Photoactivated Formation of an Extravascular Dynamic Hydrogel as an Intelligent Blood Flow Regulator to Reprogram the Immunogenic Landscape.

Long-term tumor starvation may be a potential strategy to elevate the antitumor immune response by depriving nutrients. However, combining long-term starvation therapy with immunotherapy often yields limited efficacy due to the blockage of immune cell migration pathways. Herein, an intelligent blood flow regulator (BFR) is first established through photoactivated in situ formation of the extravascular dynamic hydrogel to compress blood vessels, which can induce long-term tumor starvation to elicit metabolic stress in tumor cells without affecting immune cell migration pathways. By leveraging methacrylate-modified nanophotosensitizers (HMMAN) and biodegradable gelatin methacrylate (GelMA), the developed extravascular hydrogel dynamically regulates blood flow via enzymatic degradation. Additionally, aPD-L1 loaded into HMMAN continuously blocks immune checkpoints. Systematic in vivo experiments demonstrate that the combination of immune checkpoint blockade (ICB) and BFR-induced metabolic stress (BIMS) significantly delays the progression of Lewis lung and breast cancers by reshaping the tumor immunogenic landscape and enhancing antitumor immune responses.

Hybrid Membrane-Camouflaged Biomimetic Immunomodulatory Nanoturrets with Sequential Multidrug Release for Potentiating T Cell-Mediated Anticancer Immunity.

Ascorbic acid (AA) has been attracting great attention with its emerging potential in T cell-dependent antitumor immunity. However, premature blood clearance and immunologically "cold" tumors severely compromise its immunotherapeutic outcomes. As such, the reversal of the immunosuppressive tumor microenvironment (TME) has been the premise for improving the effectiveness of AA-based immunotherapy, which hinges upon advanced AA delivery and amplified immune-activating strategies. Herein, a novel Escherichia coli (E. coli) outer membrane vesicle (OMV)-red blood cell (RBC) hybrid membrane (ERm)-camouflaged immunomodulatory nanoturret is meticulously designed based on gating of an AA-immobilized metal-organic framework (MOF) onto bortezomib (BTZ)-loaded magnesium-doped mesoporous silica (MMS) nanovehicles, which can realize immune landscape remodeling by chemotherapy-assisted ascorbate-mediated immunotherapy (CAMIT). Once reaching the acidic TME, the acidity-sensitive MOF gatekeeper and MMS core within the nanoturret undergo stepwise degradation, allowing for tumor-selective sequential release of AA and BTZ. The released BTZ can evoke robust immunogenic cell death (ICD), synergistically promote dendritic cell (DC) maturation in combination with OMV, and ultimately increase T cell tumor infiltration together with Mg2+. The army of T cells is further activated by AA, exhibiting remarkable antitumor and antimetastasis performance. Moreover, the CD8-deficient mice model discloses the T cell-dependent immune mechanism of the AA-based CAMIT strategy. In addition to providing a multifunctional biomimetic hybrid nanovehicle, this study is also anticipated to establish a new immunomodulatory fortification strategy based on the multicomponent-driven nanoturret for highly efficient T cell-activation-enhanced synergistic AA immunotherapy.

The relationship between inflammation, impaired glymphatic system, and neurodegenerative disorders: A vicious cycle.

The term "glymphatic" emerged roughly a decade ago, marking a pivotal point in neuroscience research. The glymphatic system, a glial-dependent perivascular network distributed throughout the brain, has since become a focal point of investigation. There is increasing evidence suggesting that impairment of the glymphatic system appears to be a common feature of neurodegenerative disorders, and this impairment exacerbates as disease progression. Nevertheless, the common factors contributing to glymphatic system dysfunction across most neurodegenerative disorders remain unclear. Inflammation, however, is suspected to play a pivotal role. Dysfunction of the glymphatic system can lead to a significant accumulation of protein and waste products, which can trigger inflammation. The interaction between the glymphatic system and inflammation appears to be cyclical and potentially synergistic. Yet, current research is limited, and there is a lack of comprehensive models explaining this association. In this perspective review, we propose a novel model suggesting that inflammation, impaired glymphatic function, and neurodegenerative disorders interconnected in a vicious cycle. By presenting experimental evidence from the existing literature, we aim to demonstrate that: (1) inflammation aggravates glymphatic system dysfunction, (2) the impaired glymphatic system exacerbated neurodegenerative disorders progression, (3) neurodegenerative disorders progression promotes inflammation. Finally, the implication of proposed model is discussed.

MRI-Based Radiomics and Deep Learning in Biological Characteristics and Prognosis of Hepatocellular Carcinoma: Opportunities and Challenges.

Hepatocellular carcinoma (HCC) is the fifth most common malignancy and the third leading cause of cancer-related death worldwide. HCC exhibits strong inter-tumor heterogeneity, with different biological characteristics closely associated with prognosis. In addition, patients with HCC often distribute at different stages and require diverse treatment options at each stage. Due to the variability in tumor sensitivity to different therapies, determining the optimal treatment approach can be challenging for clinicians prior to treatment. Artificial intelligence (AI) technology, including radiomics and deep learning approaches, has emerged as a unique opportunity to improve the spectrum of HCC clinical care by predicting biological characteristics and prognosis in the medical imaging field. The radiomics approach utilizes handcrafted features derived from specific mathematical formulas to construct various machine-learning models for medical applications. In terms of the deep learning approach, convolutional neural network models are developed to achieve high classification performance based on automatic feature extraction from images. Magnetic resonance imaging offers the advantage of superior tissue resolution and functional information. This comprehensive evaluation plays a vital role in the accurate assessment and effective treatment planning for HCC patients. Recent studies have applied radiomics and deep learning approaches to develop AI-enabled models to improve accuracy in predicting biological characteristics and prognosis, such as microvascular invasion and tumor recurrence. Although AI-enabled models have demonstrated promising potential in HCC with biological characteristics and prognosis prediction with high performance, one of the biggest challenges, interpretability, has hindered their implementation in clinical practice. In the future, continued research is needed to improve the interpretability of AI-enabled models, including aspects such as domain knowledge, novel algorithms, and multi-dimension data sources. Overcoming these challenges would allow AI-enabled models to significantly impact the care provided to HCC patients, ultimately leading to their deployment for clinical use. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.

pH Mapping of Gliomas Using Quantitative Chemical Exchange Saturation Transfer MRI: Quasi-Steady-State, Spillover-, and MT-Corrected Omega Plot Analysis.

BACKGROUND: Quantitative in-situ pH mapping of gliomas is important for therapeutic interventions, given its significant association with tumor progression, invasion, and metastasis. Although chemical exchange saturation transfer (CEST) offers a noninvasive way for pH imaging based on the pH-dependent exchange rate (ksw ), the reliable quantification of ksw in glioma remains constrained due to technical challenges. PURPOSE: To quantify the pH of gliomas by measuring the proton exchange rate through optimized omega plot analysis. STUDY TYPE: Prospective. PHANTOMS/ANIMAL MODEL/SUBJECTS: Creatine and murine brain lysates phantoms, six rats with glioma xenograft model, and three patients with World Health Organization grade 2-4 gliomas. FIELD STRENGTH/SEQUENCE: 11.7 T, 7.0 T, CEST imaging, T2 -weighted (T2 W) imaging, and T1 -mapping. ASSESSMENT: Omega plot analysis, quasi-steady-state (QUASS) analysis, multi-pool Lorentzian fitting, amine and amide concentration-independent detection, pH enhanced method with the combination of amide and guanidyl (pHenh ), and magnetization transfer ratio (MTR) were utilized for pH metric quantification. The clinical outcomes were determined through radiologic follow-up and histopathological analysis. STATISTICAL TESTS: Mann-Whitney U test was performed to compare glioma with normal tissue, and Pearson's correlation analysis was used to assess the relationship between ksw and other parameters. RESULTS: In vitro experiments reveal that the determined ksw at 2 ppm increases exponentially with pH (creatine phantoms: ksw = 106 + 0.147 × 10(pH-4.198) ; lysates: ksw = 185.1 + 0.101 × 10(pH-3.914) ). Omega plot analysis exhibits a linear correlation between 1/MTRRex and 1/ω1 2 in the glioma xenografts (R2 > 0.98) and glioma patients (R2 > 0.99). The exchange rate in the rat glioma decreases compared to the contralateral normal tissue (349.46 ± 30.40 s-1 vs. 403.54 ± 51.01 s-1 , P = 0.025), while keeping independence from changes in concentration (r = 0.5037, P = 0.095). Similar pattern was observed in human data. DATA CONCLUSION: Utilizing QUASS-based, spillover-, and MT-corrected omega plot analysis for the measurement of exchange rates, offers a feasible method for quantifying pH within glioma. LEVEL OF EVIDENCE: NA TECHNICAL EFFICACY: Stage 1.

Signal enhancement ratio of multi-phase contrast-enhanced MRI: an imaging biomarker for survival in pancreatic adenocarcinoma.

OBJECTIVES: To evaluate signal enhancement ratio (SER) for tissue characterization and prognosis stratification in pancreatic adenocarcinoma (PDAC), with quantitative histopathological analysis (QHA) as the reference standard. METHODS: This retrospective study included 277 PDAC patients who underwent multi-phase contrast-enhanced (CE) MRI and whole-slide imaging (WSI) from three centers (2015-2021). SER is defined as (SIlt - SIpre)/(SIea - SIpre), where SIpre, SIea, and SIlt represent the signal intensity of the tumor in pre-contrast, early-, and late post-contrast images, respectively. Deep-learning algorithms were implemented to quantify the stroma, epithelium, and lumen of PDAC on WSIs. Correlation, regression, and Bland-Altman analyses were utilized to investigate the associations between SER and QHA. The prognostic significance of SER on overall survival (OS) was evaluated using Cox regression analysis and Kaplan-Meier curves. RESULTS: The internal dataset comprised 159 patients, which was further divided into training, validation, and internal test datasets (n = 60, 41, and 58, respectively). Sixty-five and 53 patients were included in two external test datasets. Excluding lumen, SER demonstrated significant correlations with stroma (r = 0.29-0.74, all p < 0.001) and epithelium (r = -0.23 to -0.71, all p < 0.001) across a wide post-injection time window (range, 25-300 s). Bland-Altman analysis revealed a small bias between SER and QHA for quantifying stroma/epithelium in individual training, validation (all within ± 2%), and three test datasets (all within ± 4%). Moreover, SER-predicted low stromal proportion was independently associated with worse OS (HR = 1.84 (1.17-2.91), p = 0.009) in training and validation datasets, which remained significant across three combined test datasets (HR = 1.73 (1.25-2.41), p = 0.001). CONCLUSION: SER of multi-phase CE-MRI allows for tissue characterization and prognosis stratification in PDAC. CLINICAL RELEVANCE STATEMENT: The signal enhancement ratio of multi-phase CE-MRI can serve as a novel imaging biomarker for characterizing tissue composition and holds the potential for improving patient stratification and therapy in PDAC. KEY POINTS: Imaging biomarkers are needed to better characterize tumor tissue in pancreatic adenocarcinoma. Signal enhancement ratio (SER)-predicted stromal/epithelial proportion showed good agreement with histopathology measurements across three distinct centers. Signal enhancement ratio (SER)-predicted stromal proportion was demonstrated to be an independent prognostic factor for OS in PDAC.

Enzymatic bionanocatalysts for combating peri-implant biofilm infections by specific heat-amplified chemodynamic therapy and innate immunomodulation.

Bacterial biofilm-associated infection is a life-threatening emergency contributing from drug resistance and immune escape. Herein, a novel non-antibiotic strategy based on the synergy of bionanocatalysts-driven heat-amplified chemodynamic therapy (CDT) and innate immunomodulation is proposed for specific biofilm elimination by the smart design of a biofilm microenvironment (BME)-responsive double-layered metal-organic framework (MOF) bionanocatalysts (MACG) composed of MIL-100 and CuBTC. Once reaching the acidic BME, the acidity-triggered degradation of CuBTC allows the sequential release of glucose oxidase (GOx) and an activable photothermal agent, 2,2'-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS). GOx converts glucose into H2O2 and gluconic acid, which can further acidify the BME to accelerate the CuBTC degradation and GOx/ABTS release. The in vitro and in vivo results show that horseradish peroxidase (HRP)-mimicking MIL-100 in the presence of self-supplied H2O2 can catalyze the oxidation of ABTS into oxABTS to yield a photothermal effect that breaks the biofilm structure via eDNA damage. Simultaneously, the Cu ion released from the degraded CuBTC can deplete glutathione and catalyze the splitting of H2O2 into •OH, which can effectively penetrate the heat-induced loose biofilms and kill sessile bacteria (up to 98.64%), such as E. coli and MRSA. Particularly, MACG-stimulated M1-macrophage polarization suppresses the biofilm regeneration by secreting pro-inflammatory cytokines (e.g., IL-6, TNF-α, etc.) and forming a continuous pro-inflammatory microenvironment in peri-implant biofilm infection animals for at least 14 days. Such BME-responsive strategy has the promise to precisely eliminate refractory peri-implant biofilm infections with extremely few adverse effects.

Association between Liver MRI Proton Density Fat Fraction and Liver Disease Risk.

Background A better understanding of the association between liver MRI proton density fat fraction (PDFF) and liver diseases might support the clinical implementation of MRI PDFF. Purpose To quantify the genetically predicted causal effect of liver MRI PDFF on liver disease risk. Materials and Methods This population-based prospective observational study used summary-level data mainly from the UK Biobank and FinnGen. Mendelian randomization analysis was conducted using the inverse variance-weighted method to explore the causal association between genetically predicted liver MRI PDFF and liver disease risk with Bonferroni correction. The individual-level data were downloaded between August and December 2020 from the UK Biobank. Logistic regression analysis was performed to validate the association between liver MRI PDFF polygenic risk score and liver disease risk. Mediation analyses were performed using multivariable mendelian randomization. Results Summary-level and individual-level data were obtained from 32 858 participants and 378 436 participants (mean age, 57 years ± 8 [SD]; 203 108 female participants), respectively. Genetically predicted high liver MRI PDFF was associated with increased risks of malignant liver neoplasm (odds ratio [OR], 4.5; P < .001), alcoholic liver disease (OR, 1.9; P < .001), fibrosis and cirrhosis of the liver (OR, 3.0; P < .004), fibrosis of the liver (OR, 3.6; P = .002), cirrhosis of the liver (OR, 3.8; P < .001), nonalcoholic steatohepatitis (OR, 7.7; P < .001), and nonalcoholic fatty liver disease (NAFLD) (OR, 4.4; P < .001). Individual-level evidence supported these associations after grouping participants based on liver MRI PDFF polygenic risk score (all P < .004). The mediation analysis indicated that genetically predicted high-density lipoprotein cholesterol, type 2 diabetes mellitus, and waist-to-hip ratio (mediation effects, 25.1%-46.3%) were related to the occurrence of fibrosis and cirrhosis of the liver, cirrhosis of the liver, and NAFLD at liver MRI PDFF (all P < .05). Conclusion This study provided evidence of the association between genetically predicted liver MRI PDFF and liver health. © RSNA, 2023 Supplemental material is available for this article. See also the editorials by Reeder and Starekova and Monsell in this issue.

Predicting Microvascular Invasion in Hepatocellular Carcinoma Using CT-based Radiomics Model.

Background Prediction of microvascular invasion (MVI) may help determine treatment strategies for hepatocellular carcinoma (HCC). Purpose To develop a radiomics approach for predicting MVI status based on preoperative multiphase CT images and to identify MVI-associated differentially expressed genes. Materials and Methods Patients with pathologically proven HCC from May 2012 to September 2020 were retrospectively included from four medical centers. Radiomics features were extracted from tumors and peritumor regions on preoperative registration or subtraction CT images. In the training set, these features were used to build five radiomics models via logistic regression after feature reduction. The models were tested using internal and external test sets against a pathologic reference standard to calculate area under the receiver operating characteristic curve (AUC). The optimal AUC radiomics model and clinical-radiologic characteristics were combined to build the hybrid model. The log-rank test was used in the outcome cohort (Kunming center) to analyze early recurrence-free survival and overall survival based on high versus low model-derived score. RNA sequencing data from The Cancer Image Archive were used for gene expression analysis. Results A total of 773 patients (median age, 59 years; IQR, 49-64 years; 633 men) were divided into the training set (n = 334), internal test set (n = 142), external test set (n = 141), outcome cohort (n = 121), and RNA sequencing analysis set (n = 35). The AUCs from the radiomics and hybrid models, respectively, were 0.76 and 0.86 for the internal test set and 0.72 and 0.84 for the external test set. Early recurrence-free survival (P < .01) and overall survival (P < .007) can be categorized using the hybrid model. Differentially expressed genes in patients with findings positive for MVI were involved in glucose metabolism. Conclusion The hybrid model showed the best performance in prediction of MVI. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Summers in this issue.

Value of Non-Contrast-Enhanced Vessel Wall MR Imaging in Assessing Vascular Invasion of Retroperitoneal Tumors.

BACKGROUND: Due to their location and growth patterns, retroperitoneal tumors often involve the surrounding blood vessels. Clinical decisions on a proper treatment depend on the information on this condition. Evaluation of blood vessels using non-contrast-enhanced vessel wall MRI may provide noninvasive assessment of the extent of tumor invasion to assist clinical decision-making. PURPOSE: To investigate the performance and potential of non-contrast-enhanced vessel wall MRI in evaluating the degree of vessel wall invasion of retroperitoneal tumors. STUDY TYPE: Prospective. POPULATION: Thirty-seven participants (mean age: 60.59 ± 11.77 years, 59% male) with retroperitoneal tumors close to vessels based on their diagnostic computer tomography. FIELD STRENGTH/SEQUENCES: 3 T; vessel wall MRI sequences: two-dimensional T2-weighted MultiVane XD turbo spin-echo (2D-T2-MVXD-TSE) and three-dimensional T1-weighted motion sensitized driven equilibrium fat suppression turbo spin-echo (3D-T1-MSDE-TSE) sequences; conventional MRI sequences: T2-weighted fat suppression turbo spin-echo (T2-FS-TSE), T2-weighted turbo spin-echo (T2-TSE), modified Dixon T1-weighted fast field echo (T1-mDixon-FFE), and diffusion-weighted echo planar imaging (DWI-EPI) sequences. ASSESSMENT: All patients underwent preoperative imaging using both non-contrast conventional and vessel wall MRI sequences. Images obtained from conventional and vessel wall MRI sequences were evaluated independently by three junior radiologists (3 and 2 years of experience in reading MRI) and reviewed by one senior radiologist (25 years of experience in reading MRI) to assess the degree of vessel wall invasion. MRI were validated results from the clinical standard diagnosis based on surgical confirmation or histopathological reports. Interobserver agreement was determined based on the reports from three readers with similar years of experiences. Intraobserver variability was assessed based on categorizing and recategorizing the vessels of 37 patients 1 month apart. STATISTICAL TESTS: Intra-class correlation efficient (ICC), Chi-square test, McNemar test, area under the receiver-operating characteristic curve (AUC), Delong test, P < 0.05 was considered significant. RESULTS: The accuracy of vessel wall MRI (91.96%, 95% CI: 85.43-95.71; 103 of 112) in detecting the degree of vessel wall invasion was significantly higher than that of conventional MRI (75%, 95% CI: 66.24-82.10; 84 of 112). The interobserver variability or reproducibility in categorization of the degree of vascular wall invasion was good in evaluating images from conventional and vessel wall MRI sequences (ICC = 0.821, 95% CI: 0.765-0.867 and ICC = 0.881, 95% CI: 0.842-0.913, respectively). DATA CONCLUSION: Diagnosis of vessel wall invasion of retroperitoneal tumors and assessment of its severity can be improved by using non-contrast-enhanced vessel wall MRI. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 3.

Abnormal [18F]FDG uptake in liver and adipose tissue: a potential imaging biomarker for cancer-associated cachexia.

OBJECTIVES: This study aims to investigate and develop imaging biomarkers for the diagnosis of cancer-associated cachexia based on the organ and tissue-specific abnormal metabolisms measured by fluorine-18-fluorodeoxyglucose (18F-FDG) PET/CT. METHODS: FDG PET/CT data from 390 cancer patients were analyzed retrospectively. Patients were divided into a development cohort and a validation cohort. Cachexia was defined as weight loss > 5% in 6 months or BMI < 20 and weight loss > 2%. According to the above definitions, patients were divided into cachexia and non-cachexia groups. Results of the clinical laboratory tests for metabolic levels and organ and tissue-specific FDG uptake obtained from the cachexia and non-cachexia groups were compared statistically. Logistic regression analysis was performed to identify independent variables associated with cachexia in the development cohort for generating the regression model. The performance of the model was tested using the data from a validation cohort and evaluated by area under the receiver operating characteristic curve (AUC). RESULTS: Based on the data from the development cohort of 286 patients and a validation cohort of 104 patients, it is found that age, white blood cell count, peak standardized uptake value (SUV) of the liver, and minimum SUV of lean body mass of visceral fat and subcutaneous fat were independently associated with cachexia. The model incorporating these variables reached an AUC of 0.777 (95% confidence interval (CI): 0.721, 0.833) in the development cohort and an AUC of 0.729 (95% CI: 0.629, 0.829) in the validation cohort. CONCLUSION: Organ and tissue-specific abnormal glucose metabolism as measured by PET/CT can be used as a biomarker for cancer-associated cachexia. KEY POINTS: • Patients with cancer-associated cachexia have reduced FDG uptake in the liver and increased FDG uptake in visceral fat and subcutaneous fat. • FDG uptake of the liver, visceral fat, and subcutaneous fat can be independent risk factors for identifying cancer-associated cachexia. • Cancer-associated cachexia can be classified using the model that incorporates age, white blood cell count, FDG uptake of the liver, and visceral and subcutaneous fat can diagnose with an AUC of 0.729.

An imaging-based machine learning model outperforms clinical risk scores for prognosis of cirrhotic variceal bleeding.

OBJECTIVES: To develop and validate a machine learning model based on contrast-enhanced CT to predict the risk of occurrence of the composite clinical endpoint (hospital-based intervention or death) in cirrhotic patients with acute variceal bleeding (AVB). METHODS: This retrospective study enrolled 330 cirrhotic patients with AVB between January 2017 and December 2020 from three clinical centers. Contrast-enhanced CT and clinical data were collected. Centers A and B were divided 7:3 into a training set and an internal test set, and center C served as a separate external test set. A well-trained deep learning model was applied to segment the liver and spleen. Then, we extracted 106 original features of the liver and spleen separately based on the Image Biomarker Standardization Initiative (IBSI). We constructed the Liver-Spleen (LS) model based on the selected radiomics features. The performance of LS model was evaluated by receiver operating characteristics and calibration curves. The clinical utility of models was analyzed using decision curve analyses (DCA). RESULTS: The LS model demonstrated the best diagnostic performance in predicting the composite clinical endpoint of AVB in patients with cirrhosis, with an AUC of 0.782 (95% CI 0.650-0.882) and 0.789 (95% CI 0.674-0.878) in the internal test and external test groups, respectively. Calibration curves and DCA indicated the LS model had better performance than traditional clinical scores. CONCLUSION: A novel machine learning model outperforms previously known clinical risk scores in assessing the prognosis of cirrhotic patients with AVB CLINICAL RELEVANCE STATEMENT: The Liver-Spleen model based on contrast-enhanced CT has proven to be a promising tool to predict the prognosis of cirrhotic patients with acute variceal bleeding, which can facilitate decision-making and personalized therapy in clinical practice. KEY POINTS: • The Liver-Spleen machine learning model (LS model) showed good performance in assessing the clinical composite endpoint of cirrhotic patients with AVB (AUC ≥ 0.782, sensitivity ≥ 80%). • The LS model outperformed the clinical scores (AUC ≤ 0.730, sensitivity ≤ 70%) in both internal and external test cohorts.

Changes in Resting-State Neural Activity and Nerve Fibres in Ischaemic Stroke Patients with Hemiplegia.

Many neuroimaging studies have reported that stroke induces abnormal brain activity. However, little is known about resting-state networks (RSNs) and the corresponding white matter changes in stroke patients with hemiplegia. Here, we utilized functional magnetic resonance imaging (fMRI) to measure neural activity and related fibre tracts in 14 ischaemic stroke patients with hemiplegia and 12 healthy controls. Fractional amplitude of low-frequency fluctuations (fALFF) calculation and correlation analyses were used to assess the relationship between regional neural activity and movement scores. Tractography was performed using diffusion tensor imaging (DTI) data to analyse the fibres passing through the regions of interest. Compared with controls, stroke patients showed abnormal functional connectivity (FC) between some brain regions in the RSNs. The fALFF was increased in the contralesional parietal lobe, with the regional fALFF being correlated with behavioural scores in stroke patients. Additionally, the passage of fibres across regions with reduced FC in the RSNs was increased in stroke patients. This study suggests that structural remodelling of functionally relevant white matter tracts is probably an adaptive response that compensates for injury to the brain.

Clinical Value of a Novel Magnetic Resonance Imaging Protocol and Prognostic Model Establishment for Sudden Sensorineural Hearing Loss: A Prospective Study.

INTRODUCTION: Sudden sensorineural hearing loss (SSNHL) is one of the most common acute symptoms in the otolaryngology department. Etiological diagnosis is the premise of effective treatment of SSNHL, and prognostic evaluation is the key. However, most of the patients are diagnosed as idiopathic due to a lack of overall assessment, while prognostic factors of SSNHL are numerous and controversial. Our purpose was to validate the potential value of a novel three-dimensional fluid-attenuated inversion recovery (3D-FLAIR) MR protocol in SSNHL and to establish a clinical-image prognostic model for unilateral SSNHL. METHODS: This prospective study included consecutive patients from May 2019 to November 2021. Pathogenic diagnosis relied on expertise-based estimation and the associations of MR findings with clinical features of unilateral SSNHL were assessed. The prognostic evaluation of unilateral SSNHL was adopted for recovery and no recovery groups and complete and incomplete recovery groups. Significant clinical and MR features were compared and screened out by single-factor analyses. The primary clinical-image prognosis assessment model was built by multifactor logistic regression analyses. RESULTS: A total of 101 patients were enrolled in our study who acquired the correct etiological diagnosis based on the novel 3D-FLAIR MR combined with clinical examination. Among the 93 patients with unilateral SSNHL, 30.1% (28/93) showed labyrinthine abnormalities on 3D-FLAIR images. The severity of initial hearing loss in the MR+ group was worse than that in the MR- group (p < 0.05), and patients with positive MR findings tended to have poor recovery. An excellent prognostic model was built for hearing complete recovery and no recovery. The combination of three independent risk factors, including abnormal distortion products otoacoustic emission and transient evoked otoacoustic emission, the period from onset to treatment, and PTA at the onset, was adopted for hearing recovery/no recovery (accuracy = 90.2%, AUC = 0.820). Furthermore, adding the factor of positive MRI findings could improve the confidence for the judgment of hearing no recovery. The only independent risk factor, PTA at the onset, was adopted for complete/incomplete hearing recovery (accuracy = 86.1%, AUC = 0.874). CONCLUSION: The novel MR protocol had a good advantage in pathogenic diagnosis. Labyrinthine MR 3D-FLAIR signal abnormalities were related to the severity of an initial hearing loss and had a greater tendency to be found in patients with no recovery. A prognostic model with two main steps of unilateral SSNHL, mainly for SSNHL with no recovery and complete recovery, was built successfully and needed further verification by larger series of patients.

Energy spectrum CT index-based machine learning model predicts the effect of intravenous thrombolysis in lower limbs.

To develop a noninvasive machine learning (ML) model based on energy spectrum computed tomography venography (CTV) indices for preoperatively predicting the effect of intravenous thrombolytic treatment in lower limbs. A total of 3492 slices containing thrombus regions from 58 veins in lower limbs in a cohort of 18 patients, divided in good and poor thrombolysis prognosis groups, were analyzed. Key indices were selected by univariate analysis and Pearson correlation coefficient test. A support vector machine classifier-based model was developed through ten-fold cross validation. Model performance was assessed in terms of discrimination, calibration, and clinical usefulness at both per-slice and per-vessel levels. Continuous variables and categorical variables were compared between good and poor thrombolysis prognosis group by Mann-Whitney U-test and chi-square test, respectively. A nomogram was built by integrating clinical factors and the energy spectrum CTV index-based score calculated by the model. Six indices selected from 192 indices were used to build the predictive model. The ML model achieved area under the curves (AUCs) of 0.838 and 0.767 [95% CI (confidence interval), 0.825-0.850, 0.752-0.781] in the training and validation datasets at the per-slice level, and the per-vessel level AUCs were 0.945 and 0.876 (95% CI, 0.852-0.988, 0.763-0.948) in the training and validation datasets, respectively. The nomogram showed better performance with the per-vessel level AUC, accuracy, sensitivity and specificity, yielding 0.901(95% CI, 0.793-0.964), 86.2%, 87.9% and 84.0% in the validation dataset, respectively. There was no significant difference in the vessel distribution between good and poor thrombolysis prognosis groups (chi-square test, p = 0.671). The energy spectrum CTV index-based ML model achieved favorable effectiveness in predicting the outcome of vessel-level intravenous thrombolysis. A nomogram integrating clinical factors, and risk score calculated by the developed model showed improved performance and had potential to be used as a noninvasive preoperative tool for clinicians.

Preoperative Microvascular Invasion Prediction to Assist in Surgical Plan for Single Hepatocellular Carcinoma: Better Together with Radiomics.

BACKGROUND: Prediction models with or without radiomic analysis for microvascular invasion (MVI) in hepatocellular carcinoma (HCC) have been reported, but the potential for model-predicted MVI in surgical planning is unclear. Therefore, we aimed to explore the effect of predicted MVI on early recurrence after anatomic resection (AR) and non-anatomic resection (NAR) to assist surgical strategies. METHODS: Patients with a single HCC of 2-5 cm receiving curative resection were enrolled from 2 centers. Their data were used to develop (n = 230) and test (n = 219) two prediction models for MVI using clinical factors and preoperative computed tomography images. The two prediction models, clinico-radiologic model and clinico-radiologic-radiomic (CRR) model (clinico-radiologic variables + radiomic signature), were compared using the Delong test. Early recurrence based on model-predicted high-risk MVI was evaluated between AR (n = 118) and NAR (n = 85) via propensity score matching using patient data from another 2 centers for external validation. RESULTS: The CRR model showed higher area under the curve values (0.835-0.864 across development, test, and external validation) but no statistically significant improvement over the clinico-radiologic model (0.796-0.828). After propensity score matching, difference in 2-year recurrence between AR and NAR was found in the CRR model predicted high-risk MVI group (P = 0.005) but not in the clinico-radiologic model predicted high-risk MVI group (P = 0.31). CONCLUSIONS: The prediction model incorporating radiomics provided an accurate preoperative estimation of MVI, showing the potential for choosing the more appropriate surgical procedure between AR and NAR.

Designing thermal radiation metamaterials via a hybrid adversarial autoencoder and Bayesian optimization.

Designing thermal radiation metamaterials is challenging especially for problems with high degrees of freedom and complex objectives. In this Letter, we develop a hybrid materials informatics approach which combines the adversarial autoencoder and Bayesian optimization to design narrowband thermal emitters at different target wavelengths. With only several hundreds of training data sets, new structures with optimal properties can be quickly determined in a compressed two-dimensional latent space. This enables the optimal design by calculating far less than 0.001% of the total candidate structures, which greatly decreases the design period and cost. The proposed design framework can be easily extended to other thermal radiation metamaterials design with higher dimensional features.

Size-changeable nanoprobes for the combined radiotherapy and photodynamic therapy of tumor.

PURPOSE: Radiation therapy (RT) and photodynamic therapy (PDT) are promising while challenging in treating tumors. The potential radiation resistance of tumor cells and side effects to healthy tissues restrict their clinical treatment efficacy. Effective delivery of therapeutic agents to the deep tumor tissues would be available for tumor-accurate therapy and promising for the tumor therapy. Thus, developing nanoprobes with effectively delivering radiotherapy sensitizers and photosensitizers to the interior of tumors is needed for the accurate combined RT and PDT of tumor. METHODS: The size-changeable nanoprobes of Gd2O3@BSA-BSA-Ce6 (BGBC) were synthesized with a crosslinking method. Magnetic resonance imaging (MRI) and in vivo near-infrared (NIR) imaging were measured to evaluate the nanoprobes' tumor accumulation and intratumor penetration effect. The tumor suppression effect of combined RT and PDT with these nanoprobes was also studied for the 4T1 bearing Balb/c mice. RESULTS: The nanoprobes BGBC showed high tumor accumulation and disintegrated into small particles responding to the photo-irradiation-produced reactive oxygen species (ROS), allowing for tumor penetration. Abundant radiotherapy sensitizers and photosensitizers were delivered to the deep tumor tissues, which is available for the accurate therapy of tumor. In addition, the BGBC displayed outstanding MRI and fluorescence imaging effects for evaluating the biodistribution and tumor suppression effect of nanoprobes. Consequently, significant tumor suppression effect was obtained based on the accurate tumor treatment with the combined RT and PDT. CONCLUSION: The designed size-changeable nanoprobes BGBC showed excellent tumor accumulation and deep tumor penetration, resulting in a significant tumor suppression effect based on the combined RT and PDT. This study provides a novel strategy for dual delivery of radiotherapy sensitizers and photosensitizers into the deep tumor tissues and is promising for the accurate theranostics of tumor.

Insulin Resistance and Cognitive Impairment: Evidence From Neuroimaging.

Insulin is a peptide well known for its role in regulating glucose metabolism in peripheral tissues. Emerging evidence from human and animal studies indicate the multifactorial role of insulin in the brain, such as neuronal and glial metabolism, glucose regulation, and cognitive processes. Insulin resistance (IR), defined as reduced sensitivity to the action of insulin, has been consistently proposed as an important risk factor for developing neurodegeneration and cognitive impairment. Although the exact mechanism of IR-related cognitive impairment still awaits further elucidation, neuroimaging offers a versatile set of novel contrasts to reveal the subtle cerebral abnormalities in IR. These imaging contrasts, including but not limited to brain volume, white matter (WM) microstructure, neural function and brain metabolism, are expected to unravel the nature of the link between IR, cognitive decline, and brain abnormalities, and their changes over time. This review summarizes the current neuroimaging studies with multiparametric techniques, focusing on the cerebral abnormalities related to IR and therapeutic effects of IR-targeting treatments. According to the results, brain regions associated with IR pathophysiology include the medial temporal lobe, hippocampus, prefrontal lobe, cingulate cortex, precuneus, occipital lobe, and the WM tracts across the globe. Of these, alterations in the temporal lobe are highly reproducible across different imaging modalities. These structures have been known to be vulnerable to Alzheimer's disease (AD) pathology and are critical in cognitive processes such as memory and executive functioning. Comparing to asymptomatic subjects, results are more mixed in patients with metabolic disorders such as type 2 diabetes and obesity, which might be attributed to a multifactorial mechanism. Taken together, neuroimaging, especially MRI, is beneficial to reveal early abnormalities in cerebral structure and function in insulin-resistant brain, providing important evidence to unravel the underlying neuronal substrate that reflects the cognitive decline in IR. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 2.

Disturbed Interhemispheric Functional and Structural Connectivity in Type 2 Diabetes.

BACKGROUND: Type 2 diabetes mellitus (T2DM) is associated with cognitive decline and altered brain structure and function. However, the interhemispheric coordination of T2DM patients is unclear. PURPOSE: To investigate interhemispheric functional and anatomic connectivity in T2DM, and their associations with cognitive performance and endocrine parameters. STUDY TYPE: Prospective. SUBJECTS: 38 T2DM patients and 42 matched controls. FIELD STRENGTH/SEQUENCES: 3.0 T magnetic resonance imaging (MRI) scanner; magnetization-prepared rapid acquisition gradient echo sequence; fluid-attenuated inversion recovery sequence; single-shot, gradient-recalled echo-planar imaging sequence (resting-state functional MRI); and diffusion-weighted spin-echo-based echo-planar sequence (diffusion tensor imaging). ASSESSMENT: Voxel-mirrored homotopic connectivity (VMHC) value was calculated based on the functional images. Fibers passing through the regions with significant VMHC differences were identified using an atlas-guided track recognition. The mean fractional anisotropy (FA), mean diffusivity (MD), and fiber length were extracted and compared between the two groups. Finally, correlational analyses were performed to examine the relationships between abnormal interhemispheric connectivity, cognitive performances, and endocrine parameters. STATISTICAL TESTS: Two-sample t-tests were performed controlling for confounding factors, with partial correlation analysis. False discovery rate (FDR) correction was used for multiple comparisons. A P value <0.05 was considered statistically significant. RESULTS: T2DM patients exhibited significantly decreased VMHC between bilateral lingual gyrus and sensorimotor cortex. The fibers connecting lingual gyrus in patients showed significantly lower FA (P = 0.011) and shorter fiber length (P < 0.001), while the differences in sensorimotor fibers were insignificant (P = 0.096 for FA, P = 0.739 for fiber length and P = 0.150 for MD). The FA value in the lingual fibers was negatively correlated with insulin resistance (IR) level in T2DM group after FDR correction (R = -0.635). DATA CONCLUSION: We noted disruptions in interhemispheric coordination in T2DM patients, involving both functional and anatomical connectivities. IR might be a promising therapeutic target in the intervention of T2DM-related cognitive impairment. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 2.