Artificial Intelligence, Machine Learning, and Deep Learning in the Diagnosis and Management of Hepatocellular Carcinoma
Abstract
:1. Introduction
2. Screening and Detection
Author, Year | Model Design | Population | AI Methodology | Accuracy |
---|---|---|---|---|
Blanes-Vidal et al. (2022) [16] | Prediction of liver fibrosis using clinical data readily available to primary care physicians | Low-prevalence primary care population | Ensemble learning model | AUC: 0.86–0.94 |
Ioannou et al. (2020) [18] | Identification of patients at high risk of developing HCC by extracting data from electronic medical records | Patients with known Hepatitis C Virus and cirrhosis | Recurrent neural network | AUROC: 0.759 |
Yasaka et al. (2018) [20] | Differentiation of liver masses on CT, with categorization into HCC, other liver tumors, hemangiomas, or cysts | Patients who had undergone dynamic contrast-enhanced CT for evaluation of liver lesions | Convolutional neural network | AUROC: 0.92 |
Mokrane et al. (2020) [21] | Diagnosis of liver nodules as HCC vs. non-HCC based on quantitative features extracted from triphasic CT | Patients with cirrhosis and biopsy-proven indeterminate liver nodules | Machine learning-based radiomic signature | AUROC: 0.66 |
Schmauch et al. (2019) [22] | Detection and characterization of focal liver lesions as benign- vs. malignant-based on ultrasound characteristics | Patients with known liver nodules | Residual neural network | AUROC: 0.935 |
3. Prognosis and Treatment
3.1. HCC Prognosis and Risk of Recurrence
Author, Year | Model Design | Pertinent Risk Factors | Population | AI Methodology | Accuracy |
---|---|---|---|---|---|
Chaudhary et al. (2018) [27] | Predictive model for HCC prognosis based on molecular signature and multi-omic data |
| HCC patients within the Genome Cancer Atlas (TCGA) | Deep learning | C-index: 0.68 |
Liu et al. (2021) [30] | Prediction of MVI preoperatively based on CT imaging characteristics and patient clinical factors |
| Patients with HCC | Residual Neural Network | AUC: 0.845 |
Chong et al. (2021) [32] | Creation of radiomic-based nomogram to preoperatively predict risk of MVI and recurrence-free survival, based. on MRI characteristics and clinical data |
| Patients with solitary HCC smaller than 5cm | Random Forrest | AUC: 0.92 |
Ji et al. (2020) [35] | Creation of radiomic signature with pre- and post-resection features to predict recurrence for early-stage HCC |
| Patients with HCC that met the Milan Criteria and underwent curative intent resection | Machine learning-based radiomic signature | C-index: 0.77 |
3.2. Pathologic Assessment
Author, Year | Model Design | Population | AI Methodology | Accuracy |
---|---|---|---|---|
Qu et al. (2022) [36] | Creation of histological score using whole-slide imaging to predict HCC recurrence | Patients with early-stage HCC who had undergone surgical resection in a single institutional dataset and the TCGA dataset | Convolutional neural network | C-index: 0.804 |
Saillard et al. (2020) [37] | Use of whole-slide imaging to predict risk of HCC recurrence and stratifying it into low- and high-risk subgroups | Patients with HCC who had undergone surgical resection in a single institutional dataset and the TCGA dataset | Convolutional neural network | C-index: 0.72 |
Yamashita et al. (2021) [40] | Use of whole-slide imaging to formulate a risk score predictive of HCC recurrence | Patients with HCC in the TCGA and Stanford-HCC dataset | Convolutional neural network | C-index: 0.724 |
Zeng et al. (2022) [42] | Prediction of activation of immune gene signatures based on whole-slide imaging | Patients with HCC who had undergone surgical resection in the TCGA dataset | Clustering-constrained attention multiple instance learning | AUROC: 0.78–0.91 |
3.3. Locoregional Therapies
3.4. Automatic Methods for Liver and Tumor Segmentation
3.5. Surgical Complications
Author, Year | Model Design | Population | AI Methodology | Accuracy |
---|---|---|---|---|
Wu et al. (2017) [42] | Prediction of disease-free survival after radiofrequency ablation based on clinical variables | Patients who underwent CT-guided radiofrequency ablation | Artificial neural network | AUC: 0.75–0.84 |
Liu et al. (2020) [44] | Prediction of response to first TACE session using contrast- enhanced liver ultrasound | Patients who underwent ultrasound within one week of TACE for HCC | Radiomic-based deep learning | AUC: 0.81–0.93 |
Meng et al. (2020) [50] | Automatic liver parenchyma and liver tumor segmentation from CT images | Multi-institutional liver tumor segmentation (LiTS) dataset | Dual path multiscale convolutional neural network | Dice: 0.689–0.965 |
Zheng et al. (2022) [51] | Automatic segmentation of HCC lesions based on dynamic MRI | Patients with HCC who underwent dynamic contrast-enhanced MRI | Convolutional neural network and recurrence neural network | Dice: 0.825 |
Wang et al. (2022) [52] | Prediction of post-hepatectomy liver failure based on clinical characteristics and surgical variables | Patients with HCC who underwent hepatectomy | Light gradient boosting machine learning | AUC: 0.822–0.944 |
4. Intraoperative Use of Artificial Intelligence
5. Challenges and Future Directions
5.1. Barriers to Clinical Implementation
5.2. The Role of AI in Healthcare Disparities
5.3. Potential Applications
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Larrain, C.; Torres-Hernandez, A.; Hewitt, D.B. Artificial Intelligence, Machine Learning, and Deep Learning in the Diagnosis and Management of Hepatocellular Carcinoma. Livers 2024, 4, 36-50. https://doi.org/10.3390/livers4010004
Larrain C, Torres-Hernandez A, Hewitt DB. Artificial Intelligence, Machine Learning, and Deep Learning in the Diagnosis and Management of Hepatocellular Carcinoma. Livers. 2024; 4(1):36-50. https://doi.org/10.3390/livers4010004
Chicago/Turabian StyleLarrain, Carolina, Alejandro Torres-Hernandez, and Daniel Brock Hewitt. 2024. "Artificial Intelligence, Machine Learning, and Deep Learning in the Diagnosis and Management of Hepatocellular Carcinoma" Livers 4, no. 1: 36-50. https://doi.org/10.3390/livers4010004
APA StyleLarrain, C., Torres-Hernandez, A., & Hewitt, D. B. (2024). Artificial Intelligence, Machine Learning, and Deep Learning in the Diagnosis and Management of Hepatocellular Carcinoma. Livers, 4(1), 36-50. https://doi.org/10.3390/livers4010004