Comparison of international guidelines for noninvasive diagnosis of hepatocellular carcinoma: 2018 update
Bibliographic record
Abstract
The goal of this review is to present the similarities and differences among the latest guidelines for noninvasive diagnosis of hepatocellular carcinoma (HCC) of American Association for the Study of Liver Disease (AASLD), European Association for the Study of the Liver (EASL), Liver Imaging Reporting and Data System (LI-RADS), Asian Pacific Association for the Study of the Liver (APASL), and Korean Liver Cancer Association- National Cancer Center (KLCA-NCC) of Korea. In 2018, major guideline updates have been proposed by the AASLD, EASL and KLCA-NCC; AASLD newly incorporated LI-RADS into their HCC diagnostic algorithm. The AASLD and EASL guidelines now include magnetic resonance imaging (MRI) using hepatobiliary contrast media as a first-line diagnostic test in addition to dynamic computed tomography and MRI using extracellular contrast media and the KLCA-NCC and EASL guidelines also include contrast-enhanced ultrasound as a second-line diagnostic test. We will comprehensively review the HCC surveillance and diagnostic algorithms and compare and highlight key features for each guideline. We also address limitations of current systems for the noninvasive diagnosis of HCC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".