Imageable Radioembolization Microspheres for Treatment of Unresectable Hepatocellular Carcinoma: Interim Results from a First-in-Human Trial
Bibliographic record
Abstract
PURPOSE: Y) radioembolization for unresectable hepatocellular carcinoma (HCC) in a first-in-human trial. MATERIALS AND METHODS: Y microspheres visible on computed tomography (CT) and single photon emission CT (SPECT), were used to treat 6 subjects with unresectable HCC. Patients underwent selective (≤2 segments) treatment in a prospective open-label pilot trial. Key inclusion criteria included liver-only HCC, performance status ≤1, total lesion diameter ≤9 cm, and Child-Pugh A status. Prospective partition dosimetry was utilized. Safety (measured by Common Terminology Criteria for Adverse Events [CTCAE] v5), multimodal imageability on CT and SPECT, and 3- and 6-month imaging response by modified Response Evaluation Criteria in Solid Tumors on magnetic resonance (MR) imaging were evaluated. RESULTS: Seven tumors in 6 subjects were treated and followed to 180 days. Administration success was 100%. Microsphere distribution measured by radiopacity on CT correlated with SPECT. Ninety-day target lesion complete response (CR) was observed in 3 of 6 subjects (50%) and partial response (PR) in 2 (33.3%). At 180 days, target lesion CR was maintained in 3 subjects (50%) and PR in 1 (16.7%). Two subjects could not be reassessed, having undergone intervening chemoembolization. All subjects reported adverse events (AEs), and 5 reported AEs related to treatment. There were no treatment-related Grade ≥3 AEs. CONCLUSIONS: Radioembolization using imageable microspheres was safe and effective in 6 subjects with unresectable HCC at 6-month interim analysis. Microsphere distribution by radiopacity on CT correlated with radioactivity distribution by SPECT, providing previously unavailable CT-based tumor targeting information.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".