Lentigo maligna (melanoma): A systematic review and meta‐analysis on surgical techniques and presurgical mapping by reflectance confocal microscopy
Bibliographic record
Abstract
Because of an increased risk of local recurrence following surgical treatment of lentigo maligna (melanoma) (LM/LMM), the optimal surgical technique is still a matter of debate. We aimed to evaluate the effect of different surgical techniques and reflectance confocal microscopy (RCM) on local recurrence and survival outcomes. We searched MEDLINE, Embase and PubMed databases through 20 May 2022. Randomized and observational studies with ≥10 lesions were eligible for inclusion. Bias assessment was performed using the Methodological Index for Non-Randomized Studies instrument. Meta-analysis was performed for local recurrence, as there were insufficient events for the other clinical outcomes. We included 41 studies with 5059 LM and 1271 LMM. Surgical techniques included wide local excision (WLE) (n = 1355), staged excision (n = 2442) and Mohs' micrographic surgery (MMS) (n = 2909). Six studies included RCM. The guideline-recommended margin was insufficient in 21.6%-44.6% of LM/LMM. Local recurrence rate was lowest for patients treated by MMS combined with immunohistochemistry (<1%; 95% CI, 0.3%-1.9%), and highest for WLE (13%; 95% CI, 7.2%-21.6%). The mean follow-up varied from 27 to 63 months depending on surgical technique with moderate to high heterogeneity for MMS and WLE. Handheld-RCM decreased both the rate of positive histological margins (p < 0.0001) and necessary surgical stages (p < 0.0001). The majority of regional (17/25) and distant (34/43) recurrences occurred in patients treated by WLE. Melanoma-associated mortality was low (1.5%; 32/2107), and more patients died due to unrelated causes (6.7%; 107/1608). This systematic review shows a clear reduction in local recurrences using microscopically controlled surgical techniques over WLE. The use of HH-RCM showed a trend in the reduction in incomplete resections and local recurrences even when used with WLE. Due to selection bias, heterogeneity, low prevalence of stage III/IV disease and limited survival data, it was not possible to determine the effect of the different surgical techniques on survival outcomes.
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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.023 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".