Computational intelligence identifies alkaline phosphatase (ALP), alpha-fetoprotein (AFP), and hemoglobin levels as most predictive survival factors for hepatocellular carcinoma

Anaemia is independently associated with mortality in patients with hepatocellular carcinoma

BACKGROUND

Anaemia is frequent in patients with cancer and/or liver cirrhosis and is associated with impaired quality of life. Here, we investigated the impact of anaemia on overall survival (OS) and clinical characteristics in patients with hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

HCC patients treated between 1992 and 2018 at the Medical University of Vienna were retrospectively analysed. Anaemia was defined as haemoglobin level <13 g/dl in men and <12 g/dl in women.

RESULTS

Of 1262 assessable patients, 555 (44.0%) had anaemia. The main aetiologies of HCC were alcohol-related liver disease (n = 502; 39.8%) and chronic hepatitis C (n = 375; 29.7%). Anaemia was significantly associated with impaired liver function, portal hypertension, more advanced Barcelona Clinic Liver Cancer stage and elevated C-reactive protein (CRP). In univariable analysis, anaemia was significantly associated with shorter median OS [9.5 months, 95% confidence interval (95% CI) 7.3-11.6 months] versus patients without anaemia (21.5 months, 95% CI 18.3-24.7 months) (P < 0.001). In multivariable analysis adjusted for age, Model for End-stage Liver Disease, number of tumour nodules, size of the largest nodule, macrovascular invasion, extrahepatic spread, first treatment line, alpha-fetoprotein and CRP, anaemia remained an independent predictor of mortality (adjusted hazard ratio 1.23, 95% CI 1.06-1.43, P = 0.006).

CONCLUSIONS

Anaemia was significantly associated with mortality in HCC patients, independent of established liver- and tumour-related prognostic factors. Whether adequate management of anaemia can improve outcome of HCC patients needs further evaluation.

Prognostic Impact of Metabolic Syndrome and Steatotic Liver Disease in Hepatocellular Carcinoma Using Machine Learning Techniques

Metabolic dysfunction-associated steatotic liver disease (MASLD) currently represents the predominant cause of chronic liver disease and is closely linked to a significant increase in the risk of hepatocellular carcinoma (HCC), even in the absence of liver cirrhosis. In this retrospective multicenter study, machine learning (ML) methods were employed to investigate the relationship between metabolic profile and prognosis at diagnosis in a total of 219 HCC patients. The eXtreme Gradient Boosting (XGB) method demonstrated superiority in identifying mortality predictors in our patients. Etiology was the most determining prognostic factor followed by Barcelona Clinic Liver Cancer (BCLC) and Eastern Cooperative Oncology Group (ECOG) classifications. Variables related to the development of hepatic steatosis and metabolic syndrome, such as elevated levels of alkaline phosphatase (ALP), uric acid, obesity, alcohol consumption, and high blood pressure (HBP), had a significant impact on mortality prediction. This study underscores the importance of metabolic syndrome as a determining factor in the progression of HCC secondary to MASLD. The use of ML techniques provides an effective tool to improve risk stratification and individualized therapeutic management in these patients.

Risk Models for Predicting the Recurrence and Survival in Patients With Hepatocellular Carcinoma Undergoing Radio-Frequency Ablation

Background

Hepatocellular carcinoma (HCC) patients have a poor prognosis after radio-frequency ablation (RFA), and investigating the risk factors affecting RFA and establishing predictive models are important for improving the prognosis of HCC patients.

Methods

Patients with HCC undergoing RFA in Shenzhen People's Hospital between January 2011 and December 2021 were included in this study. Using the screened independent influences on recurrence and survival, predictive models were constructed and validated, and the predictive models were then used to classify patients into different risk categories and assess the prognosis of different categories.

Results

Cox regression model indicated that cirrhosis (hazard ratio [HR] = 1.65), alpha-fetoprotein (AFP) ⩾400 ng/mL (HR = 2.03), tumor number (multiple) (HR = 2.11), tumor diameter ⩾20 mm (HR = 2.30), and platelets (PLT) ⩾ 244 (109/L) (HR = 2.37) were independent influences for recurrence of patients after RFA. On the contrary, AFP ⩾400 ng/mL (HR = 2.48), tumor number (multiple) (HR = 2.52), tumor diameter ⩾20 mm (HR = 2.25), PLT ⩾244 (109/L) (HR = 2.36), and hemoglobin (HGB) ⩾120 (g/L) (HR = 0.34) were regarded as independent influences for survival. The concordance index (C-index) of the nomograms for predicting disease-free survival (DFS) and overall survival (OS) was 0.727 (95% confidence interval [CI] = 0.770-0.684) and 0.770 (95% CI = 0.821-7.190), respectively. The prognostic performance of the nomograms was significantly better than other staging systems by analysis of the time-dependent C-index and decision curves. Each patient was scored using nomograms and influencing factors, and patients were categorized into low-, intermediate-, and high-risk groups based on their scores. In the Kaplan-Meier survival curve, DFS and OS were significantly better in the low-risk group than in the intermediate- and high-risk groups.

Conclusions

The 2 prediction models created in this work can effectively predict the recurrence and survival rates of HCC patients following RFA.

Transhepatectomy combined with arterial chemoembolization and transcatheter arterial chemoembolization in the treatment of hepatocellular carcinoma: a clinical prognostic analysis

BACKGROUND

The prognosis of patients undergoing hepatectomy combined with transarterial chemoembolization (TACE) and TACE alone was examined in order to better understand the role of hepatectomy in the treatment of hepatocellular carcinoma (HCC). In this work, we also created a model and investigated the variables influencing overall survival (OS) in HCC patients.

METHODS

Retrospective analysis of 1083 patients who received TACE alone as the control group and 188 patients who received TACE after surgery in a total of 1271 HCC patients treated with LR + TACE or TACE at three third-class hospitals in China. It was done using the Propensity Score Matching (PSM) technique. The differences in OS between the two groups were compared, and OS-influencing factors were looked at. The main endpoint is overall survival. In this study, the COX regression model was used to establish the nomogram.

RESULTS

The median OS of the LR + TACE group was not attained after PSM. The median OS for the TACE group was 28.8 months (95% CI: 18.9-38.7). The median OS of the LR + TACE group was higher than that of the TACE group alone, indicating a significant difference between the two groups (χ2 = 16.75, P < 0.001). While it was not achieved in the LR + TACE group, the median OS for patients with lymph node metastases in the TACE group alone was 18.8 months. The two groups differed significantly from one another (χ2 = 4.105, P = 0.043). In patients with distant metastases, the median OS of the LR + TACE treatment group was not achieved, and the median OS of the TACE group alone was 12.0 months. The difference between the two groups was sizable (χ2 = 5.266, P = 0.022). The median OS for patients with PVTT following PSM was 30.1 months in the LR + TACE treatment group and 18.7 months in the TACE alone group, respectively. The two groups differed significantly from one another (χ2 = 5.178, P = 0.023); There was no discernible difference between the two groups in terms of median overall survival (OS), which was 30.1 months for patients with lymph node metastasis and 19.2 months for those without (P > 0.05); Regarding the median OS for patients with distant metastases, which was not achieved and 8.5 months, respectively, there was a significant difference between the two groups (χ2 = 5.759, P = 0.016). We created a new nomogram to predict 1-, 2-, and 3-year survival rates based on multiple independent predictors in COX multivariate analysis. The cohort's C-index is 0.705. The area under the curve (AUC value) for predicting 1-, 2-, and 3-year survival rates were shown by the subject operating characteristic (ROC) curve linked to the nomogram to be 0.730, 0.728, and 0.691, respectively.

CONCLUSIONS

LR + TACE can increase OS, delay tumor recurrence, and improve prognosis in HCC patients when compared to TACE alone. Additionally, the nomogram we created does a good job of forecasting the 1-year survival rate of hepatocellular carcinoma.

Eleven quick tips for data cleaning and feature engineering

Applying computational statistics or machine learning methods to data is a key component of many scientific studies, in any field, but alone might not be sufficient to generate robust and reliable outcomes and results. Before applying any discovery method, preprocessing steps are necessary to prepare the data to the computational analysis. In this framework, data cleaning and feature engineering are key pillars of any scientific study involving data analysis and that should be adequately designed and performed since the first phases of the project. We call "feature" a variable describing a particular trait of a person or an observation, recorded usually as a column in a dataset. Even if pivotal, these data cleaning and feature engineering steps sometimes are done poorly or inefficiently, especially by beginners and unexperienced researchers. For this reason, we propose here our quick tips for data cleaning and feature engineering on how to carry out these important preprocessing steps correctly avoiding common mistakes and pitfalls. Although we designed these guidelines with bioinformatics and health informatics scenarios in mind, we believe they can more in general be applied to any scientific area. We therefore target these guidelines to any researcher or practitioners wanting to perform data cleaning or feature engineering. We believe our simple recommendations can help researchers and scholars perform better computational analyses that can lead, in turn, to more solid outcomes and more reliable discoveries.

Boron attenuated diethylnitrosamine induced hepatocellular carcinoma in C3H/HeN mice via alteration of oxidative stress and apoptotic pathway

BACKGROUND

Reactive oxygen species (ROS) regulate various cellular signaling pathways and play an important role in the occurrence and development of hepatocellular carcinoma (HCC). Excessive accumulation of ROS can promote HCC. Trace element boron has a wide range of biological effects, including anti-oxidation, anti-tumor, immune regulation and so on.

METHODS

In this study, we investigated the anticancer effects of Sodium tetraborate decahydrate (NaB) in improving oxidative stress and regulating apoptosis in mouse HCC. HCC was induced by intraperitoneal injection of diethylnitrosamine (DEN) 25 mg/kg once at the age of 2 weeks and 100 mg/kg again at the age of 6 weeks in healthy C3H/HeN male mice. At 8 weeks of age, different concentrations of NaB were given intragastric treatment once a day for 20 weeks. Oxidative stress markers, antioxidant status and liver enzyme analysis were detected to evaluate the effectiveness of NaB in inhibiting cancer induction. The anticancer properties of NaB were confirmed by observing the liver index and morphology, and analyzing the expression of apoptotic genes and proteins. Our results showed that boron significantly reduced the production of ROS, and down-regulated the expression of the anti-apoptotic protein Bcl2 and up-regulated the expression of the pro-apoptotic proteins P53, Bax, and caspase 3.

CONCLUSION

Boron has great potential to reduce the effects of oxidative stress, which may help it inhibit the progression of HCC.

Machine-learning algorithms based on personalized pathways for a novel predictive model for the diagnosis of hepatocellular carcinoma

BACKGROUND

At present, the diagnostic ability of hepatocellular carcinoma (HCC) based on serum alpha-fetoprotein level is limited. Finding markers that can effectively distinguish cancer and non-cancerous tissues is important for improving the diagnostic efficiency of HCC.

RESULTS

In this study, we developed a predictive model for HCC diagnosis using personalized biological pathways combined with a machine learning algorithm based on regularized regression and carry out relevant examinations. In two training sets, the overall cross-study-validated area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve and the Brier score of the diagnostic model were 0.987 [95%confidence interval (CI): 0.979-0.996], 0.981 and 0.091, respectively. Besides, the model showed good transferability in external validation set. In TCGA-LIHC cohort, the AUROC, AURPC and Brier score were 0.992 (95%CI: 0.985-0.998), 0.967 and 0.112, respectively. The diagnostic model has accomplished very impressive performance in distinguishing HCC from non-cancerous liver tissues. Moreover, we further analyzed the extracted biological pathways to explore molecular features and prognostic factors. The risk score generated from a 12-gene signature extracted from the characteristic pathways was correlated with some immune related pathways and served as an independent prognostic factor for HCC.

CONCLUSION

We used personalized biological pathways analysis and machine learning algorithm to construct a highly accurate HCC diagnostic model. The excellent interpretable performance and good transferability of this model enables it with great potential for personalized medicine, which can assist clinicians in diagnosis for HCC patients.

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.