Different spatial distributions of brain metastases from lung cancer by histological subtype and mutation status of epidermal growth factor receptor
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to test the hypothesis that the genetic backgrounds of lung cancers could affect the spatial distribution of brain metastases. METHODS: CT or MR images of 200 patients with a total of 1033 treatment-naive brain metastases from lung cancer were retrospectively reviewed (23 by CT and 177 by MRI). All images were standardized to the human brain MRI atlas provided by the Montreal Neurological Institute 152 database. Locations, depths from the brain surface, and sizes of the lesions after image standardization were analyzed. RESULTS: The posterior fossa, the anatomic "watershed areas," and the gray-white matter junction were confirmed to be more commonly affected by lung cancer brain metastases, and brain metastases with epidermal growth factor receptor (EGFR) L858R mutation occurred more often in the caudate, cerebellum, and temporal lobe than those with exon 19 deletion of EGFR. Median depths of the lesions from the brain surface were 13.7 mm (range, 8.6-21.9) for exon 19 deleted EGFR, 11.5 mm (6.6-16.8) for L858R mutated, and 15.0 mm (10.0-20.7) for wild-type EGFR. Lesions with L858R mutated EGFR were located significantly closer to the brain surface than lesions with exon 19 deleted or wild-type EGFR (P = .0032 and P < .0001, respectively). Furthermore, brain metastases of adenocarcinoma lung cancer patients with a history of chemotherapy but not molecular targeted therapy were located significantly deeper from the brain surface (P = .0002). CONCLUSION: This analysis is the first to reveal the relationship between EGFR mutation status and the spatial distribution of brain metastases of lung cancer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".