Chronic radiation dermatitis in breast cancer patients: pathophysiology, prevention and management strategies, and clinical impact
Bibliographic record
Abstract
Radiation therapy (RT) is a common component of breast cancer treatment. Chronic radiation dermatitis (CRD), defined as occurring after or extending beyond 90 days following the completion of RT, can be progressive and irreversible and manifest as changes in skin pigmentation, fibrosis, telangiectasia, ulceration, necrosis, and contribute to the development of cutaneous malignancy. There is limited existing research on the incidence, management approaches, and prevention strategies for CRD. The literature that does exist reports widely varying rates of CRD, reports the various manifestations individually, uses different grading scales, and has varying follow-up durations, making comparison between studies challenging. Methods that have been reported to specifically prevent CRD include massage therapy and physical activity. As increased severity of acute radiation dermatitis (ARD) may be associated with increased risk of CRD, methods to prevent ARD may also help prevent CRD. Topical corticosteroid creams currently have the greatest consensus for the prevention of ARD, but there is also evidence for barrier films such as Mepitel Film, StrataXRT, and Hydrofilm. A variety of methods have also been proposed to help manage the manifestations of CRD, including pulsed dye laser therapy for telangiectasia, flap reconstruction for ulceration, and vitamin E/pentoxifylline and fat grafting for cutaneous fibrosis. Standardization of scoring of CRD, aligning study endpoints, and further research into the prevention and management of CRD are needed, as many patients are living for years following breast RT, and CRD represents a poor cosmetic outcome that can lead to significant health problems, and can reduce quality of life. Herein, we aim to provide a comprehensive literature review of the pathophysiology, clinical manifestations, prevention and management strategies, and quality of life impact of CRD.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".