Minimally Invasive Conversion Surgery for Unresectable Gastric Cancer with Splenic Metastasis and Splenic Vein Tumor Thrombus: A Case Report
Bibliographic record
Abstract
While the importance of conversion surgery has increased with the development of systemic chemotherapy for gastric cancer (GC), reports of conversion surgery for patients with GC with distant metastasis and tumor thrombus are extremely scarce, and a definitive surgical strategy has yet to be established. Herein, we report a 67-year-old man with left abdominal pain referred to our hospital following a diagnosis of unresectable GC. Esophagogastroduodenoscopy and contrast-enhanced abdominal computed tomography (CT) revealed advanced GC with splenic metastasis. A splenic vein tumor thrombus (SVTT) and a continuous thrombus to the main trunk of the portal vein were detected. The patient was treated with anticoagulation therapy and systemic chemotherapy comprising S-1 and oxaliplatin. One year following chemotherapy initiation, a CT scan revealed progressive disease (PD); therefore, the chemotherapy regimen was switched to ramucirumab with paclitaxel. After 10 courses of chemotherapy resulting in primary tumor and SVTT shrinkage, the patient underwent laparoscopic total gastrectomy (LTG) and distal pancreaticosplenectomy (DPS). He was discharged without complications and remained alive 6 months postoperatively without recurrence. In summary, the wait-and-see approach was effective in a patient with GC with splenic metastasis and SVTT, ultimately leading to an R0 resection performed via LTG and DPS.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".