Role of Radiation Therapy for Biliary Tract Cancers
Bibliographic record
Abstract
Biliary tract cancers include cholangiocarcinoma, gallbladder cancer, and ampullary cancer. Although overall rare, the incidence is increasing globally, particularly the subset of intrahepatic cholangiocarcinoma. Surgery is currently considered to be the only curative treatment approach; however, survival outcomes after surgery remain poor. Moreover, many patients already have advanced-stage, unresectable disease at the time of diagnosis. Herein, we will review the role of adjuvant radiotherapy to improve local control after surgery, the role of neoadjuvant radiotherapy to increase the proportion of patients able to undergo surgery, and the use of definitive/palliative radiotherapy to provide local control/symptom relief for patients who have inoperable disease. Most studies observed a survival benefit associated with radiotherapy, with the strongest evidence for those with high-risk disease features (e.g., positive surgical margins, lymph node involvement). However, due to the low incidence of biliary tract cancers, most existing studies are retrospective; there is very limited randomized data and prospective studies tend to have small sample sizes, underscoring the need for more high-quality research on radiotherapy for biliary tract cancers. As some studies show evidence of a dose-dependent response, further investigation into the delivery of dose-escalated radiotherapy with modern techniques such as proton therapy is warranted.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".