Next-Generation Sequencing in the Diagnosis of Metastatic Lesions: Reclassification of a Glioblastoma as an Endometrial Cancer Metastasis to the Brain
Bibliographic record
Abstract
Endometrial cancer is the most common gynecologic cancer in the U.S., but metastasis to the brain is rare, and diagnosis can be challenging. Traditional tools for determining if a tumor is a primary or metastatic lesion include pan-imaging, histopathologic studies, and immunohistochemistry. Molecular testing with next-generation sequencing has been increasingly used to augment these tests. We present a case of a patient who initially presented with a brain lesion diagnosed as glioblastoma on histology and immunohistochemistry, but whose diagnosis was later changed to metastasis from an endometrial primary based on molecular findings. The two tumors shared a common microsatellite instability signature and 51 DNA variants, including oncogenic driver mutations KRAS p.G13D, PIK3CA p.E545A, and PTEN p.I135V and p.K267Rfs*9. This highlights the power of molecular analysis in making the diagnosis in cases of rare metastases. KEY POINTS: Brain metastasis from endometrial primary is rare, and histopathological features may be augmented with molecular analysis to aid in diagnosis. Comparison of the molecular makeup of the primary endometrial lesion with the metastatic lesion may reveal high-risk molecular features that may be indicative of metastatic potential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".