Dynamic risk profiling of HCC recurrence after curative intent liver resection
Bibliographic record
Abstract
BACKGROUND AND AIMS: Following liver resection (LR) for HCC, the likelihood of survival is dynamic, in that multiple recurrences and/or metastases are possible, each having variable impacts on outcomes. We sought to evaluate the natural progression, pattern, and timing of various disease states after LR for HCC using multistate modeling and to create a practical calculator to provide prognostic information for patients and clinicians. APPROACH AND RESULTS: Adult patients undergoing LR for HCC between January 2000 and December 2018 were retrospectively identified at a single center. Multistate analysis modeled post-LR tumor progression by describing transitions between distinct disease states. In this model, the states included surgery, intrahepatic recurrence (first, second, third, fourth, fifth), distant metastasis with or without intrahepatic recurrence, and death. Of the 486 patients included, 169 (34.8%) remained recurrence-free, 205 (42.2%) developed intrahepatic recurrence, 80 (16.5%) developed distant metastasis, and 32 (7%) died. For an average patient having undergone LR, there was a 33.1% chance of remaining disease-free, a 31.0% chance of at least one intrahepatic recurrence, a 16.3% chance of distant metastasis, and a 19.8% chance of death within the first 60 months post-LR. The transition probability from surgery to first intrahepatic recurrence, without a subsequent state transition, increased from 3% (3 months) to 17.4% (30 months) and 17.2% (60 months). Factors that could modify these probabilities included tumor size, satellite lesions, and microvascular invasion. The online multistate model calculator can be found on https://multistatehcc.shinyapps.io/home/. CONCLUSIONS: In contrast to standard single time-to-event estimates, multistate modeling provides more realistic prognostication of outcomes after LR for HCC by taking into account many postoperative disease states and transitions between them. Our multistate modeling calculator can provide meaningful data to guide the management of patients undergoing postoperative surveillance and therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".