Comparing Magnetic Resonance Imaging and Contrast‐Enhanced Ultrasound (<scp>CEUS</scp>) for the Characterization of Nodules Found on Hepatocellular Carcinoma Surveillance
Bibliographic record
Abstract
Surveillance CEUS is Our Clear Choice S ince 2007, hepatologists in Calgary have had access to contrast-enhanced ultrasound (CEUS) and it has become an invaluable tool in the diagnosis of management of our patients with hepatocellular carcinoma (HCC).Global guidelines from hepatology societies endorse HCC surveillance using ultrasound (US), alone, or in combination with alpha-fetoprotein (AFP), every 6 months for patients who are high risk for HCC due to cirrhosis or chronic HBV.1,2 In at-risk patients, a diagnosis of HCC can be established with contrast-enhanced (CE) imaging, without the need for a liver biopsy.In 2011, the American Association for the Study of Liver Disease (AASLD) updated their guidelines to recommend either CEcomputerized tomography (CT) or CE-magnetic resonance imaging (MRI) as the first test to investigate nodules found on surveillance US measuring 1 cm or more (Figure 1A). 3 If the non-invasive criteria of arterial phase hyperenhancement (APHE) and portal venous washout (PVWO) was not present, they recommended obtaining the other CE-imaging study (CE-MRI or CE-CT) before considering a biopsy.Based on our local experience with CEUS, in 2017 our center suggested that CEUS should be incorporated into the AASLD diagnostic algorithm (Figure 1A). 4 Like the AASLD, the European Association for the Study of the Liver (EASL) 2018 guidelines recommend first using either CE-CT or CE-MRI (with extracellular contrast agents) or gadoxeticenhanced MRI (GE-MRI) with a specific hepatobiliary contrast agent (eg, Eovist ® /Primovist ® ; Figure 1B). 2 EASL and AASLD guidelines both highlight that MRI has higher sensitivity, and similar specificity, to multiphasic CT scan for the diagnosis of HCC.1,2 CEUS is included in the EASL diagnostic algorithm if the first CT or MRI is inconclusive, given that CEUS in this setting (utilizing the updated Liver Image Reporting and Data System [LIRADS], where LR-5 is defined as APHE with late and weak washout after 60 seconds), had comparable sensitivity and superior specificity for diagnosing lesions 1-2 cm as HCC. 5 In 2018, the AASLD updated their diagnostic algorithm to include LIRADS CT/MRI criteria (Figure 1C). 1 Although AASLD acknowledges that CEUS, with a sensitivity of 85% and a specificity of 91%, can be used for the diagnosis of HCC in expert centers, they cite a lack of "prospective studies in U.S. populations" as the reason not to include it in their diagnostic algorithm.1 A recent meta-analysis of individual patient data from of 32 studies with 1170 CT, 3341 MRI, and 853 CEUS observations, looked at the predictive value of the LIRADS components.6 They found that all CT/MRI LIRADS features, except for interval growth, were associated with HCC diagnosis and for CEUS LIRADS, APHE (OR = 7.3), late and mild washout (OR = 4.1), and size ≥2 cm (OR = 1.6), but not 1-2 cm, were associated with HCC. 6 The study by Hu et al in the Journal in Ultrasound Medicine 7 provides further real-world evidence Kelly W. Burak received honorarium from Canadian Multidisciplinary HCC meeting for organizing and speaking in 2021 and from CADTH for expert review of "Y90 for HCC" Health Technology Assessment in 2020.Lisa Doulgas has nothing to declare.Stephen E. Congly has research grants from Bristol-Myers Sqibb Canada, has received consulting fees from AstraZeneca and is on the Board of Directors for the
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".