Redefining postmastectomy radiation contouring in the era of immediate breast reconstruction: An accurate assessment of local recurrence risk
Bibliographic record
Abstract
INTRODUCTION: Most studies report post-mastectomy local recurrences as chest wall recurrences without clarifying whether the recurrence is in the subcutaneous tissue, muscle or underlying rib. Post-mastectomy chest wall radiation is recommended in patients at increased risk of locoregional recurrence. Chest wall radiation-related fibrosis has become an important clinical consideration in the era of immediate implant-based breast reconstruction. In patients with commonly performed subpectoral implant-based reconstruction, the pectoralis major becomes relocated anterior to the implant and just deep to skin, therefore raising the question of value in radiating deep chest wall structures. This study assessed the rate of recurrence in each anatomical region of chest wall in post-mastectomy patients. METHODS: A comprehensive breast cancer database of 4287 patients at a single regional cancer center from 2006 to 2018 was retrospectively analyzed to identify 1571 mastectomy patients. Recurrences were classified as local skin/subcutaneous, pectoralis muscle (pectoralis major), deep chest wall (pectoralis minor, intercostal muscle or rib) or regional axillary recurrence. RESULTS: A total of 26 patients with locoregional recurrence were identified. Most recurrences were in the skin/subcutaneous level. Of 1571 mastectomy patients, only one patient developed a local recurrence posterior to pectoralis major. Our literature search and meta-analysis revealed that local recurrences post-mastectomy are much more likely to be in subcutaneous tissues/pectoralis major versus deeper chest wall. CONCLUSION: A reduced clinical target volume which encompasses skin/subcutaneous and pectoralis muscle layers without treating deep chest wall may be more appropriate to reduce radiation-associated toxicity since avoiding circumferential radiation of an implant may prevent capsular contracture without compromising treatment benefit.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".