Phase III HEAT Study Adding Lyso-Thermosensitive Liposomal Doxorubicin to Radiofrequency Ablation in Patients with Unresectable Hepatocellular Carcinoma Lesions
Bibliographic record
Abstract
Purpose: Lyso-thermosensitive liposomal doxorubicin (LTLD) consists of doxorubicin contained within a heat-sensitive liposome. When heated to ≥40°C, LTLD locally releases a high concentration of doxorubicin. We aimed to determine whether adding LTLD improves the efficacy of radiofrequency ablation (RFA) for hepatocellular carcinoma (HCC) lesions with a maximum diameter (dmax) of 3 to 7 cm.Experimental Design: The HEAT Study was a randomized, double-blind, dummy-controlled trial of RFA ± LTLD. The 701 enrolled patients had to have ≤4 unresectable HCC lesions, at least one of which had a dmax of 3 to 7 cm. The primary endpoint was progression-free survival (PFS) and a key secondary endpoint was overall survival (OS). Post hoc subset analyses investigated whether RFA duration was associated with efficacy.Results: The primary endpoint was not met; in intention-to-treat analysis, the PFS HR of RFA + LTLD versus RFA alone was 0.96 [95% confidence interval (CI), 0.79-1.18; P = 0.71], and the OS HR ratio was 0.95 (95% CI, 0.76-1.20; P = 0.67). Among 285 patients with a solitary HCC lesion who received ≥45 minutes RFA dwell time, the OS HR was 0.63 (95% CI, 0.41-0.96; P < 0.05) in favor of combination therapy. RFA + LTLD had reversible myelosuppression similar to free doxorubicin.Conclusions: Adding LTLD to RFA was safe but did not increase PFS or OS in the overall study population. However, consistent with LTLD's heat-based mechanism of action, subgroup analysis suggested that RFA + LTLD efficacy is improved when RFA dwell time for a solitary lesion ≥45 minutes. Clin Cancer Res; 24(1); 73-83. ©2017 AACR.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".