Immune-Checkpoint Induced Skin Toxicity Masked as Squamous Cell Carcinoma: Case Report on Mimickers of Dermatological Toxicity with PD-1 Inhibition
Bibliographic record
Abstract
BACKGROUND: Immune checkpoint inhibitors (ICI) are increasingly the mainstay of oncology treatment. Immune-related adverse events (irAEs) from ICI therapy differ from cytotoxic adverse events. Cutaneous irAEs are one of the most common irAEs and require careful attention to optimize the quality of life for oncology patients. PATIENT AND METHODS: These are two cases of patients with advanced solid-tumour malignancies treated with PD-1 inhibitor therapy. RESULTS: Both patients developed multiple pruritic hyperkeratotic lesions, which were initially diagnosed as squamous cell carcinoma from skin biopsies. The presentation as squamous cell carcinoma was atypical and, upon further pathology review, the lesions were more in keeping with a lichenoid immune reaction stemming from the immune checkpoint blockade. With the use of oral or topical steroids and immunomodulators, the lesions resolved. CONCLUSIONS: These cases emphasize that patients on PD-1 inhibitor therapy who develop lesions resembling squamous cell carcinoma on initial pathology may require an additional pathology review to assess for immune-mediated reactions, allowing appropriate immunosuppressive therapy to be initiated.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".