Concurrent or Sequential Hormonal and Radiation Therapy in Breast Cancer: A Literature Review.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Adjuvant hormonal therapy is frequently used in the treatment of women with estrogen receptor (ER)/progesterone receptor (PR) positive breast cancer. When radiotherapy is given, hormone therapy may be delivered in a concurrent or sequential manner. Hormonal blockade with tamoxifen or aromatase inhibitors is thought to arrest hormonally dependent cancer cells in the early G1 phase of the cell cycle. This has been theorized to reduce the efficacy of radiation, which is known to be more effective in cells that are actively dividing. Therefore, there has been a reluctance by many to treat with concurrent hormonal and radiation therapy. METHODS: We performed a search of the Medline database that led to the identification of 39 studies. Abstract and full-text review of these studies led to the identification of seven English non-review studies in peer-reviewed literature between 1995 and 2015 that addressed the question of timing of radiation and hormonal therapy. Outcome measures were captured from each of the studies. RESULTS: No difference in survival or local-regional recurrence was identified between concurrent versus sequential treatment. Furthermore, no difference in cosmetic outcome or adverse effects was noted for either approach. However, when comparing radiation alone or radiation and hormonal therapy, there was an increased risk of breast and lung fibrosis with combined treatment. CONCLUSIONS: Hormone therapy, concurrent or sequential, with radiation results in comparable disease-related outcomes, including survival and recurrence. However, given the theoretical reduction in efficacy and increased rates of fibrosis with concurrent use, it is reasonable to support the use of sequential therapy.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".