MétaCan
Menu
Back to cohort
Record W4415945845 · doi:10.1007/s40123-025-01267-z

From Scalpel to Syringe: Intralesional Interleukin-2-Based Therapy is Effective for Locally Advanced Periocular Cutaneous Squamous Cell Carcinoma

2025· article· en· W4415945845 on OpenAlexaff
Sorayya Seddigh, F. K. T. Lee, Dejan Vidovic, Jennette R. Gruchy, Carman A. Giacomantonio, Ahsen Hussain

Bibliographic record

VenueOphthalmology and Therapy · 2025
Typearticle
Languageen
FieldMedicine
TopicNonmelanoma Skin Cancer Studies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsBasal cellCarcinomaRadiation therapySkin cancerBasal cell carcinoma

Abstract

fetched live from OpenAlex

INTRODUCTION: Cutaneous squamous cell carcinoma (cSCC) is a common eyelid malignancy that is typically treated by surgical excision. Locally destructive periocular cSCC may not be amenable to surgery in cases where extensive resection would result in structural or functional compromise. METHODS: We report the first series of biopsy-confirmed periocular cSCC cases treated with intralesional interleukin-2 (IL-2)-based therapy. RESULTS: Treatment courses for five patients are summarized, with one representative case detailed here. A 74-year-old man presented with a large, painful, centrally pedunculated mass on the left upper eyelid, measuring 5.5 cm by 2.5 cm. Mass excisional biopsy and reconstruction revealed moderately differentiated invasive SCC involving deep and peripheral margins. Given the risks associated with further resection, the patient opted to pursue local immunotherapy. He received five doses of intralesional IL-2 every 2 weeks. The lesion was completely clinically cleared at 6 weeks, and there was no recurrence noted at 15-month follow-up. CONCLUSION: Local intralesional IL-2-based therapy may be a treatment option for periocular cSCC in cases that may result in significant functional or aesthetic compromise, or in those who have failed prior standard of care.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.415
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.298
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueOphthalmology and TherapySame topicNonmelanoma Skin Cancer StudiesFrench-language works237,207