Development of diagnostic and prognostic molecular biomarkers in hepatocellular carcinoma using machine learning: A systematic review

Abstract

Hepatocellular carcinoma (HCC) is a leading cause of cancer‐related mortality and morbidity worldwide. Machine learning (ML) tools have been developed in recent years to generate diagnostic and prognostic molecular biomarkers for this high‐fatality cancer. To delineate the landscape of ML in HCC, we performed a systematic search of Ovid Medline, Ovid Embase, Cochrane Database of Systematic Reviews (Ovid) and Cochrane CENTRAL (Ovid) to identify studies of HCC molecular biomarkers using ML strategies. In total, 75 studies met our inclusion criteria, 53 of which were pertinent to diagnosis of HCC and 22 of which were pertinent to prognostication of HCC. Genomic, transcriptomic, epigenomic, proteomic and metabolomic signatures were derived using various ML techniques (supervised, unsupervised and deep learning approaches) using serum, urine and tissue samples of HCC. The ML algorithms achieved a sensitivity of up to 95% for the diagnosis of HCC. Through pathway analysis of the signatures derived by ML tools, we identified regulators of epithelial‐mesenchymal transition and the cancer pathway Ras/Raf/MAPK as being particularly prognostic of HCC outcome. The application of ML to molecular data in HCC has thus far resulted in the generation of highly sensitive diagnostic and prognostic signatures. In future, development of ML algorithms that incorporate clinical, laboratory, alongside molecular features will be needed to fulfil the promise of personalized HCC diagnosis and treatment.

Decision-Making on the Diagnosis of Oncological Diseases Using Cost-Sensitive SVM Classifiers Based on Datasets with a Variety of Features of Different Natures

“…Currently, especially in 2022-2023, there is a significant increase in the number of studies addressing aspects of the use of ML and DL technologies in the context of solving the problem of early diagnosis of cancer [3][4][5][6][7][11][12][13][14]20,26,37,38,[60][61][62][63][64][65][66]. At the same time, many of them are aimed at solving the problem of diagnosing ODs on the basis of biomarkers, including blood protein markers [13,14,[20][21][22][23][24]27,28,37,38,60].…”

“…It increases at least like s 2 when the value of the regularization parameter C is small and like s 3 when the value of the regularization parameter C is large [81,82]. In general, the computational complexity of the standard SVM classifier training can be estimated as O qs 3 .…”

“…The design of intellectual analysis tools for processing medical data of large and ultra-large volumes with the involvement of advanced machine learning (ML) [3][4][5][6][7] and deep learning (DL) [8][9][10][11][12] technologies provides an opportunity to receive and analyze new previously hidden knowledge both directly in the medical sphere and in related ones. These technologies are actively applied in solving various problems of medical diagnosis and, in particular, in solving problems of diagnosis of oncological diseases (ODs).…”

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

“…From this discovery, they formulated two clinically independent indicators to proxy this level: tumor necrosis distribution and the necrosis area fraction. Brar et al suggested the use of a detailed systemic approach incorporating ML to survey several databases for possible biomarkers with prognostic potential in liver cancer [49]. They included peer-reviewed literature that had applied ML to molecular tissue data from liver cancers to predict prognosis for patients with primary HCC, which varies from the usual methodology of radiography to diagnose and prognosticate liver cancer.…”