Treatment of Basal Cell Carcinoma with Electrochemotherapy: Insights from the InspECT Registry (2008–2019)
Bibliographic record
Abstract
This prospective registry-based study aims to describe electrochemotherapy (ECT) modalities in basal cell carcinoma (BCC) patients and evaluate its efficacy, safety, and predictive factors. The International Network for Sharing Practices of Electrochemotherapy (InspECT) multicentre database was queried for BCC cases treated with bleomycin-ECT between 2008 and 2019 (n = 330 patients from seven countries, with 623 BCCs [median number: 1/patient; range: 1–7; size: 13 mm, range: 5–350; 85% were primary, and 80% located in the head and neck]). The procedure was carried out under local anaesthesia in 68% of cases, with the adjunct of mild sedation in the remaining 32%. Of 300 evaluable patients, 242 (81%) achieved a complete response (CR) after a single ECT course. Treatment naïvety (odds ratio [OR] 0.35, 95% confidence interval [C.I.] 0.19–0.67, p = 0.001) and coverage of deep tumour margin with electric pulses (O.R. 5.55, 95% C.I. 1.37–21.69, p = 0.016) predicted CR, whereas previous radiation was inversely correlated (O.R. 0.25, p = 0.0051). Toxicity included skin ulceration (overall, 16%; G3, 1%) and hyperpigmentation (overall, 8.1%; G3, 2.5%). At a 17-month follow-up, 28 (9.3%) patients experienced local recurrence/progression. Despite no convincing evidence that ECT confers improved outcomes compared with standard surgical excision, it can still be considered an opportunity to avoid major resection in patients unsuitable for more demanding treatment. Treatment naïvety and coverage of the deep margin predict tumour clearance and may inform current patient selection and management.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".