Safety of CDK4/6 Inhibitors Combined with Radiotherapy in Patients with Metastatic Breast Cancer: A Review of the Literature
Bibliographic record
Abstract
Recent evidence suggests that cyclin-dependent kinase 4/6 (CDK4/6) inhibitors significantly improve progression-free survival and overall survival among metastatic breast cancer patients. However, given the effects on cell cycle arrest, there is potential for CDK4/6 inhibitors and radiotherapy (RT) to work synergistically, enhancing the effect and toxicities of RT. A comprehensive review of the literature on the combination of RT and CDK4/6 inhibitors was performed with 19 eligible studies included in the final analysis. A total of 373 patients who received radiotherapy combined with CDK4/6 inhibitors were evaluated across 9 retrospective studies, 4 case reports, 3 case series, and 3 letters to the editor. The CDK4/6 inhibitor used, RT target, and RT technique were assessed in terms of toxicities. This literature review demonstrates generally limited toxicities with the combination of CDK4/6 inhibitors and palliative radiotherapy to metastatic breast cancer patients. The current evidence is nonetheless limited, and further results of ongoing prospective clinical trials will help clarify whether these treatments can be safely combined.
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 | 0.003 |
| 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 | 0.000 |
| 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 | 0.000 |
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".