Reconstructing strategies for precision diagnosis and treatment of liver cancer based on multi-modal data

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.

EUS-guided FNA could be another important tool for the early diagnosis of hepatocellular carcinoma

To explore simultaneous magnetic resonance imaging (MRI) for multiple hepatoma-bearing rats in a single session suppressing motion- and flow-related artifacts to conduct preclinical cancer research efficiently. Our institutional Animal Experimental Committee approved this study. We acquired PROPELLER (periodically rotated overlapping parallel lines with enhanced reconstruction) T2 - and diffusion-weighted images of the liver in one healthy and 11 N1-S1 hepatoma-bearing rats in three sessions using a 3-T clinical scanner and dedicated multiarray coil. We compared tumor volumes on MR images and those on specimens, evaluated apparent diffusion coefficients (ADC) of the tumor, and compared them to previously reported values. Each MRI session took 39-50 minutes from anesthesia induction to the end of scans for four rats (10-13 minutes per rat). PROPELLER provided artifact-reduced T2 - and diffusion-weighted images of the rat livers. Tumor volumes on MR images ranged from 0.04-1.81 cm(3) and were highly correlated with those on specimens. The ADC was 1.57 ± 0.37 × 10(-3) mm(2) /s (average ± SD), comparable to previously reported values. PROPELLER allowed simultaneous acquisition of artifact-reduced T2 - and diffusion-weighted images of multiple hepatoma-bearing rats. This technique can promote high-throughput preclinical MR research for liver cancer.

Surveillance for Hepatocellular Carcinoma in Patients With Cirrhosis

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.

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.

Clinical decision making and research in hepatocellular carcinoma: Pivotal role of imaging techniques

Chapter 2: Diagnosis and surveillance

SummaryBackgroundLiver cancer has one of the fastest rising incidence and mortality rates among all cancers in the UK, but it receives little attention. This study aims to understand the disparities in epidemiology and clinical pathways of primary liver cancer and identify the gaps for early detection and diagnosis of liver cancer in England.MethodsThis study used a dynamic English primary care cohort of 8.52 million individuals aged ≥25 years in the QResearch database during 2008–2018, followed up to June 2021. The crude and age-standardised incidence rates, and the observed survival duration were calculated by sex and three liver cancer subtypes, including hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (CCA), and other specified/unspecified primary liver cancer. Regression models were used to investigate factors associated with an incident diagnosis of liver cancer, emergency presentation, late stage at diagnosis, receiving treatments, and survival duration after diagnosis by subtype.Findings7331 patients were diagnosed with primary liver cancer during follow-up. The age-standardised incidence rates increased over the study period, particularly for HCC in men (increased by 60%). Age, sex, socioeconomic deprivation, ethnicity, and geographical regions were all significantly associated with liver cancer incidence in the English primary care population. People aged ≥80 years were more likely to be diagnosed through emergency presentation and in late stages, less likely to receive treatments and had poorer survival than those aged <60 years. Men had a higher risk of being diagnosed with liver cancer than women, with a hazard ratio (HR) of 3.9 (95% confidence interval 3.6–4.2) for HCC, 1.2 (1.1–1.3) for CCA, and 1.7 (1.5–2.0) for other specified/unspecified liver cancer. Compared with white British, Asians and Black Africans were more likely to be diagnosed with HCC. Patients with higher socioeconomic deprivation were more likely to be diagnosed through the emergency route. Survival rates were poor overall. Patients diagnosed with HCC had better survival rates (14.5% at 10-year survival, 13.1%–16.0%) compared to CCA (4.4%, 3.4%–5.6%) and other specified/unspecified liver cancer (12.5%, 10.1%–15.2%). For 62.7% of patients with missing/unknown stage in liver cancer, their survival outcomes were between those diagnosed in Stages III and IV.InterpretationThis study provides an overview of the current epidemiology and the disparities in clinical pathways of primary liver cancer in England between 2008 and 2018. A complex public health approach is needed to tackle the rapid increase in incidence and the poor survival of liver cancer. Further studies are urgently needed to address the gaps in early detection and diagnosis of liver cancer in England.FundingThe Early Detection of Hepatocellular Liver Cancer (DeLIVER) project is funded by 10.13039/501100000289Cancer Research UK (Early Detection Programme Award, grant reference: C30358/A29725).

NIR-II fluorescence-guided liver cancer surgery by a small molecular HDAC6 targeting probe

Event Abstract Back to Event Constructing subject-specific virtual brains from multimodal neuroimaging data Michael Schirner1, 2, Simon Rothmeier1, 2 and Petra Ritter1, 2* 1 Charité Berlin, Germany 2 Bernstein Center for Computational Neuroscience, Bernstein Focus State Dependencies of Learning, Germany Large amounts of multimodal neuroimaging data are acquired every year worldwide. In order to extract high dimensional information for computational neuroscience applications standardized data fusion and efficient reduction into integrative data structures are required. Such self-consistent multimodal data sets can be used for computational brain modeling to constrain models with individual measurable features of the brain, such as done with The Virtual Brain (TVB). TVB is a simulation platform that uses empirical structural and functional data to build full brain models of individual humans. For convenient model construction, we developed a shell scripted processing pipeline for structural, functional and diffusion-weighted magnetic resonance imaging (MRI) and optionally electroencephalography (EEG) data. The pipeline combines several state-of-the-art neuroinformatics tools to generate subject-specific cortical and subcortical parcellations, surface-tessellations, structural and functional connectomes, lead field matrices, electrical source activity estimates and region-wise aggregated blood oxygen level dependent (BOLD) functional MRI (fMRI) time-series. The output files of the pipeline can be directly uploaded to TVB to create and simulate individualized large-scale network models. We detail the pitfalls of the individual processing streams and discuss ways of validation. With the pipeline we also introduce novel ways of estimating the transmission strengths of fiber tracts in whole-brain structural connectivity (SC) networks and compare the outcomes of different tractography or parcellation approaches. We tested the functionality of the pipeline on 50 multimodal data sets. In order to quantify the robustness of the connectome extraction part of the pipeline we computed several metrics that quantify its rescan reliability and compared them to other tractography approaches. Together with the pipeline we present several principles to guide future efforts to standardize brain model construction. The code of the pipeline and the fully processed data sets are made available to the public via The Virtual Brain website (thevirtualbrain.org) and via Github (https://github.com/BrainModes/TVB-empirical-data-pipeline). Furthermore, the pipeline can be directly used with High Performance Computing (HPC) resources on the Neuroscience Gateway Portal (http://www.nsgportal.org) through a convenient web-interface. References Michael Schirner, Simon Rothmeier, Viktor K. Jirsa, Anthony Randal McIntosh, Petra Ritter, An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data, NeuroImage, Available online 31 March 2015, ISSN 1053-8119, http://dx.doi.org/10.1016/j.neuroimage.2015.03.055 Keywords: multi modal data, the virtual brain, connectome, tractography, computational modeling Conference: Neuroinformatics 2015, Cairns, Australia, 20 Aug - 22 Aug, 2015. Presentation Type: Poster, to be considered for oral presentation Topic: Computational neuroscience Citation: Schirner M, Rothmeier S and Ritter P (2015). Constructing subject-specific virtual brains from multimodal neuroimaging data. Front. Neurosci. Conference Abstract: Neuroinformatics 2015. doi: 10.3389/conf.fnins.2015.91.00005 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 29 May 2015; Published Online: 05 Aug 2015. * Correspondence: Dr. Petra Ritter, Charité Berlin, Berlin, Germany, petra.ritter@charite.de Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract Supplemental Data The Authors in Frontiers Michael Schirner Simon Rothmeier Petra Ritter Google Michael Schirner Simon Rothmeier Petra Ritter Google Scholar Michael Schirner Simon Rothmeier Petra Ritter PubMed Michael Schirner Simon Rothmeier Petra Ritter Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Magnetic Resonance Imaging (MRI) has become an important tool for doctors to diagnose liver cancer for decays. The survival rate of liver cancer patients can be significantly improved by an early diagnosis. In this paper, we present a computer aided kernel based support vector machine (SVM) algorithm for diagnosing liver cancer in early stage by applying our proposed method to the patients' magnetic resonance (MR) images. We apply the histogram-based feature extraction method to extract feature information from each raw MR image acquired. And 100 confirmed liver cancer and 100 confirmed benign type liver tumor (BLT) patients' feature information are used to form our training data set to train or SVM classification engine. The model is tested with a set of 30 confirmed early stage liver cancer and 30 BLT samples. Our trained SVM achieves an accuracy of 86.67% in classifying early stage liver cancer and 80.00% in classifying BLT.

Background and ObjectiveRecently, there has been a growing interest in the use of deep learning methods within the multi-modal domain of breast cancer research. Integrating multi-modal data for breast cancer prediction can generate richer and more diverse set of information, leading to a greater robustness in prediction outcomes as compared to single-modal approaches. This review comprehensively summarizes the advancements in multi-modal breast cancer research over the past 5 years and critically assesses the related opportunities and challenges, serving as a valuable reference for future studies. The application of deep learning techniques to the processing of multi-modal breast cancer data is discussed in depth, and the latest strategies and potential future directions in this area are examined.MethodsA systematic analysis of studies on deep learning methods for breast cancer diagnosis based on multi-modal data was conducted. A comprehensive literature search was performed across PubMed, Web of Science, Cochrane Library, and Google Scholar for studies published between January 2019 and April 2025. To ensure the representativeness of the included research, studies were evaluated according to three aspects: types of multi-modal data used, the fusion strategies adopted, and their clinical relevance.Key Content and FindingsThis review systematically traces the development of deep learning approaches for multi-modal breast cancer data, from foundational to more advanced methodologies. First, the paper categorizes common data types and core tasks related to breast cancer prediction. Subsequently, it classifies multi-modal data fusion strategies into three types—feature-level fusion, decision-level fusion, and hybrid fusion—providing a detailed explanation of the prediction steps for each category and comparing their effectiveness. Finally, the common challenges in multi-modal breast cancer research and insights into potential directions for future research are identified and discussed.ConclusionsAt present, although numerous deep learning–based multi-modal studies on breast cancer have been proposed, multi-modal fusion remains in the exploratory stage. Future research should focus on addressing the scarcity of high-quality public datasets, as well as developing more robust network architectures and adaptive fusion strategies to better capture complementary information across modalities.

Hepatocellular carcinoma (HCC) ranks among the most lethal malignancies worldwide, characterized by its high metastatic potential and poor prognosis. Early and precise detection and diagnosis of HCC remain a major clinical challenge. Magnetic resonance imaging (MRI), as the most widely used noninvasive technique for diagnosing liver diseases, currently suffers from limitations in traditional contrast agents, including low specificity and limited sensitivity, particularly when detecting small lesions. The emergence of nanotechnology offers novel approaches to enhance the diagnostic accuracy and therapeutic efficacy for HCC. Under the framework of big data driven precision medicine, this study explores the application of nanomaterials in HCC MRI enhancement and multimodal therapy. This review comprehensively summarizes two types of responsive nanomaterials: (1) Chiral Ni(OH)2 nanoparticles, which suggeste enhanced contrast in T1 weighted MRI and selective imaging capabilities for primary HCC and lung metastases; (2) β Lapachone loaded mesoporous MnO2 nanoparticles (HLMn), which effectively enhance the generation of reactive oxygen species (ROS) within tumor cells, disrupt redox homeostasis, and significantly improve the efficacy of chemo dynamic therapy (CDT). These nanoplatforms also exhibit potential to activate the c-GAS STING innate immune pathway, thereby augmenting antitumor immune responses. Nanomaterials hold great promise not only as enhanced contrast agents but also as precise therapeutic carriers. By integrating radiomics based imaging features with biological markers, we summarize current personalized HCC diagnosis and treatment planning models based on multimodal data. Simultaneously, we provide a critical summary of the synergistic application of advanced imaging and therapeutic nanotechnologies. In the future, leveraging big data for precise HCC diagnosis and treatment is anticipated to significantly improve patient survival.

Norman Kneteman, Tito Livraghi, David Madoff, Eduardo de Santibanez, and Michael Kew Division of Transplantation, Department of Surgery, University of Alberta, Edmonton, Alberta, Canada; Department of Interventional Radiology, Istituto Clinico Humanitas, Istituto di Ricovero e Cura a Carattere Scientifico, Milan, Italy; Division of Interventional Radiology, New York-Presbyterian/Weill Cornell Medical Center, New York, NY; General Surgery and Liver Transplant Unit, Hospital Italiano de Buenos Aires, Buenos Aires, Argentina; and Department of Medicine, Groote Schuur Hospital, Cape Town, South Africa