The Current Role of Radiation in the Management of Cholangiocarcinoma—A Narrative Review
Bibliographic record
Abstract
Cholangiocarcinoma (CCA) is a rare cancer of bile ducts. It is associated with a poor prognosis. The incidence of CCA is rising worldwide. Anatomical subgroups have been used to classify patients for treatment and prognosis. There is a growing understanding of clinically important distinctions based on underlying genetic differences that lead to different treatment options and outcomes. Its management is further complicated by a heterogeneous population and relative rarity, which limits the conduct of large trials to guide management. Surgery has been the primary method of therapy for localized disease; however, recurrence and death remain high with or without surgery. Therefore, there have been concerted efforts to investigate new treatment options, such as the use of neoadjuvant treatments to optimize surgical outcomes, targeted therapy, leveraging a new understanding of immunobiology and stereotactic radiation. In this narrative review, we address the evidence to improve suboptimal outcomes in unresectable CCA with radiation, as well as the role of radiation in neoadjuvant and postoperative treatment. We also briefly discuss the recent developments in systemic treatment with targeted therapies and immune checkpoint inhibitors.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".