Evaluating Difluoromethylornithine Safety and Efficacy for Non-Melanoma Skin Cancer Chemoprevention: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: Recent FDA approval of difluoromethylornithine (DFMO), an inhibitor of ornithine decarboxylase for the prevention of neuroblastoma in children, has renewed interest in this medication for the prevention of other cancers including keratinocyte carcinomas (KCs). It has been investigated for cancer chemoprevention, including neoplasms of the colon, breast, and prostate. METHODS: We assessed the current body of literature that determines DFMO efficacy and safety in non-melanoma skin cancer prevention. A systematic search of PubMed Central, and Web of Sciences was performed. RESULTS: In this analysis, 12 studies were included evaluating 1618 patients. Most patients were Caucasian 90% (1452/1618) with a mean age of 61 years, and 73% (1214/1618) had previously been diagnosed with KC. For oral DFMO, reduction in KC was significant in 24% (291/1214) of patients. Nonsignificant reduction was observed in 17% (207/1214) of patients. The remaining studies, representing 59% (716/1214) of patients explored DFMO's pharmacological/biological effects without elucidating its direct impact on KC. Topical DFMO shows modest efficacy in reducing the number of actinic keratosis (AK), as indicated in 4 studies representing 38.12% (154/404) of patients. For patients taking the oral eflornithine, the most frequently reported adverse events included reversible ototoxicity (11% of patients) gastrointestinal disturbances (10.39%). For the topical DFMO transient local cutaneous eruptions were common impacting 28.76% (111/386) of patients. CONCLUSION: Current evidence highlights the lack of conclusive data supporting the efficacy of oral DFMO, making it difficult to recommend its use. Conversely, topical DFMO demonstrates more promising outcomes in preventing AKs, presenting a potentially useful alternative in select patients.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".