Open-Source Throttling of CD8+ T Cells in Brain with Low-Intensity Focused Ultrasound-Guided Sequential Delivery of CXCL10, IL-2, and aPD-L1 for Glioblastoma Immunotherapy.

Improving clinical immunotherapy for glioblastoma (GBM) relies on addressing the immunosuppressive tumor microenvironment (TME). Enhancing CD8+ T cell infiltration and preventing its exhaustion holds promise for effective GBM immunotherapy. Here, a low-intensity focused ultrasound (LIFU)-guided sequential delivery strategy is developed to enhance CD8+ T cells infiltration and activity in the GBM region. The sequential delivery of CXC chemokine ligand 10 (CXCL10) to recruit CD8+ T cells and interleukin-2 (IL-2) to reduce their exhaustion is termed an "open-source throttling" strategy. Consequently, up to 3.39-fold of CD8+ T cells are observed with LIFU-guided sequential delivery of CXCL10, IL-2, and anti-programmed cell death 1 ligand 1 (aPD-L1), compared to the free aPD-L1 group. The immune checkpoint inhibitors (ICIs) therapeutic efficacy is substantially enhanced by the reversed immunosuppressive TME due to the expansion of CD8+ T cells, resulting in the elimination of tumor, prolonged survival time, and long-term immune memory establishment in orthotopic GBM mice. Overall, LIFU-guided sequential cytokine and ICIs delivery offers an "open-source throttling" strategy of CD8+ T cells, which may present a promising strategy for brain-tumor immunotherapy.

Engineering AIEgens-Tethered Gold Nanoparticles with Enzymatic Dual Self-Assembly for Amplified Cancer-Specific Phototheranostics.

Accurate imaging and precise treatment are critical to controlling the progression of pancreatic cancer. However, current approaches for pancreatic cancer theranostics suffer from limitations in tumor specificity and invasive surgery. Herein, a pancreatic cancer-specific phototheranostic modulator (AuHQ) dominated by aggregation-induced emission (AIE) luminogens-tethered gold nanoparticles is meticulously designed to facilitate prominent fluorescence-photoacoustic bimodal imaging-guided photothermal immunotherapy. Once reaching the pancreatic tumor microenvironment (TME), the peptide Ala-Gly-Phe-Ser-Leu-Pro-Ala-Gly-Cys (AGFSLPAGC) linkage within AuHQ can be specifically cleaved by the overexpressed enzyme Cathepsin E (CTSE), triggering the dual self-assembly of AuNPs and AIE luminogens. The aggregation of AuNPs mediated by enzymatic cleavage results in potentiated photothermal therapy (PTT) under near-infrared (NIR) laser irradiation, induced immunogenic cell death (ICD), and enhanced photoacoustic imaging. Simultaneously, AIE luminogen aggregates formed by hydrophobic interaction can generate AIE fluorescence, enabling real-time and specific fluorescence imaging of pancreatic cancer. Furthermore, coadministration of an indoleamine 2,3-dioxygenase 1 (IDO1) inhibitor with AuHQ can address the limitations of PTT efficacy imposed by the immunosuppressive TME and leverage the synergistic potential to activate systemic antitumor immunity. Thus, this well-designed phototheranostic modulator AuHQ facilitates the intelligent enzymatic dual self-assembly of imaging and therapeutic agents, providing an efficient and precise approach for pancreatic cancer theranostics.

EGFR-targeting oxygen-saturated nanophotosensitizers for orchestrating multifaceted antitumor responses by counteracting immunosuppressive milieu.

High Epidermal growth factor receptor (EGFR) in Cutaneous Squamous Cell Carcinoma (cSCC) is associated with poor prognosis and advanced metastatic stages, severely impeding the efficacy of EGFR-targeting immunotherapy. This is commonly attributed to the combinatory outcomes of hypoxic tumor microenvironment (TME) and immunosuppressive effector cells together. Herein, a novel paradigm of EGFR-targeting oxygen-saturated nanophotosensitizers, designated as CHPFN-O2, has been specifically tailored to mitigate tumor hypoxia in EGFR-positive cSCC and achieve Cetuximab (CTX)-mediated immunotherapy (CIT). The conjugated CTX in CHPFN-O2 serves to initiate immune responses by recruiting Fc receptor (FcR)-expressing immune effector cells towards tumor cells, thereby eliciting antibody-dependent cellular phagocytosis (ADCP), antibody-dependent cellular trogocytosis (ADCT) and antibody-dependent cellular cytotoxicity (ADCC). Besides, CHPFN-O2 can engender a shift from a tumor-friendly to a tumor-hostile one through improved tumor oxygenation, contributing to oxygen-elevated photodynamic therapy (oxPDT). Notably, the combination of oxPDT and CIT eventually promotes T-cell-mediated antitumor activity and successfully inhibits the growth of EGFR-expressing cSCC with good safety profiles. This comprehensive oxPDT/CIT integration aims not only to enhance therapeutic efficacy against EGFRhigh cSCC but also to extend its applicability to other EGFRhigh malignancies, thus delineating a new avenue for the highly efficient synergistic treatment of EGFR-expressing malignancies.

Combinations of Clinical Factors, CT Signs, Radiomics for Differentiating High-density Areas After Mechanical Thrombectomy in Patients with Acute Ischemic Stroke.

BACKGROUND AND PURPOSE: Clinically, hemorrhagic transformation (HT) after mechanical thrombectomy (MT) is a common complication. This study is aim to investigate the value of clinical factors, CT signs, and radiomics in the differential diagnosis of high-density areas (HDAs) in the brain after MT in patients with acute ischemic stroke with large vessel occlusion (AIS-LVO). MATERIALS AND METHODS: A total of 156 eligible patients with AIS-LVO in Center Ⅰ from December 2015 to June 2023 were retrospectively enrolled and randomly divided into training (n=109) and internal validation (n=47) sets at a ratio of 7:3. The data of 63 patients in Center Ⅱ were collected as an external validation set. According to the diagnostic criteria, the patients in the three datasets were divided into a HT group and a non-HT group. The clinical and imaging data from Centers Ⅰ and Ⅱ were used to construct a clinical factor and CT-sign model, a radiomic model and a combined model by logistic regression (LR). Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic efficacy of each model in the three datasets. RESULTS: Clinical blood glucose (Glu) and the maximum cross-sectional area (Areamax) on CT were associated with the nature of the HDA according to multivariate LR analyses (P < 0.05). Among the three models, the combined model had the highest diagnostic efficiency, with area under the curve (AUC) values of 0.895, 0.882, and 0.820 in the three datasets, which were significantly greater than the AUC values of the radiomic model (0.887, 0.898, 0.798) and clinical factor and CT sign model (0.831, 0.744, 0.684). CONCLUSIONS: The combined model based on radiomics had the best performance, indicating that radiomic features can be used as imaging biomarkers to aid in the clinical judgment of the nature of HDA after MT. ABBREVIATIONS: HDA =high-density area; HT =hemorrhagic transformation; MT =mechanical thrombectomy; AIS-LVO =acute ischemic stroke with large vessel occlusion; LR =logistic regression; AUC =area under the curve; ICE =iodine contrast extravasation; DECT =dual energy CT; IOM =iodine overlay map; VNC =virtual noncontrast; Glu =glucose; LASSO =least absolute shrinkage and selection operator; ICC =intraclass correlation coefficient; ROC =receiver operating characteristic; DCA =decision curve analysis.

Fully automated hybrid approach on conventional MRI for triaging clinically significant liver fibrosis: A multi-center cohort study.

Establishing reliable noninvasive tools to precisely diagnose clinically significant liver fibrosis (SF, ≥F2) remains an unmet need. We aimed to build a combined radiomics-clinic (CoRC) model for triaging SF and explore the additive value of the CoRC model to transient elastography-based liver stiffness measurement (FibroScan, TE-LSM). This retrospective study recruited 595 patients with biopsy-proven liver fibrosis at two centers between January 2015 and December 2021. At Center 1, the patients before December 2018 were randomly split into training (276) and internal test (118) sets, the remaining were time-independent as a temporal test set (96). Another data set (105) from Center 2 was collected for external testing. Radiomics scores were built with selected features from Deep learning-based (ResUNet) automated whole liver segmentations on MRI (T2FS and delayed enhanced-T1WI). The CoRC model incorporated radiomics scores and relevant clinical variables with logistic regression, comparing routine approaches. Diagnostic performance was evaluated by the area under the receiver operating characteristic curve (AUC). The additive value of the CoRC model to TE-LSM was investigated, considering necroinflammation. The CoRC model achieved AUCs of 0.79 (0.70, 0.86), 0.82 (0.73, 0.89), and 0.81 (0.72-0.91), outperformed FIB-4, APRI (all p < 0.05) in the internal, temporal, and external test sets and maintained the discriminatory power in G0-1 subgroups (AUCs range, 0.85-0.86; all p < 0.05). The AUCs of joint CoRC-LSM model were 0.86 (0.79-0.94), and 0.81 (0.72-0.90) in the internal and temporal sets (p = 0.01). The CoRC model was useful for triaging SF, and may add value to TE-LSM.

Carvedilol to prevent hepatic decompensation of cirrhosis in patients with clinically significant portal hypertension stratified by new non-invasive model (CHESS2306).

Background & Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvedilol-treating cohort. Results: In the meta-analysis with six studies (n = 819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new "CSPH risk" model. In the HVPG cohort (n = 151), the new model accurately predicted CSPH with cutoff values of 0 and -0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n = 1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <-0.68 (low-risk), -0.68 to 0 (medium-risk), and >0 (high-risk). In the carvedilol-treated cohort, patients with high-risk CSPH treated with carvedilol (n = 81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n = 613 before propensity score matching [PSM], n = 162 after PSM). Conclusions: Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Corrigendum to 'Predicting post-resection recurrence by integrating imaging-based surrogates of distinct vascular patterns of hepatocellular carcinoma' (JHEP Reports 5 [2023] 100806).

[This corrects the article DOI: 10.1016/j.jhepr.2023.100806.].

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.

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.

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.

LiverRisk score: An accurate, cost-effective tool to predict fibrosis, liver-related, and diabetes-related mortality in the general population.

BACKGROUND: Noninvasive and early assessment of liver fibrosis is of great significance and is challenging. We aimed to evaluate the predictive performance and cost-effectiveness of the LiverRisk score for liver fibrosis and liver-related and diabetes-related mortality in the general population. METHODS: The general population from the NHANES 2017-March 2020, NHANES 1999-2018, and UK Biobank 2006-2010 were included in the cross-sectional cohort (n = 3,770), along with the NHANES follow-up cohort (n = 25,317) and the UK Biobank follow-up cohort (n = 17,259). The cost-effectiveness analysis was performed using TreeAge Pro software. Liver stiffness measurements ≥10 kPa were defined as compensated advanced chronic liver disease (cACLD). FINDINGS: Compared to conventional scores, the LiverRisk score had significantly better accuracy and calibration in predicting liver fibrosis, with an area under the receiver operating characteristic curve (AUC) of 0.76 (0.72-0.79) for cACLD. According to the updated thresholds of LiverRisk score (6 and 10), we reclassified the population into three groups: low, medium, and high risk. The AUCs of LiverRisk score for predicting liver-related and diabetes-related mortality at 5, 10, and 15 years were all above 0.8, with better performance than the Fibrosis-4 score. Furthermore, compared to the low-risk group, the medium-risk and high-risk groups in the two follow-up cohorts had a significantly higher risk of liver-related and diabetes-related mortality. Finally, the cost-effectiveness analysis showed that the incremental cost-effectiveness ratio for LiverRisk score compared to FIB-4 was USD $18,170 per additional quality-adjusted life-year (QALY) gained, below the willingness-to-pay threshold of $50,000/QALY. CONCLUSIONS: The LiverRisk score is an accurate, cost-effective tool to predict liver fibrosis and liver-related and diabetes-related mortality in the general population. FUNDING: The National Natural Science Foundation of China (nos. 82330060, 92059202, and 92359304); the Key Research and Development Program of Jiangsu Province (BE2023767a); the Fundamental Research Fund of Southeast University (3290002303A2); Changjiang Scholars Talent Cultivation Project of Zhongda Hospital of Southeast University (2023YJXYYRCPY03); and the Research Personnel Cultivation Program of Zhongda Hospital Southeast University (CZXM-GSP-RC125).

TPA: Two-stage progressive attention segmentation framework for hepatocellular carcinoma on multi-modality MRI.

BACKGROUND: Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) plays a crucial role in the diagnosis and measurement of hepatocellular carcinoma (HCC). The multi-modality information contained in the multi-phase images of DCE-MRI is important for improving segmentation. However, this remains a challenging task due to the heterogeneity of HCC, which may cause one HCC lesion to have varied imaging appearance in each phase of DCE-MRI. In particular, some phases exhibit inconsistent sizes and boundaries will result in a lack of correlation between modalities, and it may pose inaccurate segmentation results. PURPOSE: We aim to design a multi-modality segmentation model that can learn meaningful inter-phase correlation for achieving HCC segmentation. METHODS: In this study, we propose a two-stage progressive attention segmentation framework (TPA) for HCC based on the transformer and the decision-making process of radiologists. Specifically, the first stage aims to fuse features from multi-phase images to identify HCC and provide localization region. In the second stage, a multi-modality attention transformer module (MAT) is designed to focus on the features that can represent the actual size. RESULTS: We conduct training, validation, and test in a single-center dataset (386 cases), followed by external test on a batch of multi-center datasets (83 cases). Furthermore, we analyze a subgroup of data with weak inter-phase correlation in the test set. The proposed model achieves Dice coefficient of 0.822 and 0.772 in the internal and external test sets, respectively, and 0.829, 0.791 in the subgroup. The experimental results demonstrate that our model outperforms state-of-the-art models, particularly within subgroup. CONCLUSIONS: The proposed TPA provides best segmentation results, and utilizing clinical prior knowledge for network design is practical and feasible.

Redox-responsive polymer micelles co-encapsulating immune checkpoint inhibitors and chemotherapeutic agents for glioblastoma therapy.

Immunotherapy with immune checkpoint blockade (ICB) for glioblastoma (GBM) is promising but its clinical efficacy is seriously challenged by the blood-tumor barrier (BTB) and immunosuppressive tumor microenvironment. Here, anti-programmed death-ligand 1 antibodies (aPD-L1) are loaded into a redox-responsive micelle and the ICB efficacy is further amplified by paclitaxel (PTX)-induced immunogenic cell death (ICD) via a co-encapsulation approach for the reinvigoration of local anti-GBM immune responses. Consequently, the micelles cross the BTB and are retained in the reductive tumor microenvironment without altering the bioactivity of aPD-L1. The ICB efficacy is enhanced by the aPD-L1 and PTX combination with suppression of primary and recurrent GBM, accumulation of cytotoxic T lymphocytes, and induction of long-lasting immunological memory in the orthotopic GBM-bearing mice. The co-encapsulation approach facilitating efficient antibody delivery and combining with chemotherapeutic agent-induced ICD demonstrate that the chemo-immunotherapy might reprogram local immunity to empower immunotherapy against GBM.

Long-range connections damage in white matter hyperintensities affects information processing speed.

White matter hyperintensities, one of the major markers of cerebral small vessel disease, disrupt the integrity of neuronal networks and ultimately contribute to cognitive dysfunction. However, a deeper understanding of how white matter hyperintensities related to the connectivity patterns of brain hubs at the neural network level could provide valuable insights into the relationship between white matter hyperintensities and cognitive dysfunction. A total of 36 patients with moderate to severe white matter hyperintensities (Fazekas score ≥ 3) and 34 healthy controls underwent comprehensive neuropsychological assessments and resting-state functional MRI scans. The voxel-based graph-theory approach-functional connectivity strength was employed to systematically investigate the topological organization of the whole-brain networks. The white matter hyperintensities patients performed significantly worse than the healthy controls in episodic memory, executive function and information processing speed. Additionally, we found that white matter hyperintensities selectively affected highly connected hub regions, predominantly involving the medial and lateral prefrontal, precuneus, inferior parietal lobule, insula and thalamus. Intriguingly, this impairment was connectivity distance-dependent, with the most prominent disruptions observed in long-range connections (e.g. 100-150 mm). Finally, these disruptions of hub connectivity (e.g. the long-range functional connectivity strength in the left dorsolateral prefrontal cortex) positively correlated with the cognitive performance in white matter hyperintensities patients. Our findings emphasize that the disrupted hub connectivity patterns in white matter hyperintensities are dependent on connection distance, especially longer-distance connections, which in turn predispose white matter hyperintensities patients to worse cognitive function.

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.

Tunable Thermal Conductivity of Sustainable Geopolymers by the Si/Al Ratio and Moisture Content: Insights from Atomistic Simulations.

In this work, the effects of the Si/Al ratio and moisture content on thermal transport in sustainable geopolymers have been comprehensively investigated by using the molecular dynamics simulation. The thermal conductivity of geopolymer systems increases with the increase of Si/Al ratio, and the phonon vibration frequency region, which plays a major role in the main increase of its thermal conductivity, is 8-25 THz, while the rest of the frequency interval contributes less. With the increase of moisture content, the thermal conductivity of geopolymer systems decreases at first, then increases, and finally stabilizes, which is contrary to the changing trend of the porosity of the system. This is mainly because the existence of pores leads to phonon scattering during thermal transport, which, in turn, affects the thermal conductivity of the system. When the moisture content is 5%, the thermal conductivity reaches a minimum value of about 1.103 W/(m·K), which is 40.2% lower than the thermal conductivity of the system without a water molecule. This work will help to enhance the physical level understanding of the relationship between the geopolymer structures and thermal transport properties.

Diagnostic performance of MRI for residual or recurrent hepatocellular carcinoma after locoregional treatment according to contrast agent type: a systematic review and meta­analysis.

PURPOSE: The ideal contrast agent for imaging patients with hepatocellular carcinoma (HCC) following locoregional therapies (LRT) remains uncertain. We conducted a meta-analysis to assess the diagnostic performance of magnetic resonance imaging with extracellular contrast agent (ECA-MRI) and hepatobiliary agent (EOB-MRI) in detecting residual or recurrence HCC following LRT. METHODS: Original studies comparing the diagnostic performance of ECA-MRI and EOB-MRI were systematically identified through comprehensive searches in PubMed, EMBASE, Cochrane Library and Web of Science databases. The pooled sensitivity and specificity of ECA-MRI and EOB-MRI were calculated using a bivariate-random-effects model. Subgroup-analyses were conducted to compare the diagnostic performance of ECA-MRI and EOB-MRI according to different variables. Meta-regression analysis was employed to explore potential sources of study heterogeneity. RESULTS: A total of 15 eligible studies encompassing 803 patients and 1018 lesions were included. Comparative analysis revealed no significant difference between ECA-MRI and EOB-MRI in the overall pooled sensitivity (87% vs. 79%) and specificity (92% vs. 96%) for the detection of residual or recurrent HCC after LRT (P = 0.41), with comparable areas under the HSROC of 0.95 and 0.92. Subgroup analyses indicated no significant diagnostic performance differences between ECA-MRI and EOB-MRI according to study design, type of LRT, most common etiology of liver disease, baseline lesion size, time of post-treated examination and MRI field strength (All P > 0.05). CONCLUSION: ECA-MRI exhibited overall comparable diagnostic performance to EOB-MRI in assessing residual or recurrent HCC after LRT.

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.

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.