961 Oncological Safety and Patient Satisfaction of Immediate Lipofilling in Breast Conserving Surgery: A Systematic Review and Subgroup Meta-Analysis Comparing Outcomes with No Lipofilling
Bibliographic record
Abstract
Abstract Aim To evaluate the oncological safety and aesthetic outcomes of immediate lipofilling following wide local excision in breast-conserving surgery, compared with no lipofilling. Method A systematic search was conducted across PubMed, MEDLINE, Embase, and Cochrane databases up to November 2024. Keywords included “Immediate Lipofilling,” “Immediate Lipomodelling,” “Autologous Fat Grafting,” and “Breast-Conserving Surgery.” Of the 49 studies screened, 10 were included in the analysis. The quality of the studies was assessed using the Newcastle-Ottawa Scale. Outcomes analysed included cancer recurrence, recurrence site, complications, patient satisfaction, and surgical techniques. A meta-analysis compared oncological and satisfaction outcomes between lipofilling and no-lipofilling groups. Results Data from 10 studies (819 patients) showed no significant difference in cancer recurrence rates between groups (RR: 0.97, 95% CI: 0.38–2.43). Recurrence rates ranged from 0–7.5% in the lipofilling group and 0–10% in the controls. Follow-up durations spanned 9.5–67.43 months. Funnel plot analysis demonstrated no evidence of publication bias. Local recurrences were primarily observed in breast tissue and lymph nodes, while distant metastases were rare. Patient satisfaction was higher in the lipofilling group, with tools such as Breast-Q indicating improved aesthetic and functional outcomes. Most studies utilised peri-tumoral and subcutaneous fat injection techniques. Conclusions Immediate lipofilling after breast-conserving surgery is oncologically safe, with local recurrence rates comparable to standard procedures. It achieves superior aesthetic outcomes and high levels of patient satisfaction.
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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.034 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".