Does Post-Mastectomy Radiation Therapy Worsen Outcomes in Immediate Autologous Breast Flap Reconstruction? A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background There is great uncertainty regarding the practice of immediate autologous breast reconstruction (IBR) when post-mastectomy radiotherapy (PMRT) is indicated. Many plastic surgery units differ in their protocols, with some recommending delayed breast reconstruction (DBR) instead. Nevertheless, the cosmetic and psychosocial benefits offered by IBR are significant. The aim of this study was to comprehensively review and analyse existing literature to compare irradiated and unirradiated autologous flaps. Methods A comprehensive search in MEDLINE, EMBASE and CENTRAL databases was conducted in November 2020 for primary studies assessing outcomes of IBR with and without PMRT. Primary outcomes were the incidence of clinical complications, observer- and patient-reported outcomes. Meta-analyses were performed to obtain the pooled risk ratio of individual complications where possible. Results Twenty-one articles involving 3817 patients were included. Meta-analysis of pooled data demonstrated risk ratios for fat necrosis (RR=1.91, p<0.00001), secondary surgery (RR=1.62, p=0.03) and volume loss (RR=8.16, p<0.00001) favouring unirradiated flaps, but no significant difference in all other reported complications. The unirradiated group scored higher in observer-reported outcome measures, but self-reported aesthetic and general satisfaction rates were similar. Conclusions IBR should still be offered to patients as a viable option after mastectomy, even if they require PMRT. Despite the statistically significant higher risks of fat necrosis and contracture, these changes appear to be less clinically relevant, as corroborated by generally positive self-reported scores from patients who developed the aforementioned complications. Preoperative and intraoperative measures can further optimize reconstruction and mitigate post-radiation sequelae. Careful management of patients’ expectations is also imperative.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.037 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".