Risk Stratification and Contemporary Predictors of Survival in Hepatocellular Carcinoma Treated With Transarterial Radioembolization
Bibliographic record
Abstract
Background: Identifying risk factors for poor outcomes in patients with hepatocellular carcinoma (HCC) treated with transarterial radioembolization (TARE) can aid in developing personalized management strategies, such as the early use of immune checkpoint inhibitors (ICIs). Methods: In this retrospective review, we included HCC patients who received TARE at The Ohio State University Comprehensive Cancer Center from January 1, 2015, to August 30, 2022. The Kaplan-Meier method was used to estimate progression-free survival (PFS) and overall survival (OS). Cox proportional hazard analysis was conducted to find the independent predictors of PFS and OS. Results: We included 141 patients (median age of 65 years; 80% Caucasian; 80% male). Better PFS was associated with higher albumin (alb) (hazard ratio (HR) = 0.58, P = 0.005) and lower total bilirubin (T bili) levels (HR = 0.70, P = 0.034). Better OS was associated with a history of ablation (HR = 0.35, P < 0.001) and higher pre-TARE alb (HR = 0.63, P = 0.01); OS was worse in those with hepatic encephalopathy (HR = 2.01, P = 0.006). There was a notable trend toward worse OS in patients with ascites (HR = 1.71, P = 0.06) and metabolic-dysfunction-associated fatty liver disease (MAFLD)-associated HCC (HR = 1.86, P = 0.08). The receipt of ICI therapy was associated with a significantly better OS (P = 0.016), with a median OS of 1,102 days (95% confidence interval (CI): 884 - 1,509) compared to 614 days (95% CI: 493 - 829). Conclusion: We present pretreatment risk factors (low alb, high T bili, MAFLD, hepatic encephalopathy, and ascites) that can predict poor outcomes in HCC patients treated with TARE. Preemptively treating such high-risk patients with ICI could improve their outcomes.
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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 | 0.001 | 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".