Diagnosis Of Liver Cancer Research Articles

Epidemiological and clinical characteristics of hepatocellular carcinoma in Xiamen

ObjectiveTo investigate the epidemiological characteristics of hepatocellular carcinoma in Xiamen and offer recommendations for its control and prevention. Methods: Data obtained from liver cancer screening in 2016–2018 and 2019–2020 in patients with hepatocellular carcinoma in Xiamen were collected using Xiamen Healthcare Big Data Platform. Epidemiological characteristics were analyzed descriptively by different years. Results: From 2016–2020, there were 3740 patients with hepatocellular carcinoma in Xiamen. In the patients, the percentage of males was higher than that of females (83.13 % vs 16.87 %). From 2019–2020, there was an increase in the detection rates for disorders including hepatitis B, hypertension, diabetes, fatty liver and cirrhosis of the liver, and there was a decrease in the prevalence of hepatitis C. There was an increase in the values of all hematological indicators of infection in patients with liver cancer in 2019–2020 compared with the previous three years. From 2016–2020, there was a year-on-year increase in the prevalence of liver cancer and fatty liver. From 2018–2020, there was an increasing trend in the prevalence of liver cancer in overweight patients in the population with fatty liver. Conclusion: In 2019–2020, there was an increase in the prevalence of chronic diseases in patients with hepatocellular carcinoma. Comprehensive liver cancer screening provides an important guide for the prevention, early diagnosis and treatment of liver cancer and for clinicians to assess patients’ condition.

Inhibition of M2 tumor-associated macrophages polarization by modulating the Wnt/β-catenin pathway as a possible liver cancer therapy method

The problem of liver cancer is becoming increasingly important due to the epidemic of metabolic diseases and persistent high alcohol consumption. This determines great attention to the development and improvement of methods for early diagnosis and treatment of liver cancer. Huang et al presented a study in the World Journal of Gastroenterology , in which they showed that the use of the traditional Chinese medicine Calculus bovis (CB) can suppress tumor growth in mice by inhibiting M2 tumor-associated macrophages (TAM) through modulating the activity of the Wnt/β-catenin pathway. The interaction of CB components with the Wnt/β-catenin pathway, M2 TAM polarization, and tumor dynamics were studied using network pharmacology, transcriptomics, and molecular docking. It is now generally accepted that the polarization of TAM and the differentiation of the functions of M1 and M2 phagocytes are of great importance for the progression of neoplasms. It is assumed that M2 TAM promote proliferation and migration of tumor cells. Attempts to medicinally influence the Wnt/β-catenin pathway in order to modulate phagocyte polarization now belong to one of the most promising areas of immunotherapy of oncological diseases. Undoubtedly, the work of the Chinese authors deserves attention and further development.

RMCNet: A Liver Cancer Segmentation Network Based on 3D Multi-Scale Convolution, Attention, and Residual Path

Abdominal CT images are important clues for diagnosing liver cancer lesions. However, liver cancer presents challenges such as significant differences in tumor size, shape, and location, which can affect segmentation accuracy. To address these challenges, we propose an end-to-end 3D segmentation algorithm, RMCNet. In the shallow encoding part of RMCNet, we incorporated a 3D multiscale convolution (3D-Multiscale Convolution) module to more effectively extract tumors of varying sizes. Moreover, the convolutional block attention module (CBAM) is used in the encoding part to help the model focus on both the shape and location of tumors. Additionally, a residual path is introduced in each encoding layer to further enrich the extracted feature maps. Our method achieved DSC scores of 76.56% and 72.96%, JCC scores of 75.82% and 71.25%, HD values of 11.07 mm and 17.06 mm, and ASD values of 2.54 mm and 10.51 mm on the MICCAI 2017 Liver Tumor Segmentation public dataset and the 3Dircadb-01 public dataset, respectively. Compared to other methods, RMCNet demonstrates superior segmentation performance and stronger generalization capability.

Pump-free SERS microfluidic chip based on an identification-competition strategy for ultrasensitive and efficient simultaneous detection of liver cancer-related microRNAs

In this study, we present a pump-free SERS microfluidic chip capable of detecting liver cancer-related miR-21 and miR-155 concurrently with ultra-sensitivity and high efficiency. We employed a Fe3O4@cDNA-AuNPs@Raman reporter@H composite structure and a recognition competition strategy. When the target miRNAs (miR-21 and miR-155) are present in the test liquid, they specifically compete with the nucleic acid complementary strand(H) of Fe3O4@cDNA-AuNPs@Raman reporter@H, causing AuNPs to competitively detach from the surface of Fe3O4, resulting in a decrease in the SERS signal. Consequently, this pump-free SERS microfluidic chip enables the detection of the target miRNAs more rapidly and accurately in complex environments. This method offers an approach for the simultaneous and efficient detection of miRNAs and holds promising applications in the early diagnosis of liver cancer.

The role of exosomes in liver cancer: comprehensive insights from biological function to therapeutic applications.

In recent years, cancer, especially primary liver cancer (including hepatocellular carcinoma and intrahepatic cholangiocarcinoma), has posed a serious threat to human health. In the field of liver cancer, exosomes play an important role in liver cancer initiation, metastasis and interaction with the tumor microenvironment. Exosomes are a class of nanoscale extracellular vesicles (EVs)secreted by most cells and rich in bioactive molecules, including RNA, proteins and lipids, that mediate intercellular communication during physiological and pathological processes. This review reviews the multiple roles of exosomes in liver cancer, including the initiation, progression, and metastasis of liver cancer, as well as their effects on angiogenesis, epithelial-mesenchymal transformation (EMT), immune evasion, and drug resistance. Exosomes have great potential as biomarkers for liver cancer diagnosis and prognosis because they carry specific molecular markers that facilitate early detection and evaluation of treatment outcomes. In addition, exosomes, as a new type of drug delivery vector, have unique advantages in the targeted therapy of liver cancer and provide a new strategy for the treatment of liver cancer. The challenges and prospects of exosome-based immunotherapy in the treatment of liver cancer were also discussed. However, challenges such as the standardization of isolation techniques and the scalability of therapeutic applications remain significant hurdles.

Machine Learning Empowered Breast Cancer Diagnosis: Insights from Coimbra Dataset

Aim: The aim of this work is to succinctly communicate the key aspects of a research study on breast cancer. This includes highlighting the global impact and prevalence of breast cancer, emphasizing the challenges of early diagnosis, discussing the potential of technological advancements, and showcasing the role of machine learning algorithms in the context of liver cancer diagnosis. Background: Cancer, notably breast cancer, represents a global health challenge, claiming a significant toll with 12.5% of new cancer cases annually. The prevalence of breast cancer among women worldwide is alarming, resulting in 2.26 million incidents and the unfortunate loss of 685,000 lives. Objective: This article focuses on the critical aspect of early breast cancer diagnosis, acknowledging its heightened difficulty in developing nations compared to developed counterparts. The potential for advancements in technology to serve as a beacon of hope lies in early identification and timely treatment, offering salvation to numerous women and significantly elevating survival chances. Method: In this intricate landscape, machine learning algorithms, particularly in diagnosing liver cancer at its nascent stages, emerge as instrumental tools. The study employs the latest Coimbra dataset, encompassing nine key attributes and a binary classification attribute, with values 1 and 2 signifying benign and malignant cases, respectively. Result: Supervised machine learning algorithms, including Bayes net, multilayer perceptron, IBK, random committee, and random tree, are meticulously applied. Certain models exhibit superior accuracy, precision, recall, and performance, positioning them as promising cornerstones for breast cancer analysis. Conclusion: This structured abstract highlights the urgent need for effective screening and prevention strategies, emphasizing the potential of advanced technology and machine learning algorithms to play a pivotal role in the early detection and analysis of breast cancer, offering hope for improved outcomes and survival rates.

A flexible deep learning framework for liver tumor diagnosis using variable multi-phase contrast-enhanced CT scans

BackgroundLiver cancer is a significant cause of cancer-related mortality worldwide and requires tailored treatment strategies for different types. However, preoperative accurate diagnosis of the type presents a challenge. This study aims to develop an automatic diagnostic model based on multi-phase contrast-enhanced CT (CECT) images to distinguish between hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and normal individuals.MethodsWe designed a Hierarchical Long Short-Term Memory (H-LSTM) model, whose core components consist of a shared image feature extractor across phases, an internal LSTM for each phase, and an external LSTM across phases. The internal LSTM aggregates features from different layers of 2D CECT images, while the external LSTM aggregates features across different phases. H-LSTM can handle incomplete phases and varying numbers of CECT image layers, making it suitable for real-world decision support scenarios. Additionally, we applied phase augmentation techniques to process multi-phase CECT images, improving the model’s robustness.ResultsThe H-LSTM model achieved an overall average AUROC of 0.93 (0.90, 1.00) on the test dataset, with AUROC for HCC classification reaching 0.97 (0.93, 1.00) and for ICC classification reaching 0.90 (0.78, 1.00). Comprehensive validation in scenarios with incomplete phases was performed, with the H-LSTM model consistently achieving AUROC values over 0.9.ConclusionThe proposed H-LSTM model can be employed for classification tasks involving incomplete phases of CECT images in real-world scenarios, demonstrating high performance. This highlights the potential of AI-assisted systems in achieving accurate diagnosis and treatment of liver cancer. H-LSTM offers an effective solution for processing multi-phase data and provides practical value for clinical diagnostics.

Signal-Boosted Electrochemical Lateral Flow Immunoassay for Early Point-of-Care Detection of Liver Cancer Biomarker.

The early diagnosis of cancer in a point-of-need manner is of great significance, yet it remains challenging to achieve the necessary sensitivity and speed. Traditional lateral flow immunoassay (LFIA) methods are limited in accuracy and quantification, restricting their suitability for home-based applications. Thus, we explored a new and user-friendly electrochemical LFIA (e-LFIA) test strip to detect α-fetoprotein (AFP), a diagnostic marker for liver cancer. The specific electrochemical immunoprobe utilized in this e-LFIA test strip is characterized by significant signal boosting, resulted from the loading Ag shell into a gold nanoparticle (AuNP)-coated dendritic mesoporous silica nanoscaffold (DMSN). Leveraging the distinct electrochemical characteristics of Ag anodic stripping and the high volume-to-surface area ratio of DMSNs, the developed DMSNs/AuNPs@Ag-based e-LFIA test strip is capable of detecting AFP at a low concentration of 0.85 ng/mL within a rapid 20 min timespan, both of these values are smaller than those in current clinical testing. Furthermore, we utilized homemade screen-printed electrodes in this sensing prototype and demonstrated the high versatility and reliability of this e-LFIA device. We envision that this DMSNs/AuNPs@Ag-based e-LFIA holds substantial potential for the early diagnosis of liver cancer and household health monitoring.

Computer-aided diagnosis of liver cancer with improved SegNet and deep stacking ensemble model

Liver cancer is a leading cause of cancer-related deaths, often diagnosed at advanced stages due to reliance on traditional imaging methods. Existing computer-aided diagnosis systems struggle with noise, anatomical complexity, and ineffective feature integration, leading to inaccuracies in lesion segmentation and classification. By effectively addressing these challenges, the model aims to enhance early detection and assist clinicians in making informed decisions. Ultimately, this research seeks to contribute to more efficient and accurate liver cancer diagnosis. This paper presents a novel model for liver cancer classification, called SegNet-based Liver Cancer Classification via SqueezeNet (SgN-LCC-SqN). The model effectively executes liver cancer segmentation and classification through four key steps: preprocessing, segmentation, feature extraction, and classification. During preprocessing, Quadratic Mean Estimated Wiener Filtering (QMEWF) is utilized to minimize image noise. Segmentation divides the image into segments using Enhanced Feature Pyramid SegNet (EFP-SgN), which is essential for precise diagnosis. Feature extraction encompasses color features, Local Directional Pattern Variance, and Correlation Filtering-Local Gradient Increasing Pattern (CF-LGIP) features. The extracted features are then processed through an ensemble model, Deep Convolutional, Recurrent, Long Short Term Memory with SqueezeNet (DCR-LSTM-SqN), which includes Deep Convolutional Neural Network (DCNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Modified Loss Function in SqueezeNet (MLF-SqN) classifiers, sequentially analyzing the feature sets through DCNN, RNN, and LSTM before classification by MLF-SqN. The performance of the suggested DCR-LSTM-SqN model is evaluated over conventional methods for positive, negative and other metrics. The DCR-LSTM-SqN model consistently demonstrates superior accuracy, ranging from 0.947 to 0.984, across all training data percentages. Thus, the proposed model effectively segments liver lesions and classifies cancerous areas, demonstrating its potential as a valuable resource for clinicians to enhance the efficiency and accuracy of liver cancer diagnosis.

Utilization of Microfluidic Droplet-Based Methods in Diagnosis and Treatment Methods of Hepatocellular Carcinoma: A Review.

Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide and is associated with high morbidity and mortality. One of the main challenges in the management of HCC is late clinical presentation and thus diagnosis of the disease, which results in poor survival. The pathogenesis of HCC is complex and involves chronic liver injury and genetic alterations. Diagnosis of HCC can be made either by biopsy or imaging; however, conventional tissue-based biopsy methods and serological biomarkers such as AFP have limited clinical applications. While hepatocellular carcinoma is associated with a range of molecular alterations, including the activation of oncogenic signaling pathways, such as Wnt-TGFβ, PI3K-AKT-mTOR, RAS-MAPK, MET, IGF, and Wnt-β-catenin and TP53 and TERT promoter mutations, microfluidic applications have been limited. Early diagnosis is crucial for advancing treatments that would address the heterogeneity of HCC. In this context, microfluidic droplet-based methods are crucial, as they enable comprehensive analysis of the genome and transcriptome of individual cells. Single-cell RNA sequencing (scRNA-seq) allows the examination of individual cell transcriptomes, identifying their heterogeneity and cellular evolutionary relationships. Other microfluidic methods, such as Drop-seq, InDrop, and ATAC-seq, are also employed for single-cell analysis. Here, we examine and compare these microfluidic droplet-based methods, exploring their advantages and limitations in liver cancer research. These technologies provide new opportunities to understand liver cancer biology, diagnosis, treatment, and prognosis, contributing to scientific efforts in combating this challenging disease.

Profound primary prevention of liver cancer following a natural experiment in China: A 50‐year perspective and public health implications

AbstractLiver cancer causes upwards of 1 million cancer deaths annually and is projected to rise by at least 55% over the next 15 years. Two of the major risk factors contributing to liver cancer have been well documented by multiple epidemiologic studies and the hepatitis B virus (HBV) and aflatoxin show a synergy that increases by more than 8‐fold the risk of liver cancer relative to HBV alone. Using the population‐based cancer registry established by the Qidong Liver Cancer Institute in 1972 and aflatoxin‐specific biomarkers, we document that reduction of aflatoxin exposure has likely contributed to a nearly 70% decline in age‐standardized liver cancer incidence over the past 30 years despite an unchanging prevalence of HBV infection in cases. A natural experiment of economic reform in the 1980s drove a rapid switch from consumption of heavily contaminated corn to minimally, if any, contaminated rice and subsequent dietary diversity. Aflatoxin consumption appears to accelerate the time to liver cancer diagnosis; lowering exposure to this carcinogen adds years of life before a cancer diagnosis. Thus, in 1990 the median age of diagnosis was 48 years, while increasing to 67 years by 2021. These findings have important translational public health implications since up to 5 billion people worldwide might be routinely exposed to dietary aflatoxin, especially in societies using corn as the staple food. Interventions against aflatoxin are an achievable outcome leading to a reduction in liver cancer incidence and years of delay of its nearly always fatal diagnosis.

Protein tyrosine phosphatase non-receptor II: A possible biomarker of poor prognosis and mediator of immune evasion in hepatocellular carcinoma.

The incidence of primary liver cancer is increasing year by year. In 2022 alone, more than 900000 people were diagnosed with liver cancer worldwide, with hepatocellular carcinoma (HCC) accounting for 75%-85% of cases. HCC is the most common primary liver cancer. China has the highest incidence and mortality rate of HCC in the world, and it is one of the malignant tumors that seriously threaten the health of Chinese people. The onset of liver cancer is occult, the early cases lack typical clinical symptoms, and most of the patients are already in the middle and late stage when diagnosed. Therefore, it is very important to find new markers for the early detection and diagnosis of liver cancer, improve the therapeutic effect, and improve the prognosis of patients. Protein tyrosine phosphatase non-receptor 2 (PTPN2) has been shown to be associated with colorectal cancer, triple-negative breast cancer, non-small cell lung cancer, and prostate cancer, but its biological role and function in tumors remain to be further studied. To combine the results of relevant data obtained from The Cancer Genome Atlas (TCGA) to provide the first in-depth analysis of the biological role of PTPN2 in HCC. The expression of PTPN2 in HCC was first analyzed based on the TCGA database, and the findings were then verified by immunohistochemical staining, quantitative real-time polymerase chain reaction (qRT-PCR), and immunoblotting. The value of PTPN2 in predicting the survival of patients with HCC was assessed by analyzing the relationship between PTPN2 expression in HCC tissues and clinicopathological features. Finally, the potential of PTPN2 affecting immune escape of liver cancer was evaluated by tumor immune dysfunction and exclusion and immunohistochemical staining. The results of immunohistochemical staining, qRT-PCR, and immunoblotting in combination with TCGA database analysis showed that PTPN2 was highly expressed and associated with a poor prognosis in HCC patients. Kyoto Encyclopedia of Genes and Genomes enrichment analysis showed that PTPN2 was associated with various pathways, including cancer-related pathways, the Notch signaling pathway, and the MAPK signaling pathway. Gene Set Enrichment Analysis showed that PTPN2 was highly expressed in various immune-related pathways, such as the epithelial mesenchymal transition process. A risk model score based on PTPN2 showed that immune escape was significantly enhanced in the high-risk group compared with the low-risk group. This study investigated PTPN2 from multiple biological perspectives, revealing that PTPN2 can function as a biomarker of poor prognosis and mediate immune evasion in HCC.

Primary Liver Cancer Risk and Mortality in Patients With Alcohol-Related Cirrhosis in England and Denmark: Observational Cohort Studies.

Patients with alcohol-related cirrhosis (ALD cirrhosis) have an increased risk of primary liver cancer (hepatocellular carcinoma [HCC] or intrahepatic cholangiocarcinoma [iCCA]). England recommends surveillance for HCC in these patients, while Denmark does not. We performed an observational cohort study using the English Clinical Practice Research Datalink and the nationwide Danish healthcare registries to identify 17,110 English (2000-2016) and 22,122 Danish (1994-2022) patients with diagnosis codes of ALD cirrhosis. We computed and compared incidence rates and cumulative incidence of primary liver cancer, annual ultrasound scan rates, and mortality following diagnosis of primary liver cancer. The overall risk of primary liver cancer was similar in England and Denmark: 5-year risk was 2.24% (95% confidence interval 2.00-2.49) in England (iCCA 0.07%, HCC 2.16%) and 2.36% (2.15-2.57) in Denmark (iCCA 0.05%, HCC 2.30%). The annual rate of ultrasound scans per person was 0.65 (0.63-0.67) in England and 0.44 (0.42-0.46) in Denmark. The 1-year mortality after a diagnosis of primary liver cancer was 59.2% (54.4-64.0) in England and 60.9% (57.4-64.4) in Denmark. The 3-year risks of HCC in those on vs off surveillance in England were 2.3% (1.0-4.6) vs 1.5% (1.0-2.2). The risk of primary liver cancer was the same in English and Danish patients with ALD cirrhosis, and HCCs constituted 97% of primary liver cancers. Mortality with primary liver cancer was equally high in both countries. Notably, in England, where guidance recommends biannual HCC surveillance with ultrasound, patients with ALD cirrhosis were undergoing fewer than 1 ultrasound scan per year.

Natural killer (NK) cells-related gene signature reveals the immune environment heterogeneity in hepatocellular carcinoma based on single cell analysis

The early diagnosis of liver cancer is crucial for the treatment and depends on the coordinated use of several test procedures. Early diagnosis is crucial for precision therapy in the treatment of the hepatocellular carcinoma (HCC). Therefore, in this study, the NK cell-related gene prediction model was used to provide the basis for precision therapy at the gene level and a novel basis for the treatment of patients with liver cancer. Natural killer (NK) cells have innate abilities to recognize and destroy tumor cells and thus play a crucial function as the “innate counterpart” of cytotoxic T cells. The natural killer (NK) cells is well recognized as a prospective approach for tumor immunotherapy in treating patients with HCC. In this research, we used publicly available databases to collect bioinformatics data of scRNA-seq and RNA-seq from HCC patients. To determine the NK cell-related genes (NKRGs)-based risk profile for HCC, we isolated T and natural killer (NK) cells and subjected them to analysis. Uniform Manifold Approximation and Projection plots were created to show the degree of expression of each marker gene and the distribution of distinct clusters. The connection between the immunotherapy response and the NKRGs-based signature was further analyzed, and the NKRGs-based signature was established. Eventually, a nomogram was developed using the model and clinical features to precisely predict the likelihood of survival. The prognosis of HCC can be accurately predicted using the NKRGs-based prognostic signature, and thorough characterization of the NKRGs signature of HCC may help to interpret the response of HCC to immunotherapy and propose a novel tumor treatment perspective.

Feasibility and safety of transarterial chemoembolization in patients with liver cancer via the distal radial approach: a single-center retrospective cohort study.

The femoral artery is the standard route for transarterial chemoembolization (TACE); however, it is negatively associated with the quality of life of patients, and carries an increased risk of deep vein thrombosis in the lower limbs. We employed the distal radial approach to TACE to assess its feasibility and safety. We conducted a retrospective study at the First Hospital of Jilin University from August 1, 2020 to October 31, 2023. To be eligible for inclusion in the study, the patients had to meet the following main inclusion criteria: (I) have undergone a preoperative imaging (abdominal computed tomography enhancement or magnetic resonance dynamic enhancement) examination, or have a pathologically confirmed diagnosis of primary liver cancer, and a Child-Pugh score of A or B; and (II) have undergone distal radial artery puncture. The primary endpoint of this study was the success rate of distal radial artery puncture. The secondary endpoints were complications and the duration of the puncture. Among the 343 patients with primary liver cancer (of whom 236 were male and 107 were female), a total of 1,315 distal radial artery punctures were attempted. The success rate was remarkably high at 95.13% (1,251/1,315), with only 64 cases requiring an alternative approach due to failed puncture. The average puncture duration was 20±7.43 minutes. No bleeding and hematoma, no arterial dissection and pseudoaneurysm formation were observed on ultrasound, and the radial pulse was palpable in all patients, highlighting the safety of the procedure. Further, no adverse events of vascular occlusion were observed among the 12 patients who received 6 or more punctures, indicating the sustainability of the distal radial artery access under the premise of adequate vascular protection. The development of this technique requires a learning curve of at least 50 cases to break through the learning baseline and be proficient in distal radial artery blind puncture. This may be the reason why many interventional physicians are reluctant to perform this procedure, adapting to the femoral approach with a shorter learning curve. The distal radial artery approach is feasible and safe in hepatic arterial chemoembolization, and should be widely promoted in TACE.

Machine learning approach to predict blood-secretory proteins and potential biomarkers for liver cancer using omics data

Identifying non-invasive blood-based biomarkers is crucial for early detection and monitoring of liver cancer (LC), thereby improving patient outcomes. This study leveraged computational approaches to predict potential blood-based biomarkers for LC. Machine learning (ML) models were developed using selected features from blood-secretory proteins collected from the curated databases. The logistic regression (LR) model demonstrated the optimal performance. Transcriptome analysis across 7 LC cohorts revealed 231 common differentially expressed genes (DEGs). The encoded proteins of these DEGs were compared with the ML dataset, revealing 29 proteins overlapping with the blood-secretory dataset. The LR model also predicted 29 additional proteins as blood-secretory with the remaining protein-coding genes. As a result, 58 potential blood-secretory proteins were obtained. Among the top 20 genes, 13 common hub genes were identified. Further, area under the receiver operating characteristic curve (ROC AUC) analysis was performed to assess the genes as potential diagnostic blood biomarkers. Six genes, ESM1, FCN2, MDK, GPC3, CTHRC1 and COL6A6, exhibited an AUC value higher than 0.85 and were predicted as blood-secretory. This study highlights the potential of an integrative computational approach for discovering non-invasive blood-based biomarkers in LC, facilitating for further validation and clinical translation. SignificanceLiver cancer is one of the leading causes of premature death worldwide, with its prevalence and mortality rates projected to increase. Although current diagnostic methods are highly sensitive, they are invasive and unsuitable for repeated testing. Blood biomarkers offer a promising non-invasive alternative, but their wide dynamic range of protein concentration poses experimental challenges. Therefore, utilizing available omics data to develop a diagnostic model could provide a potential solution for accurate diagnosis. This study developed a computational method integrating machine learning and bioinformatics analysis to identify potential blood biomarkers. As a result, ESM1, FCN2, MDK, GPC3, CTHRC1 and COL6A6 biomarkers were identified, holding significant promise for improving diagnosis and understanding of liver cancer. The integrated method can be applied to other cancers, offering a possible solution for early detection and improved patient outcomes.

Vibrational Spectrophotometry: A Comprehensive Review on the Diagnosis of Gastric and Liver Cancer

Vibrational Spectrophotometry: A Comprehensive Review on the Diagnosis of Gastric and Liver Cancer

DESMPA-Deep CNN: Double Exponential Smoothing Marine Predator Algorithm-Based Deep CNN for Liver Tumor Segmentation and Classification

Nowadays, liver cancers are well-known cancers attacking the human body. The larger segment of liver carcinomas is highly affected by alcohol-associated hepatitis as well as cirrhosis circumstances. In order to increase the survival rate of humans, premature and early diagnosis of liver cancer is very vital. Currently, discriminating the tumor and liver segments from medical imaging with the help of entirely automated computer-aided software is a highly difficult task, because the liver tumor may differ from one human being to another human being. Thus, this paper endeavors to present a new liver tumor segmentation and classification approach by incorporating an optimization-enabled deep learning model. Here, the adopted model comprises four stages, pre-processing, liver segmentation, liver tumor detection, and classification. To begin with, the collected images are given to the pre-processing stage using the African vulture’s optimization-based adaptive histogram equalization (AVOAHE). The integration of the African vulture’s optimization (AVO) concept in adaptive histogram equalization is named AVOAHE. After the image pre-processing phase, the liver region is segmented by utilizing kernel-based fuzzy C-means clustering. Then, the detection of liver tumor is performed using the Swin-Unet. Finally, tumor detection is done by utilizing the deep convolutional neural network (Deep CNN). Moreover, a hybrid optimization approach, called double exponential smoothing (DES)-marine predator algorithm (MPA), is developed by combining DES and MPA, which is used to train Deep CNN and serves as an enhancement. Finally, the experimental analysis has shown that the adopted technique has achieved better results in terms of accuracy, specificity, sensitivity, and precision with values of 0.957, 0.969, 0.941 and 0.915, respectively.

Liver Cancer Diagnosis: Enhanced Deep Maxout Model with Improved Feature Set

This work proposed a liver cancer classification scheme that includes Preprocessing, Feature extraction, and classification stages. The source images are pre-processed using Gaussian filtering. For segmentation, this work proposes a LUV transformation-based adaptive thresholding-based segmentation process. After the segmentation, certain features are extracted that include multi-texon based features, Improved Local Ternary Pattern (LTP-based features), and GLCM features during this phase. In the Classification phase, an improved Deep Maxout model is proposed for liver cancer detection. The adopted scheme is evaluated over other schemes based on various metrics. While the learning rate is 60%, an improved deep maxout model achieved a higher F-measure value (0.94) for classifying liver cancer; however, the previous method like Support Vector Machine (SVM), Random Forest (RF), Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), K-Nearest Neighbor (KNN), Deep maxout, Convolutional Neural Network (CNN), and DL model holds less F-measure value. An improved deep maxout model achieved minimal False Positive Rate (FPR), and False Negative Rate (FNR) values with the best outcomes compared to other existing models for liver cancer classification.

Current status and new advancements in molecular imaging of liver cancer

Early-stage diagnosis of liver cancer is challenging, with an overall poor prognosis. The tumor microenvironment of primary liver cancer is complex, exhibiting significant heterogeneity both interpersonally and intratumorally. Therefore, it is of paramount importance to dynamically analyze biological markers in the tumor microenvironment of primary liver cancer in vivo. In recent years, significant progress has been made in the imaging diagnosis and treatment of liver cancer with the development of molecular imaging. Molecular imaging techniques utilize specific nano-imaging probes to evaluate pathological changes of liver cancer at the molecular and cellular levels in real-time. These techniques enable precise imaging to reveal key molecular biomarkers involved in the occurrence and progression of liver cancer, exploring their associations with cancer progression and outcomes. This article focuses on molecular imaging, emphasizing the current research status and latest advancements in the field of liver cancer diagnosis and therapy using techniques such as CT, MRI, optical imaging, PET imaging, and multimodal imaging. It also identifies important future directions and significant challenges for further development.