Gastric Schwannomas Misdiagnosed as GIST: A Comparative Study of Clinic Strategies Based on Membrane Marker Detection
Bibliographic record
Abstract
Gastric schwannomas are one of the rarest gastric tumors originating from the nerve plexus of the gut wall. Because most of these tumors dont have any specific symptom and the majority of gastric mesenchymal tumors are gastrointestinal stromal tumors (GISTs), many are therefore misdiagnosed as GISTs. In addition, gastric schwannoma is the benign and slow-growing lesion in the stomach, but GISTs had poor outcomes due to lack of response to nonsurgical interventions. In our study, we analyzed two cases of these tumors. Computer tomography (CT), contrast-enhanced CT, gastroscopy, endoscopic ultrasonography (EUS) were applied to diagnose these two patients. In addition, histological examination and immunohistochemistry (IHC) were used to confirm the final diagnosis. All imageological examination such as CT, contrast-enhanced CT, gastroscopy and EUS, diagnosed these two patients as gastrointestinal stromal tumors. Surprisingly, after the subtotal gastric surgery, histological examination showed that these lesions were composed of spindle cells. Those cells presenting in the bundle or fence-like arrangement were mildly heterologous. The outcomes of immunohistochemistry of the cell membrane markers (CD117 / DOG-1 negative, CD34 mild positive or negative) were the exact opposite of the characteristic presentation of GIST. These pathological findings refused the primary diagnosis, and were in coincidence with the characteristics of gastric schwannomas. To our best knowledge, these tumors are really rare that only two cases could be reported and analyzed clinically. CT and EUS could help diagnose gastric schwannomas before pathological examination results, but in order to define this diagnosis correctly. Pathological examination and IHC staining should be applied after surgery. To avoid the recurrence, it is better to resect the lesion completely, regardless of the malignant or benign disease.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".