Deep learning model for prediction of hepatocellular carcinoma in patients with HBV-related cirrhosis on antiviral therapy

The power of deep learning in simplifying feature selection for hepatocellular carcinoma: a review

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.

Developing deep learning-based strategies to predict the risk of hepatocellular carcinoma among patients with nonalcoholic fatty liver disease from electronic health records

OBJECTIVE

The accuracy of deep learning models for many disease prediction problems is affected by time-varying covariates, rare incidence, covariate imbalance and delayed diagnosis when using structured electronic health records data. The situation is further exasperated when predicting the risk of one disease on condition of another disease, such as the hepatocellular carcinoma risk among patients with nonalcoholic fatty liver disease due to slow, chronic progression, the scarce of data with both disease conditions and the sex bias of the diseases. The goal of this study is to investigate the extent to which the aforementioned issues influence deep learning performance, and then devised strategies to tackle these challenges. These strategies were applied to improve hepatocellular carcinoma risk prediction among patients with nonalcoholic fatty liver disease.

METHODS

We evaluated two representative deep learning models in the task of predicting the occurrence of hepatocellular carcinoma in a cohort of patients with nonalcoholic fatty liver disease (n = 220,838) from a national EHR database. The disease prediction task was carefully formulated as a classification problem while taking censorship and the length of follow-up into consideration.

RESULTS

We developed a novel backward masking scheme to deal with the issue of delayed diagnosis which is very common in EHR data analysis and evaluate how the length of longitudinal information after the index date affects disease prediction. We observed that modeling time-varying covariates improved the performance of the algorithms and transfer learning mitigated reduced performance caused by the lack of data. In addition, covariate imbalance, such as sex bias in data impaired performance. Deep learning models trained on one sex and evaluated in the other sex showed reduced performance, indicating the importance of assessing covariate imbalance while preparing data for model training.

CONCLUSIONS

The strategies developed in this work can significantly improve the performance of hepatocellular carcinoma risk prediction among patients with nonalcoholic fatty liver disease. Furthermore, our novel strategies can be generalized to apply to other disease risk predictions using structured electronic health records, especially for disease risks on condition of another disease.

Developing deep learning-based strategies to predict the risk of hepatocellular carcinoma among patients with nonalcoholic fatty liver disease from electronic health records

Background

Deep learning models showed great success and potential when applied to many biomedical problems. However, the accuracy of deep learning models for many disease prediction problems is affected by time-varying covariates, rare incidence, and covariate imbalance when using structured electronic health records data. The situation is further exasperated when predicting the risk of one disease on condition of another disease, such as the hepatocellular carcinoma risk among patients with nonalcoholic fatty liver disease due to slow, chronic progression, the scarce of data with both disease conditions and the sex bias of the diseases.

Objective

The goal of this study is to investigate the extent to which time-varying covariates, rare incidence, and covariate imbalance influence deep learning performance, and then devised strategies to tackle these challenges. These strategies were applied to improve hepatocellular carcinoma risk prediction among patients with nonalcoholic fatty liver disease.

Methods

We evaluated two representative deep learning models in the task of predicting the occurrence of hepatocellular carcinoma in a cohort of patients with nonalcoholic fatty liver disease (n = 220,838) from a national EHR database. The disease prediction task was carefully formulated as a classification problem while taking censorship and the length of follow-up into consideration.

Results

We developed a novel backward masking scheme to evaluate how the length of longitudinal information after the index date affects disease prediction. We observed that modeling time-varying covariates improved the performance of the algorithms and transfer learning mitigated reduced performance caused by the lack of data. In addition, covariate imbalance, such as sex bias in data impaired performance. Deep learning models trained on one sex and evaluated in the other sex showed reduced performance, indicating the importance of assessing covariate imbalance while preparing data for model training.

Conclusions

Devising proper strategies to address challenges from time-varying covariates, lack of data, and covariate imbalance can be key to counteracting data bias and accurately predicting disease occurrence using deep learning models. The novel strategies developed in this work can significantly improve the performance of hepatocellular carcinoma risk prediction among patients with nonalcoholic fatty liver disease. Furthermore, our novel strategies can be generalized to apply to other disease risk predictions using structured electronic health records, especially for disease risks on condition of another disease.

Current Status and Analysis of Machine Learning in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a common tumor. Although the diagnosis and treatment of HCC have made great progress, the overall prognosis remains poor. As the core component of artificial intelligence, machine learning (ML) has developed rapidly in the past decade. In particular, ML has become widely used in the medical field, and it has helped in the diagnosis and treatment of cancer. Different algorithms of ML have different roles in diagnosis, treatment, and prognosis. This article reviews recent research, explains the application of different ML models in HCC, and provides suggestions for follow-up research.

A machine learning model for predicting hepatocellular carcinoma risk in patients with chronic hepatitis B

BACKGROUND

Machine learning (ML) algorithms can be used to overcome the prognostic performance limitations of conventional hepatocellular carcinoma (HCC) risk models. We established and validated an ML-based HCC predictive model optimized for patients with chronic hepatitis B (CHB) infections receiving antiviral therapy (AVT).

METHODS

Treatment-naïve CHB patients who were started entecavir (ETV) or tenofovir disoproxil fumarate (TDF) were enrolled. We used a training cohort (n = 960) to develop a novel ML model that predicted HCC development within 5 years and validated the model using an independent external cohort (n = 1937). ML algorithms consider all potential interactions and do not use predefined hypotheses.

RESULTS

The mean age of the patients in the training cohort was 48 years, and most patients (68.9%) were men. During the median 59.3 (interquartile range 45.8-72.3) months of follow-up, 69 (7.2%) patients developed HCC. Our ML-based HCC risk prediction model had an area under the receiver-operating characteristic curve (AUC) of 0.900, which was better than the AUCs of CAMD (0.778) and REAL B (0.772) (both p < .05). The better performance of our model was maintained (AUC = 0.872 vs. 0.788 for CAMD and 0.801 for REAL B) in the validation cohort. Using cut-off probabilities of 0.3 and 0.5, the cumulative incidence of HCC development differed significantly among the three risk groups (p < .001).

CONCLUSIONS

Our new ML model performed better than models in terms of predicting the risk of HCC development in CHB patients receiving AVT.

A hierarchical multilabel graph attention network method to predict the deterioration paths of chronic hepatitis B patients

OBJECTIVE

Estimating the deterioration paths of chronic hepatitis B (CHB) patients is critical for physicians' decisions and patient management. A novel, hierarchical multilabel graph attention-based method aims to predict patient deterioration paths more effectively. Applied to a CHB patient data set, it offers strong predictive utilities and clinical value.

MATERIALS AND METHODS

The proposed method incorporates patients' responses to medications, diagnosis event sequences, and outcome dependencies to estimate deterioration paths. From the electronic health records maintained by a major healthcare organization in Taiwan, we collect clinical data about 177 959 patients diagnosed with hepatitis B virus infection. We use this sample to evaluate the proposed method's predictive efficacy relative to 9 existing methods, as measured by precision, recall, F-measure, and area under the curve (AUC).

RESULTS

We use 20% of the sample as holdouts to test each method's prediction performance. The results indicate that our method consistently and significantly outperforms all benchmark methods. It attains the highest AUC, with a 4.8% improvement over the best-performing benchmark, as well as 20.9% and 11.4% improvements in precision and F-measures, respectively. The comparative results demonstrate that our method is more effective for predicting CHB patients' deterioration paths than existing predictive methods.

DISCUSSION AND CONCLUSION

The proposed method underscores the value of patient-medication interactions, temporal sequential patterns of distinct diagnosis, and patient outcome dependencies for capturing dynamics that underpin patient deterioration over time. Its efficacious estimates grant physicians a more holistic view of patient progressions and can enhance their clinical decision-making and patient management.

Performance de l’ADRESS-HCC score dans l‘évaluation du risque de carcinome hépatocellulaire

INTRODUCTION

The ADRESS-HCC score allows predicting the risk of occurrence of Hepatocellular carcinoma in cirrhosis at one year of follow-up.

AIM

Measuring the performance of ADRESS-HCC in predicting the risk of degeneration on post-viral cirrhosis, in a gastroenterology department in Tunisia.

METHODS

Retrospective study, including patients followed for compensated viral cirrhosis in the gastroenterology department of the Mohamed Taher Maamouri hospital. The ADRESS-HCC score was calculated at diagnosis of cirrhosis. We divided patients into two groups depending on whether they developed Hepatocellular carcinoma or not. We evaluated the performance of the ADRESS-HCC score in predicting the risk of Hepatocellular carcinoma according to a threshold value.

RESULTS

We enrolled 60 patients; the mean age was 62 years. Twenty-five patients developed hepatocellular carcinoma during follow-up. The mean value of ADRESS-HCC score was 5.08. To predict the occurrence of hepatocellular carcinoma at 1 year of follow-up, the area under the curve of the ADRESS-HCC score was 0.74 (p=0.01). For a threshold value of 5.63 its sensitivity was 91 % with a negative predictive value of 95.83%.

CONCLUSION

The ADRESS-HCC score had an average performance in predicting degeneration in post-viral cirrhosis.

A novel NHEJ gene signature based model for risk stratification and prognosis prediction in hepatocellular carcinoma

BACKGROUND

Non-homologous DNA end joining (NHEJ) is the predominant DNA double-strand break (DSB) repair pathway in human. However, the relationship between NHEJ pathway and hepatocellular carcinoma (HCC) is unclear. We aimed to explore the potential prognostic role of NHEJ genes and to develop an NHEJ-based prognosis signature for HCC.

METHODS

Two cohorts from public database were incorporated into this study. The Kaplan-Meier curve, the Least absolute shrinkage and selection operator (LASSO) regression analysis, and Cox analyses were implemented to determine the prognostic genes. A NHEJ-related risk model was created and verified by independent cohorts. We derived enriched pathways between the high- and low-risk groups using Gene Set Enrichment Analysis (GSEA). CIBERSORT and microenvironment cell populations-counter algorithm were used to perform immune infiltration analysis. XRCC6 is a core NHEJ gene and immunohistochemistry (IHC) was further performed to elucidate the prognostic impact. In vitro proliferation assays were conducted to investigate the specific effect of XRCC6.

RESULTS

A novel NHEJ-related risk model was developed based on 6 NHEJ genes and patients were divided into distinct risk groups according to the risk score. The high-risk group had a poorer survival than those in the low-risk group (P < 0.001). Meanwhile, an obvious discrepancy in the landscape of the immune microenvironment also indicated that distinct immune status might be a potential determinant affecting prognosis as well as immunotherapy reactiveness. High XRCC6 expression level associates with poor outcome in HCC. Moreover, XRCC6 could promote HCC cell proliferation in vitro.

CONCLUSIONS

In brief, this work reveals a novel NHEJ-related risk signature for prognostic evaluation of HCC patients, which may be a potential biomarker of HCC immunotherapy.

The Applications of Artificial Intelligence in Digestive System Neoplasms: A Review

Importance

Digestive system neoplasms (DSNs) are the leading cause of cancer-related mortality with a 5-year survival rate of less than 20%. Subjective evaluation of medical images including endoscopic images, whole slide images, computed tomography images, and magnetic resonance images plays a vital role in the clinical practice of DSNs, but with limited performance and increased workload of radiologists or pathologists. The application of artificial intelligence (AI) in medical image analysis holds promise to augment the visual interpretation of medical images, which could not only automate the complicated evaluation process but also convert medical images into quantitative imaging features that associated with tumor heterogeneity.

Highlights

We briefly introduce the methodology of AI for medical image analysis and then review its clinical applications including clinical auxiliary diagnosis, assessment of treatment response, and prognosis prediction on 4 typical DSNs including esophageal cancer, gastric cancer, colorectal cancer, and hepatocellular carcinoma.

Conclusion

AI technology has great potential in supporting the clinical diagnosis and treatment decision-making of DSNs. Several technical issues should be overcome before its application into clinical practice of DSNs.

Artificial Intelligence in Hepatology- Ready for the Primetime

Artificial Intelligence (AI) is a mathematical process of computer mediating designing of algorithms to support human intelligence. AI in hepatology has shown tremendous promise to plan appropriate management and hence improve treatment outcomes. The field of AI is in a very early phase with limited clinical use. AI tools such as machine learning, deep learning, and 'big data' are in a continuous phase of evolution, presently being applied for clinical and basic research. In this review, we have summarized various AI applications in hepatology, the pitfalls and AI's future implications. Different AI models and algorithms are under study using clinical, laboratory, endoscopic and imaging parameters to diagnose and manage liver diseases and mass lesions. AI has helped to reduce human errors and improve treatment protocols. Further research and validation are required for future use of AI in hepatology.

Suboptimal Performance of Hepatocellular Carcinoma Prediction Models in Patients with Hepatitis B Virus-Related Cirrhosis

This study aimed to evaluate the predictive performance of pre-existing well-validated hepatocellular carcinoma (HCC) prediction models, established in patients with HBV-related cirrhosis who started potent antiviral therapy (AVT). We retrospectively reviewed the cases of 1339 treatment-naïve patients with HBV-related cirrhosis who started AVT (median period, 56.8 months). The scores of the pre-existing HCC risk prediction models were calculated at the time of AVT initiation. HCC developed in 211 patients (15.1%), and the cumulative probability of HCC development at 5 years was 14.6%. Multivariate Cox regression analysis revealed that older age (adjusted hazard ratio [aHR], 1.023), lower platelet count (aHR, 0.997), lower serum albumin level (aHR, 0.578), and greater LS value (aHR, 1.012) were associated with HCC development. Harrell’s c-indices of the PAGE-B, modified PAGE-B, modified REACH-B, CAMD, aMAP, HCC-RESCUE, AASL-HCC, Toronto HCC Risk Index, PLAN-B, APA-B, CAGE-B, and SAGE-B models were suboptimal in patients with HBV-related cirrhosis, ranging from 0.565 to 0.667. Nevertheless, almost all patients were well stratified into low-, intermediate-, or high-risk groups according to each model (all log-rank p < 0.05), except for HCC-RESCUE (p = 0.080). Since all low-risk patients had cirrhosis at baseline, they had unneglectable cumulative incidence of HCC development (5-year incidence, 4.9−7.5%). Pre-existing risk prediction models for patients with chronic hepatitis B showed suboptimal predictive performances for the assessment of HCC development in patients with HBV-related cirrhosis.

Precision medicine in the era of potent antiviral therapy for chronic hepatitis B

With the wide use of potent and safe nucloes(t-)ide analogues (NAs) treatment, patient-centered care is getting important. Intensive care for comorbidity has gain utmost importance in care of aging chronic hepatitis B (CHB) patients with life-long antiviral treatment. Linkage to care of patients with CHB is essential for the goal of hepatitis B virus (HBV) eradication. As long-term suppression of HBV DNA replication does not prevent hepatocellular carcinoma (HCC), prevention of HCC is another challenge for NAs treatment. There is a possibility of hepatocarcinogenesis in the immune-tolerant phase and risk of loss of patients during active monitoring seeking the time point for antiviral treatment initiation. Initiation of NAs treatment from the immune-tolerant phase would improve the linkage to care. However, universal recommendation is premature and evidence for cost-effectiveness needs to be accumulated. Early initiation of NAs in the evidence of significant disease progression, either HBV associated or comorbidity associated, would be a better strategy to reduce the risk of HCC in patients located in the gray zone.

Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities

Hepatocellular carcinoma (HCC) constitutes the fifth most frequent malignancy worldwide and the third most frequent cause of cancer-related deaths. Currently, treatment selection is based on the stage of the disease. Emerging fields such as three-dimensional (3D) printing, 3D bioprinting, artificial intelligence (AI), and machine learning (ML) could lead to evidence-based, individualized management of HCC. In this review, we comprehensively report the current applications of 3D printing, 3D bioprinting, and AI/ML-based models in HCC management; we outline the significant challenges to the broad use of these novel technologies in the clinical setting with the goal of identifying means to overcome them, and finally, we discuss the opportunities that arise from these applications. Notably, regarding 3D printing and bioprinting-related challenges, we elaborate on cost and cost-effectiveness, cell sourcing, cell viability, safety, accessibility, regulation, and legal and ethical concerns. Similarly, regarding AI/ML-related challenges, we elaborate on intellectual property, liability, intrinsic biases, data protection, cybersecurity, ethical challenges, and transparency. Our findings show that AI and 3D printing applications in HCC management and healthcare, in general, are steadily expanding; thus, these technologies will be integrated into the clinical setting sooner or later. Therefore, we believe that physicians need to become familiar with these technologies and prepare to engage with them constructively.

Deep learning in hepatocellular carcinoma: Current status and future perspectives

Hepatocellular carcinoma (HCC) is among the leading causes of cancer incidence and death. Despite decades of research and development of new treatment options, the overall outcomes of patients with HCC continue to remain poor. There are areas of unmet need in risk prediction, early diagnosis, accurate prognostication, and individualized treatments for patients with HCC. Recent years have seen an explosive growth in the application of artificial intelligence (AI) technology in medical research, with the field of HCC being no exception. Among the various AI-based machine learning algorithms, deep learning algorithms are considered state-of-the-art techniques for handling and processing complex multimodal data ranging from routine clinical variables to high-resolution medical images. This article will provide a comprehensive review of the recently published studies that have applied deep learning for risk prediction, diagnosis, prognostication, and treatment planning for patients with HCC.

Nano-Gold PCR in Detection of TERT Methylation and Its Correlation with Hepatitis B-Related Hepatocellular Carcinoma

This study aimed to introduce nano-gold PCR for detection of TERT methylation, and explore the correlation between TERT methylation and prognosis of hepatocellular carcinoma (HCC). From March 2016 to March 2018, 154 HBV carriers treated in our hospital were enrolled in the study and divided into HCC (68 cases), cirrhosis (45 cases) and chronic hepatitis (CH) groups (41 cases) based on clinical disease. HCC patients were further divided into methylation (30 cases) and non-methylation (38 cases) subgroup based on methylation status of the TERT. TERT methylation of HCC specimens were 44.12% and 35.24% by nano-PCR and conventional PCR, respectively. The TERT methylation and TERT expression in HCC specimens were higher than for cirrhosis and CH specimens. A significant positive correlation was observed between TERT methylation and TERT expression. AFP, Edmondson classification, tumor size, hilar lymph node and intrahepatic metastasis, and TNM staging in the methylation group were higher than in non-methylation group. Further, overall survival and progression-free survival were significantly shorter. Nano-gold PCR is more sensitive in detecting TERT methylation. As CHB progresses, TERT methylation increases. Greater methylation of the gene is associated with worse prognosis in HCC patients.

Artificial intelligence in precision medicine in hepatology

The advancement of investigation tools and electronic health records (EHR) enables a paradigm shift from guideline-specific therapy toward patient-specific precision medicine. The multiparametric and large detailed information necessitates novel analyses to explore the insight of diseases and to aid the diagnosis, monitoring, and outcome prediction. Artificial intelligence (AI), machine learning, and deep learning (DL) provide various models of supervised, or unsupervised algorithms, and sophisticated neural networks to generate predictive models more precisely than conventional ones. The data, application tasks, and algorithms are three key components in AI. Various data formats are available in daily clinical practice of hepatology, including radiological imaging, EHR, liver pathology, data from wearable devices, and multi-omics measurements. The images of abdominal ultrasonography, computed tomography, and magnetic resonance imaging can be used to predict liver fibrosis, cirrhosis, non-alcoholic fatty liver disease (NAFLD), and differentiation of benign tumors from hepatocellular carcinoma (HCC). Using EHR, the AI algorithms help predict the diagnosis and outcomes of liver cirrhosis, HCC, NAFLD, portal hypertension, varices, liver transplantation, and acute liver failure. AI helps to predict severity and patterns of fibrosis, steatosis, activity of NAFLD, and survival of HCC by using pathological data. Despite of these high potentials of AI application, data preparation, collection, quality, labeling, and sampling biases of data are major concerns. The selection, evaluation, and validation of algorithms, as well as real-world application of these AI models, are also challenging. Nevertheless, AI opens the new era of precision medicine in hepatology, which will change our future practice.

Artificial intelligence in liver disease

Artificial intelligence (AI) is a branch of computer science that attempts to mimic human intelligence, such as learning and problem-solving skills. The use of AI in hepatology occurred later than in gastroenterology. Nevertheless, studies on applying AI to liver disease have recently increased. AI in hepatology can be applied for detecting liver fibrosis, differentiating focal liver lesions, predicting prognosis of chronic liver disease, and diagnosing of nonalcoholic fatty liver disease. We expect that AI will eventually help manage patients with liver disease, predict the clinical outcomes, and reduce medical errors. However, there are several hurdles that need to be overcome. Here, we will briefly review the areas of liver disease to which AI can be applied.