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.

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.

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.

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.

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.

Development of a radiomics-based model to predict occult liver metastases of pancreatic ductal adenocarcinoma: a multicenter study.

BACKGROUND: Undetectable occult liver metastases block the long-term survival of pancreatic ductal adenocarcinoma (PDAC). This study aimed to develop a radiomics-based model to predict occult liver metastases and assess its prognostic capacity for survival. MATERIALS AND METHODS: Patients who underwent surgical resection and were pathologically proven with PDAC were recruited retrospectively from five tertiary hospitals between January 2015 and December 2020. Radiomics features were extracted from tumors, and the radiomics-based model was developed in the training cohort using LASSO-logistic regression. The model's performance was assessed in the internal and external validation cohorts using the area under the receiver operating curve (AUC). Subsequently, the association of the model's risk stratification with progression-free survival (PFS) and overall survival (OS) was then statistically examined using Cox regression analysis and the log-rank test. RESULTS: A total of 438 patients [mean (SD) age, 62.0 (10.0) years; 255 (58.2%) male] were divided into the training cohort ( n =235), internal validation cohort ( n =100), and external validation cohort ( n =103). The radiomics-based model yielded an AUC of 0.73 (95% CI: 0.66-0.80), 0.72 (95% CI: 0.62-0.80), and 0.71 (95% CI: 0.61-0.80) in the training, internal validation, and external validation cohorts, respectively, which were higher than the preoperative clinical model. The model's risk stratification was an independent predictor of PFS (all P <0.05) and OS (all P <0.05). Furthermore, patients in the high-risk group stratified by the model consistently had a significantly shorter PFS and OS at each TNM stage (all P <0.05). CONCLUSION: The proposed radiomics-based model provided a promising tool to predict occult liver metastases and had a great significance in prognosis.

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.

Prediction of preoperative microvascular invasion by dynamic radiomic analysis based on contrast-enhanced computed tomography.

PURPOSE: Microvascular invasion (MVI) is a common complication of hepatocellular carcinoma (HCC) surgery, which is an important predictor of reduced surgical prognosis. This study aimed to develop a fully automated diagnostic model to predict pre-surgical MVI based on four-phase dynamic CT images. METHODS: A total of 140 patients with HCC from two centers were retrospectively included (training set, n = 98; testing set, n = 42). All CT phases were aligned to the portal venous phase, and were then used to train a deep-learning model for liver tumor segmentation. Radiomics features were extracted from the tumor areas of original CT phases and pairwise subtraction images, as well as peritumoral features. Lastly, linear discriminant analysis (LDA) models were trained based on clinical features, radiomics features, and hybrid features, respectively. Models were evaluated by area under curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive values (PPV and NPV). RESULTS: Overall, 86 and 54 patients with MVI- (age, 55.92 ± 9.62 years; 68 men) and MVI+ (age, 53.59 ± 11.47 years; 43 men) were included. Average dice coefficients of liver tumor segmentation were 0.89 and 0.82 in training and testing sets, respectively. The model based on radiomics (AUC = 0.865, 95% CI: 0.725-0.951) showed slightly better performance than that based on clinical features (AUC = 0.841, 95% CI: 0.696-0.936). The classification model based on hybrid features achieved better performance in both training (AUC = 0.955, 95% CI: 0.893-0.987) and testing sets (AUC = 0.913, 95% CI: 0.785-0.978), compared with models based on clinical and radiomics features (p-value < 0.05). Moreover, the hybrid model also provided the best accuracy (0.857), sensitivity (0.875), and NPV (0.917). CONCLUSION: The classification model based on multimodal intra- and peri-tumoral radiomics features can well predict HCC patients with MVI.

Low Dose Iodinated Contrast Material and Radiation for Virtual Monochromatic Imaging in Craniocervical Dual-Layer Spectral Detector Computed Tomography Angiography: A Prospective and Randomized Study.

RATIONALE AND OBJECTIVES: To investigate the feasibility of virtual monochromatic imaging (VMI) of dual-layer spectral detector computed tomography (SDCT) to reduce iodinated contrast material (CM) and radiation dose in craniocervical computed tomography angiography (CTA). MATERIALS AND METHODS: A total of 280 consecutively selected patients performed craniocervical CTA with SDCT were prospectively selected and randomly divided into four groups (A, DoseRight index (DRI) 31, iopromide 370mgI/mL, volume 0.8 mL/kg; B, DRI 26, iopromide 370mgI/mL, volume 0.4 mL/kg; C, DRI 26, ioversol 320mgI/mL, volume 0.4 mL/kg; D, DRI 26, iohexol 300mgI/mL, volume 0.4 mL/kg). 50-70 kiloelectron volts (keV) VMIs in group B were reconstructed and compared to group A to select the optimal keV. Then, the optimal keV in groups B, C and D was reconstructed and compared. Objective image quality, including vascular attenuation, image noise, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), was evaluated. Subjective image quality was assessed using a 5-point Likert scale. In addition, the effective dose (ED), iodine load and iodine delivery rate (IDR) were compared between groups A and D. RESULTS: 55 keV VMI was the optimal VMI in group B. The objective and subjective image quality of 55 keV VMI in group B were equal to or better than those of the CI in group A. The SNR, CNR and subjective image quality in group D were similar to those in group B (P > 0.05). The ED, iodine load and IDR of group D were reduced by 44%, 59% and 19%, respectively, when compared to those of group A. CONCLUSION: Low dose iodinated CM and radiation for 55 keV VMI in craniocervical CTA using SDCT could still provide equivalent or better image quality than the conventional scanning protocol.

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.

The image quality and diagnostic performance of CT perfusion-derived CT angiography versus that of conventional CT angiography.

Background: The combination of computed tomography angiography (CTA) and computed tomography perfusion (CTP) evaluation of cerebral perfusion status and vascular conditions can improve the diagnostic accuracy of infarction, ischemia, and vascular occlusion in stroke patients, as well as a comprehensive assessment of cerebral edema, collateral circulation, and blood perfusion in the lesion area. However, the consequent radiation safety and contrast agent nephropathy have aroused increasing concern. The purpose of this study was to assess the image quality and diagnostic accuracy of CTA images derived from CTP data, and to explore the feasibility of replacing conventional CTA. Methods: A total of 31 consecutive patients with suspected acute ischemic stroke were retrospectively analyzed. All patients underwent head and neck CTA and brain CTP examinations. All the CTP images were transmitted to the ShuKun artificial intelligence system, which reconstructs CTA derived from CTP (CTA-DF-CTP). The images were divided into 2 groups, including CTA-DF-CTP (Group A) and conventional CTA (Group B). The CT attenuation values, subjective image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), image quality, CT volume dose index (CTDIvol), dose length product (DLP), and effective radiation dose (ED) were compared between the 2 groups. Moreover, the consistency of vascular stenosis and stenosis degree between the 2 groups were measured and evaluated. Results: There were no significant differences in image noise, SNR, or CNR between Groups A and B (P>0.05). The CT attenuation values of the arteries were higher in Group A than in B [internal carotid artery (ICA) =548±112 vs. 454±85 Hounsfield units (HU), middle cerebral artery (MCA) =453±118 vs. 388±70 HU, and basilar artery (BA) =431±99 vs. 360±83 HU] (P<0.01). The image quality of the 2 groups met the requirement of clinical diagnosis (4.97±0.18 vs. 4.94±0.25). No significant difference was found in subjective evaluation (P>0.05). In Group A compared with Group B, the following reductions were observed: CTDIvol (10.7%; 100.8 vs. 112.9 mGy), DLP (23.0%; 1,613±0 vs. 2,093±88 mGy·cm), and ED (23.0%; 5.00±0.00 vs. 6.49±0.27 mSv). Conclusions: CTA-DF-CTP data provide diagnostic accuracy and image quality similar to those of conventional CTA of head and neck CTA.

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 post-resection recurrence by integrating imaging-based surrogates of distinct vascular patterns of hepatocellular carcinoma.

Background & Aims: Distinct vascular patterns, including microvascular invasion (MVI) and vessels encapsulating tumour clusters (VETC), are associated with poor outcomes of hepatocellular carcinoma (HCC). Imaging surrogates of these vascular patterns potentially help to predict post-resection recurrence. Herein, a prognostic model integrating imaging-based surrogates of these distinct vascular patterns was developed to predict postoperative recurrence-free survival (RFS) in patients with HCC. Methods: Clinico-radiological data of 1,285 patients with HCC from China undergoing surgical resection were retrospectively enrolled from seven medical centres between 2014 and 2020. A prognostic model using clinical data and imaging-based surrogates of MVI and VETC patterns was developed (n = 297) and externally validated (n = 373) to predict RFS. The surrogates (i.e. MVI and VETC scores) were individually built from preoperative computed tomography using two independent cohorts (n = 360 and 255). Whether the model's stratification was associated with postoperative recurrence following anatomic resection was also evaluated. Results: The MVI and VETC scores demonstrated effective performance in their respective training and validation cohorts (AUC: 0.851-0.883 for MVI and 0.834-0.844 for VETC). The prognostic model incorporating serum alpha-foetoprotein, tumour multiplicity, MVI score, and VETC score achieved a C-index of 0.748-0.764 for the developing and external validation cohorts and generated three prognostically distinct strata. For patients at model-predicted medium risk, anatomic resection was associated with improved RFS (p <0.05). By contrast, anatomic resection had no impact on RFS in patients at model-predicted low or high risk (both p >0.05). Conclusions: The proposed model integrating imaging-based surrogates of distinct vascular patterns enabled accurate prediction for RFS. It can potentially be used to identify HCC surgical candidates who may benefit from anatomic resection. Impact and implications: MVI and VETC are distinct vascular patterns of HCC associated with aggressive biological behaviour and poor outcomes. Our multicentre study provided a model incorporating imaging-based surrogates of these patterns for preoperatively predicting RFS. The proposed model, which uses imaging detection to estimate the risk of MVI and VETC, offers an opportunity to help shed light on the association between tumour aggressiveness and prognosis and to support the selection of the appropriate type of surgical resection.

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.

Recruiting T-Cells toward the Brain for Enhanced Glioblastoma Immunotherapeutic Efficacy by Co-Delivery of Cytokines and Immune Checkpoint Antibodies with Macrophage-Membrane-Camouflaged Nanovesicles.

Immunotherapy with immune checkpoint inhibitors (CPIs) shows promising prospects for glioblastoma multiforme (GBM) but with restricted results, mainly attributed to the immunosuppressive tumor microenvironment (TME) and the limited antibody permeability of the blood-tumor barrier (BTB) in GBM. Here, nanovesicles with a macrophage-mimicking membrane are described, that co-deliver chemotactic CXC chemokine ligand 10 (CXCL10), to pre-activate the immune microenvironment, and anti-programmed death ligand 1 antibody (aPD-L1), to interrupt the immune checkpoint, aiming to enhance the effect of GBM immunotherapy. Consequently, the tumor tropism of the macrophage membrane and the receptor-mediated transcytosis of the angiopep-2 peptide allow the nanovesicle to effectively cross the BTB and target the GBM region, with 19.75-fold higher accumulation of antibodies compared to the free aPD-L1 group. The CPI therapeutic efficacy is greatly enhanced by CXCL10-induced T-cells recruitment with significant expansion of CD8+ T-cells and effector memory T-cells, leading to the elimination of tumor, prolonged survival time, and long-term immune memory in orthotopic GBM mice. The nanovesicles, that relieve the tumor immunosuppressive microenvironment by CXCL10 to enhance aPD-L1 efficacy, may present a promising strategy for brain-tumor immunotherapy.

Visual Investigation of Tumor-Promoting Fibronectin Potentiated by Obesity in Pancreatic Ductal Adenocarcinoma Using an MR/NIRF Dual-Modality Dendrimer Nanoprobe.

Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease characterized by dense stroma. Obesity is an important metabolic factor that greatly increases PDAC risk and mortality, worsens progression and leads to poor chemotherapeutic outcomes. With omics analysis, magnetic resonance and near-infrared fluorescence (MR/NIRF) dual-modality imaging and molecular functional verification, obesity as an important risk factor is proved to modulate the extracellular matrix (ECM) components and enhance Fibronectin (FN) infiltration in the PDAC stroma, that promotes tumor progression and worsens response to chemotherapy by reducing drug delivery. In the study, to visually evaluate FN in vivo and guide PDAC therapy, an FN-targeted nanoprobe, NP-CREKA, is synthesized by conjugating gadolinium chelates, NIR797 and fluorescein isothiocyanate to a polyamidoamine dendrimer functionalized with targeting peptides. A dual-modality strategy combining MR and NIRF imaging is applied, allowing effective visualization of FN in orthotopic PDAC with high spatial resolution, ideal sensitivity and excellent penetrability, especially in obese mice. In conclusion, the findings provide new insights into the potential of FN as an ideal target for therapeutic evaluation and improving treatment efficacy in PDAC, hopefully improving the specific management of PDAC in lean and obese hosts.

YY1 complex in M2 macrophage promotes prostate cancer progression by upregulating IL-6.

BACKGROUND: Tumor-associated macrophages are mainly polarized into the M2 phenotype, remodeling the tumor microenvironment and promoting tumor progression by secreting various cytokines. METHODS: Tissue microarray consisting of prostate cancer (PCa), normal prostate, and lymph node metastatic samples from patients with PCa were stained with Yin Yang 1 (YY1) and CD163. Transgenic mice overexpressing YY1 were constructed to observe PCa tumorigenesis. Furthermore, in vivo and in vitro experiments, including CRISPR-Cas9 knock-out, RNA sequencing, chromatin immunoprecipitation (ChIP) sequencing, and liquid-liquid phase separation (LLPS) assays, were performed to investigate the role and mechanism of YY1 in M2 macrophages and PCa tumor microenvironment. RESULTS: YY1 was highly expressed in M2 macrophages in PCa and was associated with poorer clinical outcomes. The proportion of tumor-infiltrated M2 macrophages increased in transgenic mice overexpressing YY1. In contrast, the proliferation and activity of anti-tumoral T lymphocytes were suppressed. Treatment targeting YY1 on M2 macrophages using an M2-targeting peptide-modified liposome carrier suppressed PCa cell lung metastasis and generated synergistic anti-tumoral effects with PD-1 blockade. IL-4/STAT6 pathway regulated YY1, and YY1 increased the macrophage-induced PCa progression by upregulating IL-6. Furthermore, by conducting H3K27ac-ChIP-seq in M2 macrophages and THP-1, we found that thousands of enhancers were gained during M2 macrophage polarization, and these M2-specific enhancers were enriched in YY1 ChIP-seq signals. In addition, an M2-specific IL-6 enhancer upregulated IL-6 expression through long-range chromatin interaction with IL-6 promoter in M2 macrophages. During M2 macrophage polarization, YY1 formed an LLPS, in which p300, p65, and CEBPB acted as transcriptional cofactors. CONCLUSIONS: Phase separation of the YY1 complex in M2 macrophages upregulated IL-6 by promoting IL-6 enhancer-promoter interactions, thereby increasing PCa progression.

A Bio-Liposome Activating Natural Killer Cell by Illuminating Tumor Homogenization Antigen Properties.

Natural killer (NK) cell therapies, primarily based on chimeric antigen receptor NK cells (CAR-NK), have been developed and applied clinically for therapeutic treatment of patients with mid-to-late-stage tumors. However, NK cell therapy has limited efficacy due to insufficient antigen expression on the tumor cell surface. Here, a universal "illuminate tumor homogenization antigen properties" (ITHAP) strategy to achieve stable and controlled antigen expression on the surface of tumor cells using nanomedicine, thus significantly enhancing the immune recognizability of tumor cells, is described. The ITHAP strategy is used to generate bio-liposomes (Pt@PL-IgG) composed of intermingled platelet membranes and liposomes with NK-activatable target antigen (IgG antibodies) and cisplatin pre-drug. It is demonstrated that Pt@PL-IgG successfully targets tumor cells using the autonomous drive of platelet membranes and achieves IgG implantation on tumor cells by utilizing membrane fusion properties. Moreover, it is shown that the Pt-DNA complex combined with NK cell-induced pyroptosis causes substantial interferon (IFN) secretion, thus providing a synthase-stimulator of interferon genes (STING)-IFN-mediated positive immune microenvironment to further potentiate NK therapy. These results show that anchoring cancer cells with NK-activatable target antigens is a promising translational strategy for addressing therapeutic challenges in tumor heterogeneity.