Emerging Role of Radiotherapy in the Management of Liver Metastases
Bibliographic record
Abstract
Improvements with systemic therapy in controlling occult metastatic disease in patients with colorectal cancer and other solid malignancies have raised renewed interest in local therapies that can treat isolated or "oligo" sites of metastatic disease within the liver. Radiotherapy (RT) is a treatment option that can be offered to patients unsuitable for surgery or other ablative therapies. Technological advances in RT planning and delivery have made it possible to administer high doses conformally around focal liver metastases effectively. Methods to facilitate safe delivery of high-dose RT include conformal RT planning, stereotactic body RT, breathing motion management, and image-guided RT. The clinical experience in conformal RT and stereotactic body RT for liver metastases is emerging, with phase I and II trials demonstrating excellent local control and occasional long-term survivors. With appropriate patient selection and sparing of the uninvolved liver, serious toxicity can be avoided. Out-of-field recurrences are common, providing rationale for combining systemic or regional therapies with RT for these patients. Finally, randomized trials of RT for liver metastases are needed to better define the benefits of RT for these patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".