Tyrosine Kinase Inhibitors in the Treatment of Choroidal Metastases from Non-Small-Cell Lung Cancer: A Case Report and Review of Literature
Bibliographic record
Abstract
BACKGROUND: Choroidal metastases being the sole presenting feature of lung cancer is rare. Erlotinib, a tyrosine kinase inhibitor (TKI), is used in the treatment of lung adenocarcinoma where tumor cells exhibit epidermal growth factor receptor (EGFR) mutations. We report a case of metastatic non-small-cell lung cancer (NSCLC) with choroidal metastasis, which was the sole presenting feature and which responded to erlotinib. METHODS: We performed a retrospective case review. CASE: A 78-year-old man presented with a choroidal mass which was found to be the presenting feature of metastatic NSCLC. Our patient, a nonsmoker, had disseminated bony metastases, and therefore was advised to undergo palliative chemotherapy, which he refused. He was therefore instituted on oral erlotinib. RESULTS: Tumor cells expressing EGFR mutations are known to be susceptible to TKIs. Even though the tumor in our case showed no mutation, i.e. was classified as 'wild-type', our patient showed a dramatic response to erlotinib. At 1 year, the choroidal lesion had regressed and visual acuity had recovered. CONCLUSIONS: TKIs may be beneficial in patients with choroidal metastases from NSCLC, especially those in which an EGFR mutation is noted. Even in the absence of such mutations, choroidal metastases may show a favorable effect in response to TKIs, such as erlotinib.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".