PurposeTo assess the possibility of employing whole-volume ADC histogram analysis for predicting programmed cell death ligand 1 (PD-L1) expression in periampullary carcinomas (PCs).Materials and methodsWe retrospectively evaluated imaging records of 65 patients with PC who received pancreaticoduodenectomy in our hospital. PD-L1 expression was systematically categorized as positive or negative based on the tumor proportion score (TPS), immune cell score (ICS), and the combined positive score (CPS), with an immunohistochemistry assay. Univariate analysis was conducted to assess differences in parameters between PD-L1-positive and PD-L1-negative groups. Spearman’s correlation analysis was utilized to explore associations between variables and PD-L1 expression. Receiver operating characteristic (ROC) analysis was performed to evaluate the differential diagnostic performance of parameters in distinguishing two groups.ResultsSeveral ADC histogram parameters were significantly different between PD-L1-positive and PD-L1-negative group, and showed significant correlations with PD-L1 expression, most notably the 5th and 10th percentiles. In TPS grouping, the 5th percentile demonstrated the highest area under the curve (AUC) of 0.690, which was improved to 0.740 when combined with tumor size and carbohydrate antigen 19-9. In ICS grouping, the 10th percentile showed the highest AUC of 0.690, which was improved to 0.772 when integrated with the degree of differentiation. In CPS grouping, the 5th percentile demonstrated the highest AUC of 0.694, which was improved to 0.752 when combined with tumor size and carcinoembryonic antigen.ConclusionWhole-volume ADC histogram parameters of primary tumors hold great potential in predicting PD-L1 expression in PCs.

Background:Pericarotid fat density (PFD) obtained by computed tomography angiography (CTA) could serve as a surrogate biomarker reflecting local inflammation. The study aimed to quantitatively estimate the impact of PFD on cognitive and functional outcomes in patients with cerebral small vessel disease (CSVD). Methods:A total of 71 symptomatic and 55 asymptomatic CSVD patients were prospectively recruited. Imaging characteristics comprising CSVD markers and carotid artery markers (PFD values, calcification, stenosis degree, ulceration, and maximum wall thickness) were analyzed. Cognitive assessments were performed 90 days after acute infarcts, including diverse neuropsychological scales (the Montreal Cognitive Assessment, the Shape Trail Test, the Stroop test, and the Rey-Osterrieth Complex Figure Test) and sensitive event-related potential (ERP) detection. Worse functional outcomes were defined as a Modified Rankin Scale change score (ΔmRS) ≥ 0 (ΔmRS = mRS90-day-mRSbaseline) in the symptomatic CSVD cohort. Results:Symptomatic CSVD showed significantly higher PFD values than asymptomatic CSVD. As indicated by multiple scales and P3a/P3b amplitude, cognitive function in symptomatic CSVD was poorer than that in asymptomatic CSVD. Symptomatic CSVD showed numerically stronger correlations between PFD values and cognitive impairment, with more parameters involved and the strongest correlations existing among ERP data. Maximum PFD was identified as an independent predictor of adverse functional outcomes (p < 0.001), and a predictive model incorporating maximum PFD showed better performance (AUC = 0.939). Conclusion:The increased PFD values were closely associated with cognitive impairment and adverse functional outcomes in CSVD patients, especially among those with symptoms. Our findings could assist in the risk stratification and tailored treatment of CSVD patients to facilitate prognosis.

The aim of this study was to investigate additional risk stratification benefits of myocardial radiomics derived from coronary CTA (CCTA) over clinical factors, anatomic and hemodynamic CTA findings in patients with stable angina. 2171 multicenter patients with stable angina who underwent CCTA were retrospectively included. The endpoints were major adverse cardiovascular events (MACE). Clinical factors and CTA findings were analyzed to identify the independent predictors. The left ventricular myocardium was segmented from CCTA, and the radiomics features were assessed to build a radiomics signature (RS). A radiomics nomogram (RN) was constructed by combining the independent clinical and CTA predictors with the RS. Previous myocardial infarction, hyperlipidemia, and the change in CT derived fractional flow reserve (ΔFFRCT) were proven as independent predictors for MACE (C-index, 0.733, test cohort). The RS was significantly associated with MACE (C-index, 0.713, test cohort). By adding the RS to the clinical and CTA predictors, the RN provided a higher C-index (0.788, test cohort), and higher time-AUCs. In the test cohort, the IDIs of RN to the clinical and CTA predictors were 3.10

To evaluate the association between coronary computed tomography angiography (CCTA)-based parameters and major adverse cardiovascular events (MACEs) in liver transplantation (LT) recipients and assess the incremental prognostic value of CCTA over the cardiovascular risk in orthotopic liver transplantation (CAR-OLT) score. A dual-center 395 LT recipients were enrolled. The endpoints were 1-year MACE and MACE after LT. The CAR-OLT score was calculated. An imaging model for predicting 1-year MACE after LT was constructed based on the independent CCTA-based predictors. A combined model was established by integrating the independent imaging predictors with the CAR-OLT score. Coronary artery disease-reporting and data system (CAD-RADS) score (odds ratio [OR], 3.15; P = .020) and CT-derived translesional fractional flow reserve gradient across the lesion with the highest-grade stenosis (ΔFFRCT) (OR 2.23, P = .001) were identified as the independent imaging predictors for 1-year MACE after LT. The combined model outperformed the CAR-OLT score with a higher area under the curve value for predicting 1-year MACE after LT (0.793 vs 0.668, P = .044) and a higher C-index for predicting MACE after LT (0.766 vs 0.659, P < .001). CAD-RADS score and ΔFFRCT can predict MACE after LT and provide incremental value to the CAR-OLT score.

This commentary discusses the recent study by Feng et al published in World Journal of Gastroenterology . Serum origin recognition complex subunit 1 (ORC1) was identified as a potential biomarker for hepatitis B virus-related hepatocellular carcinoma (HCC). The study reported improved diagnostic accuracy when ORC1 was combined with extra spindle pole bodies-like 1 and alpha-fetoprotein, providing an opportunity to contextualize ORC1 within existing multi-marker biomarker strategies for hepatitis B virus-related HCC surveillance. These observations, along with the limitations of the study, are briefly discussed. Several suggestions have also been made regarding prospective validation and the incorporation of ORC1 into broader multi-marker surveillance strategies.

Background:The risk stratification of sudden cardiac death (SCD) in patients with heart failure (HF) with mildly reduced ejection fraction (HFmrEF) remains suboptimal. This study aims to evaluate the role of late gadolinium enhancement (LGE) and T1 mapping in predicting SCD in patients with HFmrEF and to provide an improved clinical risk stratification algorithm. Methods:A total of 1855 patients with HFmrEF from five tertiary medical centers were enrolled in this study, all of whom underwent echocardiography and cardiovascular magnetic resonance (CMR). Data analysis utilized a multicenter development cohort (n = 1417) and a multicenter external validation cohort (n = 438). The primary endpoints were sudden cardiac death, appropriate ICD shocks, and resuscitated cardiac arrest. Secondary endpoints were a composite of HF death, unplanned HF hospitalization, or cardiac transplantation. Based on validated MRI predictors, a risk algorithm and corresponding clinical workflow were developed for SCD risk assessment. Findings:During a median follow-up of 42 months in the development cohort, 142 and 140 patients experienced primary and secondary endpoints, respectively. Adjusted analyses identified the following as significant predictors of SCD-related events: LGE ≥6.3% (HR 5.33, 95% CI: 3.74-7.60; P < 0.001), ECV ≥28.4% (HR 3.44, 95% CI: 2.32-5.11; P < 0.001), and a native T1 z-score ≥2.7 (HR 2.40, 95% CI 1.67-3.48; P < 0.001). Patients with ECV >28.4% and no LGE showed higher SCD risk compared to those with ECV <28.4% and LGE <6.3% or mid-wall LGE. Conversely, patients with ln (NT-proBNP) <6.2, LGE <6.3%, and ECV <28.4% had a lower SCD risk, with an annual event rate of 0.4%. Notably, LGE ≥6.3% was associated with a high annual SCD event rate of 7.9%, irrespective of ECV, NT-proBNP levels, and LGE distribution. Interpretation:In HFmrEF, at least 6.3% of LGE serves as a strong predictor of SCD risk, irrespective of its distribution. ECV significantly enhances risk stratification, particularly in patients with negative or mid-wall LGE. Funding:This study was supported by the National Natural Science Foundation of China (Grant Nos. 81871354, 81571672) and Key R&D Program of Shandong Province, China (2025CXPT106, 2025CXGC020305).

In clinical practice, approximately 15–25

Background and Aims Patients with heart failure with mildly reduced or preserved ejection fraction remain at substantial risk of adverse clinical outcomes. This study aimed to evaluate the prognostic value of the triglyceride-glucose index, body mass index, and epicardial adipose tissue index and to determine whether their combination improves risk stratification. Methods and Results This multicenter retrospective study included 712 patients (316 with mildly reduced and 396 with preserved ejection fraction). Epicardial adipose tissue was quantified by cardiac magnetic resonance and indexed to body surface area. Cox regression and Kaplan–Meier analyses were performed for the composite endpoint of all-cause mortality or heart failure hospitalization. During a median follow-up of 25 months, 184 patients (25.8%) experienced the primary endpoint. Higher triglyceride-glucose index and epicardial adipose tissue index were independently associated with increased risk of adverse outcomes, whereas body mass index was not. Patients with concomitantly elevated triglyceride-glucose index and epicardial adipose tissue index had the highest risk. Adding these two indices to the clinical model improved discrimination and risk reclassification. Conclusion The triglyceride-glucose index and epicardial adipose tissue index were independently associated with adverse outcomes in patients with heart failure with mildly reduced or preserved ejection fraction. Their combination provided incremental prognostic information beyond traditional clinical factors, supporting their potential utility for improved risk stratification.

Extracellular matrix (ECM) remodeling is essential for glioma invasion, yet lacks non-invasive assessment methods. This study employs radiogenomics to enable non-invasive survival prediction and ECM remodeling assessment in glioma. Utilizing a multi-dataset data (n = 891), an 11-feature radiomics signature is developed stratifying patients into low-and high-Rad-score groups (area under the receiver operator characteristic curve [AUC] = 0.886, 95% confidence interval [CI]: 0.807-0.964 in the training set from two local centers; AUC = 0.828, 95% CI: 0.796-0.893 in the validation set from five public datasets). Radiogenomic analysis (n = 572) reveals differentially expressed genes significantly associated with Rad-scores, particularly enriched in pathways associated with ECM remodeling, and identifies seven related hub genes (MMP2, MMP9, CXCL8, TIMP1, IL-6, COL1A2, and CCL2). These findings are validated using an external radiogenomic dataset and orthotopic (both syngeneic and xenograft) mouse models, where silencing MMP2 reduced Rad-scores and tumor infiltration. This study highlights the potential of MRI-based radiomics signatures in assessing ECM remodeling for survival prediction and improved glioma clinical management.

In this retrospective study, we aimed to assess the predictive value of the Carotid Plaque-RADS (Reporting and Data System) for coronary functional stenosis in candidates for carotid revascularization, using high-resolution magnetic resonance imaging (HR-MRI) coupled with computed tomography-derived fractional flow reserve (CT-FFR). A retrospective analysis was performed on data of 101 patients with carotid atherosclerosis who underwent HR-MRI for Carotid Plaque evaluation, and CT-FFR for coronary assessment was conducted. Patients were divided into two groups based on a CT-FFR threshold of ≤ 0.80. Logistic regression, correlation analyses, and receiver operating characteristic curve analyses were used to identify predictors of coronary functional stenosis. In the functional stenosis group (n = 76), both plaque volume and Carotid Plaque-RADS categories had higher values than those observed in the non-functional group (n = 25). Univariate analysis showed that Carotid Plaque-RADS, Carotid Plaque volume, and hypertension were associated with functional stenosis. After adjustment, Carotid Plaque-RADS remained an independent predictor (odds ratio: 2.35, p < 0.01) and demonstrated the strongest correlation (ρ = 0.51, p < 0.01). It also demonstrated good diagnostic performance (area under the curve [AUC]: 0.81; sensitivity: 85

This study aimed to evaluate the predictive value of quantitative gadobenate dimeglumine-enhanced MRI parameters in aggressiveness and prognosis of intrahepatic mass-forming cholangiocarcinoma (IMCC). A total of 158 patients with IMCC who underwent preoperative MRI at three centers were included, and their clinical and imaging data were analyzed retrospectively. Multimodal quantitative parameters were measured in various tumor areas, including relative intensity ratio (RIR) and relative enhancement ratio (RER) of the central and rim areas of the tumor to the liver in the hepatobiliary phase, and the center area-tumor volume ratio. Patients were classified into low-aggressiveness (Ki-67 LI < 25

Background and objective Early detection of hemodynamically obstructive coronary artery disease (HOCAD) is essential for guiding clinical intervention. However, traditional methods relying on standard 12-lead electrocardiography (ECG) exhibit limitations in detecting HOCAD because of subtle dynamic changes during early-stage ischemia. This study aims to develop an ensemble learning method that integrates clinical information, static ECG features, and dynamic cardiodynamicsgram (CDG) features to enhance HOCAD detection. Methods First, CDG were constructed using deterministic learning theory. Subsequently, feature engineering was applied to extract waveform characteristics and dynamic features from ECG data. Four independent ensemble learning models for HOCAD detection were then developed: CatBoost, XGBoost, LightGBM and GBDT. Finally, the developed ensemble models were trained and tested on a real-world clinical dataset comprising 350 patients. This dataset included clinical information, 20-second 12-lead ECG recordings and CT imaging data, with 139 HOCAD-positive patients and 211 non-HOCAD patients. A stratified 5-fold cross-validation scheme was employed to ensure robust performance estimation. Results All four ensemble models achieved over 80% accuracy in patient-level HOCAD detection. The optimal CatBoost model attained an accuracy of 86.29%, a sensitivity of 87.77%, and a specificity of 85.31%. Comprehensive comparisons with classical machine learning, advanced tabular models, and hybrid deep learning architectures demonstrated the superior performance of the developed boosting framework. Conclusion The developed boosting ensemble learning approach, integrating ECG-based dynamic-static features, achieves high sensitivity and specificity for non-invasive HOCAD detection and provides vessel-level diagnostic insights, offering a promising low-cost screening tool for early CAD management.

The gold standard assessment of coarctation of aorta (CoA) was achieved invasively by cardiac catheterization, which is associated with several risks including radiation exposure. The present study aimed to validate a multimodal imaging-based non-invasive computational framework for assessment of trans-coarctation pressure gradients (PG) in patients with severe coarctation of the aorta (CoA). Here we developed a non-invasive computational fluid dynamics (CFD) modeling framework based on multidetector computed tomography angiography (MDCTA) and ultrasound-derived input parameters, which incorporated into a lumped parameter model (LPM) and validated the results against measurements obtained via cardiac catheterization, both preoperatively and postoperatively. We used conventional Doppler estimates to make these correlations and to compare their diagnostic performance in identifying critical PG. The results indicated that for 18 patients with severe CoA the CFD simulation exhibited better concordance and correlation with catheter measurements compared to Doppler gradients (pre-intervention: 58.44 ± 17.77 vs. 55.72 ± 19.71 vs.57.78 ± 18.02 mmHg; post-intervention: 17.94 ± 10.54 vs. 15.65 ± 5.15 vs.20.61 ± 7.43 mmHg). Specifically, the CFD-derived PG showed a stronger correlation with catheter measurements (pre-intervention r = 0.89, post-intervention r = 0.90) than did Doppler-derived PG (pre-intervention r = 0.71, post-intervention r = 0.30). This CFD framework facilitated reliable quantification of PG and visualization of hemodynamic forces in patient-specific geometric models, and suggesting its potential as a non-invasive and effective approach for the assessment of CoA.

Lingxin Kong,1,2,&ast; Bowen Li,1,2,&ast; Yicheng Han,2 Xueqiao Yang,1,2 Ruyue Ding,1,2 Sen Zhang,1,2 Xinwu Ma,2 Hui Gu,2 Ximing Wang2,31School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences), Jinan, Shandong, People’s Republic of China; 2Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, People’s Republic of China; 3Department of Radiology, Shandong University, Jinan, Shandong, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Ximing Wang, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324, Jing 5 Road, Jinan, Shandong, 250021, People’s Republic of China, Tel +86 15168886672, Email wxming369@163.comPurpose: To evaluate the correlation between pericoronary fat attenuation index (pFAI) and the presence, characteristics, and number of high-risk plaque (HRP) features identified using photon-counting computed tomography coronary computed tomography angiography (PCCT-CCTA) in patients with suspected coronary artery disease (CAD).Patients and Methods: This was a single-center prospective cross-sectional study. We prospectively enrolled 119 consecutive patients with suspected CAD who underwent PCCT-CCTA at a local hospital between August and September, 2025. pFAI was measured from coronary computed tomography angiography (CCTA) images, and its correlation with individual HRP features and their cumulative number was investigated.Results: A total of 119 patients with suspected CAD were prospectively enrolled (mean age: 58.9± 11.3 years; 62.1% male). pFAI was significantly higher in the HRP group than in the non-HRP group (− 90.1± 7.5 HU vs. − 98.4± 6.7 HU, P< 0.001). Patients with ≥ 2 high-risk features exhibited significantly higher pFAI scores than those without HRP features. The pFAI was not significantly increased in patients with only one HRP feature (P=0.408). Furthermore, pFAI was significantly higher in patients with spotty calcification (SC), positive remodeling (PR), or napkin-ring sign (NRS) (all P< 0.05), but no significant increase in pFAI was found in patients with low-attenuation plaque (LAP). Multivariable analysis identified non-calcified plaque (NCP) volume, SC, and PR as independent factors associated with pFAI (all P < 0.05).Conclusion: The pFAI was significantly elevated in patients with HRP. Coronary arterial inflammation plays a pivotal role in HRP formation. The PCCT is a powerful tool for detecting HRP and assessing coronary arterial inflammation.Keywords: photon-counting computed tomography, coronary computed tomography angiography, coronary artery disease, pericoronary adipose tissue

BACKGROUND AND PURPOSE:Ischemic stroke poses a significant global health burden. Accurately identifying symptomatic carotid atherosclerotic plaques, beyond relying solely on stenosis degree, remains a critical challenge for precise stroke risk stratification. We aimed to develop and validate a deep learning radiomics (DLR) signature based on multicontrast MRI to identify symptomatic carotid plaques accurately. MATERIALS AND METHODS:In this retrospective multicenter study, 409 carotid arteries from 355 patients with carotid atherosclerosis were enrolled (219 training, 95 internal validation, 95 external test). Deep learning (DL) and radiomics features were extracted and combined from automatically segmented plaque regions on multicontrast MRI. The optimized DLR signature derived from a 3-stage feature selection pipeline was leveraged to train diverse machine learning classifiers for robust identification of symptomatic carotid plaques. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC) and compared against clinical models, radiomics-only models, and DL-only models. Subgroup analysis across stenosis severities and comparison of MRI-based American Heart Association lesion types between DLR-defined risk groups were performed. RESULTS:The DLR model with logistic regression demonstrated excellent performance in identifying symptomatic plaques, achieving AUROCs of 0.975 (95% CI, 0.954-0.992), 0.933 (95% CI, 0.876-0.976), and 0.881 (95% CI, 0.807-0.939) in the training, internal validation, and external validation cohorts, respectively. It significantly outperformed the clinical model (AUROCs of 0.701, 0.749, 0.711; P < .05), radiomics-only model (AUROCs of 0.877, 0.839, 0.789; P < .05), and DL-only model (AUROCs of 0.948, 0.894, 0.845; P < .05 in training/external). Performance remained consistently high across stenosis severity subgroups (AUROCs of 0.895-0.982 for severe, 0.863-0.971 for mild-moderate stenosis). DLR-defined symptomatic groups showed significantly higher prevalence of complex type VI lesions (internal: 50.0% versus 14.8%, P < .001; external: 48.7% versus 20.7%, P = .004) and lower prevalence of predominantly calcified type VII lesions (external: 8.1% versus 43.1%, P < .001) compared with asymptomatic groups. CONCLUSIONS:The developed multicontrast MRI-based DLR signature provides a highly accurate and robust tool for the automated identification of symptomatic carotid plaques, underscoring its potential value as a noninvasive tool to guide personalized stroke prevention strategies.

Pancreatic ductal adenocarcinoma (PDAC), one of the deadliest solid malignancies, is often detected at a late and inoperable stage. Retrospective reviews of prediagnostic CT scans, when conducted by expert radiologists aware that the patient later developed PDAC, frequently reveal lesions that were previously overlooked. To help detecting these lesions earlier, we developed an automated system named ePAI (early Pancreatic cancer detection with Artificial Intelligence). It was trained on data from 1,598 patients from a single medical center. In the internal test involving 1,009 patients, ePAI achieved an area under the receiver operating characteristic curve (AUC) of 0.939-0.999, a sensitivity of 95.3%, and a specificity of 98.7% for detecting small PDAC less than 2 cm in diameter, precisely localizing PDAC as small as 2 mm. In an external test involving 7,158 patients across 6 centers, ePAI achieved an AUC of 0.918-0.945, a sensitivity of 91.5%, and a specificity of 88.0%, precisely localizing PDAC as small as 5 mm. Importantly, ePAI detected PDACs on prediagnostic CT scans obtained 3 to 36 months before clinical diagnosis that had originally been overlooked by radiologists. It successfully detected and localized PDACs in 75 of 159 patients, with a median lead time of 347 days before clinical diagnosis. Our multi-reader study showed that ePAI significantly outperformed 30 board-certified radiologists by 50.3% (P < 0.05) in sensitivity while maintaining a comparable specificity of 95.4% in detecting PDACs early and prediagnostic. These findings suggest its potential of ePAI as an assistive tool to improve early detection of pancreatic cancer.

This invited commentary discusses the recent study by Alali et al , published in the World Journal of Gastrointestinal Endoscopy , which investigated the feasibility and safety of endoscopic ultrasound-guided liver biopsy (EUS-LB) for diagnosing parenchymal liver disease. The study demonstrated a high diagnostic yield and a low rate of serious complications, supporting the efficacy of EUS-LB as an alternative to percutaneous liver biopsy. The study also highlighted technical factors that improve tissue acquisition. While commending the multi-center findings and technical insights, we discuss limitations of the retrospective design and modest sample, compare EUS-LB with traditional biopsy modalities, and emphasize the need for larger prospective studies to validate and generalize these results.

Aims Patent foramen ovale (PFO) is associated with a variety of clinical events; however, its association with major adverse cardiovascular events (MACEs) in patients with coronary artery disease (CAD) remains unclear. The objective of our study was to compare the prognosis between patients with and without PFO and to investigate the association between PFO and MACEs in patients with CAD. Methods and results This study retrospectively included 2667 patients who underwent coronary computed tomography angiography (CCTA) for the evaluation of suspected CAD. MACEs were defined as cardiac death, non-fatal myocardial infarction, and ischemic stroke. The primary endpoint was a composite of cardiac death and non-fatal myocardial infarction. The secondary endpoint was defined as ischemic stroke. After a median follow-up of 86 months (interquartile range: 74-99 months), 288 patients experienced MACEs, among whom 203 patients had the primary endpoint and 85 patients had the secondary endpoint. Compared with patients without PFO, patients with PFO had a higher cumulative incidence of MACEs (P < 0.001), as well as higher cumulative incidence rates for both the primary endpoint (P < 0.001) and secondary endpoint (P < 0.001). PFO was significantly associated with MACEs in patients with CAD (HR = 3.82, P < 0.001). In multivariate analysis, PFO remained an independent predictor of MACE in patients with CAD (HR = 2.98, P < 0.001), with consistent results observed in the analyses of both the primary endpoint and secondary endpoint (All P < 0.001). Conclusion PFO is associated with adverse prognosis in patients with CAD, and patients with PFO have a higher risk of MACEs.