model fit（P=0.462). Conclusion The proposed MRI-based scoring model serves as an effective tool for distinguishing benign from malignant O-RADS category 4 cystic-solid ovarian-adnexal lesions,potentially aiding in more precise preoperative risk stratification.
Objective To develop and validate a multimodal model integrating ZOOMit-based multi-b-value DWI quantitative parameters with clinical features for preoperative identification of deficient mismatch repair(dMMR)status in patients with Endometrial Cancer(EC). Methods A total of 195 patients with EC who underwent preoperative MRI within 2 weeks prior to surgery were retrospectively enrolled.Patients were randomly divided into a training set(n=138)and a validation set(n=57)at a ratio of 7∶3.Models based on quantitative parameters from ZOOMit multi-b-value DWI(b=0-1600 s/mm²)and clinical characteristics were constructed,including DKI,IVIM,Imaging model,clinical model,and multimodal fusion model.Univariate and multivariate Logistic regression analyses were used to identify independent predictors.Receiver operating characteristic(ROC)curves,decision curve analysis(DCA),and calibration curves were used to evaluate the models' discrimination,clinical utility,and calibration.A nomogram was then constructed based on the optimal model for visual prediction. Results Multivariate Logistic regression analysis revealed that the independent predictors varied across different models.The final fusion model identified ADC,Dp,CA125 level,and FIGO stage as independent predictors for dMMR status(all P<0.05),achieving AUCs of 0.840(95%CI:0.768-0.912)in the training set and 0.817(95%CI:0.696-0.938)in the validation set.DCA indicated favorable clinical net benefit. Conclusion The multimodal fusion model based on ZOOMit multi-b-value DWI enables effective noninvasive prediction of dMMR status in EC patients,providing a reliable imaging biomarker for preoperative molecular stratification and individualized treatment planning.
Objective To compare the diagnostic performance of the Ovarian-Adnexal Reporting and Data System（O-RADS）MRI score and the modified O-RADS score in differentiating benign and malignant ovarian masses containing solid tissue,and to evaluate the value of clinical features for diagnostic improvement. Methods 276 patients with 276 ovarian masses containing solid tissue were retrospectively collected from three centers.O-RADS MRI scores and modified O-RADS scores were respectively assigned to all ovarian masses.The histopathological results were used as the reference standard.Receiver operating characteristic（ROC）curves were drawn.The area under the curve（AUC),sensitivity,specificity,positive predictive value,and negative predictive value were calculated.Univariate and multivariate Logistic regression analyses were performed to evaluate the value of significant features in the prediction of ovarian malignancies.The diagnostic performance of the two scoring methods and the combined prediction model was compared used by the DeLong test. Results The AUC of the modified O-RADS score was higher than that of the O-RADS score（0.787 vs. 0.639,P<0.001).Univariate and multivariate Logistic regression showed that the combination of the modified O-RADS score with HE4 level,the maximum diameter and the high b-value DWI signal of the lesion further improved the diagnostic performance,with a sensitivity of 86.7%,a specificity of 82.5%,and an AUC of 0.922. Conclusion sThe modified O-RADS scoring system combined with clinical features can significantly improve the diagnostic performance in predicting malignant tumors.
Objective To explore the effectiveness of deep learning-based super-resolution dynamic enhancement MRI radiomics in predicting deep myometrial invasion of endometrial cancer. Methods A total of 212 patients with endometrial cancer who underwent surgical treatment were retrospectively collected and divided into superficial and deep myometrial invasion group based on pathological results.The original resolution dynamic enhancement MR images were converted into super-resolution images through super-resolution reconstruction technology,and radiomics features were extracted separately.A support vector machine algorithm was used to build models for original and super-resolution images.The performance of the models was evaluated using receiver operating characteristic curves and decision curve analysis. Results The diagnostic performance of the super-resolution model（area under the curve in training and testing set：0.877 and 0.872）was better than that of the original resolution model（area under the curve:0.787 and 0.717）and showed greater clinical benefits. Conclusion Deep learning-based super-resolution reconstruction technology can enhance the ability of dynamic enhancement MRI radiomics models to predict deep myometrial invasion in endometrial cancer,and may provide more accurate information for clinical treatment.
Objective To evaluate the ability of large language models（LLMs）combined with the Bone Tumor Risk Stratification and Management System（Bone-RADS）based on radiographs to distinguish benign and malignant bone tumors and provide histological diagnoses. Methods Bone tumor cases from two public medical imaging databases（Eurorad and Radiopaedia）and one radiology journal（Skeletal Radiology）were identified,with a deadline of September 30,2025.We collected the age,gender,clinical history,radiological descriptions,and pathological diagnoses of the cases.The DeepSeek-R1-0528 was used for testing,and was required to output Bone-RADS classification and one definitive diagnosis and three differential diagnoses,based on patients’ data and radiological descriptions.The performance of the LLM in distinguishing benign and malignant tumors,and making histological diagnoses was analyzed. Results A total of 511 cases were included in the study, among which 310（60.67%）were male.The median age（interquartile range）was 30.00（15.00,55.00）years.The primary lesion locations included the lower extremities（246 cases,48.14%),upper extremities（115 cases,22.50%),and multiple sites（49 cases,9.59%).Histological diagnoses were categorized as benign（277 cases,54.21%),intermediate（54 cases,10.57%),and malignant（180 cases,35.23%).For distinguishing benign and malignant tumors based on Bone-RADS classification generated by the LLM,the sensitivity was 0.876（95%CI：0.829-0.924),specificity was 0.699（95%CI：0.644-0.754),and the area under the receiver operating characteristic curve was 0.788（95%CI：0.751-0.824).Regarding histological diagnosis,the LLM directly achieved correct diagnoses in 306 cases（59.88%),achieved correct diagnoses among differential options in 84 cases（16.44%),and failed to make correct diagnoses in 121 cases（23.68%).Notably,among the cases that directly achieved correct diagnoses,14.91%（41/275）were misclassified in terms of benign/malignant nature excluding Bone-RADS 0 and Bone-RADS 5 cases. Conclusion LLMs have value in assisting with risk assessment of bone tumors on radiographs and suggesting potential histological diagnoses.However,there are certain differences between their diagnostic generation process and the diagnostic thinking of radiologists.Their output should be critically interpreted by radiologists.
Objective To analyze the computed tomography（CT）and magnetic resonance imaging（MRI）features of osseous cystic echinococcosis and explore the key points for its differential diagnosis. Methods The imaging data of 25 patients with pathologically confirmed osseous echinococcosis treated between June 2009 and May 2025 were retrospectively collected. Results All 25 patients underwent CT examination,with 19 of them also receiving concurrent MRI.The lesion sites included the ilium in 15 cases（60.0%),ribs in 8 cases（32.0%),pubis in 1 case（4.0%),and scapula in 1 case（4.0%).Involvement of the ischium was observed in 7 cases（28.0%),and vertebral bodies in 6 cases（24.0%).All 25 patients exhibited varying degrees of soft tissue involvement.CT imaging revealed:unilocular type in 20 cases（80.0%）and multilocular（multi-vesicular）type in 5 cases（20.0%).Cystic expansive bone destruction was observed in 21 cases（84.0%),osteolytic destruction in 2 cases（8.0%),moth-eaten destruction in 1 case（4.0%),and a combination of expansive and moth-eaten destruction in 1 case（4.0%).MRI findings showed:cystic expansive bone destruction in 16 cases（84.2%),osteolytic destruction in 2 cases（10.5%),and moth-eaten destruction in 1 case（5.3%);unilocular type in 15 cases（78.9%),appearing as low signal on T1WI,high signal on T2WI,high signal on STIR sequence,with the cyst wall showing low signal;multilocular type in 4 cases（21.1%),where the maternal cyst with low or slightly low signal on T1WI and high or slightly high signal on T2WI contained small vesicles exhibiting even lower signal on T1WI and higher signal on T2WI. Conclusion Osseous cystic echinococcosis primarily manifests as cystic expansive bone destruction,with a minority presenting as osteolytic or moth-eaten destruction.It frequently invades surrounding soft tissues and is often accompanied by calcification.The multilocular cystic structure is a characteristic imaging finding,which aids in improving the diagnostic accuracy of osseous echinococcosis.
Objective To identify independent factors associated with difficult laryngoscopic exposure in infants with Pierre Robin sequence（PRS）using quantitative three-dimensional computed tomography（3D-CT）parameters. Methods This single-center retrospective study included 110 infants with PRS who underwent mandibular distraction osteogenesis between January and December 2024.Preoperative 3D-CT was used to measure upper airway parameters.Patients were categorized into grade 1-2（n=54）and grade 3-4（n=56）groups based on the Cormack-Lehane laryngoscopic grading.Univariate and multivariate Logistic regression analyses were employed to identify independent factors associated with difficult laryngoscopy.The model's performance was evaluated by assessing discrimination using the receiver operating characteristic（ROC）curve,calibration using the Hosmer-Lemeshow test,and stability via Bootstrap internal validation.A nomogram was constructed,and decision curve analysis（DCA）was performed to validate clinical utility. Results Intra-observer consistency was excellent（intraclass correlation coefficients:0.84-0.99).Univariate analysis revealed that the difficult laryngoscopy group had significantly smaller values in several parameters,including the distance from the tongue root to the posterior pharyngeal wall（D4),sagittal oropharyngeal cross-sectional area（S2),and the angle between the tongue root and epiglottic root（A2)（all P<0.05).Multivariate analysis identified D4,S2,and A2 as independent factors（all P<0.05).The prediction model incorporating these three parameters demonstrated excellent discrimination[area under the curve（AUC)=0.872,95%CI:0.806-0.937],good calibration（Hosmer-Lemeshow test P=0.664),and high stability upon internal Bootstrap validation（mean AUC=0.865).The model was developed into a nomogram,and the decision curve analysis demonstrated a positive net clinical benefit. Conclusion The 3D-CT quantitative parameters D4,S2,and A2 are independent factors associated with difficult laryngoscopy in infants with PRS.The prediction model based on these three parameters demonstrates excellent performance and can serve as an objective,quantitative tool for preoperative difficult airway risk assessment.
Objective This study aimed to evaluate changes in glymphatic system function in patients with ulcerative colitis（UC）by calculating the ALPS index based on DTI and DKI,and to analyze its association with systemic inflammatory markers and cognitive impairment. Methods A total of 79 subjects were enrolled,including 44 healthy controls（HC),24 UC patients with normal cognition（UC-CN),and 11 UC patients with cognitive impairment（UC-CI).DTI and DKI sequences were used to assess diffusion and kurtosis properties along different directions of fiber tracts,and the ALPS index was calculated.Analysis of covariance and multiple comparisons were performed to analyze differences in the ALPS index and related parameters among the three groups.Correlations between the ALPS index and inflammatory markers（INFLA score,SII,SIRI,NLR,CRP,ESR,and IL-6）were analyzed. Results Compared with the HC group,UC patients showed significantly lower bilateral and mean DTI-ALPS and DKI-ALPS indices（P<0.001).The UC-CI group had a lower DKI-ALPS index than the UC-CN group,but no statistically significant difference was found in the DTI-ALPS index between these two UC subgroups.Significant differences were observed among the three groups in the diffusion rate and kurtosis along the z-direction for association fibers,suggesting microstructural white matter alterations and the presence of neuroinflammation.The DKI-ALPS index showed significant negative correlations with inflammatory markers（INFLA,SIRI,SII,NLR,CRP,etc.;r=-0.469 to -0.764).These correlations were stronger than those for the DTI-ALPS index.Notably,within the UC-CI group,only the DKI-ALPS index was negatively correlated with inflammatory markers（INFLA,NLR,CRP;r=-0.647 to -0.764). Conclusion The DKI-ALPS index demonstrated superior sensitivity compared to the DTI-ALPS index in assessing glymphatic dysfunction and neuroinflammation in UC patients and was significantly associated with systemic low-grade chronic inflammation.This provides new neuroimaging evidence for impaired glymphatic function within the gut-brain axis mechanism.Our findings support the value of the DKI-ALPS index as a potential biomarker for neuroinflammation,highlighting its important clinical relevance for understanding UC-related cognitive impairment.
Objective To investigate the correlation between atherogenic index of plasma（AIP),uric acid/high-density lipoprotein cholesterol ratio（UHR),and fat distribution（visceral fat area VFA,subcutaneous fat area SFA,liver fat content LFC）measured by quantitative computed tomography（QCT）in middle-aged and elderly populations. Method A retrospective cohort study was conducted involving a middle-aged and elderly population who underwent QCT examinations at the Health Management Centre of Henan Provincial People's Hospital between January 2020 and August 2024.General and clinical data were collected.QCT technology was employed to measure participants' VFA,SFA,and LFC.Participants were categorised into male and female groups based on gender.Independent samples t-tests and Mann-Whitney U tests were employed to compare general and clinical data between genders.Univariate linear regression analysis was used to assess correlations between AIP,UHR,and fat distribution,and to screen covariates for inclusion in multiple linear regression models.Covariates were progressively incorporated using a stratified approach.Multiple linear regression models were constructed with VFA,SFA,and LFC as dependent variables and AIP and UHR as independent variables,respectively,to evaluate the independent associations between AIP,UHR,and fat distribution. Results Comparisons by gender revealed that males exhibited significantly higher levels of age,body mass index（BMI),triglycerides（TG),uric acid（UA),AIP,UHR,VFA,and LFC levels were significantly higher than those in females,whereas females exhibited higher levels of total cholesterol（TC),low-density lipoprotein cholesterol（LDL-C),high-density lipoprotein cholesterol（HDL-C),and SFA（all P<0.05).Multiple linear regression analysis revealed:AIP exhibited significant positive correlations with VFA across all three models（Model 1:B=126.256,Beta=0.439;Model 2:B=95.623,Beta=0.333;Model 3:B=38.932,Beta=0.136;all P<0.05);The relationship with SFA exhibited dynamic variation,showing positive correlations in Models 1 and 2（B=16.733,34.555;Beta=0.100,0.206),but shifting to a weak negative correlation in Model 3（B=-3.654;Beta=-0.022;P<0.05);A significant positive correlation with LFC was observed across all three models（Model 1:B=5.939,Beta=0.326;Model 2:B=5.670,Beta=0.311;Model 3:B=3.253,Beta=0.178;all P<0.05).UHR and VFA exhibited significant positive correlations in all three models（Model 1:B=972.261,Beta=0.529;Model 2:B=607.304,Beta=0.331;Model 3:B=147.274,Beta=0.080;all P<0.05).The association with SFA exhibited dynamic changes:no significant association in Model 1（B=-1.484,Beta=-0.001,P=0.890),a significant positive correlation in Model 2（B=271.198,Beta=0.253),and a weak positive correlation in Model 3（B=27.251,Beta=0.025,P<0.05).Model 2:B=36.458,Beta=0.313;Model 3:B=17.489,Beta=0.150;all P<0.05). Conclusion AIP and UHR in the middle-aged and elderly population exhibit stable positive correlations with both VFA and LFC,with these associations remaining significant after adjusting for multiple covariates.Their relationship with SFA undergoes dynamic changes modulated by factors such as age,gender,and BMI.QCT technology enables precise quantification of fat distribution,suggesting AIP and UHR may serve as non-invasive reference indicators for assessing visceral fat accumulation and hepatic fat deposition in this demographic.
Objective To investigate the clinical utility of unsupervised diffusion model for synthesizing CT images from CBCT in radiotherapy,with the aim of reducing patient radiation exposure and enhancing adaptive radiotherapy precision. Methods This paper introduces the Wavelet-domain High-frequency Guided Diffusion model（WHGD).Based on the Daubechies-4 wavelet frequency domain framework for four-level decoupling of CBCT images, precisely separating low-frequency structures and high-frequency details to ensure anatomical integrity in synthesized CT images;a novel Sobel-VAE-GAN guidance mechanism that leverages gradient feature mapping and shared latent space to accurately recover CBCT high-frequency anatomical details during reverse diffusion;and unsupervised cross-domain generalization to enhance robustness in clinical radiotherapy scenarios.The model was trained and validated using public and in-house datasets（a total of 231 head/pelvis/chest pairs),which were split into training,validation,and test sets at a 7∶1∶2 ratio.Image quality was assessed using peak signal-to-noise ratio（PSNR),structural similarity index（SSIM),and mean squared error（MSE),with comparisons against six state-of-the-art baseline models. Results On the head dataset,WHGD achieved a PSNR of 29.975,SSIM of 0.939,and MSE comparable to the top baseline.Relative to CycleGAN,PSNR improved by 6.1% and SSIM by 2.1%;compared to MUNIT,PSNR increased by 8.1% and SSIM by 2.4%;versus the diffusion-based TPDM,PSNR rose by 42.0% and SSIM by 12.9%.For the pelvis dataset,WHGD yielded a PSNR of 28.171,SSIM of 0.947,and MSE of 0.00136,surpassing the second-best FGDM method with a 5.7% PSNR gain and 24.0% MSE reduction.On the chest dataset,WHGD attained a PSNR of 25.175,SSIM of 0.932,and MSE of 0.00222,outperforming all comparators.Visual evaluations highlighted WHGD's excellence in suppressing metal implant artifacts and preserving soft tissue fidelity.Ablation studies validated the efficacy and superior performance of individual modules. Conclusion This approach establishes an efficient paradigm for unsupervised CBCT-to-CT synthesis,holding promise for substantially mitigating patient radiation risks and advancing precision radiotherapy in clinical practice.
Objective To optimize the technical process of hepatic artery cannulation in rats and compare the feasibility and safety of different transarterial approaches,with the aim of standardizing the interventional technology for liver cancer and improving the accessibility of interventional technology for liver cancer in rats. Methods First,healthy SD rats were trained on 4 types of access intubation techniques:gastroduodenal artery（GDA),femoral artery（FA),left common carotid artery（LCCA),and ventral caudal artery（VCA),and feasible accesses were screened.According to the screened accesses,rat liver cancer models were divided into the LCCA access group（Group A）and the VCA access group（Group B).The optimized combination of“20-gauge intravenous catheter+Y Hemostasis Connector+coaxial catheter”was used to perform hepatic artery intubation.The puncture success rate,intubation success rate,operation time,rat survival time,and complications between the two groups were compared. Results In the training of healthy rats,the GDA approach puncture success rate was 0%,the FA approach intubation failed,and the LCCA and VCA approach success rates were higher.In the rat liver cancer models:the puncture success rates in groups A and B were 92.2% and 90.0% respectively（P=0.546).After excluding rats with puncture failure,the success rates of hepatic artery intubation were 78.7% and 94.4% respectively（P=0.124).The median operation time in group A was significantly longer than that in group B（54min vs. 41min,P=0.008).The 7-day survival rate of rats after surgery in group B was higher than that in group A（80.0% vs. 42.1%,P=0.004).The risk of early death（≤2 days）in group B was significantly reduced（OR=6.375,P=0.003).In group A,9 rats died during the operation（9/51,17.6%),and 12 rats died within 24 hours after the operation,while in group B,there were no deaths during the operation or within 24 hours after the operation.Complications such as puncture bleeding and hepatic artery rupture were more common in group A. Conclusion Both the optimized LCCA and VCA approaches are feasible methods for rat hepatic artery cannulation.The VCA approach is more worthy of promotion due to its convenient operation,minimal trauma,and high postoperative survival rate,and can provide standardized technical support for basic research on TACE in HCC.
Objective To explore the diagnostic value of CT lymphangiography(CTL)for generalized lymphatic anomaly. Methods The clinical and imaging data of 56 patients diagnosed with generalized lymphatic anomaly were retrospectively collected.All patients underwent CTL.The observation indicators included:(1)abnormal distribution sites and ranges of lipiodol in the neck,chest,abdomen,pelvis,etc.;(2)abnormal CT manifestations in the lungs;(3)other abnormal manifestations related to lymphatic vessels.The composition ratio of qualitative data was used for statistical description. Results Abnormal lipiodol distribution was observed in the CTL of all 56 patients.(1)Neck and chest region:abnormal lipiodol deposition was observed at the terminal of the thoracic duct in 42 cases,at the terminal of the right lymphatic duct in 7 cases,at the pulmonary hilar area in 15 cases,around the bronchovascular bundles in 11 cases,in the mediastinum in 31 cases,in the pericardium in 11 cases,around the trachea in 24 cases,in the pleura in 23 cases,and in the axilla in 6 cases;(2)Abdomen and pelvis region:abnormal lipiodol deposition was observed around the pancreas in 3 cases,in the liver parenchyma in 1 case,at the hepatic hilum in 4 cases,in the renal sinus in 2 cases,around the small intestine in 3 cases,in the retroperitoneum in 13 cases,in the pelvic cavity in 14 cases,in the pelvic wall in 5 cases,and in the perineum in 5 cases,and in the iliac fossa in 11 cases;(3)Others:abnormal lipiodol deposition was observed in the bones in 2 cases.Abnormal CT manifestations in the lungs included:39 cases of ground-glass opacity in the lungs,including 8 cases of central type,10 cases of peripheral type,21 cases of patchy or diffuse type;6 cases of consolidation;18 cases of thickening of the interlobular septum;13 cases of thickening of the bronchovascular bundle;4 cases of paving stone sign;19 cases of nodule;27 cases of atelectasis;4 cases of frog egg sign;21 cases of pleural effusion.Other abnormal manifestations related to lymphatic vessels:lymphatic vessel abnormalities were observed in the mediastinum in 43 cases,on the chest wall in 8 cases,in the axilla in 11 cases,in the neck in 17 cases,in the abdominal and pelvic cavity in 13 cases,in the retroperitoneum in 12 cases,in the abdominal and pelvic wall in 4 cases,and in the bones in 24 cases. Conclusion CTL can reveal the systemic lymphatic vessel abnormalities of generalized lymphatic anomaly as well as the abnormal lesions in the lungs,which is helpful to provide imaging evidence for the diagnosis and differentiation of this disease.
