A Novel Online Calculator to Predict Risk of Microvascular Invasion in the Preoperative Setting for Hepatocellular Carcinoma Patients Undergoing Curative-Intent Surgery
Bibliographic record
Abstract
BACKGROUND: The presence of microvascular invasion (MVI) has been highlighted as an important determinant of hepatocellular carcinoma (HCC) prognosis. We sought to build and validate a novel model to predict MVI in the preoperative setting. METHODS: Patients who underwent curative-intent surgery for HCC between 2000 and 2020 were identified using a multi-institutional database. Preoperative predictive models for MVI were built, validated, and used to develop a web-based calculator. RESULTS: Among 689 patients, MVI was observed in 323 patients (46.9%). On multivariate analysis in the test cohort, preoperative parameters associated with MVI included α-fetoprotein (AFP; odds ratio [OR] 1.50, 95% confidence interval [CI] 1.23-1.83), imaging tumor burden score (TBS; hazard ratio [HR] 1.11, 95% CI 1.04-1.18), and neutrophil-to-lymphocyte ratio (NLR; OR 1.18, 95% CI 1.03-1.35). An online calculator to predict MVI was developed based on the weighted β-coefficients of these three variables ( https://yutaka-endo.shinyapps.io/MVIrisk/ ). The c-index of the test and validation cohorts was 0.71 and 0.72, respectively. Patients with a high risk of MVI had worse disease-free survival (DFS) and overall survival (OS) compared with low-risk MVI patients (3-year DFS: 33.0% vs. 51.9%, p < 0.001; 5-year OS: 44.2% vs. 64.8%, p < 0.001). DFS was worse among patients who underwent an R1 versus R0 resection among those patients at high risk of MVI (R0 vs. R1 resection: 3-year DFS, 36.3% vs. 16.1%, p = 0.002). In contrast, DFS was comparable among patients at low risk of MVI regardless of margin status (R0 vs. R1 resection: 3-year DFS, 52.9% vs. 47.3%, p = 0.16). CONCLUSION: Preoperative assessment of MVI using the online tool demonstrated very good accuracy to predict MVI.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".