Optimization of the Clinical Effectiveness of Radioembolization in Hepatocellular Carcinoma with Dosimetry and Patient-Selection Criteria
Bibliographic record
Abstract
Selective internal radiation therapy (SIRT) is part of the treatment strategy for hepatocellular carcinoma (HCC). Strong clinical data demonstrated the effectiveness of this therapy in HCC with a significant improvement in patient outcomes. Recent studies demonstrated a strong correlation between the tumor response and the patient outcome when the tumor-absorbed dose was assessed by nuclear medicine imaging. Dosimetry plays a key role in predicting the clinical response and can be optimized using a personalized method of activity planning (multi-compartmental dosimetry). This paper reviews the main clinical results of SIRT in HCC and emphasizes the central role of dosimetry for improving it effectiveness. Moreover, some patient and tumor characteristics predict a worse outcome, and toxicity related to SIRT treatment of advanced HCC patient selection based on the performance status, liver function, tumor characteristics, and tumor targeting using technetium-99m macro-aggregated albumin scintigraphy can significantly improve the clinical performance of SIRT.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".