Characteristics of Long-Term Survivors With EGFR-Mutant Metastatic NSCLC
Bibliographic record
Abstract
Introduction Characteristics of long-term survivors in EGFR-mutant (EGFRm) NSCLC are not fully understood. This retrospective analysis evaluated a multi-institution cohort of patients with EGFRm NSCLC treated in the pre-osimertinib era and sought to describe characteristics of long-term survivors. Methods Clinical characteristics and outcomes were abstracted from the electronic medical records of patients with EGFRm metastatic NSCLC who started first-line therapy before 2015. Demographics and comutations were compared between greater than or equal to 5-year survivors and less than 5-year survivors. Multivariable Cox proportional hazard and logistic regression models were used to evaluate factors associated with survival and the odds of death within 5 years, respectively. Results Overall, 133 patients were greater than or equal to 5-year survivors; 127 were less than 5-year survivors. Burden of pathogenic comutations including TP53 and PIK3CA was similar between greater than or equal to 5-year survivors and less than 5-year survivors. Receipt of first-line chemotherapy rather than EGFR tyrosine kinase inhibitor was similar between the groups (22% of <5-y versus 31% of ≥5-y). Baseline brain metastasis and history of smoking were associated with higher odds of death within 5 years (odds ratio = 2.16, p = 0.029 and odds ratio = 1.90, p = 0.046, respectively). Among patients without baseline brain metastases, cumulative incidence of brain metastases at 5 years was 42.3%. Both baseline and post-baseline brain metastasis were associated with worse overall survival compared with no brain metastasis (hazard ratio = 3.26, p < 0.001 and hazard ratio = 4.99, p < 0.001, respectively). Conclusions Within patients treated for EGFRm metastatic NSCLC before 2015, absence of brain metastasis and nonsmoking status were predictive of 5-year survival. Our findings help to define a subset of patients with EGFRm NSCLC with excellent survival outcomes who may not require intensification of initial therapy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".