THER-03. USING SUCCESSIVE EGF RECEPTOR ANTAGONISTS TO TREAT A PATIENT WITH EXTENSIVE METASTATIC DISEASE: CASE REPORT AND REVIEW OF THE LITERATURE
Bibliographic record
Abstract
Abstract INTRODUCTION: EGFR-targeted agents can be useful in the treatment of systemic metastatic cancer including that which has spread to the brain. We present the case of a patient with two different EGFR mutations that responded to receptor blockade. CASE REPORT: A 38 year old right-handed female presented with a one week history of progressive left-sided weakness and focal seizures. Neuroimaging revealed multiple enhancing brain lesions and a lesion in the left maxillary antrum. Body imaging revealed a right lung mass, hilar and mediastinal nodes and multiple bony lesions. Biopsy of the maxillary antrum lesion showed metastatic poorly differentiated adenocarcinoma, TTF-1 positive, suggesting a lung primary. ALK was not mutated but there was an EGFR mutation (exon 19 deletion). The patient underwent treatment with dexamethasone, levetiracetam, whole brain radiation and afatinib, an oral EGFR-targeted agent. Most of the brain lesions responded completely with only two small residual lesions. Seizures were controlled. There was major partial response from the systemic lesions. Two years later the patient was clinically well but the lung lesion, mediastinal nodes and bony lesions were all enlarging. A new pituitary lesion was identified on brain MRI. A liquid biopsy (blood) revealed a T790M mutation and the patient underwent stereotactic body radiation and EGFR-targeted therapy with osimertinib. All lesions responded to treatment and four years after initial diagnosis the patient is clinically well with stable disease. DISCUSSION: Successful treatment of widespread metastatic disease is possible with the use of multiple EGFR-targeted agents in certain patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".