Second‐line treatment of hepatocellular carcinoma after sorafenib: Characterizing treatments used over the past 10 years and real‐world eligibility for cabozantinib, regorafenib, and ramucirumab
Bibliographic record
Abstract
BACKGROUND: The CELESTIAL, RESORCE, and REACH-2 trials showed survival benefit of cabozantinib, regorafenib, and ramucirumab, respectively, in hepatocellular carcinoma (HCC) patients treated with sorafenib who had good performance status (ECOG 0-1) and liver function (Child-Pugh-A). This study characterizes subsequent treatments received by HCC patients after sorafenib, and determines the proportion of patients eligible for novel therapies if strict eligibility criteria (SEC) were utilized compared to more liberal modified eligibility criteria (MEC, including ECOG 2, Child-Pugh-B7). METHODS: HCC patients who received sorafenib between 2008 and 2017 were included from the Canadian HCC CHORD Database. Patients were classified as eligible or ineligible based on available CELESTIAL, RESORCE, and REACH-2 trial SEC or MEC. Median overall survival (mOS) was assessed using the Kaplan-Meier method. RESULTS: A total of 730 patients were identified; and 172 (23.6%) received subsequent treatment. Patients who received subsequent treatment had longer mOS than those who did not (12.1 vs 3.3 months; P < .001). Using SEC, only 13.1% of patients would be eligible for cabozantinib, regorafenib, or ramucirumab. Expanding eligibility to include patients who meet MEC increased the proportion of eligible patients to 31.7%. Higher ineligibility for regorafenib and ramucirumab was driven by trial-specific criteria, including sorafenib intolerance (28%) for RESORCE and AFP <400 (58.9%) for REACH-2. CONCLUSIONS: A small proportion of real-world HCC patients would be eligible for cabozantinib, regorafenib, or ramucirumab if SEC in clinical trials were followed, while more than double would be eligible if MEC were applied. Patients who received subsequent treatment had improved mOS, regardless of whether they met SEC or MEC.
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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) | 0.001 | 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".