The widespread adoption of computed tomography has increased the detection of lung nodules. However, deep learning methods for classification of benign and malignant nodules often fail to comprehensively integrate global and local features, and most of these methods have not been validated through clinical trials. Here we developed DeepFAN, a transformer-based model trained on more than 10,000 pathology-confirmed nodules, and conducted a multireader, multicase clinical trial (Chinese Clinical Trial Registry: ChiCTR2400084624) to evaluate its efficacy in assisting junior radiologists. DeepFAN achieved diagnostic area under the curve (AUC) values of 0.939 (95% CI 0.930-0.948) on an internal test set and 0.954 (95% CI 0.934-0.973) on a clinical trial dataset involving 400 cases across three independent medical institutions. Explainability analysis indicated higher contributions from global than local features. The average performance of 12 readers improved significantly: by 10.9% (95% CI 8.3-13.5%) for AUC, 10.0% (95% CI 8.9-11.1%) for accuracy, 7.6% (95% CI 6.1-9.2%) for sensitivity and 12.6% (95% CI 10.9-14.3%) for specificity (all P < 0.001). Nodule-level interreader diagnostic consistency improved from fair to moderate (overall κ: 0.313 versus 0.421; P = 0.019). These results indicate that DeepFAN can effectively assist junior radiologists and could help to homogenize diagnostic quality and reduce unnecessary follow-up of patients with indeterminate pulmonary nodules.

Giant nodular goiters are hypervascular benign thyroid lesions whose surgical resection carries substantial risks. This study evaluates the feasibility, efficacy, and safety of transarterial chemoembolization (TACE) with bleomycin for symptomatic giant nodular goiter. This single-center retrospective study included patients with giant nodular goiter (≥ 4 cm) treated with TACE using 15,000 U bleomycin and 200–400 μm drug-loaded microspheres between May 2024 and May 2025. Primary outcomes were volumetric reduction and symptom relief. Secondary outcomes included complications and thyroid function changes. Non-parametric paired comparisons were used for statistical analysis. Fifty-one cases (mean age, 58 years [IQR, 42.75–64]) were evaluated. Technical success was achieved in all patients. Median goiter volume decreased from 192.7 cm³ (IQR, 125.4–350.1) to 70.45 cm³ (IQR, 54.04–102.1) at 6-month follow-up (p < 0.0001), representing a median volume reduction rate of 72.13

RATIONALE AND OBJECTIVES:To evaluate the clinical impact of low-dose ultra-high-resolution temporal bone imaging using photon-counting detector CT (PCD-CT) with 100 kVp tin filtration (Sn100 kVp), focusing on radiation dose reduction and image quality compared with conventional energy-integrating detector CT (EID-CT). MATERIALS AND METHODS:Patients with suspected or confirmed temporal bone lesions were prospectively enrolled and underwent PCD-CT scanning. Their prior EID-CT scans were retrospectively retrieved for comparison. PCD-CT was performed at Sn100 kVp, and images were reconstructed at 0.2 mm (PCD-0.2) and 0.6 mm (PCD-0.6) slice thicknesses. The EID-CT scans were acquired at 120 kVp with 0.6 mm reconstruction (EID-0.6). Quantitative parameters including CT attenuation, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured. In addition, qualitative assessment of six normal anatomical structures, preference ranking and radiation dose metrics were performed for comparison. RESULTS:A total of 46 patients (24 females; mean age, 40.37 ± 15.89 years) contributed 55 temporal bone CT image datasets. Compared with EID-CT, PCD-CT achieved a 53 % reduction in radiation dose (effective dose: 0.35 ± 0.05 vs. 0.75 ± 0.12 mSv; p < 0.001). PCD-0.6 demonstrated significantly lower image noise (55.51 ± 7.66 vs. 79.70 ± 13.04 HU; p < 0.001) and higher CNR of vestibule, muscle and bone than in EID-0.6 (all p < 0.001). PCD‑0.2 was preferred over PCD‑0.6, and both PCD reconstructions were preferred over EID‑0.6 for visualization of six anatomical structures and overall image quality (all p < 0.05). CONCLUSION:In this comparison of real-world clinical protocols with multiple varying parameters, the low-dose Sn100 kVp PCD-CT protocol reduced dose by 53 % and improved image quality versus EID-CT. Ultra-thin 0.2 mm reconstruction, despite higher noise, leveraged PCD-CT's high spatial resolution to better depict fine temporal bone details.

OBJECTIVE:To investigate the nationwide prevalence, technical standardization, and reporting practices of prostate MRI and Prostate Imaging-Reporting and Data System (PI-RADS) adoption across China's Mainland. METHODS:A nationwide cross-sectional survey was conducted from May 1 to June 30, 2023. All hospitals with a Department of Radiology in China's Mainland were invited to report their prostate MRI practices for the year 2022. The questionnaire collected information on hospital characteristics, MRI scanner type, scanning protocols, reporting formats, and examination volume. Hospitals were categorized into high- and low-volume institutions based on the 75th percentile of annual prostate MRI examinations. Statistical analyses were performed using the Mann-Whitney U test, χ2 test, or Fisher's exact test, as appropriate. RESULTS:Of the 9,070 hospitals invited, 4,620 were eligible hospitals performing prostate MRI, with 3,919 (84.8%) completed the survey. Most were public, general, and tertiary-level hospitals. The majority used 1.5-T scanners (61.2%) and adopted both biparametric and multiparametric MRI (mpMRI) protocols (60.2%). However, only 58.7% included high b-value DWI, and 55.3% used unstructured reporting formats. Hospitals with higher annual prostate MRI volumes were more likely to use 3.0-T scanners, perform multiparametric MRI, include essential sequences, and adopt PI-RADS reporting. Factors significantly associated with PI-RADS usage included hospital level, economic zone, MRI scanner type, scanning protocol, and examination volume (all P < .05). CONCLUSION:This nationwide survey reveals substantial variability in prostate MRI practices and limited standardization in PI-RADS implementation across China's Mainland, which highlights the need for national quality control efforts to standardize prostate MRI scanning and reporting and improve diagnostic consistency for prostate cancer.

Risk stratification is crucial for outcome comparison and standardization of treatment and follow-up in non-muscle-invasive bladder cancer (NMIBC). To investigate the feasibility of using CT imaging features to evaluate the risk stratification of NMIBC. Data from 168 patients pathologically diagnosed with NMIBC were retrospectively collected. Cases were stratified into low (n = 50), medium (n = 23), and high-risk groups (n = 95) according to the European Association of Urology guidelines. Preoperative CT imaging features were evaluated independently by two radiologists. Interobserver agreement was assessed using kappa and intraclass correlation coefficients. Univariate analysis was performed using Fisher’s exact test, the chi-square test, and ordinal logistic regression. Variables with significant associations were included in the generalized linear model (GLM). The GLM showed that tumor location, number, and long diameter helped evaluate risk stratification. Tumors located in the posterior wall (p = 0.004), side wall (p = 0.001), and ureteral orifice (p = 0.032) had a higher probability of being in the high-risk group. Tumors with longer maximal diameters tended to exhibit a higher risk (p = 0.005). The presence of multiple tumors had a probability of being in a relatively higher risk group than single tumors (p = 0.002). The model’s AUC values for predicting low, medium, and high-risk tumors are 0.83 [95

BACKGROUND:The redistribution of pulmonary blood volume (PBV) across COVID-19 severity levels and its prognostic value for the less pathogenic, predominantly upper respiratory tract-infecting Omicron variant remain unclear. This study investigates PBV distribution patterns and validates its predictive utility for Omicron outcomes. METHODS:This retrospective study enrolled consecutive patients (November 2022-January 2023) with baseline CT and clinical data, followed for six months. Patients were divided into mild/moderate (MM) and severe/critical (SC) groups according to COVID-19 severity. Pre-trained deep learning algorithms quantified total, lobar, and vessel-size-specific PBV. Adjusted multivariable analyses determined odds ratios (OR) for clinical outcomes, and logistic regression models based on PBV were constructed to predict adverse events. RESULTS:Among 921 patients (61 ± 20 years, 460 men), 755 were in the MM group and 166 in the SC group. Compared to MM patients, SC patients showed significantly lower total PBV (259 mL vs. 239 mL, p = 0.002) and redistribution from lower to upper lobes (upper vs. lower; MM, 21% vs. 23%; SC, 23% vs. 18%) and from small-calibre (≤5 mm2, 44% vs. 32%, p < 0.0005) to large-calibre (>10 mm2, 39% vs. 51%, p < 0.0005) vessels. PBV (especially in vessels ≤5 mm2) predicted six-month composite outcomes (OR = 4.66, AUC = 0.79, sensitivity = 92%) and mortality (OR = 3.34, AUC = 0.75, sensitivity = 93%) for the Omicron variant with high sensitivity, but at a higher risk threshold (42%) than that reported for more pathogenic variants in previous publications. CONCLUSIONS:Severe/critical COVID-19 is associated with reduced PBV and its redistribution across lung regions and vessel sizes. PBV retains predictive value for clinical outcomes in the immune-evasive Omicron variant.

AIMS:This study aimed to evaluate the prognostic value of the systemic immune-inflammation index (SII) and prognostic nutritional index (PNI) for overall survival (OS) in colorectal cancer (CRC) patients with liver metastasis after transcatheter arterial chemoembolization (TACE). PATIENTS AND METHODS:A retrospective analysis of 270 CRC patients who underwent TACE was conducted. Baseline comparisons were made between survivors (n = 142) and non-survivors (n = 128) focusing on tumor size, AFP, SII, and PNI. Prognostic factors were analyzed using Cox regression, ROC curves, and Kaplan-Meier analysis. RESULTS:Significant differences in tumor size, AFP, SII, and PNI were found between the two groups. Multivariate Cox regression revealed that larger tumor size (HR = 1.110, p < 0.001), higher AFP (HR = 1.003, p < 0.001), and elevated SII (HR = 1.001, p < 0.001) were associated with poorer OS, while higher PNI (HR = 0.944, p < 0.001) was protective. ROC analysis yielded AUCs of 0.852 for SII and 0.876 for PNI, with a combined model improving to 0.948. Kaplan-Meier showed that high SII (≥1324.165) and low PNI (<40.915) were associated with poorer 3-year OS (p < 0.001). CONCLUSIONS:SII and PNI are valuable prognostic indicators for OS in CRC patients post-TACE. Elevated SII and reduced PNI predict worse outcomes, and their combination enhances survival prediction.

BACKGROUND:Accurate preoperative grading of bladder cancer is important for determining treatment and prognosis. PURPOSE:To investigate the diagnostic efficacy of MR cytometry imaging in differentiating high- and low-grade bladder cancer. STUDY TYPE:Prospective. POPULATION:Sixty-participants (male: 27, mean age: 65 years) with pathologically confirmed bladder cancer (37 high-grade, 23 low-grade). FIELD STRENGTH/SEQUENCE:3.0 T, pulsed gradient spin-echo (PGSE) and oscillating gradient spin-echo (OGSE, 20 and 40 Hz) diffusion-weighted imaging. ASSESSMENT:All tumors were manually delineated independently by two radiologists, and inter-observer agreement was assessed using intraclass correlation coefficient (ICC). Time-dependent apparent diffusion coefficients (ADCs), including OGSE at 20 HZ (ADC20HZ), OGSE at 40 HZ (ADC40HZ), and PGSE (ADCPGSE), and MR cytometry-derived microstructural parameters (cell diameter [ d ], intracellular volume fraction [ v in ], extracellular diffusivity [ D ex ], and cellularity [ ρ ]) were calculated. Histopathological examination of surgical specimens served as the reference standard for tumor grading. STATISTICAL TESTS:Mann-Whitney U test was used for group comparisons. Diagnostic performance was evaluated by logistic regression and receiver operating characteristic (ROC) analysis; area under the ROC curve (AUCs) was compared with the DeLong test. Statistical significance was set at p < 0.05. RESULTS:High-grade tumors showed significantly higher v in (median: 0.31 vs. 0.20), ρ (1.97 vs. 1.33 × 10-2 μm-1), and lower ADCs than low-grade tumors while d (p = 0.85, 95% confidence interval [CI] of mean difference: -0.822 to -0.820) and D ex (p = 0.053, 95% CI of mean difference: 0.025 to 0.352) were not different. v in demonstrated the highest AUC (0.89; 95% CI: 0.80-0.97) among single parameters, and the combined model of v in , D ex , and ADCPGSE achieved the highest diagnostic accuracy (AUC = 0.92; 95% CI: 0.86-0.99). DATA CONCLUSION:MR cytometry noninvasively differentiates high- from low-grade bladder cancer. v in showed good discriminatory performance, and combining v in , D ex , and ADCPGSE further improves preoperative assessment. EVIDENCE LEVEL:1. TECHNICAL EFFICACY:Stage 3: Diagnostic Thinking.

ObjectiveTo explore the clinical value of a multimodal predictive model based on multiparametric magnetic resonance imaging(MRI) radiomics combined with deep learning(DL) features for the preoperative noninvasive assessment of mismatch repair-deficient(MMRd) status in endometrial cancer(EC).MethodsPatients diagnosed with EC at Peking Union Medical College Hospital from January 2015 to December 2021 were retrospectively enrolled and randomly divided into a training set and a validation set at a ratio of 8∶2. Relevant clinical data were collected, and radiomics features and DL features were extracted from preoperative contrast-enhanced T1-weighted imaging(CE-T1WI), fat-suppressed T2-weighted imaging(fs-T2WI), and diffusion-weighted imaging(DWI) sequences. High-dimensional feature selection and dimensionality reduction were performed sequentially using the recursive feature elimination(RFE) algorithm to generate a radiomics score(Rad-score) and a deep learning score(DL-score), respectively. Multivariate logistic regression was utilized to construct a clinical model, a pure radiomics model, a clinical-radiomics model, and an integrated multimodal model incorporating clinical indicators, Rad-score, and DL-score. Model performance was assessed and compared using area under receiver operating characteristic curve(AUC) and DeLong test.ResultsA total of 509 patients were enrolled in this study, comprising 413 in the training cohort and 96 in the validation cohort. Independent predictors: Multivariate analysis indicated that preoperative fasting blood glucose level, histological grade, lymph node metastasis status, Rad-score, and DL-score were all independent significant predictors of MMRd status in EC patients. The integrated multimodal model demonstrated optimal predictive performance with an AUC of 0.699(95% CI: 0.635-0.763) in the training set, which was superior to the clinical model(AUC=0.629, 95% CI: 0.561-0.697) and the pure radiomics model(AUC=0.641, 95% CI: 0.575-0.706). In the validation set, the integrated model maintained good generalizability, achieving an AUC of 0.655(95% CI: 0.535-0.775), and its diagnostic efficacy was higher than that of the clinical model(AUC=0.578, 95% CI: 0.450-0.705) and the pure radiomics model(AUC=0.611, 95% CI: 0.488-0.734). According to the DeLong test, the incorporation of DL features resulted in the clinicalradiomicsdeep learning model performing better than both the clinicalonly model(P=0.027) and the radiomicsonly model(P=0.044) in the training cohort.ConclusionsThe initially developed clinical-radiomics-deep learning model exhibits a certain predictive potential for the MMRd status in patients with EC. The inclusion of DL features may help complement the limitations of traditional evaluations, offering a preliminary radiological reference for preoperative non-invasive screening. However, given the current diagnostic performance, its overall accuracy and clinical generalizability warrant further validation in multi-center, large-sample external cohort studies.

Accurate preoperative assessment of internal carotid artery (ICA) invasion is crucial in managing glomus jugulare tumors. This study evaluated the efficacy of contrast-enhanced 3D BRAin VOlume (BRAVO) imaging compared to enhanced fast spin-echo (FSE) T1-weighted imaging and enhanced computed tomography (CT). Retrospective analysis was performed on imaging data from surgically confirmed glomus jugulare tumors, including temporal bone enhanced BRAVO, enhanced T1-weighted FSE, and temporal bone enhanced CT sequences. ICA encasement and stenosis by tumor were graded and compared based on intraoperative assessment. According to Fisch criteria, the preoperative image C-type based on BRAVO, FSE and CT were separately and compared with surgical C-type (gold standard). Among 21 patients, For image Fisch C-type, BRAVO showed excellent agreement with surgical C-type (κ = 1.000, P < 0.001), outperforming enhanced FSE (κ = 0.561) and CT (κ = 0.702). For ICA encasement, BRAVO had moderate agreement (κ = 0.513), slightly better than enhanced FSE (κ = 0.431) but inferior to enhanced CT (κ = 0.648). For ICA stenosis, BRAVO (κ = 0.588) surpassed enhanced FSE (κ = 0.339) but was less accurate than enhanced CT (κ = 0.716). Enhanced BRAVO and temporal bone enhanced CT are complementary for assessing ICA involvement in glomus jugulare tumors, offering superior accuracy over conventional FSE imaging.

BACKGROUND. In patients undergoing cardiac MRI after ST-elevation myocardial infarction (STEMI), microvascular obstruction (MVO) often decreases in size between early gadolinium enhancement (EGE) and late gadolinium enhancement (LGE) images. Persistence of MVO between these images may indicate greater microvascular injury. OBJECTIVE. The purpose of this study was to evaluate the prognostic utility of measures of MVO persistence between EGE and LGE images in patients undergoing cardiac MRI after STEMI. METHODS. This retrospective study included 584 patients (mean age, 60 ± 11 [SD] years; 507 men, 77 women) enrolled in the multicenter Early Assessment of Myocardial Tissue Characteristics by CMR in STEMI (EARLY-MYO-CMR) registry from June 2017 to March 2023 who underwent cardiac MRI, including EGE and LGE imaging, within 1 week after percutaneous intervention for STEMI. Using semiautomated software, a radiologist measured MVO volumes (i.e., hypointense cores within hyperenhancing territories) on EGE and LGE images. The MVO persistence index was calculated as the ratio of MVO volume between LGE and EGE images. Patients were assigned to one of four MVO patterns (none [absent on EGE and LGE images]; reversible [present on EGE, absent on LGE images]; partially reversible [present on both images, persistence index < 40%]; persistent [present on both images, persistence index ≥ 40%]). Cox regression models were performed to predict major adverse cardiovascular event (MACE, including all-cause death, heart failure hospitalization, and reinfarction), adjusted for established clinical and MRI risk factors including static EGE and LGE MVO volumes. Propensity-score matching (PSM) analysis was performed between partially reversible and persistent MVO patterns. RESULTS. No MVO, reversible MVO, partially reversible MVO, and persistent MVO patterns were observed in 157, 133, 195, and 99 patients, respectively. In separate models, increased risk of MACE (n = 103) showed independent associations with MVO persistence index (HR per 10% increase: 1.36; p < .001) and persistent MVO pattern (HR vs no MVO pattern: 5.14; p < .001). Heart failure hospitalizations and reinfarctions also showed significant independent associations with MVO persistence index (HR: 1.26-1.45) and persistent MVO pattern (HR: 7.06-11.16). In PSM analysis (99 patients per group), MACE was independently associated with the persistent MVO pattern relative to the partially reversible MVO pattern (HR: 3.33; p = .004). CONCLUSION. MVO persistence between EGE and LGE images was a significant independent predictor of MACE. CLINICAL IMPACT. Measures of MVO dynamics provide prognostic information beyond standard static MVO measures.

The accurate identification of children with refractory Mycoplasma pneumoniae pneumonia (RMPP) remains challenging. This study aimed to develop a transformer-based model utilizing clinically indicated chest computed tomography (CT) to stratify pediatric RMPP risk at a critical decision point. Non-contrast chest CT data from a multicenter retrospective cohort of 1224 pediatric patients with Mycoplasma pneumoniae pneumonia who underwent clinically indicated CT were used to develop a transformer-based deep learning framework (trans-DLF). The primary cohort comprised training (n = 506), validation (n = 140), and internal testing (n = 139) cohorts, with two independent external cohorts (n = 331 and n = 108) used to evaluate generalizability. Model performance was assessed by the area under the receiver operating characteristic curve (AUC) and compared against a three-dimensional convolutional neural network (3D-CNN), a clinical model, and a multimodal nomogram. Interpretability was examined using gradient-weighted class activation mapping (Grad-CAM). The median age was 6.83 years (interquartile range, 5.0–8.6 years), and 609 (49.8

BACKGROUND:Accurate prediction of early recurrence (ER) after radical resection remains a critical challenge in pancreatic ductal adenocarcinoma (PDAC). This study aimed to develop and validate an integrated radiomic-pathology (Rad-Path) model for ER prediction and to elucidate its underlying biological mechanisms. METHODS:A retrospective cohort of 225 PDAC patients who underwent R0 resection was included. Preoperative CT images and whole-slide images (WSI) were collected for the extraction of radiomic features and computational pathology features. Selected features were used to develop 11 distinct machine learning models. The SHapley Additive exPlanations (SHAP) algorithm was employed to evaluate feature importance. Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) were performed on prospectively collected specimens. RESULTS:The final Rad-Path model achieved AUCs of 0.851 and 0.814 in the internal and external validation cohorts, respectively. The predicted ER group was specifically linked to the enrichment of fibroblasts and pancreatic stellate cells, as well as dysregulation in extracellular matrix (ECM)-related pathways. This finding was validated histopathologically, as predicted ER patients predominantly displayed a "reactive-dominant" phenotype marked by abundant activated fibroblasts and ECM deposition. CONCLUSION:Our study offers a high-performance predictive model for ER in PDAC and establishes ECM remodeling as a key biological mechanism underlying the predictions.

Pelvic lipomatosis is a rare benign disease characterized by excessive deposition of adipose tissue in the pelvis. The pelvic organs are compressed by excessive adipose tissue, causing a variety of nonspecific symptoms. Therefore, the diagnosis of this disease mainly depends on imaging examinations. This review summarizes the imaging characteristics of the disease and recent research advances, aiming to support more accurate diagnosis and reduce misdiagnosis and missed diagnosis in clinical practice.

Pancreatic masses present significant challenges in clinical management due to their diverse manifestations and inherent complexity. Dual-phase contrast-enhanced CT is essential for accurate diagnosis, yet widely adopted segmentation methods rely on image registration, which compromises both precision and efficiency. In this study, we introduce a novel architecture that utilizes a cross-attention mechanism for selective feature integration across different phases, achieving registration-free dual-phase segmentation of the pancreas and pancreatic masses. Our model incorporates a dual-path encoder with symmetrical branches specifically designed for the arterial and portal venous phases, where weight-shared cross-attention modules perform symmetrical feature selection and alignment, obviating explicit registration. We further design a progressive fusion decoder that incrementally merges features from both branches through multiple cross-attention modules, ensuring optimal utilization of information from both imaging phases throughout the decoding process. Extensive evaluations on one internal and three external datasets demonstrate that our approach not only outperforms previous registration-dependent methods in accuracy (Dice: 81.86% vs 76.68%) but also improves inference speeds (10.55s vs 130.07s per scan), setting new benchmarks in the field. Additional comparative experiments underscore the efficacy and robustness of our symmetrical fusion framework, confirming its potential as a superior alternative to conventional techniques.

Purpose: Pancreatic ductal adenocarcinoma (PDAC) shows marked survival heterogeneity after curative resection, and practical genomic tools for postoperative risk stratification are lacking. We developed and validated a multi-gene mutation panel and microarray assay for prognostic stratification in resected PDAC. Methods: Among 180 patients who underwent R0 resection for PDAC (2015–2019), 169 passed specimen quality control. Ten longest- and ten shortest-survival patients underwent whole-exome sequencing (~200×). Through fold-change ranking, pathway enrichment, and literature curation, 97 candidate genes were subjected to deep targeted sequencing (~1,000×) in all 169 tumors. A 32-gene signature was derived by integrating Cox regression, pathway enrichment, and protein–protein interaction network analyses, then translated into an oligonucleotide microarray and validated in 69 independent patients (surgery 2013–2014). Results: The discovery cohort had a median overall survival (OS) of 28 months. Conventional factors including lymph node status and N stage were not significantly associated with OS, and CA 19-9 discriminated only the ultra-poor prognosis group (<12 months; P=0.963 among remaining groups). The 32-gene signature centered on KRAS, TP53, and FAS hubs spanning nine pathways. In the validation cohort, mutation-score cut-offs delineated four strata with median OS of 7, 15, 40, and 66 months (P<0.001), achieving 75.4% overall and 100% adjacent-category accuracy. Conclusions: A 32-gene mutation panel and microarray assay enable practical, mutation-based postoperative prognostic stratification in PDAC, complementing conventional factors for individualized management.

Background and aims Covert MRI markers of cerebral small vessel disease (CSVD) can coexist with large artery atherosclerosis. We aimed to explore whether the spatial distributions of these markers were diverse in people with or without intracranial artery stenosis (ICAS).Methods This cross-sectional analysis included 1206 stroke-free participants (aged 55.69±9.27, 62.94% female) with brain MRI and MR angiography from community-based Shunyi cohort. We analysed the relationships between ICAS and CSVD markers. We also compared the probability maps of lacunes, cerebral microbleeds (CMB), white matter hyperintensities (WMH) and cortex morphology at a voxel/vertex-wise level in groups with and without ICAS.Results ICAS increased the risk of lacunes by 2.99-fold (95% CI 1.99 to 4.50, p<0.001), lacunes ≥3 by 5.32 times (95% CI 2.76 to 10.28, p<0.001), correlated with WMH volume (β=0.332, SE=0.059, p<0.001), WMH Fazekas scores ≥5 (OR 4.50, 95% CI 2.44 to 8.29, p<0.001) and brain parenchymal fraction (β=−0.012, SE=0.002, p<0.001), but not with CMB. ICAS is associated with lacunes in the corresponding blood supply area. Lacunes that coexist with ICAS were prone in basal ganglia, while the lacunes without ICAS appeared in centrum semiovale more often. WMH with ICAS was prone to present in deep white matter involving the bilateral pyramidal tracts and superior thalamic radiation. People with ICAS were susceptible to worse cortical atrophy of right superior frontal and left rostral anterior cingulate. No obvious distributional differences were found for CMB between the two groups.Conclusions Since ICAS may be involved in the upstream pathogenesis of lacunes, white matter lesions and cortical atrophy, the impact of ICAS should not be ignored when evaluating MRI markers of CSVD.

This study investigates the link between CT-measured fat area and patient outcomes following chemoradiotherapy for locally advanced cervical cancer. It assesses the reliability of CT fat area as a prognostic indicator. The retrospective study included 129 cervical cancer patients with over 36 months of follow-up. All underwent abdominal CT and pelvic MRI before treatment. Clinical factors (age, BMI, tumor stage, pathology) were recorded. Fat areas—total (TFA), visceral (VFA), and subcutaneous (SFA)—were measured at the L3-L4 level via CT. MRI evaluated tumor size, parametrial invasion, lymph node score, and tumor extension to adjacent organs. The study’s endpoints were tumor progression and mortality, with progression-free survival (PFS) and overall survival (OS) determined using Kaplan-Meier method and compared with the Log-rank test. A Cox model analyzed the impact of clinical and imaging variables on survival. Patients with TFA ≤ 150 cm² or > 300 cm² experienced poorer PFS and OS compared to those with 150 cm² < TFA ≤ 300 cm². VFA ≤ 70 cm² correlated with worse OS, and SFA > 200 cm² with poorer PFS. In univariable analysis, significant factors included pathology type, parametrial invasion, lymph node score, and tumor extension (all p < 0.05). Multivariable analysis identified ultra-low/high TFA and high lymph node score as independent predictors of poor PFS and OS. Abdominal fat area, measured by CT, is significantly associated with disease prognosis in cervical cancer. Both ultra-low and ultra-high fat areas indicate worse outcomes, suggesting that CT measurements could be a reliable predictive tool for disease outcomes.

OBJECTIVE:Clonal hematopoiesis of indeterminate potential (CHIP) is an emerging risk factor for cardio-cerebrovascular diseases. This study aimed to investigate CHIP's association with cerebrovascular or glymphatic changes in a community-based population. METHODS:This study examined Chinese community cohort participants. CHIP mutations were identified through whole-exome sequencing. Intracranial arterial stenosis, silent brain infarcts, cerebral small vessel disease markers, and diffusion along the perivascular space index were identified by magnetic resonance imaging. The correlation between CHIP and neuroimaging outcomes was investigated through univariate and multivariate logistic/linear regression. The multivariate regression model was adjusted for cerebrovascular disease risk factors, including age, sex, body mass index, smoking status, hypertension, diabetes, and hyperlipidemia. RESULTS:In total, 18.2% (224 out of 1,229) participants were identified as carriers of CHIP mutations. The prevalence of CHIP generally increases with age (p = 0.009). After adjusting for vascular risk factors using multivariate regression, CHIP mutations were found to be significantly associated with increased odds of large magnetic resonance imaging-defined infarcts (>15 mm; OR 3.20; 95% CI 1.18 to 8.43; p = 0.018), inversely associated with diffusion along the perivascular space (β = -0.02; 95% CI -0.04 to 0; p = 0.034), and showed a borderline association with intracranial arterial stenosis (OR 1.52; 95% CI 0.99 to 2.30; p = 0.053). Notably, no statistically significant correlations were observed between CHIP and cerebral small vessel disease markers or brain atrophy measures. INTERPRETATION:CHIP was significantly associated with glymphatic dysfunction and large infarcts, and marginally associated with intracranial arterial stenosis. Further research is needed to elucidate the pathophysiology linking CHIP to cerebral covert changes. ANN NEUROL 2025;98:826-836.

To assess the effect of the combination of deep learning reconstruction (DLR) and time-resolved maximum intensity projection (tMIP) or time-resolved average (tAve) post-processing method on image quality of CTA derived from low-dose cerebral CTP. Thirty patients underwent regular dose CTP (Group A) and other thirty with low-dose (Group B) were retrospectively enrolled. Group A were reconstructed with hybrid iterative reconstruction (R-HIR). In Group B, four image datasets of CTA were gained: L-HIR, L-DLR, L-DLRtMIP and L-DLRtAve. The CT attenuation, image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and subjective images quality were calculated and compared. The Intraclass Correlation (ICC) between CTA and MRA of two subgroups were calculated. The low-dose group achieved reduction of radiation dose by 33