Gadoxetic Acid-Based MRI for Decision-Making in Hepatocellular Carcinoma Employing Perfusion Criteria Only—A Post Hoc Analysis from the SORAMIC Trial Diagnostic Cohort
Bibliographic record
Abstract
The value of gadoxetic acid in the diagnosis of hepatocellular carcinoma (HCC), based on perfusion criteria, is under dispute. This post-hoc analysis of the prospective, phase II, randomized, controlled SORAMIC study compared the accuracy of gadoxetic acid-enhanced dynamic magnetic resonance imaging (MRI) (arterial, portovenous, and venous phase only) versus contrast-enhanced computed tomography (CT) for stratifying patients with HCC to curative ablation or palliative treatment. Two reader groups (radiologists, R1 and R2) performed blind reads of CT and gadoxetic acid-enhanced MRI (contrast dynamics only). A truth panel, with access to clinical and imaging follow-up data, served as reference. Primary endpoint was non-inferiority (margin: 5% points) of MRI vs. CT (lower 95% confidence interval [CI] > 0.75) in a first step and superiority (complete 95% CI > 1) in a second step. The intent-to-treat population comprised 538 patients. Accuracy of treatment decisions was 73.4% and 70.8% for CT (R1 and R2, respectively) and 75.1% and 70.3% for gadoxetic acid-enhanced dynamic MRI. Non-inferiority but not superiority of gadoxetic acid-enhanced dynamic MRI versus CT was demonstrated (odds ratio 1.01; CI 0.97-1.05). Despite a theoretical disadvantage in wash-out depiction, gadoxetic acid-enhanced dynamic MRI is non-inferior to CT in accuracy of treatment decisions for curative ablation versus palliative strategies. This outcome was not subject to the use of additional MR standard sequences.
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".