Alarmin S100A8 imparts chemoresistance of esophageal cancer by reprogramming cancer-associated fibroblasts.

Chemotherapy remains the first-line treatment for advanced esophageal cancer. However, durable benefits are achieved by only a limited subset of individuals due to the elusive chemoresistance. Here, we utilize patient-derived xenografts (PDXs) from esophageal squamous-cell carcinoma to investigate chemoresistance mechanisms in preclinical settings. We observe that activated cancer-associated fibroblasts (CAFs) are enriched in the tumor microenvironment of PDXs resistant to chemotherapy. Mechanistically, we reveal that cancer-cell-derived S100A8 triggers the intracellular RhoA-ROCK-MLC2-MRTF-A pathway by binding to the CD147 receptor of CAFs, inducing CAF polarization and leading to chemoresistance. Therapeutically, we demonstrate that blocking the S100A8-CD147 pathway can improve chemotherapy efficiency. Prognostically, we found the S100A8 levels in peripheral blood can serve as an indicator of chemotherapy responsiveness. Collectively, our study offers a comprehensive understanding of the molecular mechanisms underlying chemoresistance in esophageal cancer and highlights the potential value of S100A8 in the clinical management of esophageal cancer.

Maximizing the Value of Real-World Data and Real-World Evidence to Accelerate Healthcare Transformation in China: Summary of External Advisory Committee Meetings.

Use of real-world data (RWD) is gaining wide attention. To bridge the gap between diverse healthcare stakeholders and to leverage the impact of Chinese real-world evidence (RWE) globally, a multi-stakeholder External Advisory Committee (EAC) and EAC meetings were initiated, aiming to elucidate the current and evolving RWD landscape in China, articulate the values of RWE in ensuring Chinese patients' equitable access to affordable medicines and solutions, and identify strategic opportunities and partnerships for expansion of RWE generation in China. Chinese and international experts who are clinicians and academic researchers were selected as EAC members based on their professional background and familiarity with RWD/RWE. Three EAC meetings were held quarterly in 2023. Various topics were presented and discussed for insights and suggestions. Nine experts from China, one from South Korea, and two from Europe were selected as EAC members and attended these meetings. Experts' presentations were summarized by theme, including the RWD landscape and RWE enablement in China, as well as global development of a patient-centric ecosystem. Experts' insights and suggestions on maximizing the RWD/RWE value to accelerate healthcare transformation in China were collected. We concluded that though data access, sharing, and quality are still challenging, RWD is developing to support evidence generation in the medicinal product lifecycle, inform clinical practice, and empower patient management in China. RWD/RWE creates value, accelerates healthcare transformation, and improves patient outcomes. Fostering a patient-centric ecosystem across healthcare stakeholders and maintaining global partnerships and collaboration are essential for unlocking the power of RWD/RWE.

Multiomics and blood-based biomarkers of moyamoya disease: protocol of Moyamoya Omics Atlas (MOYAOMICS).

BACKGROUND: Moyamoya disease (MMD) is a rare and complex cerebrovascular disorder characterized by the progressive narrowing of the internal carotid arteries and the formation of compensatory collateral vessels. The etiology of MMD remains enigmatic, making diagnosis and management challenging. The MOYAOMICS project was initiated to investigate the molecular underpinnings of MMD and explore potential diagnostic and therapeutic strategies. METHODS: The MOYAOMICS project employs a multidisciplinary approach, integrating various omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, to comprehensively examine the molecular signatures associated with MMD pathogenesis. Additionally, we will investigate the potential influence of gut microbiota and brain-gut peptides on MMD development, assessing their suitability as targets for therapeutic strategies and dietary interventions. Radiomics, a specialized field in medical imaging, is utilized to analyze neuroimaging data for early detection and characterization of MMD-related brain changes. Deep learning algorithms are employed to differentiate MMD from other conditions, automating the diagnostic process. We also employ single-cellomics and mass cytometry to precisely study cellular heterogeneity in peripheral blood samples from MMD patients. CONCLUSIONS: The MOYAOMICS project represents a significant step toward comprehending MMD's molecular underpinnings. This multidisciplinary approach has the potential to revolutionize early diagnosis, patient stratification, and the development of targeted therapies for MMD. The identification of blood-based biomarkers and the integration of multiple omics data are critical for improving the clinical management of MMD and enhancing patient outcomes for this complex disease.

Genomic alterations driving precancerous to cancerous lesions in esophageal cancer development.

Esophageal squamous cell carcinoma (ESCC) develops through a series of increasingly abnormal precancerous lesions. Previous studies have revealed the striking differences between normal esophageal epithelium and ESCC in copy number alterations (CNAs) and mutations in genes driving clonal expansion. However, due to limited data on early precancerous lesions, the timing of these transitions and which among them are prerequisites for malignant transformation remained unclear. Here, we analyze 1,275 micro-biopsies from normal esophagus, early and late precancerous lesions, and esophageal cancers to decipher the genomic alterations at each stage. We show that the frequency of TP53 biallelic inactivation increases dramatically in early precancerous lesion stage while CNAs and APOBEC mutagenesis substantially increase at late stages. TP53 biallelic loss is the prerequisite for the development of CNAs of genes in cell cycle, DNA repair, and apoptosis pathways, suggesting it might be one of the earliest steps initiating malignant transformation.

Collagen 1-mediated CXCL1 secretion in tumor cells activates fibroblasts to promote radioresistance of esophageal cancer.

Esophageal squamous-cell carcinoma (ESCC) is commonly treated with radiotherapy; however, radioresistance hinders its clinical effectiveness, and the underlying mechanism remains elusive. Here, we develop patient-derived xenografts (PDXs) from 19 patients with ESCC to investigate the mechanisms driving radioresistance. Using RNA sequencing, cytokine arrays, and single-cell RNA sequencing, we reveal an enrichment of cancer-associated fibroblast (CAF)-derived collagen type 1 (Col1) and tumor-cell-derived CXCL1 in non-responsive PDXs. Col1 not only promotes radioresistance by augmenting DNA repair capacity but also induces CXCL1 secretion in tumor cells. Additionally, CXCL1 further activates CAFs via the CXCR2-STAT3 pathway, establishing a positive feedback loop. Directly interfering with tumor-cell-derived CXCL1 or inhibiting the CXCL1-CXCR2 pathway effectively restores the radiosensitivity of radioresistant xenografts in vivo. Collectively, our study provides a comprehensive understanding of the molecular mechanisms underlying radioresistance and identifies potential targets to improve the efficacy of radiotherapy for ESCC.

Albumosomes formed by cytoplasmic pre-folding albumin maintain mitochondrial homeostasis and inhibit nonalcoholic fatty liver disease.

Hepatic mitochondrial dysfunction contributes to the progression of nonalcoholic fatty liver disease (NAFLD). However, the factors that maintain mitochondrial homeostasis, especially in hepatocytes, are largely unknown. Hepatocytes synthesize various high-level plasma proteins, among which albumin is most abundant. In this study, we found that pre-folding albumin in the cytoplasm is completely different from folded albumin in the serum. Mechanistically, endogenous pre-folding albumin undergoes phase transition in the cytoplasm to form a shell-like spherical structure, which we call the "albumosome". Albumosomes interact with and trap pre-folding carnitine palmitoyltransferase 2 (CPT2) in the cytoplasm. Albumosomes control the excessive sorting of CPT2 to the mitochondria under high-fat-diet-induced stress conditions; in this way, albumosomes maintain mitochondrial homeostasis from exhaustion. Physiologically, albumosomes accumulate in hepatocytes during murine aging and protect the livers of aged mice from mitochondrial damage and fat deposition. Morphologically, mature albumosomes have a mean diameter of 4µm and are surrounded by heat shock protein Hsp90 and Hsp70 family proteins, forming a larger shell. The Hsp90 inhibitor 17-AAG promotes hepatic albumosomal accumulation in vitro and in vivo, through which suppressing the progression of NAFLD in mice.

Corrigendum: Development and validation of a model for predicting the risk of brain arteriovenous malformation rupture based on three-dimensional morphological features.

[This corrects the article DOI: 10.3389/fneur.2022.979014.].

CT Angiography Radiomics Combining Traditional Risk Factors to Predict Brain Arteriovenous Malformation Rupture: a Machine Learning, Multicenter Study.

This study aimed to develop a machine learning model for predicting brain arteriovenous malformation (bAVM) rupture using a combination of traditional risk factors and radiomics features. This multicenter retrospective study enrolled 586 patients with unruptured bAVMs from 2010 to 2020. All patients were grouped into the hemorrhage (n = 368) and non-hemorrhage (n = 218) groups. The bAVM nidus were segmented on CT angiography images using Slicer software, and radiomic features were extracted using Pyradiomics. The dataset included a training set and an independent testing set. The machine learning model was developed on the training set and validated on the testing set by merging numerous base estimators and a final estimator based on the stacking method. The area under the receiver operating characteristic (ROC) curve, precision, and the f1 score were evaluated to determine the performance of the model. A total of 1790 radiomics features and 8 traditional risk factors were contained in the original dataset, and 241 features remained for model training after L1 regularization filtering. The base estimator of the ensemble model was Logistic Regression, whereas the final estimator was Random Forest. In the training set, the area under the ROC curve of the model was 0.982 (0.967-0.996) and 0.893 (0.826-0.960) in the testing set. This study indicated that radiomics features are a valuable addition to traditional risk factors for predicting bAVM rupture. In the meantime, ensemble learning can effectively improve the performance of a prediction model.

Comprehensive Analyses Reveal Effects on Tumor Immune Infiltration and Immunotherapy Response of APOBEC Mutagenesis and Its Molecular Mechanisms in Esophageal Squamous Cell Carcinoma.

The apolipoprotein B mRNA editing enzyme catalytic polypeptide (APOBEC) mutagenesis is prevalent in esophageal squamous cell carcinoma (ESCC). However, the functional role of APOBEC mutagenesis has yet to be fully delineated. To address this, we collect matched multi-omics data of 169 ESCC patients and evaluate characteristics of immune infiltration using multiple bioinformatic approaches based on bulk and single-cell RNA sequencing (scRNA-seq) data and verified by functional assays. We find that APOBEC mutagenesis prolongs overall survival (OS) of ESCC patients. The reason for this outcome is probably due to high anti-tumor immune infiltration, immune checkpoints expression and immune related pathway enrichment, such as interferon (IFN) signaling, innate and adaptive immune system. The elevated AOBEC3A (A3A) activity paramountly contributes to the footprints of APOBEC mutagenesis and is first discovered to be transactivated by FOSL1. Mechanistically, upregulated A3A exacerbates cytosolic double-stranded DNA (dsDNA) accumulation, thus stimulating cGAS-STING pathway. Simultaneously, A3A is associated with immunotherapy response which is predicted by TIDE algorithm, validated in a clinical cohort and further confirmed in mouse models. These findings systematically elucidate the clinical relevance, immunological characteristics, prognostic value for immunotherapy and underlying mechanisms of APOBEC mutagenesis in ESCC, which demonstrate great potential in clinical utility to facilitate clinical decisions.

Epithelial cells activate fibroblasts to promote esophageal cancer development.

Esophageal squamous-cell carcinoma (ESCC) develops through multistage epithelial cancer formation, i.e., from normal epithelium, low- and high-grade intraepithelial neoplasia to invasive carcinoma. However, how the precancerous lesions progress to carcinoma remains elusive. Here, we report a comprehensive single-cell RNA sequencing and spatial transcriptomic study of 79 multistage esophageal lesions from 29 patients with ESCC. We reveal a gradual and significant loss of ANXA1 expression in epithelial cells due to its transcription factor KLF4 suppression along the lesion progression. We demonstrate that ANXA1 is a ligand to formyl peptide receptor type 2 (FPR2) on fibroblasts that maintain fibroblast homeostasis. Loss of ANXA1 leads to uncontrolled transformation of normal fibroblasts into cancer-associated fibroblasts (CAFs), which can be enhanced by secreted TGF-ß from malignant epithelial cells. Given the role of CAFs in cancer, our study underscores ANXA1/FPR2 signaling as an important crosstalk mechanism between epithelial cells and fibroblasts in promoting ESCC.

Development and validation of a model for predicting the risk of brain arteriovenous malformation rupture based on three-dimensional morphological features.

Objective: Brain arteriovenous malformation (bAVM) is an important reason for intracranial hemorrhage. This study aimed at developing and validating a model for predicting bAVMs rupture by using three-dimensional (3D) morphological features extracted from Computed Tomography (CT) angiography. Materials and methods: The prediction model was developed in a cohort consisting of 412 patients with bAVM between January 2010 and December 2020. All cases were partitioned into training and testing sets in the ratio of 7:3. Features were extracted from the 3D model built on CT angiography. Logistic regression was used to develop the model, with features selected using L1 Regularization, presented with a nomogram, and assessed with calibration curve, receiver operating characteristic (ROC) curve and decision curve analyze (DCA). Results: Significant variations in associated aneurysm, deep located, number of draining veins, type of venous drainage, deep drainage, drainage vein entrance diameter (Dv), type of feeding arteries, middle cerebral artery feeding, volume, Feret diameter, surface area, roundness, elongation, mean density (HU), and median density (HU) were found by univariate analysis (p < 0.05). The prediction model consisted of associated aneurysm, deep located, number of draining veins, deep drainage, Dv, volume, Feret diameter, surface area, mean density, and median density. The model showed good discrimination, with a C-index of 0.873 (95% CI, 0.791-0.931) in the training set and 0.754 (95% CI, 0.710-0.795) in the testing set. Conclusions: This study presented 3D morphological features could be conveniently used to predict hemorrhage from unruptured bAVMs.

Development and Validation of a Deep Learning Model for Brain Tumor Diagnosis and Classification Using Magnetic Resonance Imaging.

Importance: Deep learning may be able to use patient magnetic resonance imaging (MRI) data to aid in brain tumor classification and diagnosis. Objective: To develop and clinically validate a deep learning system for automated identification and classification of 18 types of brain tumors from patient MRI data. Design, Setting, and Participants: This diagnostic study was conducted using MRI data collected between 2000 and 2019 from 37 871 patients. A deep learning system for segmentation and classification of 18 types of intracranial tumors based on T1- and T2-weighted images and T2 contrast MRI sequences was developed and tested. The diagnostic accuracy of the system was tested using 1 internal and 3 external independent data sets. The clinical value of the system was assessed by comparing the tumor diagnostic accuracy of neuroradiologists with vs without assistance of the proposed system using a separate internal test data set. Data were analyzed from March 2019 through February 2020. Main Outcomes and Measures: Changes in neuroradiologist clinical diagnostic accuracy in brain MRI scans with vs without the deep learning system were evaluated. Results: A deep learning system was trained among 37 871 patients (mean [SD] age, 41.6 [11.4] years; 18 519 women [48.9%]). It achieved a mean area under the receiver operating characteristic curve of 0.92 (95% CI, 0.84-0.99) on 1339 patients from 4 centers' data sets in diagnosis and classification of 18 types of tumors. Higher outcomes were found compared with neuroradiologists for accuracy and sensitivity and similar outcomes for specificity (for 300 patients in the Tiantan Hospital test data set: accuracy, 73.3% [95% CI, 67.7%-77.7%] vs 60.9% [95% CI, 46.8%-75.1%]; sensitivity, 88.9% [95% CI, 85.3%-92.4%] vs 53.4% [95% CI, 41.8%-64.9%]; and specificity, 96.3% [95% CI, 94.2%-98.4%] vs 97.9%; [95% CI, 97.3%-98.5%]). With the assistance of the deep learning system, the mean accuracy of neuroradiologists among 1166 patients increased by 12.0 percentage points, from 63.5% (95% CI, 60.7%-66.2%) without assistance to 75.5% (95% CI, 73.0%-77.9%) with assistance. Conclusions and Relevance: These findings suggest that deep learning system-based automated diagnosis may be associated with improved classification and diagnosis of intracranial tumors from MRI data among neuroradiologists.

Extracellular Hsp90α Supports the ePKM2-GRP78-AKT Axis to Promote Tumor Metastasis.

Tumor-secreted proteins can provide numerous molecular targets for cancer diagnosis and treatment. Of note, pyruvate kinase M2 (PKM2) is secreted by tumor cells to promote malignant progression, while its regulatory mechanism or the interacting network remains uncovered. In the present study, we identified extracellular heat shock protein 90 alpha (eHsp90α) as one potential interacting protein of ePKM2 by mass spectrometry (MS), which was further verified by pull-down and co-immunoprecipitation analysis. Later, we found that eHsp90α enhanced the effect of ePKM2 on migration and invasion of lung cancer cells. Blocking of Hsp90α activity, on the other hand, attenuated tumor migration or invasion induced by ePKM2. Eventually, the in vivo role of Hsp90α in regulating ePKM2 activity was validated by the mouse xenograft tumor model. Mechanistically, we found that eHsp90α binds to and stabilizes ePKM2 to protect it from degradation in the extracellular environment. Besides, eHsp90α promoted the interaction of ePKM2 with cell surface receptor GRP78, which leads to the activation of the ePKM2/GRP78/AKT axis. Collectively, we unraveled the novel molecular mechanism of eHsp90α in regulating ePKM2 activity during tumor progression, which is beneficial for the development of new treatments against lung cancer.

Computational Identification of Preneoplastic Cells Displaying High Stemness and Risk of Cancer Progression.

Evidence points toward the differentiation state of cells as a marker of cancer risk and progression. Measuring the differentiation state of single cells in a preneoplastic population could thus enable novel strategies for early detection and risk prediction. Recent maps of somatic mutagenesis in normal tissues from young healthy individuals have revealed cancer driver mutations, indicating that these do not correlate well with differentiation state and that other molecular events also contribute to cancer development. We hypothesized that the differentiation state of single cells can be measured by estimating the regulatory activity of the transcription factors (TF) that control differentiation within that cell lineage. To this end, we present a novel computational method called CancerStemID that estimates a stemness index of cells from single-cell RNA sequencing data. CancerStemID is validated in two human esophageal squamous cell carcinoma (ESCC) cohorts, demonstrating how it can identify undifferentiated preneoplastic cells whose transcriptomic state is overrepresented in invasive cancer. Spatial transcriptomics and whole-genome bisulfite sequencing demonstrated that differentiation activity of tissue-specific TFs was decreased in cancer cells compared with the basal cell-of-origin layer and established that differentiation state correlated with differential DNA methylation at the promoters of these TFs, independently of underlying NOTCH1 and TP53 mutations. The findings were replicated in a mouse model of ESCC development, and the broad applicability of CancerStemID to other cancer-types was demonstrated. In summary, these data support an epigenetic stem-cell model of oncogenesis and highlight a novel computational strategy to identify stem-like preneoplastic cells that undergo positive selection. SIGNIFICANCE: This study develops a computational strategy to dissect the heterogeneity of differentiation states within a preneoplastic cell population, allowing identification of stem-like cells that may drive cancer progression.

TP53 mutations upregulate RCP expression via Sp1/3 to drive lung cancer progression.

Mutant p53 (mtp53) can exert cancer-promoting activities via "gain-of-function", which has become a popular research target. Although lots of researchers focus on the tumor-suppressor role for p53, the regulation of mutant p53 remains unknown. Here, we report a mechanism by which mtp53 regulate the transcription of Rab coupling protein (RCP) to influence lung cancer behavior. First, we show that RCP is specifically expressed at high levels in lung cancer tissues and cells, and RCP knockout suppresses tumor growth and metastasis. Further mass spectrometry and functional analysis identify that Sp1, Sp3 and Stat3 are the transcriptional activators of RCP. Moreover, p53 is involved in modulating RCP expression in an Sp1/3 dependent manner. Mechanistically, in contrast to wild-type p53 suppression of RCP transcription by decreasing Sp1/3 proteins, TP53 mutations have changed on Sp1/3 expression via "loss-of-function". Surprisingly, the DNA contact mutants of p53 further robustly enhance their binding ability with Sp1/3 to drive RCP expression through the "gain-of-function" activity. Collectively, we reveal a mechanism by which p53 regulating the transcription of RCP to influence lung cancer progression, which provides new insights for treating p53 mutant lung cancer.

CstF64-Induced Shortening of the BID 3'UTR Promotes Esophageal Squamous Cell Carcinoma Progression by Disrupting ceRNA Cross-talk with ZFP36L2.

The majority of human genes have multiple polyadenylation sites, which are differentially used through the process of alternative polyadenylation (APA). Dysregulation of APA contributes to numerous diseases, including cancer. However, specific genes subject to APA that impact oncogenesis have not been well characterized, and many cancer APA landscapes remain underexplored. Here, we used dynamic analyses of APA from RNA-seq (DaPars) to define both the 3'UTR APA profile in esophageal squamous cell carcinoma (ESCC) and to identify 3'UTR shortening events that may drive tumor progression. In four distinct squamous cell carcinoma datasets, BID 3'UTRs were recurrently shortened and BID mRNA levels were significantly upregulated. Moreover, system correlation analysis revealed that CstF64 is a candidate upstream regulator of BID 3'UTR length. Mechanistically, a shortened BID 3'UTR promoted proliferation of ESCC cells by disrupting competing endogenous RNA (ceRNA) cross-talk, resulting in downregulation of the tumor suppressor gene ZFP36L2. These in vitro and in vivo results were supported by human patient data whereby 3'UTR shortening of BID and low expression of ZFP36L2 are prognostic factors of survival in ESCC. Collectively, these findings demonstrate that a key ceRNA network is disrupted through APA and promotes ESCC tumor progression.Significance: High-throughput analysis of alternative polyadenylation in esophageal squamous cell carcinoma identifies recurrent shortening of the BID 3'UTR as a driver of disease progression.

Dissecting esophageal squamous-cell carcinoma ecosystem by single-cell transcriptomic analysis.

Esophageal squamous-cell carcinoma (ESCC), one of the most prevalent and lethal malignant disease, has a complex but unknown tumor ecosystem. Here, we investigate the composition of ESCC tumors based on 208,659 single-cell transcriptomes derived from 60 individuals. We identify 8 common expression programs from malignant epithelial cells and discover 42 cell types, including 26 immune cell and 16 nonimmune stromal cell subtypes in the tumor microenvironment (TME), and analyse the interactions between cancer cells and other cells and the interactions among different cell types in the TME. Moreover, we link the cancer cell transcriptomes to the somatic mutations and identify several markers significantly associated with patients' survival, which may be relevant to precision care of ESCC patients. These results reveal the immunosuppressive status in the ESCC TME and further our understanding of ESCC.

Young and Undamaged rMSA Improves the Healthspan and Lifespan of Mice.

Improvement of longevity is an eternal dream of human beings. The accumulation of protein damages is considered as a major cause of aging. Here, we report that the injection of exogenous recombinant mouse serum albumin (rMSA) reduced the total damages of serum albumin in C57BL/6N mice, with higher level of free-thiols, lower levels of carbonyls and advanced glycation end-products as well as homocysteines in rMSA-treated mice. The healthspan and lifespan of C57BL/6N mice were significantly improved by rMSA. The grip strength of rMSA-treated female and male mice increased by 29.6% and 17.4%, respectively. Meanwhile, the percentage of successful escape increased 23.0% in rMSA-treated male mice using the Barnes Maze test. Moreover, the median lifespan extensions were 17.6% for female and 20.3% for male, respectively. The rMSA used in this study is young and almost undamaged. We define the concept "young and undamaged" to any protein without any unnecessary modifications by four parameters: intact free thiol (if any), no carbonylation, no advanced glycation end-product, and no homocysteinylation. Here, "young and undamaged" exogenous rMSA used in the present study is much younger and less damaged than the endogenous serum albumin purified from young mice at 1.5 months of age. We predict that undamaged proteins altogether can further improve the healthspan and lifespan of mice.

VAV2 is required for DNA repair and implicated in cancer radiotherapy resistance.

Radiotherapy remains the mainstay for treatment of various types of human cancer; however, the clinical efficacy is often limited by radioresistance, in which the underlying mechanism is largely unknown. Here, using esophageal squamous cell carcinoma (ESCC) as a model, we demonstrate that guanine nucleotide exchange factor 2 (VAV2), which is overexpressed in most human cancers, plays an important role in primary and secondary radioresistance. We have discovered for the first time that VAV2 is required for the Ku70/Ku80 complex formation and participates in non-homologous end joining repair of DNA damages caused by ionizing radiation. We show that VAV2 overexpression substantially upregulates signal transducer and activator of transcription 1 (STAT1) and the STAT1 inhibitor Fludarabine can significantly promote the sensitivity of radioresistant patient-derived ESCC xenografts in vivo in mice to radiotherapy. These results shed new light on the mechanism of cancer radioresistance, which may be important for improving clinical radiotherapy.

Plaque burden assessment and attenuation measurement of carotid atherosclerotic plaque using virtual monoenergetic images in comparison to conventional polyenergetic images from dual-layer spectral detector CT.

PURPOSE: To compare virtual monoenergetic images (VMIs) with conventional polyenergetic images (PI) of Dual-layer spectral detector CT angiography (DLCTA) in plaque burden assessment and attenuation measurement of carotid atherosclerotic plaques. METHODS: Supra-aortic DLCTA imaging of thirty patients (8 female, mean ages 63.1 ±â€¯7.5 years) were respectively reviewed. Lumen area, wall area, and calcified area of plaques were outlined and recorded. Normalized wall index (NWI) was calculated for plaque burden and compared between PI and different VMIs. The attenuation of the non-calcified, calcified area of the plaques, sternocleidomastoid muscle (SCM), as well as Z effective values were measured and compared. RESULTS: Fifty carotid plaques (27 left, 23 right) of thirty patients were analyzed. The average values of lumen, wall, calcified areas and NWI on PI were 34.50 ±â€¯20.57mm2, 47.61 ±â€¯19.94 mm2, 5.25 mm2 (1.35- 51.86 mm2), and 0.59 ±â€¯0.16 respectively. No significant difference was found in the lumen area (p = 0.314), wall area (p = 0.600), and NWI (p = 0.980) between different VMIs and PI. A significant difference was found in the calcified area between VMIs and PI (p = 0.009). Attenuations of non-calcified and calcified components in carotid plaques were comparable to PI for 50-120 keV (all: p > 0.05) and 60-120 keVs (all p > 0.05), respectively. Z Effective values for non-calcified, calcified and SCM were 7.67 ±â€¯0.42, 11.70 ±â€¯1.22, and 7.45 ±â€¯0.12, respectively. CONCLUSIONS: Carotid plaque burden assessment was comparable between PI and VMIs at 40-120 keVs. Attenuations of non-calcified components in carotid plaques were comparable to PI for 50-120 keV VMIs of DLCTA. VMIs might provide more information on carotid plaque features.