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The Liver Imaging and Reporting Data System (LI-RADS) is a comprehensive system for standardizing the terminology, technique, interpretation, reporting, and data collection of liver imaging with the overarching goal of improving communication, clinical care, education, and research relating to patients at risk for or diagnosed with hepatocellular carcinoma (HCC). In 2018, the American Association for the Study of Liver Diseases (AASLD) integrated LI-RADS into its clinical practice guidance for the imaging-based diagnosis of HCC. The harmonization between the AASLD and LI-RADS diagnostic imaging criteria required minor modifications to the recently released LI-RADS v2017 guidelines, necessitating a LI-RADS v2018 update. This article provides an overview of the key changes included in LI-RADS v2018 as well as a look at the LI-RADS v2018 diagnostic algorithm and criteria, technical recommendations, and management suggestions. Substantive changes in LI-RADS v2018 are the removal of the requirement for visibility on antecedent surveillance ultrasound for LI-RADS 5 (LR-5) categorization of 10-19 mm observations with nonrim arterial phase hyper-enhancement and nonperipheral "washout", and adoption of the Organ Procurement and Transplantation Network definition of threshold growth (≥ 50% size increase of a mass in ≤ 6 months). Nomenclatural changes in LI-RADS v2018 are the removal of -us and -g as LR-5 qualifiers.

Major research efforts in liver cancer have been devoted to increasing the efficacy and effectiveness of surveillance for hepatocellular carcinoma (HCC). As with other cancers, surveillance programmes aim to detect tumours at an early stage, facilitate curative-intent treatment, and reduce cancer-related mortality. HCC surveillance is supported by a large randomised-controlled trial in patients with chronic HBV infection and several cohort studies in cirrhosis; however, effectiveness in clinical practice is limited by several barriers, including inadequate risk stratification, underuse of surveillance, and suboptimal accuracy of screening tests. There are several proposed strategies to address these limitations, including risk stratification algorithms and biomarkers to better identity at-risk individuals, interventions to increase surveillance, and emerging imaging- and blood-based surveillance tests with improved sensitivity and specificity for early HCC detection. Beyond clinical validation, data are needed to establish clinical utility, i.e. increased early tumour detection and reduced HCC-related mortality. If successful, these data could facilitate a precision screening paradigm in which surveillance strategies are tailored to individual HCC risk to maximise overall surveillance value. However, practical and logistical considerations must be considered when designing and implementing these validation efforts. To address these issues, ILCA (the International Liver Cancer Association) adjourned a single topic workshop on HCC risk stratification and surveillance in June 2022. Herein, we present a white paper on these topics, including the status of the field, ongoing research efforts, and barriers to the translation of emerging strategies. [ABSTRACT FROM AUTHOR]

Background: Bibliometric analysis has become popular in recent years, and increasingly more articles focusing on a particular disease are being published. The present study was performed to analyse the 100 most frequently cited papers in liver cancer (LC). Methods: We searched the Thomson Reuters Web of Science database on 14 July 2018 to identify all potential manuscripts for this study. The search terms were 'liver cancer' and its synonyms. Manuscripts were listed in descending order by the total citations (TCs), and the 100 most frequently cited papers were identified and analysed by topic, journal, author, year and institution. Results: We retrieved 235 687 papers from the Web of Science database. The TC of the 100 most frequently cited papers in LC ranged from 612 to 5358. The 100 papers were published in 31 journals and came from nine countries. The University of Barcelona published the highest number of papers and had the most TC. Ten authors published more than one paper. Treatment of LC was the most widely studied topic. A significant correlation was found between the journal's 2017 impact factor and the TC (P = 0.003). Conclusion: We assessed the landmark papers in the field of LC. These 100 most frequently cited papers reflect major advances and several hot topics in LC during the recent decades. Our study is of great value for young investigators, provides insights into the trends of LC and can guide directions for future academic research. [ABSTRACT FROM AUTHOR]

The last 5 years have witnessed relevant advances in the systemic treatment of hepatocellular carcinoma. New data have emerged since the development of the EASL Clinical Practice Guidelines on the management of hepatocellular carcinoma in 2018. Drugs licensed in some countries now include 4 oral multi-tyrosine kinase inhibitors (sorafenib, lenvatinib, regorafenib and cabozantinib), 1 anti-angiogenic antibody (ramucirumab) and 4 immune checkpoint inhibitors, alone or in combination (atezolizumab in combination with bevacizumab, ipilimumab in combination with nivolumab, nivolumab and pembrolizumab in monotherapy). Prolonged survival in excess of 2 years can be expected in most patients with sensitive tumours and well-preserved liver function that renders them fit for sequential therapies. With different choices available in any given setting, the robustness of the evidence of efficacy and a correct matching of the safety profile of a given agent with patient characteristics and preferences are key in making sound therapeutic decisions. The recommendations in this document amend the previous EASL Clinical Practice Guidelines and aim to help clinicians provide the best possible care for patients today. In view of several ongoing and promising trials, further advances in systemic therapy of hepatocellular carcinoma are foreseen in the near future and these recommendations will have to be updated regularly. [ABSTRACT FROM AUTHOR]

Hepatitis B and C virus, Opisthorchis viverrini and Clonorchis sinensis, are all individually known to put a person at increased risk for cholangiocarcinoma and hepatocellular carcinoma. This paper seeks to determine if there is any interaction between liver flukes and hepatitis virus infection that are known to put a person at an increased risk for cholangiocarcinoma and hepatocellular carcinoma collectively. This paper seeks to determine whether there is any publicly available articles in English that determine if having a hepatitis viral co-infection along with liver flukes would influence the risk of developing liver cancer. We followed PRISMA systematic review guidelines to conduct a literature review. Three manuscripts fit the search criteria. Two presented evidence in support of a synergistic relationship between liver fluke and viral hepatitis infection while the other found no relationship. One manuscript determined that the interaction between hepatitis B and C. sinensis did not have any significant risk of liver cancer. Studies found that HBV affected progression of co-infection to liver cancer but may have its own disease state worsened by presence of liver flukes. Only one paper was found that presented data on HCV, therefore no conclusion can be drawn due to the lack of evidence discovered. Of the studies, the conclusions and strength of the data were mixed. However, the stronger studies suggested a synergistic relationship between liver flukes and HBV to increase the risk of progressing to liver cancer. [ABSTRACT FROM AUTHOR]

According to a recent World Health Organization estimate, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, which originated in China in 2019, has spread globally, infecting nearly 100 million people worldwide by January 2021. Patients with chronic liver diseases (CLD), particularly cirrhosis, hepatobiliary malignancies, candidates for liver transplantation, and immunosuppressed individuals after liver transplantation appear to be at increased risk of infections in general, which in turn translates into increased mortality. This is also the case for SARS-CoV-2 infection, where patients with cirrhosis, in particular, are at high risk of a severe COVID-19 course. Therefore, vaccination against various pathogens including SARS-CoV-2, administered as early as possible in patients with CLD, is an important protective measure. However, due to impaired immune responses in these patients, the immediate and long-term protective response through immunisation may be incomplete. The current SARS-CoV-2 pandemic has led to the exceptionally fast development of several vaccine candidates. A small number of these SARS-CoV-2 vaccine candidates have already undergone phase III, placebo-controlled, clinical trials in healthy individuals with proof of short-term safety, immunogenicity and efficacy. However, although regulatory agencies in the US and Europe have already approved some of these vaccines for clinical use, information on immunogenicity, duration of protection and long-term safety in patients with CLD, cirrhosis, hepatobiliary cancer and liver transplant recipients has yet to be generated. This review summarises the data on vaccine safety, immunogenicity, and efficacy in this patient population in general and discusses the implications of this knowledge on the introduction of the new SARS-CoV-2 vaccines. [ABSTRACT FROM AUTHOR]

The article presents several abstracts on medical topics including prognostic significance and correlation between Igr5 and regulatory T cells in gastric cancer, upper gastrointestinal toxicity in chronic ketamine abusers, and microwave versus radiofrequency ablation for hepatocellular carcinoma.

In this work, a self‐powered electrowetting valve (SPEV) driven by an energy‐harvesting triboelectric nanogenerator (TENG) is reported. The TENG (5 × 5 cm2) can produce an open‐circuit voltage of 380 V by applying a mechanical stimulus, which is much higher than the actuation voltage of the SPEV (130 V). Once actuated, the electrowetting valve can be instantly switched on at a response time of 0.18 s, allowing liquid reagent to flow through the valve. The SPEV can be used for simultaneous addition of multiple reagents in an enzyme‐linked immunosorbent assay on a paper‐based microfluidic analytical device (µPAD). This assay involves a chromogenic reaction that achieves effective detection of alpha‐fetoprotein, a critical tumor marker for early diagnosis of liver cancer. The SPEV reported in this work can be potentially used in other complex multiprocedure µPADs, which will potentially enable portable, accessible, and cost‐effective assays for early diagnosis, food safety, pollution detection, etc. A self‐powered electrowetting valve (SPEV) enabled by a triboelectric nanogenerator is demonstrated in this work. The SPEV allows an instantaneous flow of liquid reagent once agitated by a mechanical stimulus. Integrated with a microfluidic paper‐based device, the SPEV achieves simultaneous addition of multiple reagents in an enzyme‐linked immunosorbent essay, making it possible for portable, acccesible, and cost‐effective essays. [ABSTRACT FROM AUTHOR]

Enzyme-based assays have been extensively used for the early diagnosis of disease-related biomarkers. However, these assays are time-consuming, resource-intensive, and infrastructure-dependent, which renders them unsuitable and impractical for use in resource-constrained areas. Thus, there is a strong demand for a biocompatible and potentially generalizable sensor that can rapidly detect cancer biomarkers at ultralow concentration. Herein, an enzyme-free, cost-efficient, and easy-to-use assay based on a novel approach that entails fluorescent molecularly imprinting conjugated polythiophenes (FMICPs) for cancer biomarkers detection is developed. The promising conjugated polythiophenes structure, with a PLQY as high as 55%, provides a straightforward, and affordable method for free-enzyme signal generation. More importantly, the feasibility of integrating printed-paper technology with a sensitive and cost-effective smartphone and portable prototype testing device that could be utilized for rapid point-of-care (POC) cancer diagnostics is successfully introduced. Significantly, the unique structure of FMICP nanofibers (FMICP NFs) displays superior performance with enhanced sensitivity that is 80 times higher than that of pristine FMICP. This assay could lower the limits of detection to 15 fg mL−1 and 3.5 fg mL−1 for α-fetoprotein (AFP) and carcinoembryonic antigen (CEA), respectively, which are three orders of magnitude exceeding that of the standard enzyme-based assay. Moreover, the developed sensors are successfully applied to the fast diagnosis of AFP in liver cancer patients and the FMICP and FMICP NFs results are in excellent agreement with those of clinical ELISA. Image 1 • Novel imprinting conjugated polythiophene nanofibers are developed for assaying biomarkers with the naked eyes. • The sensors can detect 15.0 and 3.5 fg mL−1 concentrations of AFP and CEA biomarkers, respectively. • Point-of-care portable diagnostic paper device has been demonstrated. • The performance of this cost-efficient and user-friendly assay was validated using clinical serum samples. [ABSTRACT FROM AUTHOR]

Background: Hepatocellular Carcinoma (HCC) is a highly aggressive, prevalent, and deadly type of liver cancer. With the advent of deep learning techniques, significant advancements have been made in simplifying and optimizing the feature selection process. Objective: Our scoping review presents an overview of the various deep learning models and algorithms utilized to address feature selection for HCC. The paper highlights the strengths and limitations of each approach, along with their potential applications in clinical practice. Additionally, it discusses the benefits of using deep learning to identify relevant features and their impact on the accuracy and efficiency of diagnosis, prognosis, and treatment of HCC. Design: The review encompasses a comprehensive analysis of the research conducted in the past few years, focusing on the methodologies, datasets, and evaluation metrics adopted by different studies. The paper aims to identify the key trends and advancements in the field, shedding light on the promising areas for future research and development. Results: The findings of this review indicate that deep learning techniques have shown promising results in simplifying feature selection for HCC. By leveraging large-scale datasets and advanced neural network architectures, these methods have demonstrated improved accuracy and robustness in identifying predictive features. Conclusions: We analyze published studies to reveal the state-of-the-art HCC prediction and showcase how deep learning can boost accuracy and decrease false positives. But we also acknowledge the challenges that remain in translating this potential into clinical reality. [ABSTRACT FROM AUTHOR]

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• A highly porous poly(l -lactic) acid nanofiber membrane has been prepared and applied for constructing a low-cost and highly sensitive dual-signal bioassay to detect cancer biomarkers. • By tuning the amount of the capture antibodies grafted to p-PLLA, we make this bioassay give an obvious color mutation when the concentration of AFP reached 10 ng∙mL−1. • After the bioassay is treated with IgG-FITC solution, the precise concentration of AFP is available (LOD 0.17 pg∙mL−1) by measuring the differences in fluorescence intensity of IgG-FITC. The portable, low-cost and sensitive biosensors are essential for in vitro diagnostics. Here, with a highly porous poly(l -lactic) acid nanofiber membrane (p-PLLA), an efficient and sensitive bioassay is created for biomarker detection, giving out colorimetric and fluorescence dual signals. A sensitivity-enhancing strategy is achieved based on the high porosity, loading capacity of p-PLLA and gold nanoparticles-based signal amplification. Through a sandwich immunoreaction strategy, the gold nanoparticle-labeled detector antibodies accumulate on the p-PLLA membrane and show a naked-eye-readable red spot starting from a cut-off value of the target biomarker. After adding goat anti-mouse IgG/FITC antibody (IgG-FITC), which is bound to the detector antibody, the concentration of the target biomarker can be accurately calculated by the differences in fluorescence intensity of IgG-FITC solution before and after the immunoreaction. When the liver cancer marker (α-fetoprotein) is used as a model analyte, this bioassay performs with excellent convenience and a high level of sensitivity. The preset visual color mutation point is 10 ng mL−1 (cut-off value) and the limit of detection is 0.17 pg mL−1. It shows good prospects of screening cancer biomarkers and point-of-care diagnostics applications. [ABSTRACT FROM AUTHOR]

Liver cancer is the second leading cause of cancer death worldwide. Early tumor detection may help identify suitable treatment and increase the survival rate. Medical imaging is a non-invasive tool that can help uncover abnormalities in human organs. Magnetic Resonance Imaging (MRI), in particular, uses magnetic fields and radio waves to differentiate internal human organs tissue. However, the interpretation of medical images requires the subjective expertise of a radiologist and oncologist. Thus, building an automated diagnosis computer-based system can help specialists reduce incorrect diagnoses. This paper proposes a hybrid automated system to compare the performance of 3D features and 2D features in classifying magnetic resonance liver tumor images. This paper proposed two models; the first one employed the 3D features while the second exploited the 2D features. The first system uses 3D texture attributes, 3D shape features, and 3D graphical deep descriptors beside an ensemble classifier to differentiate between four 3D tumor categories. On top of that, the proposed method is applied to 2D slices for comparison purposes. The proposed approach attained 100% accuracy in discriminating between all types of tumors, 100% Area Under the Curve (AUC), 100% sensitivity, and 100% specificity and precision as well in 3D liver tumors. On the other hand, the performance is lower in 2D classification. The maximum accuracy reached 96.4% for two classes and 92.1% for four classes. The top-class performance of the proposed system can be attributed to the exploitation of various types of feature selection methods besides utilizing the ReliefF features selection technique to choose the most relevant features associated with different classes. The novelty of this work appeared in building a highly accurate system under specific circumstances without any processing for the images and human input, besides comparing the performance between 2D and 3D classification. In the future, the presented work can be extended to be used in the huge dataset. Then, it can be a reliable, efficient Computer Aided Diagnosis (CAD) system employed in hospitals in rural areas. [ABSTRACT FROM AUTHOR]

The liver is present underneath the diaphragm and extends from right to left upper part of the belly. The liver is an organ which has many responsibilities for producing different chemicals needed for physical body. The conversion of an image into information is helpful for research people to share. The method of conversion prevents manual error because it depends on technology and algorithm. This paper deals with the detection of liver cancer as implemented using optimization techniques. The paper discusses scanning, filtering, segmentation, feature extraction, and exhibit through artificial neural network. In real time data sets, feed forward neural network is applied for classification and detection of liver cancer. Filtering is mainly used to reduce noise and smoothing edges. Then segmentation is used to extract needed region so that result can be stored in less storage space. Feature extraction is done through gray level and co-occurrence matrix that can yield the result in various parameters. This matrix is helpful to differentiate the tumors namely benign and malignant. Artificial neural network is trained to differentiate those tumors. The result is analyzed by following the parameters like accuracy, area, correlation, entropy, homogeneity, contrast, and similarity index. [ABSTRACT FROM AUTHOR]

Automatic segmentation of the liver in abdominal CT images is critical for guiding liver cancer biopsies and treatment planning. Yet, automatic segmentation of CT liver images remains challenging due to the poor contrast between the liver and surrounding organs in abdominal CT images. In this paper, we propose a novel network for liver segmentation, and the network is essentially a U-shaped network with an encoder–decoder structure. Firstly, the complementary feature enhancement unit is designed in the network to mitigate the semantic gap between encoder and decoder. The complementary feature enhancement unit is based on subtraction, which enhances the complementary features between encoder and decoder. Secondly, this paper proposes a new cross attention model that no longer generates value by convolution, which reduces redundant information and enhances the contextual information of single sparse attention by encoding contextual information by 3 × 3 convolution. The dice score, accuracy, and precision of our network on the LiTS dataset were 95.85 % , 97.19 % , and 97.11 % , and the dice score, accuracy, and precision on the dataset consisted of 3Dircadb and CHAOS were 93.65 % , 94.38 % , and 97.53 % . [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma (HCC), recognized as a significant global health concern, ranks as the sixth most prevalent form of cancer and is the third leading cause of cancer-associated mortality. Over half of HCC patients are diagnosed at advanced stages, an unfortunate phenomenon primarily attributed to the liver’s robust compensatory mechanisms. Given the limited availability of donor livers, existing clinical surgical approaches have yet to provide universally applicable treatment strategies offering substantial prognostic improvement for late-stage cancer. Although the past few decades have witnessed significant advancements in chemotherapy and targeted therapy for HCC, the emergence of drug resistance poses a substantial impediment to their successful execution. Furthermore, issues such as diminished quality of life post-treatment and high treatment costs warrant critical attention. Consequently, the imperative for an effective treatment strategy for advanced liver cancer is unequivocal. In recent years, notable progress in the development and application of immunotherapy has sparked a revolution in advanced liver cancer treatment. This study aims to elucidate a more comprehensive understanding of the current landscape, knowledge framework, research focal points, and nascent breakthrough trends in the domain of immunotherapy for hepatocellular carcinoma via bibliometric analysis. Method: Our study involved conducting a comprehensive literature search spanning from 1999 through December 31, 2022, by utilizing the Science Citation Index Expanded (SCI-Expanded) database. Our aim was to amass all the papers and reviews related to immunotherapy for hepatocellular carcinoma. Our search strategy yielded a total of 4,486 papers. After exclusion of selfcitations, we focused our analysis on 68,925 references. These references were cited 119,523 times (excluding 97,941 self-citations), boasting an average citation frequency of 26.64 times per paper, and achieved an h-index of 135. We employed analytical software tools like Citespace and VOSviewer to perform an intricate analysis of the amassed literature, covering various aspects, including geographical location, research institutions, publishing journals, authors, references, and keywords. Our method incorporated timeline analysis, burst detection, and co-occurrence analysis. The application of these tools facilitated a thorough evaluation of research hotspots, knowledge structure, and emerging advancements within the sphere of immunotherapy for hepatocellular carcinoma. Results: Our bibliometric analysis disclosed a noteworthy escalation in the number of publications in the realm of hepatocellular carcinoma immunotherapy during the years 2021-2022, surpassing the aggregate number of papers published in the preceding decade (2011–2020). This surge underscores a sharp upturn in research interest within this field. Additionally, the research hotspot in hepatocellular carcinoma immunotherapy has perceptibly deviated from the preceding decade’s trends. In terms of geographical distribution, China emerged as the leading country, producing 50.08% of the total publications. This was followed by the United States, with 963 papers, and Japan, contributing 335 papers. Among research institutions, Sun Yat-sen University was the most prolific, while Tim F. Greten stood out as the most published author with 42 papers to his credit. A co-reference network examination uncovered a shift in research emphasis within the field of hepatocellular carcinoma immunotherapy, highlighting the evolving nature of this important area of study Conclusion: Our bibliometric study highlights the significant evolution and growth in HCC immunotherapy research over the past two decades. Looking ahead, research will focus on improving the microenvironment post-drug resistance from immune combination therapy, harnessing adoptive cellular immunity (as CAR-T), subclassify the population and developing new tumor markers. Incorporation of technologies such as nanotechnology, microbiology, and gene editing will further advance HCC treatments. This progressive trajectory in the field promises a brighter future for individuals suffering from HCC. [ABSTRACT FROM AUTHOR]

An important issue in interconnected microgrids (MGs) is the realization of balance between the generation side and the demand side. Imbalanced generation and load demands lead to security, power quality, and reliability issues. The load frequency control (LFC) is accountable for regulating MG frequency against generation/load disturbances. This paper proposed an optimized fractional order (FO) LFC scheme with cascaded outer and inner control loops. The proposed controller is based on a cascaded one plus tilt derivative (1+TD) in the outer loop and an FO tilt integrator-derivative with a filter (FOTIDF) in the inner loop, forming the cascaded (1+TD/FOTIDF) controller. The proposed 1+TD/FOTIDF achieves better disturbance rejection compared with traditional LFC methods. The proposed 1+TD/FOTIDF scheme is optimally designed using a modified version of the liver cancer optimization algorithm (MLCA). In this paper, a new modified liver cancer optimization algorithm (MLCA) is proposed to overcome the shortcomings of the standard Liver cancer optimization algorithm (LCA), which contains the early convergence to local optima and the debility of its exploration process. The proposed MLCA is based on three improvement mechanisms, including chaotic mutation (CM), quasi-oppositional based learning (QOBL), and the fitness distance balance (FDB). The proposed MLCA method simultaneously adjusts and selects the best 1+TD/FOTIDF parameters to achieve the best control performance of MGs. Obtained results are compared to other designed FOTID, TI/FOTID, and TD/FOTID controllers. Moreover, the contribution of electric vehicles and the high penetration of renewables are considered with power system parameter uncertainty to test the stability of the proposed 1+TD/FOTIDF LFC technique. The obtained results under different possible load/generation disturbance scenarios confirm a superior response and improved performance of the proposed 1+TD/FOTIDF and the proposed MLCA-based optimized LFC controller. [ABSTRACT FROM AUTHOR]

Liver cancer is one of the main causes of cancer-related mortality globally. It is a rising threat with over 700,000 deaths and 800,000 new cases annually. To tackle this, deep learning (DL) artificial intelligence (AI) tools have been researched extensively to create predictive models that aid in the early diagnosis and classification of liver cancers. The liver, however, is prone to metastasis from various gastrointestinal cancers, making it difficult to correctly classify tumors present in the organ. This paper is a thorough systematic evaluation of several published studies that used deep learning algorithms to diagnose, classify, and predict common liver tumors, including but not limited to Hepatocellular carcinoma and Intrahepatic cholangiocarcinoma. Articles selected in this review have been published between 2016 and 2024 from Google Scholar, Research Gate, Science Direct, IEEE Xplore, Springer Link, and other resources as per the PRISMA guidelines. Amongst these, the highest recorded accuracy was 99.38%, with other studies showing close results at 97% and below. The highest recorded sensitivity was 100%. Selected studies were also analyzed based on their choice of dataset, DL algorithm, and preprocessing techniques. Magnetic resonance imaging and computed tomography (CT) are by far the preferred medium for imaging data because of their high resolution; however, CT is used more often because it’s cheaper and provides a better view of extrahepatic space. Genetic datasets showed great potential in differentiating between primary and secondary liver cancers. Nonetheless, several gaps were identified across the current literature. The problem of accurately differentiating cancer types in the liver still persists. However, the use of 3D imaging datasets aids in this significantly. Though, it is clear that 3D datasets are underutilized. Dynamic imaging is also beneficial and should be considered more often. For future research, we strongly recommend the use of Gaussian Mixture Model, Watershed Transform, Sparsity classification, DLIR and Siamese algorithms as they each showed promising results. Lastly, we have shown a benchmark framework for a predictive AI model that can be referred to develop future models. Though numerous past methods have produced excellent prediction results, the prevalence of liver cancer-related deaths has not yet been reduced. To address the difficulties in the field of cancer prediction, more thorough research is required, along with the development of more efficient decision support systems using deep learning.Article Highlights: The article provides a comprehensive evaluation of various diagnostic approaches used for liver cancers and their potential in Deep Learning diagnostic models. We present a benchmark framework for an AI-based computational model for the diagnosis of diseases The article presents novel approaches for preprocessing and diagnostic techniques for liver cancer diagnosis [ABSTRACT FROM AUTHOR]

Hepatocellular Carcinoma (HCC), the most common primary liver cancer, is a significant contributor to worldwide cancer-related deaths. Various medical imaging techniques, including computed tomography, magnetic resonance imaging, and ultrasound, play a crucial role in accurately evaluating HCC and formulating effective treatment plans. Artificial Intelligence (AI) technologies have demonstrated potential in supporting physicians by providing more accurate and consistent medical diagnoses. Recent advancements have led to the development of AI-based multi-modal prediction systems. These systems integrate medical imaging with other modalities, such as electronic health record reports and clinical parameters, to enhance the accuracy of predicting biological characteristics and prognosis, including those associated with HCC. These multi-modal prediction systems pave the way for predicting the response to transarterial chemoembolization and microvascular invasion treatments and can assist clinicians in identifying the optimal patients with HCC who could benefit from interventional therapy. This paper provides an overview of the latest AI-based medical imaging models developed for diagnosing and predicting HCC. It also explores the challenges and potential future directions related to the clinical application of AI techniques. [ABSTRACT FROM AUTHOR]

Elephantopus tomentosus (ET) Linn. was reported to be an anti-tumor plant. However, the chemical composition of ET and its anti-tumor compounds and potential mechanisms still unclear. In this paper, UPLC-Q-TOF–MS/MS was firstly used to identified the ingredients in ET and UPLC was used to determine the main compounds of ET. Network pharmacology was applied to predict the potential mechanisms of anti-liver cancer. Anti-tumor nuclear activate compounds and targets of ET were obtained and the anti-liver cancer effect was validated on HepG2. Finally, Molecule docking, RT-qPCR, and western blotting were used for verification of the relationship between nuclear activate compounds and nuclear targets and the potential anti-cancer mechanisms. The result showed that 42 compounds were identified in ET, which consisted of sesquiterpene lactones, flavonoids, and phenylpropanoid compounds. Scabertopin (ST), chlorogenic acid, Isochlorogenic acid B, Isochlorogenic acid A and Isochlorogenic acid C were identified as main compounds and were determined as 0.426%, 0.457%, 0.159%, 0.701%, and 0.103% respectively. 24 compounds showed high pharmacokinetics and good drug-likeness. 520 overlapping targets of the ET compounds and liver cancer were collected. The targets were used for KEGG and GO analysis. GO enrichment analysis suggested that the targets of 24 active compound closed related to promote apoptosis, inhibit proliferation, and regulate oxidative levels. KEGG enrichment analysis suggested that pathway in cancer was enriched most and p38 MAPK/p53 signaling pathway, which closely related to promoting apoptosis and inhibiting proliferation. Compounds-targets analysis based on the parameter of Betweenness, Closeness, Information, Eigenvector, Degree, and component content indicated that ST was the nucleus anti-tumor active compound of ET. HepG2 was first used to validated the anti-tumor effect of ST and the result showed that ST significantly inhibited HepG2 proliferation with a low IC50 less than 5 μM. Nucleus active compound targets, including TP53, CASP3, BCL2, EGFR, TNF-a, IL-1β, and IL-6 were enriched based on degree value of PPI analysis. Molecule docking suggested that ST showed a good combination to TGFBR1 with the combination energy less than − 5 kcal/mol. RT-qPCR result also suggested that ST significantly medicated the mRNA expression level of TP53, CASP3, BCL2, EGFR, TNF-a, IL-1β, and IL-6. Protein expression of p-p38/p38 and p-p53/p53 notable increased by ST treatment. In conclude, combining with UPLC-Q-TOF–MS/MS qualitative analysis, UPLC quantitative analysis, network pharmacology analysis, molecule docking, and in vitro experiments on HepG2, we suggest that ST is an anti-tumor ingredient of ET, which may target to TGFBR1 and promote apoptosis and inhibited proliferation of HepG2 by activating p38 MAPK/p53 signaling pathway. ST can be regarded as a quality marker of ET. [ABSTRACT FROM AUTHOR]

In this paper, we report on the use of a purpose-built hybrid solid-state microwave and radiofrequency generator operating at frequencies of 2.45 GHz and/or 480 kHz for cancer ablation in various tissues. The hybrid generator was tested ex vivo on chicken breast and bovine liver and has demonstrated that the high accuracy of the power delivered to the sample can be achieved by controlling the emitted power versus the temperature profile of the treated sample. In particular, the hybrid generator incorporates control systems based on impedance or reflected power measurements that allow controlled ablation without causing unwanted carbonization and without including areas where tissue damage is not desired. The results of the ex vivo tests showed that radiofrequency ablation (RFA) could be effective for performing controlled ablations with minimally invasive probes, such as cardiac pathologies, small lesions, and tissues with particular composition, while microwave ablation (MWA) could be optimal for performing large ablations in highly vascularized tissues, such as liver cancer, where it is necessary to achieve higher temperatures. [ABSTRACT FROM AUTHOR]

Interventional radiologists mainly rely on visual feedback via imaging modalities to steer a needle toward a tumor during biopsy and ablation procedures. In the case of CT-guided procedures, there is a risk of exposure to hazardous X-ray-based ionizing radiation. Therefore, CT scans are usually not used continuously, which increases the chances of a misplacement of the needle and the need for reinsertion, leading to more tissue trauma. Interventionalists also encounter haptic feedback via needle–tissue interaction forces while steering a needle. These forces are useful but insufficient to clearly perceive and identify deep-tissue structures such as tumors. The objective of this paper was to investigate the effect of enhanced force feedback for sensing interaction forces and guiding the needle when applied individually and simultaneously during a virtual CT-guided needle insertion task. We also compared the enhanced haptic feedback to enhanced visual feedback. We hypothesized that enhancing the haptic feedback limits the time needed to reach the target accurately and reduces the number of CT scans, as the interventionalist depends more on real-time enhanced haptic feedback. To test the hypothesis, a simulation environment was developed to virtually steer a needle in five degrees of freedom (DoF) to reach a tumor target embedded in a liver model. Twelve participants performed in the experiment with different feedback conditions where we measured their performance in terms of the following: targeting accuracy, trajectory tracking, number of CT scans required, and the time needed to finish the task. The results suggest that the combination of enhanced haptic feedback for guidance and sensing needle–tissue interaction forces significantly reduce the number of scans and the duration required to finish the task by 32.1% and 46.9%, respectively, when compared to nonenhanced haptic feedback. The other feedback modalities significantly reduced the duration to finish the task by around 30% compared to nonenhanced haptic feedback. [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma accounts for 80% of primary liver cancers, is the most common primary liver malignancy. Hepatocellular carcinoma is the third leading cause of tumor-related deaths worldwide, with a 5-year survival rate of approximately 18%. Chemotherapy, although commonly used for hepatocellular carcinoma treatment, is limited by systemic toxicity and drug resistance. Improving targeted delivery of chemotherapy drugs to tumor cells without causing systemic side effects is a current research focus. Chitosan, a biopolymer derived from chitin, possesses good biocompatibility and biodegradability, making it suitable for drug delivery. Enhanced chitosan formulations retain the anti-tumor properties while improving stability. Chitosan-based biomaterials promote hepatocellular carcinoma apoptosis, exhibit antioxidant and anti-inflammatory effects, inhibit tumor angiogenesis, and improve extracellular matrix remodeling for enhanced anti-tumor therapy. Methods: We summarized published experimental papers by querying them. Results and Conclusions: This review discusses the physicochemical properties of chitosan, its application in hepatocellular carcinoma treatment, and the challenges faced by chitosan-based biomaterials. [ABSTRACT FROM AUTHOR]

The article presents an introduction on history of the journal "Annals of Hepatology", charts and diagrams about geographical distribution of papers and articles published in journal, and lists the top papers on non-alcoholic fatty liver disease, importance of redox state, and Hepatocellular carcinoma