Vitiligo-like Lesions as a Predictor of Response to Immunotherapy in Non-Small Cell Lung Cancer: Comprehensive Review and Case Series from a University Center
Bibliographic record
Abstract
The reference to vitiligo-like lesions (VLLs) induced by immune checkpoint inhibitors (ICIs) as a valuable predictive marker of treatment success of immunotherapy with ICIs in melanoma has been mentioned in the literature. Its role in non-small cell lung cancer (NSCLC)-treated patients remains a poorly recognized phenomenon with uncertain significance regarding its predictive value. A retrospective, observational, single-center report was performed, with descriptive analysis of clinicopathological and treatment characteristics of patients with stage IV NSCLC who developed ICI-induced VLL between January 2018 and December 2022, contextualized in a comprehensive review of the literature and reported cases regarding this phenomenon. During the first 5 years' experience of ICI use in stage IV NSCLC treatment, three cases of ICI-induced VLLs were diagnosed. In line with the previous reports, two of the three presented cases exhibited treatment response and favorable prognosis. The recognition and understanding of the pathophysiological processes underlying ICI-induced VLLs may represent a promising opportunity to identify a predictive marker of tumor response to ICIs, with impact in treatment selection and patient management. It also may contribute to the recognition of new patterns of molecular expression that could lead to improvements in therapeutic development.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".