UV-Permeable 3D Li Anodes for In Situ Fabrication of Interface-Gapless Flexible Solid-State Lithium Metal Batteries.

Flexible solid-state lithium metal batteries (SSLMBs) are highly desirable for future wearable electronics because of their high energy density and safety. However, flexible SSLMBs face serious challenges not only in regulating the Li plating/stripping behaviors but also in enabling the mechanical flexibility of the cell. Both challenges are largely associated with the interfacial gaps between the solid electrolytes and the electrodes. Here, a UV-permeable and flexible composited Li metal anode (UVp-Li), which possesses a unique light-penetrating interwoven structure similar to textiles is reported. UVp-Li allows one-step bonding of the cathode, anode, and solid electrolyte via an in situ UV-initiated polymerization method to achieve the gapless SSLMBs. The gapless structure not only effectively stabilizes the plating/stripping of Li metal during cycling, but also ensures the integrity of the cell during mechanical bending. UVp-Li symmetric cell presents a stable cycling over 1000 h at 0.5 mA cm-2. LiFePO4||UVp-Li full cells (areal capacity ranging from 0.5 to 3 mAh cm-2) show outstanding capacity retention of over 84% after 500 charge/discharge cycles at room temperature. Large pouch cells using high-loading cathodes maintain stable electrochemical performance during 1000 times of dynamic bending.

Bibliometric analysis and evaluation of publications on non-Helicobacter pylori helicobacters from 1993 to 2023.

Aim: A bibliometric analysis and evaluation of research on non-Helicobacter pylori Helicobacter species (NHPHs) is essential to determining future research directions. Materials & methods: A comprehensive search was carried out using predetermined search terms within the Web of Science Core Collection (WoSCC) to gather publications spanning from 1993 to 2023. VOSviewer and Citespace were employed for data analysis and visualization. Results: 308 publications on NHPHs were included. Among these, gastric NHPHs received more publications and attention compared with enterohepatic NHPHs. Key findings included the identification of most productive countries, institutions, journals, authors, keywords, research trends and notable perspectives in the field. Conclusion: The article guides further research and clinical applications on NHPHs.

Utility of gadoxetate disodium-enhanced magnetic resonance imaging in evaluating liver failure risk after major hepatic resection.

Background: Post-hepatectomy liver failure (PHLF) is still a predominant cause of hepatectomy-related mortality. However, it is difficult to evaluate the remnant liver functional reserve accurately before surgery to prevent PHLF. In this study, we aimed to explore the role of gadoxetate disodium-enhanced magnetic resonance imaging (MRI) in evaluating remnant liver functional reserve. Methods: For this cross-sectional study, the sample retrospectively included 56 patients undergoing liver resections of at least three segments between June 2019 and September 2022 at The General Hospital of the Western Theater Command. Pre-surgery assessments involved liver computer tomography (CT), an indocyanine green (ICG) clearance test, the Child-Pugh scoring system, and liver function serum biochemical indicators. Each patient underwent a gadoxetate disodium-enhanced MRI before the hepatectomy, and we measured the remnant hepatocellular uptake index (rHUI) as well as the standard remnant hepatocellular uptake index (SrHUI). We examined the diagnostic utility of rHUI, SrHUI, indocyanine green retention rate of 15 minutes (ICG R15), and Albumin for PHLF. Receiver operating characteristics (ROC) analyses were used to measure the preoperative liver function parameters (namely, rHUI, SrHUI, ICG R15, and Albumin) for predicting PHLF. The areas under the curve (AUCs) were calculated and compared between different preoperative liver function parameters using the Wilson/Brown method. The Pearson or Spearman correlation coefficient was used for correlation analysis between ICG R15, Albumin, and rHUI and between ICG R15, Albumin, and SrHUI, respectively. Results: Twelve patients (21.43%) had complications of PHLF. We found significant differences in rHUI, SrHUI, ICG R15, and Albumin between the non-PHLF and PHLF groups. The pooled r between ICG R15 and rHUI was -0.591 [95% confidence interval (CI): -0.740 to -0.389, P<0.001], and between ICG R15 and SrHUI was -0.534 (95% CI: -0.703 to -0.308, P<0.001). The area under the curve (AUC) values of rHUI, SrHUI, ICG R15, and Ablumin were 0.871 (sensitivity 81.82%; specificity 91.67%), 0.878 (sensitivity 79.55%; specificity 83.33%), 0.835 (sensitivity 99.73%; specificity 66.67%), and 0.782 (sensitivity 88.64%; specificity 58.33%), respectively. Conclusions: We found that the rHUI and SrHUI calculated using the gadoxetate disodium-enhanced MRI reflected a combination of remnant hepatocyte function and liver volume, and these were useful as a quantitative assessment indicator of remnant liver functional reserve and can be a better predictor of PHLF after major hepatic resection.

Radiomic signatures associated with tumor immune heterogeneity predict survival in locally recurrent nasopharyngeal carcinoma.

BACKGROUND: The prognostic value of traditional clinical indicators for locally recurrent nasopharyngeal carcinoma (lrNPC) is limited due to their inability to reflect intratumor heterogeneity. We aimed to develop a radiomic signature to reveal tumor immune heterogeneity and predict survival in lrNPC. METHODS: This multicenter, retrospective study included 921 patients with lrNPC. A machine learning signature and nomogram based on pretreatment MRI features were developed for predicting overall survival (OS) in a training cohort and validated in two independent cohorts. A clinical nomogram and an integrated nomogram were constructed for comparison. Nomogram performance was evaluated by concordance index (C-index) and receiver operating characteristic curve analysis. Accordingly, patients were classified into risk groups. The biological characteristics and immune infiltration of the signature were explored by RNA sequencing (RNA-seq) analysis. RESULTS: The machine learning signature and nomogram demonstrated comparable prognostic ability to a clinical nomogram, achieving C-indexes of 0.729, 0.718, and 0.731 in the training, internal, and external validation cohorts, respectively. Integration of the signature and clinical variables significantly improved the predictive performance. The proposed signature effectively distinguished patients between risk groups with significantly distinct OS rates. Subgroup analysis indicated the recommendation of local salvage treatments for low-risk patients. Exploratory RNA-seq analysis revealed differences in interferon response and lymphocyte infiltration between risk groups. CONCLUSIONS: An MRI-based radiomic signature predicted OS more accurately. The proposed signature associated with tumor immune heterogeneity may serve as a valuable tool to facilitate prognostic stratification and guide individualized management for lrNPC patients.

A Freestanding, Dissolution- and Diffusion-Limiting, Flexible Sulfur Electrode Enables High Specific Capacity at High Mass Loading.

The acquisition of stable and high-areal-capacity S cathodes over 10 mA h cm-2 is a critical and indispensable step to realize the high energy density configuration. However, increasing the areal capacity of S cathodes often deteriorates the specific capacity and stability due to the aggravated dissolution of S and diffusion of solvable polysulfides in the thick electrode. Herein, the design of a freestanding composite cathode that leverages 3D covalent binding sites and chemical adsorption environment to offer dissolution-limiting and diffusion-blocking functions of S species is reported. By employing this architecture, the coin cell exhibits excellent cycling stability and an exceptional specific capacity of 1444.3 mA h g-1 (13 mA h cm-2), and the pouch cell configuration manifests a noteworthy areal capacity exceeding 11 mA h cm-2. This performance is coupled with excellent flexibility, demonstrated through consecutive bending cycle tests, even at a sulfur loading of 9.00 mg cm-2. This study lays the foundation for the development of flexible Li-S batteries with increased loading capacities and exceptional performance.

Metal-Organic Framework Based Mucoadhesive Nanodrugs for Multifunction Helicobacter Pylori Targeted Eradication, Inflammation Regulation and Gut Flora Protection.

The prevalence of drug-resistant bacteria presents a significant challenge to the antibiotic treatment of Helicobacter pylori (H. pylori), while traditional antimicrobial agents often suffer from shortcomings such as poor gastric retention, inadequate alleviation of inflammation, and significant adverse effects on the gut microbiota. Here, a selenized chitosan (CS-Se) modified bismuth-based metal-organic framework (Bi-MOF@CS-Se) nanodrug is reported that can target mucin through the charge interaction of the outer CS-Se layer to achieve mucosal adhesion and gastric retention. Additionally, the Bi-MOF@CS-Se can respond to gastric acid and pepsin degradation, and the exposed Bi-MOF exhibits excellent antibacterial properties against standard H. pylori as well as clinical antibiotic-resistant strains. Remarkably, the Bi-MOF@CS-Se effectively alleviates inflammation and excessive oxidative stress by regulating the expression of inflammatory factors and the production of reactive oxygen species (ROS), thereby exerting therapeutic effects against H. pylori infection. Importantly, this Bi-MOF@CS-Se nanodrug does not affect the homeostasis of gut microbiota, providing a promising strategy for efficient and safe treatment of H. pylori infection.

Sevelamer reverses liver fibrosis by deactivation of hepatic stellate cells.

Liver fibrosis is a chronic liver disease characterized by a progressive wound healing response caused by chronic liver injury. Currently, there are no approved clinical treatments for liver fibrosis. Sevelamer is used clinically to treat hyperphosphatemia and has shown potential therapeutic effects on liver diseases. However, there have been few studies evaluating the therapeutic effects of sevelamer on liver fibrosis, and the specific mechanisms are still unclear. In this study, we investigated the antifibrotic effects of sevelamer-induced low inorganic phosphate (Pi) stress in vitro and in vivo and analyzed the detailed mechanisms. We found that low Pi stress could inhibit the proliferation of activated hepatic stellate cells (HSCs) by promoting apoptosis, effectively suppressing the migration and epithelial-mesenchymal transition (EMT) of hepatic stellate cells. Additionally, low Pi stress significantly increased the antioxidant stress response. It is worth noting that low Pi stress indirectly inhibited the activation and migration of HSCs by suppressing transforming growth factor ß (TGF-ß) expression in macrophages. In a rat model of liver fibrosis, oral administration of sevelamer significantly decreased blood phosphorus levels, improved liver function, reduced liver inflammation, and increased the antioxidant stress response in the liver. Our study revealed that the key mechanism by which sevelamer inhibited liver fibrosis involved binding to gastrointestinal phosphate, resulting in a decrease in blood phosphorus levels, the downregulation of TGF-ß expression in macrophages, and the inhibition of HSC migration and fibrosis-related protein expression. Therefore, our results suggest that sevelamer-induced low Pi stress can attenuate hepatic stellate cell activation and inhibit the progression of liver fibrosis, making it a potential option for the treatment of liver fibrosis and other refractory chronic liver diseases.

ROS-mediated up-regulation of SAE1 by Helicobacter pylori promotes human gastric tumor genesis and progression.

Helicobacter pylori (H. pylori) is a major risk factor of gastric cancer (GC). The SUMO-activating enzyme SAE1(SUMO-activating enzyme subunit 1), which is indispensable for protein SUMOylation, involves in human tumorigenesis. In this study, we used the TIMER and TCGA database to explore the SAE1 expression in GC and normal tissues and Kaplan-Meier Plotter platform for survival analysis of GC patients. GC tissue microarray and gastric samples from patients who underwent endoscopic treatment were employed to detect the SAE1expression. Our results showed that SAE1 was overexpressed in GC tissues and higher SAE1 expression was associated with worse clinical characteristics of GC patients. Cell and animal models showed that H. pylori infection upregulated SAE1, SUMO1, and SUMO2/3 protein expression. Functional assays suggested that suppression of SAE1 attenuated epithelial-mesenchymal transition (EMT) biomarkers and cell proliferation abilities induced by H. pylori. Cell and animal models of ROS inhibition in H. pylori showed that ROS could mediate the H. pylori-induced upregulation of SAE1, SUMO1, and SUMO2/3 protein. RNA sequencing was performed and suggested that knockdown of SAE1 could exert an impact on IGF-1 expression. General, increased SUMOylation modification is involved in H. pylori-induced GC.

Machine learning-based identification of contrast-enhancement phase of computed tomography scans.

Contrast-enhanced computed tomography scans (CECT) are routinely used in the evaluation of different clinical scenarios, including the detection and characterization of hepatocellular carcinoma (HCC). Quantitative medical image analysis has been an exponentially growing scientific field. A number of studies reported on the effects of variations in the contrast enhancement phase on the reproducibility of quantitative imaging features extracted from CT scans. The identification and labeling of phase enhancement is a time-consuming task, with a current need for an accurate automated labeling algorithm to identify the enhancement phase of CT scans. In this study, we investigated the ability of machine learning algorithms to label the phases in a dataset of 59 HCC patients scanned with a dynamic contrast-enhanced CT protocol. The ground truth labels were provided by expert radiologists. Regions of interest were defined within the aorta, the portal vein, and the liver. Mean density values were extracted from those regions of interest and used for machine learning modeling. Models were evaluated using accuracy, the area under the curve (AUC), and Matthew's correlation coefficient (MCC). We tested the algorithms on an external dataset (76 patients). Our results indicate that several supervised learning algorithms (logistic regression, random forest, etc.) performed similarly, and our developed algorithms can accurately classify the phase of contrast enhancement.

Bilibili, TikTok, and YouTube as sources of information on gastric cancer: assessment and analysis of the content and quality.

BACKGROUND: Gastric cancer has attracted widespread attention on social media due to its high incidence and severity. The Bilibili, TikTok, and YouTube video-sharing platforms have received considerable interest among general health consumers. Nevertheless, it remains unclear whether the information in videos on these platforms is of satisfactory content and quality. METHODS: A total of 300 eligible videos related to gastric cancer were screened from three video-sharing platforms, Bilibili, TikTok, and YouTube, for assessment and analysis. First, the basic information presented in the videos was recorded. Next, we identified the source and content type of each video. Then, the Global Quality Scale (GQS), Journal of the American Medical Association (JAMA), and Modified DISCERN were used to assess the educational content and quality of each video. A comparative analysis was undertaken of the videos procured from these three sources. RESULTS: We identified six categories of uploaders of the 300 videos: 159 videos (53%) were uploaded by health professionals, 21 videos (7%) by users in science communications, 29 videos (9.67%) by general users, 27 videos (9%) from news agencies, 63 videos (12%) by nonprofit organizations, and one video (0.33%) by a for-profit organization. In terms of the content types of the 300 videos, we identified five distinct categories. There were 48 videos (16%) on early signals, 12 videos (4%) on late symptoms, 40 videos (13.33%) on etiologies and causations, 160 videos (53.33%) on scientific introductions, and 40 videos (13.33%) on treatment methods. The overall quality of the videos was evaluated by the GQS, JAMA, and Modified DISCERN and was found to be medium, with scores of 2.6/5, 2.41/4, and 2.71/5 points, respectively. CONCLUSIONS: This innovative study demonstrates that videos on social media platforms can help the public learn about early signals, late symptoms, treatment methods, etiologies and causations, and scientific introductions of gastric cancer. However, both the content and quality of uploaded recordings are inadequate currently. More efforts should be made to enhance the content and quality of videos on gastric cancer and to increase public awareness.

LOX-1 acts as an N6-methyladenosine-regulated receptor for Helicobacter pylori by binding to the bacterial catalase.

The role of N6-methyladenosine (m6A) modification of host mRNA during bacterial infection is unclear. Here, we show that Helicobacter pylori infection upregulates host m6A methylases and increases m6A levels in gastric epithelial cells. Reducing m6A methylase activity via hemizygotic deletion of methylase-encoding gene Mettl3 in mice, or via small interfering RNAs targeting m6A methylases, enhances H. pylori colonization. We identify LOX-1 mRNA as a key m6A-regulated target during H. pylori infection. m6A modification destabilizes LOX-1 mRNA and reduces LOX-1 protein levels. LOX-1 acts as a membrane receptor for H. pylori catalase and contributes to bacterial adhesion. Pharmacological inhibition of LOX-1, or genetic ablation of Lox-1, reduces H. pylori colonization. Moreover, deletion of the bacterial catalase gene decreases adhesion of H. pylori to human gastric sections. Our results indicate that m6A modification of host LOX-1 mRNA contributes to protection against H. pylori infection by downregulating LOX-1 and thus reducing H. pylori adhesion.

Elevated expression of WSB2 degrades p53 and activates the IGFBP3-AKT-mTOR-dependent pathway to drive hepatocellular carcinoma.

Dysregulation of wild-type p53 turnover is a key cause of hepatocellular carcinoma (HCC), yet its mechanism remains poorly understood. Here, we report that WD repeat and SOCS box containing protein 2 (WSB2), an E3 ubiquitin ligase, is an independent adverse prognostic factor in HCC patients. WSB2 drives HCC tumorigenesis and lung metastasis in vitro and in vivo. Mechanistically, WSB2 is a new p53 destabilizer that promotes K48-linked p53 polyubiquitination at the Lys291 and Lys292 sites in HCC cells, leading to p53 proteasomal degradation. Degradation of p53 causes IGFBP3-dependent AKT/mTOR signaling activation. Furthermore, WSB2 was found to bind to the p53 tetramerization domain via its SOCS box domain. Targeting mTOR with everolimus, an oral drug, significantly blocked WSB2-triggered HCC tumorigenesis and metastasis in vivo. In clinical samples, high expression of WSB2 was associated with low wild-type p53 expression and high p-mTOR expression. These findings demonstrate that WSB2 is overexpressed and degrades wild-type p53 and then activates the IGFBP3-AKT/mTOR axis, leading to HCC tumorigenesis and lung metastasis, which indicates that targeting mTOR could be a new therapeutic strategy for HCC patients with high WSB2 expression and wild-type p53.

Elevated FBXL6 activates both wild-type KRAS and mutant KRASG12D and drives HCC tumorigenesis via the ERK/mTOR/PRELID2/ROS axis in mice.

BACKGROUND: Kirsten rat sarcoma (KRAS) and mutant KRASG12D have been implicated in human cancers, but it remains unclear whether their activation requires ubiquitination. This study aimed to investigate whether and how F-box and leucine-rich repeat 6 (FBXL6) regulates KRAS and KRASG12D activity in hepatocellular carcinoma (HCC). METHODS: We constructed transgenic mouse strains LC (LSL-Fbxl6KI/+;Alb-Cre, n = 13), KC (LSL-KrasG12D/+;Alb-Cre, n = 10) and KLC (LSL-KrasG12D/+;LSL-Fbxl6KI/+;Alb-Cre, n = 12) mice, and then monitored HCC for 320 d. Multiomics approaches and pharmacological inhibitors were used to determine oncogenic signaling in the context of elevated FBXL6 and KRAS activation. Co­immunoprecipitation (Co-IP), Western blotting, ubiquitination assay and RAS activity detection assay were employed to investigate the underlying molecular mechanism by which FBXL6 activates KRAS. The pathological relevance of the FBXL6/KRAS/extracellular signal-regulated kinase (ERK)/mammalian target of rapamycin (mTOR)/proteins of relevant evolutionary and lymphoid interest domain 2 (PRELID2) axis was evaluated in 129 paired samples from HCC patients. RESULTS: FBXL6 is highly expressed in HCC as well as other human cancers (P < 0.001). Interestingly, FBXL6 drives HCC in transgenic mice. Mechanistically, elevated FBXL6 promotes the polyubiquitination of both wild-type KRAS and KRASG12D at lysine 128, leading to the activation of both KRAS and KRASG12D and promoting their binding to the serine/threonine-protein kinase RAF, which is followed by the activation of mitogen-activated protein kinase kinase (MEK)/ERK/mTOR signaling. The oncogenic activity of the MEK/ERK/mTOR axis relies on PRELID2, which induces reactive oxygen species (ROS) generation. Furthermore, hepatic FBXL6 upregulation facilitates KRASG12D to induce more severe hepatocarcinogenesis and lung metastasis via the MEK/ERK/mTOR/PRELID2/ROS axis. Dual inhibition of MEK and mTOR effectively suppresses tumor growth and metastasis in this subtype of cancer in vivo. In clinical samples, FBXL6 expression positively correlates with p-ERK (χ2 = 85.067, P < 0.001), p-mTOR (χ2 = 66.919, P < 0.001) and PRELID2 (χ2 = 20.891, P < 0.001). The Kaplan-Meier survival analyses suggested that HCC patients with high FBXL6/p-ERK levels predicted worse overall survival (log­rank P < 0.001). CONCLUSIONS: FBXL6 activates KRAS or KRASG12D via ubiquitination at the site K128, leading to activation of the ERK/mTOR/PRELID2/ROS axis and tumorigenesis. Dual inhibition of MEK and mTOR effectively protects against FBXL6- and KRASG12D-induced tumorigenesis, providing a potential therapeutic strategy to treat this aggressive subtype of liver cancer.

A deep learning-based semiautomated workflow for triaging follow-up MR scans in treated nasopharyngeal carcinoma.

It is imperative to optimally utilize virtues and obviate defects of fully automated analysis and expert knowledge in new paradigms of healthcare. We present a deep learning-based semiautomated workflow (RAINMAN) with 12,809 follow-up scans among 2,172 patients with treated nasopharyngeal carcinoma from three centers (ChiCTR.org.cn, Chi-CTR2200056595). A boost of diagnostic performance and reduced workload was observed in RAINMAN compared with the original manual interpretations (internal vs. external: sensitivity, 2.5% [p = 0.500] vs. 3.2% [p = 0.031]; specificity, 2.9% [p < 0.001] vs. 0.3% [p = 0.302]; workload reduction, 79.3% vs. 76.2%). The workflow also yielded a triaging performance of 83.6%, with increases of 1.5% in sensitivity (p = 1.000) and 0.6%-1.3% (all p < 0.05) in specificity compared to three radiologists in the reader study. The semiautomated workflow shows its unique superiority in reducing radiologist's workload by eliminating negative scans while retaining the diagnostic performance of radiologists.

Does dual-layer spectral detector CT provide added value in predicting spread through air spaces in lung adenocarcinoma? A preliminary study.

OBJECTIVES: To examine the predictive value of dual-layer spectral detector CT (DLCT) for spread through air spaces (STAS) in clinical lung adenocarcinoma. METHODS: A total of 225 lung adenocarcinoma cases were retrospectively reviewed for demographic, clinical, pathological, traditional CT, and spectral parameters. Multivariable logistic regression analysis was carried out based on three logistic models, including a model using traditional CT features (traditional model), a model using spectral parameters (spectral model), and an integrated model combining traditional CT and spectral parameters (integrated model). Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were performed to assess these models. RESULTS: Univariable analysis showed significant differences between the STAS and non-STAS groups in traditional CT features, including nodule density (p < 0.001), pleural indentation types (p = 0.006), air-bronchogram sign (p = 0.031), the presence of spiculation (p < 0.001), long-axis diameter of the entire nodule (LD) (p < 0.001), and consolidation/tumor ratio (CTR) (p < 0.001). Multivariable analysis revealed that LD > 20 mm (odds ratio [OR] = 2.271, p = 0.025) and CTR (OR = 24.208, p < 0.001) were independent predictors in the traditional model, while electronic density (ED) in the venous phase was an independent predictor in the spectral (OR = 1.062, p < 0.001) and integrated (OR = 1.055, p < 0.001) models. The area under the curve (AUC) for the integrated model (0.84) was the highest (spectral model, 0.83; traditional model, 0.80), and the difference between the integrated and traditional models was statistically significant (p = 0.015). DCA showed that the integrated model had superior clinical value versus the traditional model. CONCLUSIONS: DLCT has added value for STAS prediction in lung adenocarcinoma. CLINICAL RELEVANCE STATEMENT: Spectral CT has added value for spread through air spaces prediction in lung adenocarcinoma so may impact treatment planning in the future. KEY POINTS: • Electronic density may be a potential spectral index for predicting spread through air spaces in lung adenocarcinoma. • A combination of spectral and traditional CT features enhances the performance of traditional CT for predicting spread through air spaces.

Radiomic signatures reveal multiscale intratumor heterogeneity associated with tissue tolerance and survival in re-irradiated nasopharyngeal carcinoma: a multicenter study.

BACKGROUND: Post-radiation nasopharyngeal necrosis (PRNN) is a severe adverse event following re-radiotherapy for patients with locally recurrent nasopharyngeal carcinoma (LRNPC) and associated with decreased survival. Biological heterogeneity in recurrent tumors contributes to the different risks of PRNN. Radiomics can be used to mine high-throughput non-invasive image features to predict clinical outcomes and capture underlying biological functions. We aimed to develop a radiogenomic signature for the pre-treatment prediction of PRNN to guide re-radiotherapy in patients with LRNPC. METHODS: This multicenter study included 761 re-irradiated patients with LRNPC at four centers in NPC endemic area and divided them into training, internal validation, and external validation cohorts. We built a machine learning (random forest) radiomic signature based on the pre-treatment multiparametric magnetic resonance images for predicting PRNN following re-radiotherapy. We comprehensively assessed the performance of the radiomic signature. Transcriptomic sequencing and gene set enrichment analyses were conducted to identify the associated biological processes. RESULTS: The radiomic signature showed discrimination of 1-year PRNN in the training, internal validation, and external validation cohorts (area under the curve (AUC) 0.713-0.756). Stratified by a cutoff score of 0.735, patients with high-risk signature had higher incidences of PRNN than patients with low-risk signature (1-year PRNN rates 42.2-62.5% vs. 16.3-18.8%, P < 0.001). The signature significantly outperformed the clinical model (P < 0.05) and was generalizable across different centers, imaging parameters, and patient subgroups. The radiomic signature had prognostic value concerning its correlation with PRNN-related deaths (hazard ratio (HR) 3.07-6.75, P < 0.001) and all causes of deaths (HR 1.53-2.30, P < 0.01). Radiogenomics analyses revealed associations between the radiomic signature and signaling pathways involved in tissue fibrosis and vascularity. CONCLUSIONS: We present a radiomic signature for the individualized risk assessment of PRNN following re-radiotherapy, which may serve as a noninvasive radio-biomarker of radiation injury-associated processes and a useful clinical tool to personalize treatment recommendations for patients with LANPC.

Magnetic Resonance Deep Learning Radiomic Model Based on Distinct Metastatic Vascular Patterns for Evaluating Recurrence-Free Survival in Hepatocellular Carcinoma.

BACKGROUND: The metastatic vascular patterns of hepatocellular carcinoma (HCC) are mainly microvascular invasion (MVI) and vessels encapsulating tumor clusters (VETC). However, most existing VETC-related radiological studies still focus on the prediction of VETC status. PURPOSE: This study aimed to build and compare VETC-MVI related models (clinical, radiomics, and deep learning) associated with recurrence-free survival of HCC patients. STUDY TYPE: Retrospective. POPULATION: 398 HCC patients (349 male, 49 female; median age 51.7 years, and age range: 22-80 years) who underwent resection from five hospitals in China. The patients were randomly divided into training cohort (n = 358) and test cohort (n = 40). FIELD STRENGTH/SEQUENCE: 3-T, pre-contrast T1-weighted imaging spoiled gradient recalled echo (T1WI SPGR), T2-weighted imaging fast spin echo (T2WI FSE), and contrast enhanced arterial phase (AP), delay phase (DP). ASSESSMENT: Two radiologists performed the segmentation of HCC on T1WI, T2WI, AP, and DP images, from which radiomic features were extracted. The RFS related clinical characteristics (VETC, MVI, Barcelona stage, tumor maximum diameter, and alpha fetoprotein) and radiomic features were used to build the clinical model, clinical-radiomic (CR) nomogram, deep learning model. The follow-up process was done 1 month after resection, and every 3 months subsequently. The RFS was defined as the date of resection to the date of recurrence confirmed by radiology or the last follow-up. Patients were followed up until December 31, 2022. STATISTICAL TESTS: Univariate COX regression, least absolute shrinkage and selection operator (LASSO), Kaplan-Meier curves, log-rank test, C-index, and area under the curve (AUC). P < 0.05 was considered statistically significant. RESULTS: The C-index of deep learning model achieved 0.830 in test cohort compared with CR nomogram (0.731), radiomic signature (0.707), and clinical model (0.702). The average RFS of the overall patients was 26.77 months (range 1-80 months). DATA CONCLUSION: MR deep learning model based on VETC and MVI provides a potential tool for survival assessment. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.