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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 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".