Clinical Versus Dermoscopic Evaluation of Tumor Margins Prior to Surgical Excision—A Systematic Review
Bibliographic record
Abstract
Background/Objectives: Accurate surgical margin delineation is essential in the treatment of non-melanoma skin cancers (NMSCs), particularly basal cell carcinoma (BCC) and cutaneous squamous cell carcinoma (cSCC), to reduce recurrence and metastasis. Dermoscopy improves diagnostic accuracy for skin tumors, but its utility for preoperative margin assessment remains underexplored. To compare dermoscopy-guided versus clinical visual inspection for preoperative margin assessment in NMSC excision, focusing on histological clearance rates and surgical outcomes. Methods: This systematic review and meta-analysis followed PRISMA 2020 guidelines. MEDLINE, Cochrane CENTRAL, Scopus, and Web of Science were searched from inception to 1 July 2025. Eligible studies included adult patients undergoing surgical excision of histologically confirmed BCC or cSCC, with preoperative margin evaluation using either dermoscopy or clinical examination. The primary outcome was the rate of complete histological excision. Study quality was assessed using the Newcastle–Ottawa Scale. A random-effects meta-analysis using the Freeman–Tukey transformation was performed. Results: Nine cohort studies comprising 900 NMSC lesions were included. Dermoscopy-guided excision demonstrated pooled histological clearance of 98.7% (95% CI: 97–99.8%), compared to 80–94% with clinical assessment. Moderate heterogeneity was observed (I2 = 42%). However, variability in study design and limited data for cSCC restricted broader conclusions. Conclusions: Dermoscopy may enhance margin assessment and histological clearance in NMSC surgery, especially for BCC. Further standardized, high-quality studies are needed to confirm its role in surgical planning and extend evidence to SCC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.023 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".