The Feasibility of Immunocryosurgery in the Treatment of Non-Superficial, Facial Basal Cell Carcinoma That Relapsed after Standard Surgical Excision: An Experience Report from Two Centers
Bibliographic record
Abstract
In this retrospective, chart review study, we evaluated the feasibility of immunocryosurgery in facial, non-superficial basal cell carcinomas (BCC) that had relapsed after standard surgery. Inclusion criteria were (a) ‘biopsy confirmed relapse of facial BCC’, (b) known ‘calendar year of surgical excision(s)’, and (c) ‘relapse within 10 years after the last surgical excision’. Tumors treated from 1 January 2011 to 31 December 2020 with a standard 5-week immunocryosurgery cycle (daily imiquimod application for 5 weeks and a cryosurgery session at day 14) were included. Descriptive statistics, Kaplan–Meier method, and Cox proportional hazards model were calculated with significance at p < 0.05. From the n = 27 BCC evaluated, n = 20 (74.1 ± 8.4%) cleared after one immunocryosurgery cycle. Two of the remaining cases cleared completely after a repeat cycle, one patient favored surgery, and four BCC did not clear despite additional immunocryosurgery cycles (feasibility 81.5 ± 7.5%). Of the 22 tumors with clinical outcome ‘complete clearance with immunocryosurgery’, three BCC relapsed at 9, 28, and 50 months. Overall, the 5–year treatment efficacy rate was 60.2 ± 13.4% (mean follow-up 94.6 ± 15.1 months). In total, 20/27 BCC relapses after surgery (74.1%) were tumor-free at the end of personalized follow-up times (66.7 ± 12.4% tumor free patients at 5-year follow-up). Number of tumor relapses before immunocryosurgery was the single predictor of tumor progression after immunocryosurgery (p = 0.012). Conclusively, immunocryosurgery could be further evaluated as an alternative, definitive treatment of selected facial BCC relapsing after surgery.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".