A Single-Center Prospective Evaluation of Oncoplastic Crescent Incision in the Surgical Management of Large and Multiple Benign Breast Lesions
Bibliographic record
Abstract
Background Benign breast diseases constitute a significant proportion of breast pathologies, often requiring surgical excision. Excision of large or multiple lesions by conventional approaches may compromise cosmetic outcomes, especially for peripheral lesions. Oncoplastic techniques offer a way to achieve complete excision while preserving breast aesthetics. This study aimed to evaluate the efficacy, safety, cosmetic outcomes, and patient satisfaction associated with the oncoplastic crescent incision technique for the excision of large and/or multiple benign breast lesions compared to the conventional periareolar approach. Methodology A prospective, comparative cohort study was conducted in a single tertiary care center, from April 2022 to August 2023, on 74 histologically confirmed large (>3 cm) and/or multiple benign breast lesions. Breast lesions were excised by crescent oncoplasty incision (Group A) or periareolar incision (Group B). Surgical outcomes, postoperative complications, scar quality, breast symmetry, patient satisfaction, and patient-reported outcomes were assessed using objective measurements such as the Vancouver Scar Scale, sonographic scar thickness, and the BREAST-Q questionnaire with a follow-up of three months. Results The crescent incision provided better access for peripheral lesions (mean lesion-to-nipple-areolar complex (NAC) distance: 3.20 ± 0.71 cm vs. 2.37 ± 0.82 cm, p < 0.001) and was more frequently used for lesions >5 cm (33% vs. 9%, p = 0.0051). Postoperative breast symmetry was significantly better in the crescent group based on sternal notch-to-nipple and midline-to-nipple measurements (p < 0.01). Cosmetic outcomes were superior in the crescent group based on the Vancouver Scar Scale (4.24 ± 1.93 vs. 6.62 ± 1.72, p < 0.001) and sonographic scar thickness (p = 0.02). Nipple sensation was preserved in all Group A patients. BREAST-Q scores for satisfaction, psychosocial, sexual, and physical well-being were comparable between groups throughout follow-up. Conclusions The oncoplastic crescent incision is a safe and effective alternative to the conventional periareolar approach for excising large or multiple benign breast lesions. It provides superior cosmetic outcomes, better lesion access, and comparable patient-reported quality of life, supporting its broader adoption in benign breast surgery.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".