Successful Treatment of Carcinomatous Meningitis with Gefitinib in a Patient with Lung Adenocarcinoma Harboring a Mutated EGF Receptor Gene
Bibliographic record
Abstract
Carcinomatous meningitis is a severe complication of lung cancer. Although treatment with gefitinib, a tyrosine kinase inhibitor of epidermal growth factor (EGF) receptor, has been reported to be highly effective against lung cancers harboring a mutated EGF gene, its effect against carcinomatous meningitis is unknown. Here, we report successful treatment of carcinomatous meningitis with gefitinib in a lung cancer patient suffered from meningeal metastasis. A 62-year-old, non-smoking, Japanese male was admitted for headache, failing vision, and temporary loss of consciousness and was subsequently diagnosed with stage IV lung adenocarcinoma and carcinomatous meningitis. A tumor sample revealed the in-frame deletion of codons 746 to 750 (E746 to A750) in exon 19 of the EGF gene, which leads to constitutive activation of the tyrosine kinase domain and high-affinity binding of gefitinib. The patient's performance status was poor owing to progression of the meningitis and elevated cerebrospinal fluid (CSF) pressure. Combined treatment with gefitinib (250 mg/day) and whole-brain irradiation (36 Gray total) proved to be effective. It is noteworthy that the level of gefitinib in the CSF was less than 1% of the serum level (serum: 117 nM before drug re-administration and 132 nM 2 hrs later; CSF: 0.9 nM both before and 2 hrs after drug re-administration). Gefitinib treatment should be considered for patients with carcinomatous meningitis and lung adenocarcinoma harboring a mutated EGF gene.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".