Leptomeningeal carcinomatosis and brain metastases in gastroesophageal carcinoma: a real-world analysis of clinical and pathologic characteristics and outcomes
Bibliographic record
Abstract
BACKGROUND: Brain metastasis (BrM) and Leptomeningeal Carcinomatosis (LMC) are uncommon complications in gastroesophageal carcinoma (GEC) patients. These patients have a poor prognosis and are challenging to treat. We described the clinicopathologic features and outcomes in the largest cohort of Central Nervous System (CNS) metastasis in GEC patients. METHODS: single-center retrospective study of GEC treated from 2007 to 2021. Clinicopathologic characteristics and treatment modalities were reviewed. Survival was calculated from the date of CNS diagnosis until date of death/last follow-up using the Kaplan-Meier method. A multivariable Cox proportional hazards regression model was used. RESULTS: Of 3283 GEC patients, 100 (3.04%) were diagnosed with BrM and 20 with LMC (0.61%). Patients with known human epidermal growth factor receptor 2 (HER2) status (N = 48), 60% were HER2 positive (defined as IHC 3 + or IHC 2+/FISH+). Among LMC patients most were signet-ring subtype (85%), and only 15% (2/13) were HER2 positive. Median survival was 0.7; 3.8; and 7.7 months in BrM patients treated with best supportive care, radiation, and surgery, respectively (p < 0.001). In LMC, median survival was 0.7 month in patients who had best supportive care (7/19) and 2.8 months for those who had whole brain radiation therapy (p = 0.015). Multivariate analysis showed worse outcomes in ECOG ≥ 2 (p = 0.002), number of BrM ≥ 4 (p < 0.001) and number of metastatic sites (p = 0.009). CONCLUSION: HER2 expression were enriched in patients with BrM, while it is uncommon in LMC. Patients treated with surgery followed by radiation had an improved OS in BrM and WBRT benefited patients with LMC.
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.000 | 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".