Metastatic SDH-Deficient GIST Diagnosed during Pregnancy: Approach to a Complex Case
Bibliographic record
Abstract
Gastrointestinal stromal tumors (GISTs) account for 1% of GI neoplasms in adults, and epidemiological data suggest an even lower occurrence in pregnant women. The majority of GISTs are caused by KIT and PDGFRA mutations. This is not the case in women of childbearing age. Some GISTs do not have a KIT/PDGFRA mutation and are classified as wild-type (WT) GISTs. WT-GIST includes many molecular subtypes including SDH deficiencies. In this paper, we present the first case report of a metastatic SDH-deficient GIST in a 23-year-old pregnant patient and the challenges encountered given her concurrent pregnancy. Our patient underwent a surgical tumor resection of her gastric GIST as well as a lymphadenectomy a week after induction of labor at 37 + 1 weeks. She received imatinib, sunitinib as well as regorafenib afterward. These drugs were discontinued because of disease progression despite treatment or after side effects were reported. Hence, she is currently under treatment with ripretinib. Her last FDG-PET showed a stable disease. This case highlights the complexity of GI malignancy care during pregnancy, and the presentation and management particularities of metastatic WT-GISTs. This case also emphasizes the need for a multidisciplinary approach and better clinical guidelines for offering optimal management to women in this specific context.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| 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".