Artificial intelligence in liver cancers: Decoding the impact of machine learning models in clinical diagnosis of primary liver cancers and liver cancer metastases

Bakrania, Anita K.; Joshi, Narottam; Zhao, Xun; Zheng, Gang; Bhat, Mamatha

doi:10.1016/j.phrs.2023.106706

Cited by 24 publications

(13 citation statements)

References 231 publications

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“…Liver cancer is a type of carcinoma that grows in hepatic cells (Wu et al, 2023). It is the fourth leading cancer-related cause of mortality, and approximately 800,000 cases are diagnosed each year (Bakrania et al, 2023). Certain associated risk factors that could lead to liver cancer development are chronic viral hepatitis, alcohol abuse, excess body fat, and constant smoking (Arafa et al, 2023).…”

Section: Liver Cancermentioning

confidence: 99%

Applications of nanotechnologies for miRNA-based cancer therapeutics: current advances and future perspectives

Bravo-Vázquez,

Méndez-García,

Rodríguez

et al. 2023

Front. Bioeng. Biotechnol.

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MicroRNAs (miRNAs) are short (18–25 nt), non-coding, widely conserved RNA molecules responsible for regulating gene expression via sequence-specific post-transcriptional mechanisms. Since the human miRNA transcriptome regulates the expression of a number of tumor suppressors and oncogenes, its dysregulation is associated with the clinical onset of different types of cancer. Despite the fact that numerous therapeutic approaches have been designed in recent years to treat cancer, the complexity of the disease manifested by each patient has prevented the development of a highly effective disease management strategy. However, over the past decade, artificial miRNAs (i.e., anti-miRNAs and miRNA mimics) have shown promising results against various cancer types; nevertheless, their targeted delivery could be challenging. Notably, numerous reports have shown that nanotechnology-based delivery of miRNAs can greatly contribute to hindering cancer initiation and development processes, representing an innovative disease-modifying strategy against cancer. Hence, in this review, we evaluate recently developed nanotechnology-based miRNA drug delivery systems for cancer therapeutics and discuss the potential challenges and future directions, such as the promising use of plant-made nanoparticles, phytochemical-mediated modulation of miRNAs, and nanozymes.

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Section: Liver Cancermentioning

confidence: 99%

Applications of nanotechnologies for miRNA-based cancer therapeutics: current advances and future perspectives

Bravo-Vázquez,

Méndez-García,

Rodríguez

et al. 2023

Front. Bioeng. Biotechnol.

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“…Liver cancer, a malignant tumor with consistently high global incidence and mortality rates, has long been a focal point of medical research and clinical intervention[ 1 , 2 ]. In particular, within the rapidly advancing field of immunotherapy, immune checkpoint blockade (ICB) treatment has emerged as an innovative therapeutic approach, offering renewed hope for patients with liver cancer[ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Identification of an immune-related gene signature for predicting prognosis and immunotherapy efficacy in liver cancer via cell-cell communication

Li,

Zhang,

Wang

et al. 2024

World J Gastroenterol

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BACKGROUND Liver cancer is one of the deadliest malignant tumors worldwide. Immunotherapy has provided hope to patients with advanced liver cancer, but only a small fraction of patients benefit from this treatment due to individual differences. Identifying immune-related gene signatures in liver cancer patients not only aids physicians in cancer diagnosis but also offers personalized treatment strategies, thereby improving patient survival rates. Although several methods have been developed to predict the prognosis and immunotherapeutic efficacy in patients with liver cancer, the impact of cell-cell interactions in the tumor microenvironment has not been adequately considered. AIM To identify immune-related gene signals for predicting liver cancer prognosis and immunotherapy efficacy. METHODS Cell grouping and cell-cell communication analysis were performed on single-cell RNA-sequencing data to identify highly active cell groups in immune-related pathways. Highly active immune cells were identified by intersecting the highly active cell groups with B cells and T cells. The significantly differentially expressed genes between highly active immune cells and other cells were subsequently selected as features, and a least absolute shrinkage and selection operator (LASSO) regression model was constructed to screen for diagnostic-related features. Fourteen genes that were selected more than 5 times in 10 LASSO regression experiments were included in a multivariable Cox regression model. Finally, 3 genes (stathmin 1, cofilin 1, and C-C chemokine ligand 5) significantly associated with survival were identified and used to construct an immune-related gene signature. RESULTS The immune-related gene signature composed of stathmin 1, cofilin 1, and C-C chemokine ligand 5 was identified through cell-cell communication. The effectiveness of the identified gene signature was validated based on experimental results of predictive immunotherapy response, tumor mutation burden analysis, immune cell infiltration analysis, survival analysis, and expression analysis. CONCLUSION The findings suggest that the identified gene signature may contribute to a deeper understanding of the activity patterns of immune cells in the liver tumor microenvironment, providing insights for personalized treatment strategies.

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“…By using ANNs for liver disease diagnosis, clinicians and researchers can employ computational dating power to discover relationships amongst the clinical laboratory and demographic features such as subtle patterns and interactions. It enables efficient, intelligent diagnostic models which accurately predict the presence or progression of liver disease; this ultimately leads to improve patient outcomes and promotes more effective healthcare [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Use Harris Hawks Optimization (HHO) Algorithm based on Artificial Neural Network for liver disease diagnosis

Al-safi,

Alsalihi,

Munilla

2024

icisna

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This research paper presents a new method for diagnosing liver disease using the Harris Hawks Optimization (HHO) algorithm in combination with an Artificial Neural Network (ANN). The HHO algorithm enhances the ANN's performance in liver disease classification by refining its parameters. Clinical, laboratory, and demographic data are collected from hepatitis patients and individuals without hepatic illness. The dataset is processed to handle missing values, outliers, and normalization. The HHO algorithm optimizes the weights and biases of the ANN, facilitating the identification of relevant features for accurate diagnosis. The trained ANN model is evaluated using various performance metrics, demonstrating its effectiveness in diagnosing liver disease. The HHO algorithm efficiently searches the entire search space, enhancing the ANN's ability to learn complex patterns and make accurate predictions. Evaluation metrics indicate that the optimized ANN model outperforms traditional machine learning methods, showcasing its potential as a reliable diagnostic tool. Interpretability metrics, such as feature importance and saliency maps, provide insights into the key elements of diagnosis. The proposed approach shows high diagnostic accuracy and interpretability, implying the potential for a stable decision-aid system in clinical practice. Early detection and timely intervention enabled by this method can lead to increased patient safety rates and optimized resource allocation.

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Artificial intelligence in liver cancers: Decoding the impact of machine learning models in clinical diagnosis of primary liver cancers and liver cancer metastases

Cited by 24 publications

References 231 publications

Applications of nanotechnologies for miRNA-based cancer therapeutics: current advances and future perspectives

Applications of nanotechnologies for miRNA-based cancer therapeutics: current advances and future perspectives

Identification of an immune-related gene signature for predicting prognosis and immunotherapy efficacy in liver cancer via cell-cell communication

Use Harris Hawks Optimization (HHO) Algorithm based on Artificial Neural Network for liver disease diagnosis

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