The Role of Artificial Intelligence in the Detection and Implementation of Biomarkers for Hepatocellular Carcinoma: Outlook and Opportunities

Abstract

Liver cancer is a leading cause of cancer-related death worldwide, and its early detection and treatment are crucial for improving morbidity and mortality. Biomarkers have the potential to facilitate the early diagnosis and management of liver cancer, but identifying and implementing effective biomarkers remains a major challenge. In recent years, artificial intelligence has emerged as a promising tool in the cancer sphere, and recent literature suggests that it is very promising in facilitating biomarker use in liver cancer. This review provides an overview of the status of AI-based biomarker research in liver cancer, with a focus on the detection and implementation of biomarkers for risk prediction, diagnosis, staging, prognostication, prediction of treatment response, and recurrence of liver cancers.

Biomarkers for Early Cancer Detection: A Landscape View of Recent Advancements, Spotlighting Pancreatic and Liver Cancers

“…Analyzing multiple layers of molecular information can enhance diagnostic accuracy and predictive capabilities. − The application of artificial intelligence (AI) and machine learning (ML) algorithms can aid in the analysis and interpretation of complex biomarker data. AI can assist in identifying patterns, predicting disease outcomes, and improving the accuracy and efficiency of cancer diagnosis. − MicroRNAs (miRNAs) and other non-coding RNAs (ncRNAs) have shown promise as potential biomarkers due to their involvement in gene regulation and their dysregulation in various cancers. Further research into the functional roles of these molecules can lead to the discovery of novel biomarkers for early cancer detection. ,− Combining imaging techniques such as magnetic resonance imaging (MRI), positron emission tomography (PET), and computed tomography (CT) scans with molecular biomarkers can provide a comprehensive and multi-dimensional view of cancer.…”

“…The application of artificial intelligence (AI) and machine learning (ML) algorithms can aid in the analysis and interpretation of complex biomarker data. AI can assist in identifying patterns, predicting disease outcomes, and improving the accuracy and efficiency of cancer diagnosis. − …”

Advancements in Pancreatic Cancer Detection: Integrating Biomarkers, Imaging Technologies, and Machine Learning for Early Diagnosis

“…The possibility for biomarker identification has also increased due to technological advancements, particularly with the creation of massive biological multi-omics datasets and AI algorithms [40]. Using ML and large-scale transcriptomic profiling of data from 1,665 non-tumorous tissue samples and 2,316 HCC, Kaur et al identified three platform-independent diagnostic genes -FCN3 (downregulated in HCC), CLEC1B (downregulated in HCC), and PRC1 (upregulated in HCC) -that demonstrated prognostic potential and could detect HCC with high precision (93-98%) in both training and validation datasets, demonstrating the contribution of AI-powered tools to the precise identification of relevant biomarkers [41].…”

“…AI-based biomarker research in cancer has enormous promise to improve patient outcomes and revolutionize care, despite the challenges. Sustained investigation and cooperation are therefore necessary to guarantee that its potential is fulfilled in clinical application [29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44].…”

Editorial: Prognostic factors in hepatocellular carcinoma

“…Early diagnosis using new biomarkers, combined with artificial intelligence programs ( 4 ), could extend survival expectations by a simplified approach to patients with HCC. Therefore, the identification of serum glycobiomarkers associated with HCC using Lectin microarrays represents a promising step toward the early detection of cases with HCC.…”