High-density areas (HDAs) are frequently observed in follow-up CT of large-vessel occlusions after endovascular therapy. Utilizing the established ASPECTS regions, incorporating the subarachnoid space and ventricles, we developed a novel HDA score to evaluate its correlation and predictive value for hemorrhagic transformations and clinical outcomes. This retrospective, multicenter study included consecutive patients who had HDA on follow-up CT after endovascular therapy. Multivariable logistic regression and area under the receiver operating characteristic curve (AUC) analyses assessed the associations and predictive value of HDA location and score with hemorrhagic transformations and unfavorable clinical outcomes. Among the 1130 consecutive patients treated with endovascular therapy, 542 patients (326 males; median age 70 years) had HDA were finally included. Multivariable logistic regression showed that HDA location in the lentiform nucleus (OR, 1.6; 95

Glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) often exhibit overlapping appearances on routine MRI, complicating pre-treatment diagnosis. In 1,109 patients from five centers, we constructed standard-space tumor probabilistic maps and derived atlas-anchored spatial features to augment conventional radiomics. The spatial radiomics classifier outperformed radiomics alone (external test area under the ROC curve [AUC], 0.98) with acceptable calibration and decision curve benefit, and SHapley Additive exPlanations (SHAP)-enabled anatomy-grounded interpretation. Aligning tumor localization with the Allen Human Brain Atlas and a normative functional connectome linked GBM-enriched territories to developmental-oncogenic programs and network hubness, whereas PCNSL-enriched territories showed immune-inflammatory/proliferative programs, and associations with network hubness did not survive spatial-autocorrelation correction. These results provide shareable reference maps and an interpretable, multicenter-generalizing tool for GBM-PCNSL differentiation, while offering biological context for diagnosis-specific location susceptibility.

RATIONALE AND OBJECTIVES:Extrapancreatic necrosis volume is an established prognostic marker in acute necrotizing pancreatitis, yet early diagnosis remains challenging and manual segmentation is labor-intensive. We developed and validated an automated CT volumetry tool for peripancreatic collections (PPCs) on contrast-enhanced CT (CE-CT) and evaluated its utility for early risk stratification. METHODS:This retrospective study initially screened 520 patients with acute pancreatitis and ultimately included 394. Patients from the primary center (n = 303) were temporally divided into a development cohort (n = 198) for model construction and an internal test cohort (n = 105). An additional 91 patients from two external hospitals formed the external test cohort. Using manual segmentations verified by two radiologists as the reference standard, model performance was assessed via Dice coefficients and Pearson correlation. Multivariable logistic regression and receiver operating characteristic (ROC) analyses evaluated the association between automated PPC volume and the primary outcome (organ failure) as well as the exploratory secondary outcome (infection). RESULTS:The development, internal test, and external test cohorts included 198, 105, and 91 patients, respectively (mean age, 46 years; range, 18-90). In the external test cohort, automated segmentation showed excellent agreement with manual assessment [Dice coefficient, 0.89 (95% CI: 0.87-0.90); Pearson r = 0.99 (95% CI: 0.97-1.00; P < 0.001)]. Automated peripancreatic collection (PPC) volume (per 100 mL) was independently associated with organ failure and infection in both test cohorts (all P ≤ 0.006). In the external test cohort, automated PPC volume demonstrated strong performance for risk stratification of organ failure (AUC: 0.82 vs. modified CT severity index [mCTSI]: 0.73) and infection (AUC: 0.79 vs. mCTSI: 0.72). It also significantly outperformed mCTSI in the internal test cohort for both outcomes. The framework was substantially faster than manual segmentation (median, 25.2 s vs. 12.4 min per patient; P < 0.001) and surpassed radiologists' subjective binary classification (low vs. high PPC burden). CONCLUSION:This deep learning framework enables rapid, fully automated PPC volumetry on CE-CT and provides clinically meaningful risk stratification for organ failure and infection in patients with acute pancreatitis and early peripancreatic collections.

To investigate the natural history and longitudinal imaging evolution of focal nodular hyperplasia (FNH) using gadoxetic acid–enhanced MRI (EOB-MRI) in a multicenter cohort. This retrospective multicenter cohort study enrolled patients with FNH who underwent both baseline and follow-up EOB-MRI. Lesion evolution was assessed using three complementary metrics: absolute diameter change (threshold ≥ 0.5 cm), relative percentage change (threshold ≥ 10

A noninvasive and accurate indicator for evaluating direct renal effects after remote ischemia preconditioning (RIPC) is currently lacking. To explore the feasibility of R2’ mapping in evaluating the direct effect of RIPC on rabbit kidneys and to investigate the mechanisms underlying renal changes induced by RIPC. Eighteen healthy New Zealand rabbits were used (RIPC group, N = 12; control group, N = 6). RIPC was achieved with three cycles of bilateral hindlimb ischemia (10 min/cycle, 60 min total). Magnetic resonance imaging was performed at 1 and 24 hours after RIPC. The R2’ values of the renal cortex, outer medulla, and inner medulla were then recorded. Femoral arterial blood was collected for blood gas analysis and measurements of electrolytes. Enzyme-linked immunosorbent assay was used to detect the levels of myeloperoxidase (MPO), malondialdehyde (MDA), and superoxide dismutase (SOD). Immunohistochemical staining was used to detect the average optical density (AOD) of hypoxia-inducible factor 1 alpha (HIF1α). One-way analysis of variance or the Kruskal–Wallis test was used to assess differences among the groups. Correlations were evaluated using the Spearman rank correlation coefficient. The R2’ values of the renal cortex, outer medulla, and inner medulla in the RIPC groups were significantly lower than those in the control group (RIPC 1 h group: each P < .001; RIPC 24 h group: P = .002, P = .002, P < .001, respectively). MPO levels in the RIPC 1 h and 24 h groups were significantly lower than those in the control group (P = .02, P = .004, respectively). SOD levels in the RIPC 1 h group were significantly higher than in the control group (P = .001). HIF1α AOD in the RIPC 1 h and 24 h groups were significantly higher than those in the control group (both P < .001). The R2’ values of the renal cortex, outer medulla, and inner medulla positively correlated with myeloperoxidase level (rs=0.78, P < .001; rs=0.78, P < .001; rs=0.78, P < .001), and negatively correlated with superoxide dismutase level (rs=-0.81, P < .001; rs=-0.74, P < .001; rs=-0.69, P = .002), and HIF1α AOD (rs=-0.74, P < .001; rs=-0.55, P = .02; rs=-0.71, P < .001). R2’ mapping can quantitatively assess kidney effects after remote ischemia preconditioning, and remote ischemia preconditioning can effectively enhance renal antioxidant capacity and oxygen uptake.

Purpose The differentiation grade of pancreatic ductal adenocarcinoma (PDAC) is a crucial determinant of its aggressiveness and patient prognosis. Recently, advanced diffusion models have been increasingly applied to assess the grading of many tumors. However, the value of these models in discriminating PDAC differentiation grades remains unclear. Methods A retrospective analysis was conducted on 71 patients with pathologically confirmed PDAC between July 2022 and March 2025. Clinicopathological data and conventional imaging features were collected. Eleven diffusion parameters were derived from four advanced diffusion models via multi-model diffusion imaging (DXI) technology: intravoxel incoherent motion (IVIM: f, D∗, D), diffusion kurtosis imaging (DKI: D, K), fractional order calculus (FROC: D, β, μ), and continuous-time random walk (CTRW: D, β, α). Based on postoperative histological differentiation, patients were categorized into a low-grade group (n = 38) and a high-grade group (n = 33). The clinicopathological features, imaging characteristics, and diffusion parameters were compared between the two groups. Receiver operating characteristic (ROC) curves were utilized to evaluate the diagnostic performance of these indicators in predicting the differentiation grade of PDAC, and the DeLong test was employed to compare differences in the area under the curve (AUC). Parameter stability was assessed using bootstrap internal validation with 1000 resamples. Results The high-grade PDAC group exhibited significantly lower values in IVIM-f, DKI-D, and CTRW-α metrics (all p < 0.0045). Among all metrics, CTRW-α had the numerically highest AUC of 0.823. A CTRW-α cutoff of <0.856 diagnosed high-grade PDAC with 81.8% sensitivity and 71.1% specificity. Lymph node status differed significantly between the low- and high-grade groups (p = 0.024). Conclusions DXI technology provides valuable advanced diffusion models and derived parameters for non-invasively assessing PDAC differentiation grade, providing a novel tool for clinical assessment.

Malignant cerebral edema (MCE) is a life-threatening complication after endovascular therapy (EVT) for acute ischemic stroke (AIS). In this multicenter study, we observed a specific gyral hyperdensity sign (GHS) and sought to investigate its association with MCE, analyzing the underlying factors correlated with GHS. Consecutive anterior circulation AIS patients exhibiting post-EVT hyperdensities on 24-h follow-up computed tomography were included. GHS was defined as curvilinear hyperdensities distributed along ⩾2 adjacent cerebral gyri. A total of 542 patients were enrolled. GHS was identified in 142 patients with excellent interobserver agreement (κ = 0.804). We found that GHS (OR = 1.83, 95% CI = 1.13-2.95, P = 0.014) was independently associated with MCE. Furthermore, baseline NIHSS score (OR = 1.05, 95% CI = 1.02-1.09, P = 0.003), blood glucose (OR = 1.15, 95% CI = 1.05-1.25, P = 0.002), infarct ASPECTS (OR = 0.91, 95% CI = 0.83-0.99, P = 0.024), creatinine level (OR = 3.47, 95% CI = 1.71-7.02, P < 0.001), and intracranial internal carotid artery occlusion (OR = 1.66, 95% CI = 1.09-2.55, P = 0.020) were independently correlated with GHS. These results revealed that GHS can be reliably identified and can serve as an early warning indicator for MCE.

ObjectiveThis study aimed to develop a hybrid model combining MRI-based radiomics, deep learning, and clinical variables for preoperative differentiation of Escherichia coli from non-Escherichia coli pathogens in perianal abscesses.MethodsA retrospective series of 215 patients with culture-confirmed perianal abscesses (119 Escherichia coli, 96 non-Escherichia coli) was analyzed. Preoperative MRI data were collected, with radiomic and deep learning features extracted from T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and fat-suppressed T2WI (FS-T2WI) sequences. Radiomic feature selection was performed using univariate t-tests, Pearson correlation, and least absolute shrinkage and selection operator (LASSO) regression. Clinical and MRI data were screened through univariate and multivariable logistic regression. The MRI signature was derived by averaging the probabilities from a logistic regression model (radiomics) and a k-nearest neighbors classifier (deep learning). A hybrid logistic regression model integrated the MRI signature with clinical predictors to create a nomogram. Model performance was evaluated using receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA).ResultsEscherichia coli accounted for 57.63% of all strains. Gender (odds ratio [OR] = 0.456, p = 0.001) and diabetes (OR = 9.207, p < 0.001) were significant independent predictors. The nomogram achieved an area under the curve (AUC) of 0.885 in the testing set, outperforming the MRI signature alone (AUC = 0.860), with accuracy of 0.815, sensitivity of 0.818, and specificity of 0.812. Calibration curves showed good agreement between predicted and observed outcomes, while DCA demonstrated superior clinical utility.ConclusionThe hybrid model, utilizing preoperative multi-sequence MRI, noninvasively identifies Escherichia coli, the predominant pathogen in perianal abscesses, offering significant clinical potential to transition from empirical antibiotic regimens to microbiology-guided precision strategies.

Background:T2* mapping provides a noninvasive approach for quantifying tissue oxygenation via the detection of the paramagnetic effects associated with deoxyhemoglobin, with validated correlations to hypoxia in other cancers. However, its application in stratifying hypoxic microenvironments in pancreatic ductal adenocarcinoma (PDAC) remains limited. This preliminary study aimed to evaluate T2* mapping as a quantitative imaging biomarker for hypoxia levels in PDAC in order to offer a potential tool for the noninvasive characterization of the tumor microenvironment and the development of personalized treatment strategies. Methods:This retrospective study enrolled 50 patients with PDAC pathologically confirmed between June 2022 and July 2023. Hypoxia-inducible factor-1α (HIF-1α) expression was used to evaluate PDAC hypoxia levels, and patients were stratified into low and high hypoxia groups. T2* values and other clinicoradiological indicators were compared between the two groups via binary logistic regression analysis. A logistic regression model was built with independent factors related to hypoxia levels. The predictive performance of the model was evaluated with area under the curve (AUC) and leave-one-out cross-validation (LOOCV). The correlation between the PDAC HIF-1α expression and T2* values was assessed via the Spearman rank correlation coefficient, and hypoxia levels were compared with pathological findings. Results:T2* values differed significantly between the low and high hypoxia groups (62.63±3.33 vs. 59.16±3.97 ms; P=0.002), as did rim enhancement (P<0.001). Multivariate analysis identified the independent predictors of hypoxia to be T2* values [odds ratio (OR) =0.819; 95% confidence interval (CI): 0.674-0.994; P=0.044] and rim enhancement (OR =6.261; 95% CI: 1.532-25.581; P=0.011). The logistic regression model achieved an initial AUC of 0.822, retaining diagnostic performance after LOOCV (AUC =0.785). T2* values exhibited a significant inverse correlation with HIF-1α expression (r=-0.463; P<0.001). High-hypoxia tumors were associated with poorer differentiation (P<0.001). Conclusions:T2* mapping may serve as a noninvasive imaging biomarker for stratifying the hypoxic microenvironment in PDAC.

In patients with anterior circulation large vessel occlusion stroke, hyperdense areas (HDAs) on post-thrombectomy computed tomography (CT) reflect blood–brain barrier injury and may herald malignant edema or hemorrhagic transformation. Both complications can progress to life-threatening mass effect, often necessitating surgical interventions such as decompressive craniectomy. We aimed to develop and validate a location-specific HDA score for predicting severe mass effect. This multicenter retrospective study analyzed 865 patients exhibiting HDAs on post-thrombectomy CT. The primary outcome was severe mass effect, defined as decompressive craniectomy or midline shift ≥ 5 mm on follow-up imaging. A location-specific HDA score was developed by expanding the ASPECTS template to include subarachnoid and ventricular compartments. Predictive models integrating imaging and clinical variables were constructed via LASSO regression and validated internally (bootstrapping) and externally. Three HDA locations independently predicted the outcome: insula [adjusted odds ratio (aOR) = 2.02, 95

Chronic kidney disease (CKD) represents a major global health burden, and early, reliable risk prediction remains clinically challenging. This study proposes a CKD prediction framework that integrates machine learning with Synergy-Unique-Redundant Decomposition (SURD) from causal information theory to enhance both predictive performance and interpretability. Ten classification models were developed using the UCI-CKD dataset (n = 400). Missing values were handled using multiple imputation via chained equations, and class imbalance was addressed with the synthetic minority oversampling technique. Model performance was evaluated using accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC). To rigorously assess generalizability and mitigate concerns regarding overfitting, extensive external validation was conducted using a large-scale real-world electronic health record cohort from the MIMIC-IV database (n = 27,834). While several models achieved near-perfect performance on the internal dataset, the Random Forest model demonstrated superior generalization in the external cohort, achieving an AUC of 0.990 (95% CI 0.989-0.991), compared with an AUC of AUC: 0.914 (95% CI0.912-0.916) for the baseline Decision Tree. SURD-based causal decomposition and feature importance analyses consistently identified clinically established predictors, including serum creatinine and hemoglobin. Overall, these results indicate that the proposed SURD-guided framework provides a robust and interpretable approach for early CKD risk stratification and demonstrates stable performance when transferred from benchmark datasets to real-world clinical settings.

Functional recovery after acute ischemic stroke is highly heterogeneous. This study aimed to explore and characterize distinct clinical-neuroimaging phenotypes associated with outcomes in patients with anterior circulation large vessel occlusion (LVO) by integrating multidimensional clinical and neuroimaging biomarkers using latent class analysis (LCA). In this retrospective, multicenter study, we included patients with anterior circulation LVO receiving contemporary standard acute stroke care. The analysis integrated clinical factors (age, NIHSS, atrial fibrillation, glucose) and neuroimaging markers (infarct volume, location, and brain frailty) selected via LASSO regression. LCA was performed in a derivation cohort (n = 957) to identify latent phenotypes. Associations between phenotypes and 90-day unfavorable outcomes (modified Rankin Scale 3–6) were assessed using multivariable generalized linear models, with results validated in an independent cohort (n = 131). LCA identified four distinct clinical-neuroimaging : (1) Brain Frailty (24.8

This study aimed to explore the association between obesity and renal parenchymal heterogeneity on T1 map in the general population. This population-based study included participants who underwent renal T1 mapping in UK Biobank imaging cohort. Obesity indices were body roundness index (BRI) and body mass index (BMI). The primary renal imaging parameter was corticomedullary T1 differentiation (ΔT1), quantified by manual region-of-interest placement in bilateral kidneys. Multivariable linear regression sequentially adjusted for sociodemographic, metabolic, and renal covariates. A total of 4214 participants (1812 men, 2402 women; 53.9 ± 7.6 years) were included. Both BRI and BMI were significantly and negatively associated with ΔT1 in fully adjusted models (BRI: β = − 8.507, 95

BACKGROUND:To establish and vertify a nomogram model that integrates multiparametric magnetic resonance imaging (MRI) radiomic signatures and clinical features to predict satellite nodules (SNs) and recurrence-free survival (RFS) in hepatocellular carcinoma (HCC) patients. METHODS:Data from 244 patients with HCC who underwent multiparametric MRI were analyzed and randomly assigned into a training (n = 170) dataset and a validation dataset (n = 74). A support vector machine algorithm was employed to develop T1WI (T1-weighted imaging), T2WI (T2-weighted imaging), arterial phase (AP), portal-venous phase (PVP), and integrated MRI radiomic models. The selected signatures were combined with independent clinical factors to construct a nomogram model. The performance of these predictive models in the prediction of SNs and RFS was assessed with the AUC and Kaplan-Meier analysis, respectively. RESULTS:Portal vein tumor thrombosis and peritumoral enhancement were significant clinical indicators of SNs (P < 0.05). In predicting SNs, the nomogram model demonstrated the highest AUC value of 0.94 in the training dataset and 0.83 in the validation dataset. This was followed by the integrated MRI (0.93 and 0.79), AP (0.92 and 0.82), T2WI (0.91 and 0.78), PVP (0.90 and 0.80), and T1WI models (0.88 and 0.77). Compared with SNs (-) patients, SNs (+) patients had a significantly lower median RFS (61.3 vs. 18.6 months, P < 0.001). Additionally, nomogram predicted SNs (+) had a lower median RFS compared to SNs (-) (20.5 vs. 63.1 months, P < 0.001). CONCLUSION:The nomogram model based on multiparametric MRI radiomics signatures demonstrated substantial efficacy in predicting SNs and RFS in patients with HCC.

Oxidative stress and inflammation are common medical issues contributing to the onset and progression of heart failure (HF). Sulfiredoxin 1 (Srxn1) is a key regulatory factor in the antioxidant response. This study aimed to examine the effect of Srxn1 in HF. We utilised transcriptome sequencing to screen for differentially expressed genes in cardiac remodelling. We overexpressed Srxn1 in the hearts using an adeno-associated virus 9 (AAV9) system through tail vein injection. C57BL/6 mice were subjected to transverse aortic constriction (TAC) for 4 weeks. Echocardiography was used to evaluate cardiac function, and cardiac remodelling was estimated by histopathology and molecular techniques. In addition, H9C2 cells were stimulated by Ang II to establish an in vitro model of cardiomyocyte hypertrophy, and the effects of Srxn1 overexpression on the inflammatory pathways and oxidative stress in Ang II-stimulated H9C2 cells were examined. We found that Srxn1 is downregulated after cardiac remodelling by transcriptome sequencing. Our results revealed down-regulated levels of Srxn1 in murine hearts subjected to TAC treatment, and H9C2 challenged with Ang II. Moreover, compared with WT mice, AAV-9-Srxn1 mice exhibited dramatically ameliorated TAC-induced cardiac dysfunction, hypertrophy, fibrosis, oxidative stress, and inflammation. In terms of mechanism, both in vitro and in vivo experiments confirmed that the potential positive impacts may be linked to the inhibition of TLR4/NF-κB signalling. In summary, this study is the first to demonstrate the protective effects of Srxn1 against TAC-induced cardiac oxidative stress and inflammation, which are induced by the inhibited activation of the TLR4/NF-κB signalling pathway.

Hypertensive nephropathy, a major cause of end-stage renal disease, lacks reliable noninvasive biomarkers. Renal surface nodularity (RSN) on CT may reflect nephrosclerosis, but existing methods fail to quantify nodularity heterogeneity or predict renal decline. This study developed a novel CT-based RSN metric and assessed its prognostic value in hypertensive patients. This retrospective cohort study included hypertensive patients who underwent contrast-enhanced CT. Patients with bilateral renal surface irregularities were assigned to the RSN group, with age- (± 2 year) and sex-matched controls (non-RSN group) randomly selected at a 1:1 ratio. RSN was quantified using three surface roughness metrics. A semi-quantitative RSN score was also calculated based on the distribution and depth (> 50

The purpose of this study was to determine if habitat radiomic features extracted from pretherapy multi-sequence MRI predict residual status in patients with Nasopharyngeal Carcinoma (NPC) after radical radiotherapy. The retrospective study enrolled 179 primary NPC patients, divided into training and validation cohorts at a 7:3 ratio. K-means clustering was employed to segment T2WI, CE-T1WI and FSCE-T1WI images, creating habitats within the volume of interest. Identify relevant features that can recognize NPC residuals. In the training cohort, support vector machine (SVM) models were developed utilizing the radiomic features extracted from each habitat and the entire tumor, selecting the most predictive features for each sequence. SVM models were constructed by combining the optimal radiomic features from each sequences with clinical data. Model performance was compared and validated using receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA), and differences between models were assessed using the DeLong test. The optimal clustering results revealed 4 habitats in FSCE-T1WI, while 2 habitats in both CE-T1WI and T2WI sequences. In the training cohort, we compared the predictive accuracy of SVM models based on different habitats and total tumor characteristics from three sequences, and found that the features from T2 Hab2, CE-T1 Hab1, and FSCE-T1 Hab4 images showed higher performance. Incorporation of habitat-based radiomic features and clinical variables significantly enhanced the predictive performance. The integrated model exhibits the optimal predictive performance, with the area under the curve (AUC) values of 0.921 (SEN = 0.821, SPE = 0.830) in the training cohort and 0.811 (SEN = 0.778, SPE = 0.722) in the validation cohort. Compared to conventional radiomics, habitat imaging features that distinguish intratumoral heterogeneity have higher predictive value, making them potential non-invasive biomarkers for assessing NPC residual after radiotherapy. Integration of multi-sequence MRI habitat radiomic with clinical parameters further improved predictive accuracy.

BackgroundPancreatic cancer is a highly aggressive malignancy of the digestive system, characterized by insidious onset and rapid progression. Most cases are diagnosed at advanced stages, complicating surgical resection and presenting significant challenges for clinical treatment. Recent advancements have emphasized individualized treatment strategies tailored to patients’ specific conditions. Consequently, accurate preoperative assessment is crucial, highlighting the urgent need to develop more reliable predictive models to guide personalized treatment plans.MethodsA systematic literature search was conducted using Web of Science Core Collection (WoSCC) database, covering publications from January 1, 1995, to October 25, 2024. A comprehensive bibliometric analysis was performed employing analytical tools such as VOSviewer, CiteSpace and Microsoft Excel.ResultsThis study includes 919 publications authored by 6716 researchers from 3727 institutions in 222 countries and regions. The articles were published in 301 journals, with 1,640 distinct keywords and 25,910 references. China led in publication volume, while the United States garnered the most citations. The top three research institutions in this field were Fudan University, Shanghai Jiao Tong University, and Sun Yat-sen University. Yu Xianjun from Fudan University emerged as the most prolific author with the highest citation count. Frontiers in Oncology had the highest publication volume, while the Annals of Surgery received the most citations. Medical imaging, biochemistry, immunology, bioinformatics, genetics, and interdisciplinary integrative research are the main research disciplines in the field of prognosis prediction for pancreatic cancer. The results of keyword co-occurrence and literature co-citation analysis revealed emerging hotspots and trends in this field, including CA19-9, CT, inflammation, machine learning, tumor microenvironment, radiomics, genes, nomograms, randomized controlled trials, long-term survival, and metastasis.ConclusionThis bibliometric analysis provides an overview of research conducted over the past three decades, offering insights into the current state of knowledge and outlining directions for future studies on prognosis prediction models for pancreatic cancer. Biochemical indicators have consistently emerged as key research focal points. The tumor microenvironment represents a currently popular research direction, while bioinformatics, medical imaging, and artificial intelligence are gaining traction as future trends in this field. In the future, prognostic models for pancreatic cancer require further refinement to ensure reliable guidance for therapeutic decision-making.

To evaluate the feasibility and accuracy of Fat Analysis Calculation Technique (FACT), a multi-echo Dixon-like sequence, for quantifying renal and perirenal adipose distribution at 5 T. Accuracy of FACT-based Proton density fat fraction (FACT-PDFF) was assessed by comparing with magnetic resonance spectroscopy-based PDFF (MRS-PDFF) in phantom study. In vivo FACT images from 24 volunteers (13 males and 11 females) without kidney-related diseases were acquired at 5 T and evaluated independently by two readers. Repeatability of FACT-PDFF was assessed through three consecutive scans. Spearman correlation examined associations between averaged FACT-PDFF and clinical characteristics. Linear regression, intraclass correlation coefficients (ICCs), and Bland-Altman plots assessed consistency and deviations between fat quantification methods and field strengths. The Wilcoxon signed-rank test compared image quality scores between radiologists. The paired t test compared FACT-PDFF differences across all regions of interest between bilateral kidneys and between renal cortex and medulla. Analysis of covariance compared gender-related renal fat differences. In phantom study, FACT-PDFF showed excellent agreement with MRS-PDFF at both fields (ICCs ≥ 0.995). Linear regression revealed strong correlations (R² ≥ 0.998), and Bland-Altman plots indicated minimal bias. In clinical study, FACT images achieved high quality. Repeatability was excellent (ICCs: 0.837–0.991; CVs: 0.78–4.49