Identifying and managing rare subtypes of gastrointestinal stromal tumors
Bibliographic record
Abstract
Introduction A subset of gastrointestinal stromal tumors (GISTs) lack the common mutations in KIT/PDGFRa genes. This is a rare and heterogeneous group of challenging GISTs due to their diversity and absence of sensitivity to the tyrosine kinase inhibitor (TKI) imatinib.Areas covered In this manscript, we review the pathogenesis, natural history, diagnostic features and management of KIT/PDGFRa wild-type (WT) GISTs, including SDH-deficient GISTs, GISTs with mutations in the RAS/RAF pathway, quadruple WT GISTs which lack mutations in either KIT/PDGFRa and SDH genes or components of the RAS/RAF pathway, syndromic GISTs as well as GISTs with rare KIT/PDGFRa mutations.Expert opinion Patients should be managed in referrence centers. There is progress in the understanding of the biology of these GISTs and promising therapeutic targets have been identified. In SDH-deficient GISTs, the TKI olverembatinib has shown encouraging clinical activity but requires further clinical validation, while the HIF2a inhibitor bezultifan and temozolomide alone or in combination with the death receptor agonist 5 are under clinical investigation. Targeting the RAS/RAF pathway in RAS/RAF-mutated GISTs warrants evaluation in clinical trials. Rare molecular alterations in quadruple WT GISTs require investigation for their oncogenic potential. Collaborative research and patient advocacy is critical for these extremely rare tumors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".