Are Radiation Target Volumes for Postmastectomy Radiation Therapy Too Large? Initial Report of the Complication Avoidance of Reconstruction Implant Radiation Therapy (CARIT) Study
Bibliographic record
Abstract
Following mastectomy for breast cancer, women may choose implant-based reconstruction for many reasons, such as cosmesis, self-identity, and the ability to wear particular items of clothing. However, postmastectomy radiation therapy (PMRT) can compromise these cosmetic goals, including as much as a 40% loss of implant rate. To minimize the risk of radiation toxicity, it is important to consider how clinical target volumes (CTVs) can be optimized in PMRT to preserve the implant and reduce complications. Typically, guidelines from organizations such as the Radiation Oncology Group are used, which include regions previously encompassed by tangential fields. This includes all structures below the pectoralis muscle, such as the chest wall, where the risk of recurrence is negligible; this technique often requires incidental inclusion of portions of the lung and heart plus circumferential radiation of the implant. We present the preliminary single institution case series of a technique of complication avoidance of reconstruction implant radiation therapy, called CARIT, where the chest wall, and a large proportion of the implant, is not irradiated. In a retrospective review of 30 cases in which CARIT has been attempted, it was found that 24% of patients treated required a second surgery due to Baker grade III/IV capsular contracture. Using the Modified Harvard Harris Cosmetic Scale, 66.5% of patients had cosmetic outcomes rated as "good" or "excellent". CARIT could offer a technique to reduce complications in postmastectomy implant-based reconstruction patients, with our next steps focusing on improving dosimetry, and formally comparing the cosmesis and tumor control aspects with commonly used techniques.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".