TP53 Mutation Predicts Worse Survival and Earlier Local Progression in Patients with Hepatocellular Carcinoma Treated with Transarterial Embolization
Bibliographic record
Abstract
The aim of this study was to evaluate associations between TP53 status and outcomes after transarterial embolization (TAE) for the treatment of patients with hepatocellular carcinoma (HCC). This single-institution study included patients from 1/2014 to 6/2022 who underwent TAE of HCC and genomic analysis of tumoral tissue. The primary outcome was overall survival (OS) with relation to TP53 status, and the secondary outcome was the time to progression. Survival analysis was performed using the Kaplan–Meier method. The time to progression with death or the last patient contact without progression as competing risks were used to obtain a cumulative incidence function, and the association with TP53 status was evaluated using the Gray test. In total, 75 patients (63 men) with a median age of 70.0 (IQR 62.0–76.3) years were included. Of these, 26/75 (34.7%) patients had TP53-mutant HCC. Patients with TP53-mutant HCC had a significantly worse median OS of 15.2 (95% CI, 9.5–29.3) months, versus 31.2 (95% CI, 21.2–52.4) months as the median OS (p = 0.023) for TP53 wild-type HCC. Competing risk analysis showed a shorter time to local hepatic progression (at the site of the previously treated tumor) after TAE in patients with TP53-mutant HCC. The cumulative incidences of local progression at 6 and 12 months for TP53-mutant HCC were 65.4% and 84.6%, versus 40.8% and 55.1% for TP53 wild-type HCC (p = 0.0072). A TP53 mutation may predict a worse overall survival and a shorter time to local progression in HCC patients treated with TAE.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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 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".