Annual contrast-enhanced magnetic resonance imaging is highly effective in the surveillance of hepatocellular carcinoma among cirrhotic patients
Bibliographic record
Abstract
OBJECTIVES: Biannual ultrasonography, a globally accepted surveillance method, has low sensitivity in detecting early-stage hepatocellular carcinoma (HCC). We aimed to investigate the effectiveness of a surveillance strategy using annual contrast-enhanced MRI to detect HCCs at early-stage. MATERIALS AND METHODS: We reviewed the data of 294 patients with consistent annual contrast-enhanced MRI and biannual alpha fetoprotein (AFP) surveillance between 2008 and 2017. Patients were stratified for HCC risk as low-intermediate-high risk group using Toronto risk score. HCCs were classified according to Barcelona Clinic Liver Cancer staging system. RESULTS: Thirty-five (11.9%) HCCs were detected with annual surveillance MRI. Of those, 30 (85.8%) were early-stage and 15 (42.9%) were very early-stage. The majority of patients (82.9%) with surveillance detected HCC were high risk at the entry. MRI had sensitivity of 83.3 and 80% with a specificity of 95.4 and 91.4%, for detecting early and very early-stage HCC, respectively. Addition of AFP to MRI displayed similar sensitivity and specificity rates to detect early and very early HCCs. The area under the curve of MRI alone and combination with AFP was not statistically different (Any-HCC: 0.905 vs. 0.924; Early-HCC: 0.853 vs. 0.885; Very early-HCC: 0.838 vs. 0.885, respectively, all P values >0.2). CONCLUSION: Annual MRI strategy demonstrated a satisfactory performance in the surveillance of HCC, in terms of detecting most of the lesions in earlier curable stages and indicating high sensitivity with no additional benefit of biannual AFP. New risk stratified screening algorithms may further increase the yield of HCC surveillance among cirrhotic patients.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".