Clinicopathological Features of Metastatic Gastric Tumors Originating From Breast Cancer: Analysis of Eleven Cases
Bibliographic record
Abstract
BACKGROUND: Metastatic gastric tumor originating from breast cancer (MGTBC) is rare. Endoscopically, gastric cancer (GC)-like lesions and submucosal tumors (SMT) are the features of MGTBC. Their clinicopathological characteristics are currently uncertain. We analyzed the clinicopathological characteristics of 11 patients with MGTBC to better understand the disease progression and thereby improve early detection methods. METHODS: From 2006 to 2016, 11 patients with MGTBC diagnosed by esophagogastroduodenoscopy (EGD) were included. RESULTS: All 11 patients were women, with a median age of 57 years. Histological examination revealed six cases with solid tubular components and five cases with invasive lobular carcinoma components. Melena was the most common symptom; however, asymptomatic cases were also common. Seven and four cases had GC-like lesions and SMTs, respectively. Six of the seven cases had GC-like lesions with peritoneal metastasis, whereas none of the four SMT cases had peritoneal metastasis (P = 0.015). The median overall survival (OS) in all cases was 26 months (range, 1 - 42 months). OS in cases treated with chemotherapy after a diagnosis of gastric metastases was significantly better than that in those treated without chemotherapy (P = 0.047). One patient showed gradual transformation from an SMT-type lesion to a Borrmann type 4-like appearance. CONCLUSIONS: MGTBC is a rare occurrence; however, it should be considered when gastric tumors, especially GC-like lesions in patients with a medical history of breast cancer are found. Patients with breast cancer, especially invasive lobular carcinoma, should undergo screening EGD regularly to detect gastric metastases early and receive chemotherapy to obtain good outcomes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".