Needle tract seeding and abdominal recurrence following pre-treatment biopsy of gastrointestinal stromal tumors (GIST): results of a systematic review
Bibliographic record
Abstract
BACKGROUND: Gastrointestinal stromal tumors (GIST) are rare abdominal tumors. Pretreatment biopsies may be used to diagnose a GIST and enable tailored treatment. Some experts are skeptical about biopsies because they fear tumor cell seeding. The objective of this study was to determine if pretreatment biopsy is associated with increased tumor recurrence. METHODS: We performed a systematic literature search and included studies assessing the oncological outcome of GIST patients who underwent a pre-treatment core needle biopsy or fine needle aspiration. We assessed methodological quality with the Newcastle-Ottawa-Scale for non-randomized studies. This review was registered in the PROSPERO database (CRD42021170290). RESULTS: Three non-randomized studies and eight case reports comprising 350 patients were eligible for inclusion. No prospective study designed to answer the review question was found. One case of needle tract seeding after percutaneous core needle biopsy of GIST was reported. None of the studies reported an increased rate of abdominal recurrence in patients with pretreatment biopsy. CONCLUSIONS: The existing evidence does not indicate a relevant risk of needle tract seeding or abdominal recurrence after pre-treatment biopsy of GIST. Biopsy can safely be done to differentiate GIST from other tumors and to select the most appropriate treatment.
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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 | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".