Comprehensive review of Bayesian network applications in gastrointestinal cancers

Table 6 Summary of Bayesian network applications in liver cancer research

Data type	Bayesian network algorithm	Key findings	Ref.
Radiomics features	A logistic sparsity-based feature selection model optimized using Bayesian optimization	The Bayesian optimization-based feature selection model significantly improved classification performance for HCC and other focal liver lesions, especially under limited training data conditions	[79]
Simulated concentration time curves for DCE-MRI and in vivo patient data with hepatic tumor lesions	BNN	The BNN provided more accurate parameter estimates compared to NLLS fitting and effectively identified uncertainties, particularly under high noise levels and out-of-distribution data, improving robustness for clinical applications	[84]
Genetic variation data from 33 meta-analytic studies on 45 polymorphisms across 35 genes related to HCC	BFDP	Fourteen gene polymorphisms, including CCND1, CTLA4, EGF, IL6, IL12A, KIF1B, MDM2, MICA, miR-499, MTHFR, PNPLA3, STAT4, TM6SF2, and XPD genes, were identified as significant biomarkers for HCC susceptibility	[81]
Gene expression profiles of liver tissue samples from two microarray platforms analyzed for HCC	An empirical Bayesian method	Three genes were identified as specific biomarkers for HCC diagnosis, achieving an AUC of 0.931	[85]
Single-cell multiomics data, including RNA-seq, Reduced Representation Bisulfite Sequencing, and copy number variation estimates	Bayesian network models	Best-fitted BN models identified 295 genes and provided novel insights into the mechanistic relationships of human lymphocyte antigen class I genes in HCC	[86]
miRNA and mRNA expression data from 39 HCC patients and 25 liver cirrhosis patients	A flexible Bayesian two-step integrative method	The study identified 66 significant miRNA-mRNA pairs, including molecules previously recognized as potential biomarkers in liver cancer	[82]
Multi-omics data, including genome (mutation and copy number), transcriptome, proteome, and phosphoproteome from HCC samples	A Bayesian network mixture model	The study identified three main HCC subtypes with distinct molecular characteristics, some associated with survival independent of clinical stage. Cluster-specific networks revealed connections between genotypes and molecular phenotypes	[87]
Electronic medical records from 10060 primary liver cancer patients, including TCM symptoms, signs, tongue diagnosis, and pulse diagnosis information	Bayesian network model	The Bayesian network model achieved a classification accuracy of 85.84% for syndrome diagnosis in primary liver cancer, demonstrating its effectiveness in mining nonlinear relationships in clinical data and providing reliable support for TCM-based syndrome differentiation and treatment in liver cancer	[88]
Clinical data of HCC patients, including recurrence outcomes (early, late, or no recurrence)	Bayesian network-based model	The Bayesian network model effectively distinguished between early, late, and no recurrence, significantly outperforming benchmark techniques in accuracy, precision, recall, and F-measures. It addressed the challenge of insufficient early-stage information by integrating latent variables, offering robust and reliable predictions validated across datasets, with potential implications for improving HCC recurrence management in clinical practice	[89]
Dataset of 299 HCC patients after hepatectomy, including factors like preoperative AFP level, liver function grade, tumor size, and postoperative treatment	Tree-augmented naïve Bayes algorithm	The Bayesian network model identified PVTT as the most significant predictor of survival time for HCC patients after hepatectomy. The model also highlighted the preoperative AFP level and postoperative performance of TACE as independent survival factors	[80]
Functional CT perfusion data of hepatic regions, including measurements from malignant and benign liver tissues, acquired over 590 seconds using repeated scans	A Bayesian semiparametric model	The model facilitated the clustering of liver regions based on their CT profiles, which can be used to predict and classify regions as malignant or benign, aiding in the discrimination of cancerous tissue from healthy tissue in diagnostic settings	[83]

HCC: Hepatocellular carcinoma; DCE-MRI: Dynamic contrast-enhanced magnetic resonance imaging; BNN: Bayesian neural network; NLLS: Nonlinear least squares; BFDP: Bayesian False Discovery Probability; AUC: Area under curve; BN: Bayesian network; TCM: Traditional Chinese medicine; PVTT: Portal vein tumor thrombosis; TACE: Transcatheter arterial chemoembolization; AFP: Alpha-fetoprotein; CT: Computed tomography.