Artificial Intelligence Used for the Diagnosis, Treatment and Surveillance of Hepatocellular Carcinoma: A Systematic Review
Bibliographic record
Abstract
Introduction: Hepatocellular Carcinoma (HCC) is the most common type of liver cancer, compromising about 75% of all liver cancers. The advancement in artificial intelligence (AI) has paved the way in the field of liver cancers to help clinicians with early diagnosis, treatment guidance and surveillance for HCC. The aim of this review was to summarize different AI-assisted methods that could be used in the diagnosis, treatment, and surveillance of HCC throughout the literature. Methods: PubMed and MEDLINE OVID databases were searched for primary studies involving AI and HCC published from 2012 to February 2022. Data was obtained, including study characteristics and outcome measures: accuracy, area under curve (AUC), specificity, sensitivity, and errors. A narrative synthesis was used to summarize the findings. Results: The systematic search produced 340 studies, of which 36 met the pre-determined eligibility criteria. The studies were published between 2012 to 2020. All the studies with their respective AI models/algorithms were described and summarized in the tables according to their role in the diagnosis, treatment, or surveillance of HCC. All the studies included used different AI algorithms, out of which, most were used for diagnostic purposes (44%), followed by treatment prediction (38%) and then surveillance of HCC (18%). Among studies, 38% reported their results as AUC, 33% of the studies reported accuracy, 19% reported sensitivity and specificity, 4% reported concordance indices (C-indices), 3% reported the mean errors and 2% reported AUROC values for respective AI models used. The accuracy of the diagnostic, treatment and surveillance tools range from 40% to 99%, 50% to 90% and 70% to 95% respectively. Conclusion: Many AI models are available that show promising results for the different applications in diagnosis, treatment, and surveillance of HCC. However, the demand for the generalization of these results remains. Future research should focus on improving the results and accuracy of these algorithms used for HCC to reduce the risks in complicated procedures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".