Update on Locoregional Therapies for Liver Cancer: Radiation Segmentectomy
Bibliographic record
Abstract
Over 900,000 people worldwide were diagnosed with liver cancer in 2022 alone, with hepatocellular carcinoma (HCC) accounting for 75-85% of cases. Treatment for HCC includes some combination of systemic therapies, surgery, liver transplantation, ablation, and intra-arterial therapies with transarterial chemoembolization (TACE) or transarterial radioembolization (TARE). Currently, the Barcelona Clinic Liver Cancer (BCLC) guidelines have acknowledged liver transplantation, surgical resection, and thermal ablation as curative therapies in very early to early stage HCC (BCLC-0 and BCLC-A). While these modalities are the preferred curative treatments for a very early to early stage disease, there are challenges associated with these options, such as organ availability and patient eligibility. Current data shows the role of radiation segmentectomy as a curative therapeutic option for very early to early stage HCC that is unresectable and not amenable to ablation. As future data continues to elucidate the ability for radiation segmentectomy to achieve complete pathologic necrosis, the goal is for the BCLC staging model to acknowledge its role as a curative treatment in this patient population and incorporate it into the ever-evolving guidelines.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".