Advancements in nanoultrasonics technology for the diagnosis and treatment of liver cancer: discussion on medical ethics and hospital management issues.

Background: Primary liver cancer, of which around 75–85% is hepatocellular carcinoma in China, is the fourth most common malignancy and the second leading cause of tumor-related death, thereby posing a significant threat to the life and health of the Chinese people. Summary: Since the publication of Guidelines for Diagnosis and Treatment of Primary Liver Cancer in China in June 2017, which were updated by the National Health Commission in December 2019, additional high-quality evidence has emerged from researchers worldwide regarding the diagnosis, staging, and treatment of liver cancer, that requires the guidelines to be updated again. The new edition (2022 Edition) was written by more than 100 experts in the field of liver cancer in China, which not only reflects the real-world situation in China but also may reshape the nationwide diagnosis and treatment of liver cancer. Key Messages: The new guideline aims to encourage the implementation of evidence-based practice and improve the national average 5-year survival rate for patients with liver cancer, as proposed in the “Health China 2030 Blueprint.”

Liver cancer is one of the most common malignant tumors in China, and its incidence rate is increasing year by year. Therefore, the early diagnosis and treatment of liver cancer is of great importance. Among various examinations, endoscopic ultrasound (EUS) can perform close scanning of the liver and the biliary system, which plays an indispensable role in the diagnosis, treatment, and staging of liver cancer. This article reviews the current status and future perspectives of EUS in the diagnosis and treatment of liver cancer, in order to provide a reference for the clinical diagnosis and treatment of liver cancer.

Liver cancer has high incidence and mortality rates and its treatment generally requires the use of a combination treatment strategy. Therefore, the early detection and diagnosis of liver cancer is crucial to achieving the best treatment effect. In addition, it is imperative to explore multimodal combination therapy for liver cancer treatment and the synergistic effect of two liver cancer treatment drugs while preventing drug resistance and drug side effects to maximize the achievable therapeutic effect. Gold nanoparticles are used widely in applications related to optical imaging, CT imaging, MRI imaging, biomarkers, targeted drug therapy, etc., and serve as an advanced platform for integrated application in the nano-diagnosis and treatment of diseases. Dual-drug-delivery nano-diagnostic and therapeutic agents have drawn great interest in current times. Therefore, the present report aims to review the effectiveness of dual-drug-delivery nano-diagnostic and therapeutic agents in the field of anti-tumor therapy from the particular perspective of liver cancer diagnosis and treatment.

BackgroundAs a common cancer, liver cancer imposes an unacceptable burden on patients, but its underlying molecular mechanisms are still not fully understood. Therefore, there is an urgent need to potential biomarkers and diagnostic models for liver cancer.MethodsIn this study, transcriptome and single-cell datasets related to liver cancer were downloaded from the UCSC Xena database and the Mendeley database, and differential analysis and weighted gene co-expression network analysis were used to find differentially expressed genes related to liver cancer. We used multiple machine algorithms to find hub genes related to liver cancer, and constructed new artificial neural network models based on their transcriptome expression patterns to assist in the diagnosis of liver cancer. Subsequently, we conducted survival analysis and immune infiltration analysis to explore the correlation between hub genes and immune cells, and used single-cell data to verify hub genes related to liver cancer.ResultsThis study identified MARCO, KCNN2, NTS, TERT and SFRP4 as central genes associated with liver cancer, and constructed a new artificial neural network model for molecular diagnosis of liver cancer. The diagnostic performance of the training cohort and the validation cohort was good, with the areas under the ROC curves of 1.000 and 0.986, respectively. Immune infiltration analysis determined that these central genes were closely associated with different types of immune cells. The results of immunohistochemistry and the results at the single cell level were consistent with those at the transcriptome level, and also showed obvious differences between different cell types in liver cancer and healthy states.ConclusionThis study identified MARCO, KCNN2, NTS, TERT, and SFRP4 from multiple dimensions and highlighted their key roles in the diagnosis and treatment of liver cancer from multiple dimensions, providing promising biomarkers for the diagnosis of liver cancer.

BackgroundLiver cancer is among the top five causes of cancer death in 90 countries, with China accounting for a substantial proportion of the global burden. This study aimed to analyse the national liver cancer policies and programs in China.MethodsThis study applied a documentary research method using the systematic READ approach. Six national official websites and one public policy database were searched. The document analysis was based on the WHO Health System's Six Building Blocks and the WHO's four modules of cancer control (prevention, early detection, diagnosis and treatment, and palliative care).ResultsA total of 74 liver cancer-related policies and 8 programs published from 1 January 1984 to 31 March 2025 were included in this study. The analysis revealed that liver cancer management in China mainly focused on Medical products and Technologies (n = 46, 62.16%), and Service Delivery (n = 34, 45.95%) within the WHO health system building blocks. When it came to WHO cancer control four modules, most policies (n = 39, 52.70%) targeted Diagnosis and Treatment of liver cancer, followed by Prevention of liver cancer (n = 33, 44.59%). Additionally, 8 national programs were implemented to improve the prevention, diagnosis, and treatment of liver cancer. Before 2019, the emphasis of liver cancer prevention was primarily on hepatitis prevention and control. However, since 2020, the national-level programs aimed at preventing and controlling liver cancer emphasizing patient education and treatment for high-risk groups.ConclusionsChina’s liver cancer control mainly focused heavily on prevention, diagnosis and treatment modules with special focus on medical products and technology, as well as service delivery. Currently, less attention has been given to the detection and palliative care of survivors. The control of liver cancer in China still requires further strengthening of the health system for implementation. Considering the continual increase in the burden of liver cancer, it is imperative for future efforts to develop a comprehensive national liver cancer strategy.Supplementary InformationThe online version contains supplementary material available at 10.1186/s41256-025-00450-w.

To assess the accuracy, reliability, and readability of publicly available large language models in answering fundamental questions on hepatocellular carcinoma diagnosis and management. Twenty questions on liver cancer diagnosis and management were asked in triplicate to ChatGPT-3.5 (OpenAI), Gemini (Google), and Bing (Microsoft). Responses were assessed by six fellowship-trained physicians from three academic liver transplant centers who actively diagnose and/or treat liver cancer. Responses were categorized as accurate (score 1; all information is true and relevant), inadequate (score 0; all information is true, but does not fully answer the question or provides irrelevant information), or inaccurate (score - 1; any information is false). Means with standard deviations were recorded. Responses were considered as a whole accurate if mean score was > 0 and reliable if mean score was > 0 across all responses for the single question. Responses were also quantified for readability using the Flesch Reading Ease Score and Flesch-Kincaid Grade Level. Readability and accuracy across 60 responses were compared using one-way ANOVAs with Tukey's multiple comparison tests. Of the twenty questions, ChatGPT answered nine (45%), Gemini answered 12 (60%), and Bing answered six (30%) questions accurately; however, only six (30%), eight (40%), and three (15%), respectively, were both accurate and reliable. There were no significant differences in accuracy between any chatbot. ChatGPT responses were the least readable (mean Flesch Reading Ease Score 29; college graduate), followed by Gemini (30; college) and Bing (40; college; p < 0.001). Large language models provide complex responses to basic questions on hepatocellular carcinoma diagnosis and management that are seldomly accurate, reliable, or readable.

Most patients are at advanced stages when they are diagnosed with hepatocellular carcinoma, leading to poor prognosis and a low 5-year survival rate. Serological markers, ultrasound, computed tomography, magnetic resonance imaging, positron emission tomography, and liver biopsy are the common clinical diagnostic techniques for liver cancer. Effective interventions in the early stage will be beneficial to improve the prognosis of liver cancer patients and reduce the global burden. Therefore, it is urgent to develop new diagnostic methods to improve the diagnosis and management of liver cancer. Nanotechnology has become a new frontier subject in medical detection along with the application of nanomaterials in the manufacture of drug carriers, diagnostic tools, and therapeutic devices. Many studies have shown that nanoparticles (NPs) can be applied to the clinical diagnosis of liver cancer in combination with existing technologies, providing a new method for the early diagnosis of liver cancer. In this review, we elaborate on the theoretical basis and characteristics of NPs in the diagnosis of liver cancer, and the research progress and prospects of NPs in the diagnosis of liver cancer are summarized.

Primary liver cancer is the seventh-most-common cancer worldwide and the fourth-leading cause of cancer mortality. In the current era of precision medicine, the diagnosis and management of liver cancer are full of challenges and prospects. Mesoporous nanoparticles are often designed as specific carriers of drugs and imaging agents because of their special morphology and physical and chemical properties. In recent years, the design of the elemental composition and morphology of mesoporous nanoparticles have greatly improved their drug-loading efficiency, biocompatibility and biodegradability. Especially in the field of primary liver cancer, mesoporous nanoparticles have been modified as highly tumor-specific imaging contrast agents and targeting therapeutic medicine. Various generations of complexes and structures have been determined for the complicated clinical management requirements. In this review, we summarize these advanced mesoporous designs in the different diagnostic and therapeutic fields of liver cancer and discuss the relevant advantages and disadvantages of transforming applications. By comparing the material properties, drug-delivery characteristics and application methods of different kinds of mesoporous materials in liver cancer, we try to help determine the most suitable drug carriers and information media for future clinical trials. We hope to improve the fabrication of biomedical mesoporous nanoparticles and provide direct evidence for specific cancer management.

Background: Primary liver cancer, around 90% are hepatocellular carcinoma in China, is the fourth most common malignancy and the second leading cause of tumor-related death, thereby posing a significant threat to the life and health of the Chinese people. Summary: Since the publication of Guidelines for Diagnosis and Treatment of Primary Liver Cancer (2017 Edition) in 2018, additional high-quality evidence has emerged with relevance to the diagnosis, staging, and treatment of liver cancer in and outside China that requires the guidelines to be updated. The new edition (2019 Edition) was written by more than 70 experts in the field of liver cancer in China. They reflect the real-world situation in China regarding diagnosing and treating liver cancer in recent years. Key Messages: Most importantly, the new guidelines were endorsed and promulgated by the Bureau of Medical Administration of the National Health Commission of the People’s Republic of China in December 2019.

Primary liver cancer has become the second most fatal cancer in the world, and its five-year survival rate is only 10%. Most patients are in the middle and advanced stages at the time of diagnosis, losing the opportunity for radical treatment. Liver cancer is not sensitive to chemotherapy or radiotherapy. At present, conventional molecularly targeted drugs for liver cancer show some problems, such as short residence time, poor drug enrichment, and drug resistance. Therefore, developing new diagnosis and treatment methods to effectively improve the diagnosis, treatment, and long-term prognosis of liver cancer is urgent. As an emerging discipline, nanobiotechnology, based on safe, stable, and efficient nanomaterials, constructs highly targeted nanocarriers according to the unique characteristics of tumors and further derives a variety of efficient diagnosis and treatment methods based on this transport system, providing a new method for the accurate diagnosis and treatment of liver cancer. This paper aims to summarize the latest progress in this field according to existing research and the latest clinical diagnosis and treatment guidelines in hepatocellular carcinoma (HCC), as well as clarify the role, application limitations, and prospects of research on nanomaterials and the development and application of nanotechnology in the diagnosis and treatment of HCC.

BackgroundLiver cancers are common malignancies that primarily include hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA). Currently, the most commonly used serum markers for HCC are alpha fetoprotein (AFP), AFP-L3% and protein induced by vitamin K absence or antagonist-II (PIVKA-II), while the most commonly used serum markers for CCA are carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9). In recent years, many HCC diagnostic models using the combined detection of serum AFP, AFP-L3% and PIVKA-II have been established. For serum AFP, AFP-L3%, PIVKA-II and their many diagnostic models, there has been no clear guidance on the selection of these markers and their various combinations in the diagnosis of liver cancers. The aim of this study was to evaluate and compare the efficacy of these markers and the models that incorporate them in diagnosing HCC and CCA. This could assist in identifying the optimal patterns of serum AFP, AFP-L3% and PIVKA-II for the diagnosis of liver cancers.MethodsClinical data and the results of serum AFP, AFP-L3%, PIVKA-II, CEA and CA19-9 were collected from 117 patients with HCC, 28 patients with CCA and 101 patients with benign liver diseases. Laboratory tests and detection of serum tumor markers in liver cancer patients were conducted prior to treatments. Recently published diagnostic models for AFP, AFP-L3% and PIVKA-II detection were collected; these included GALAD, ASAP, GALAD-C, GAAP, C-GALAD, C-GALAD II and GAP-TALAD.ResultsLevels of AFP-L3%, PIVKA-II, GALAD, ASAP, GALAD-C, GAAP, C-GALAD and C-GALAD II significantly differed between the patient cohorts, with the highest levels seen in HCC, followed by CCA and with the lowest levels seen in benign liver diseases (p < 0.05). Levels of CEA and CA19-9 significantly differed between cohorts, with the highest levels seen in CCA, followed by HCC and with the lowest levels seen in benign liver diseases (p < 0.05). Levels of AFP and GAP-TALAD in HCC patients were significantly higher than those in patients with CCA and patients with benign liver diseases (p < 0.05), but there were no significant differences in levels of AFP and GAP-TALAD between patients with CCA and benign liver diseases (p > 0.05). In the diagnosis of HCC, GAP-TALAD, GALAD, C-GALAD, ASAP and GALAD-C showed the highest efficacy. In the diagnosis of overall liver cancers (HCC and CCA), GALAD-C, GAAP, GALAD, ASAP and C-GALAD showed the highest efficacy. In the diagnosis of early liver cancers (early HCC and CCA), GALAD, GALAD-C, GAAP, C-GALAD and ASAP showed the highest efficacy.ConclusionsFor serum AFP, AFP-L3% and PIVKA-II, diagnostic models of combined marker detection improved efficacy in the diagnosis of liver cancers. Diagnostic models GALAD, ASAP, GALAD-C and C-GALAD showed the highest efficacy in the diagnosis of HCC, overall liver cancers (HCC + CCA) and early liver cancers, and can be used preferentially in clinical practice.

Biomarkers are critical for the diagnosis, prognostication, and management of liver cancer and have been developed significantly in the past decade, in part due to the advancement in molecular diagnostics and precision medicine. The recent updates, discovery, and validation of liver cancer biomarkers will be reviewed here, as well as the methodological considerations on cancer biomarker research. Specifically, the epidemiological considerations on cancer biomarkers are discussed including the comparisons of companion and complementary diagnostics. Practical perspectives are also provided on the established and emerging biomarkers for the diagnosis, prognosis, and treatment of liver cancer, as well as future research directions on the liver cancer biomarker discovery.

Liver cancer will continue to be a major disease threatening the lives and health of our people in the next few decades. In recent years, with the development of early diagnosis and treatment of liver cancer, precise liver resection, and the development of targeted and immunotherapeutic drugs, the survival rate of liver cancer patients has been improved. Nevertheless, due to the high heterogeneity of liver cancer, patients with liver cancer in the same clinical stage still have great differences in response to treatment and prognosis. New staging and classification indicators are urgently needed to facilitate accurate diagnosis and treatment of liver cancer, so as to further improve the survival rate of patients. The continuous progress and development of multi-omics technology, single-cell technology, tumor molecular visualization technology and medical artificial intelligence, etc., make the molecular classification of liver cancer more and more approaching the true nature of tumor biological characteristics, thus contributing to the accurate diagnosis and treatment of liver cancer.

Background and research aimThe incidence and mortality of liver cancer have been increasing in the UK in recent years. However, liver cancer is still under-studied. The Early Detection of Hepatocellular Liver Cancer (DeLIVER-QResearch) project aims to address the research gap and generate new knowledge to improve early detection and diagnosis of primary liver cancer from general practice and at the population level. There are three research objectives: (1) to understand the current epidemiology of primary liver cancer in England, (2) to identify and quantify the symptoms and comorbidities associated with liver cancer, and (3) to develop and validate prediction models for early detection of liver cancer suitable for implementation in clinical settings.MethodsThis population-based study uses the QResearch® database (version 46) and includes adult patients aged 25–84 years old and without a diagnosis of liver cancer at the cohort entry (study period: 1 January 2008–30 June 2021). The team conducted a literature review (with additional clinical input) to inform the inclusion of variables for data extraction from the QResearch database. A wide range of statistical techniques will be used for the three research objectives, including descriptive statistics, multiple imputation for missing data, conditional logistic regression to investigate the association between the clinical features (symptoms and comorbidities) and the outcome, fractional polynomial terms to explore the non-linear relationship between continuous variables and the outcome, and Cox/competing risk regression for the prediction model. We have a specific focus on the 1-year, 5-year, and 10-year absolute risks of developing liver cancer, as risks at different time points have different clinical implications. The internal–external cross-validation approach will be used, and the discrimination and calibration of the prediction model will be evaluated.DiscussionThe DeLIVER-QResearch project uses large-scale representative population-based data to address the most relevant research questions for early detection and diagnosis of primary liver cancer in England. This project has great potential to inform the national cancer strategic plan and yield substantial public and societal benefits.

Objective To obtain short peptides which specifically binds to HepG2 cell line from 12 peptide libraries, and lay foundation for screening and identifying the new liver cancer markers for early diagnosis and treatment of liver cancer. Methods The liver cancer cell line HepG2 was used as the antigen and LO-2 as the absorber cells for subtraction biopanning from a phage display peptide library at 37℃. The positive phage clones were identified by cell enzyme-linked immunosorbentassay (EL1SA), and the identity of DNA sequence and amino acids were analyzed. Results After 3 rounds of screening, 30 phage clones were identified by EL1SA, ZS-9 of them bind to the HepG2 specifically. The amino acid sequence was blast in NCBI and Swiss-Prot, the results show that the sequence has not identity with the known genes and proteins in the database, and the sequence was not reported in literature. All these show that we had discovered several novel liver cancer associated antigen ligand. Conclusion Several candidate peptide binding to liver cancer specifically have been selected by phage display technology, providing the potential uses for early diagnosis of liver cancer or targeted therapy for liver cancer. Key words: Carcinoma hepatocellular; Phage library; Peptides; HepG2; Subtract biopanning