Surgical Management of Advanced Gastrointestinal Stromal Tumors: An International Multi-Institutional Analysis of 158 Patients
Bibliographic record
Abstract
BACKGROUND: Patients with advanced gastrointestinal stromal tumors (GIST) are at high risk for recurrence after surgery. The aim of this study was to characterize outcomes of advanced GIST treated with surgery from a large multi-institutional database in the tyrosine kinase inhibitor (TKI) era. STUDY DESIGN: Patients who underwent surgery for an advanced GIST from 1998 through 2012 were identified. Demographic, clinicopathologic, perioperative, and survival data were collected and analyzed. RESULTS: There were 87 patients with locally advanced GIST and 71 patients with recurrent/metastatic GIST. The vast majority (95%) of patients with locally advanced GIST required a multivisceral resection; most patients (87%) underwent a microscopically complete (R0) resection. Although 82% of patients had high-risk tumors according to modified NIH criteria or had recurrent/metastatic disease, only 56% of patients received adjuvant TKI therapy. Among patients with locally advanced GIST, 3-year recurrence-free survival and overall survival rates were 65% and 87%, respectively. In contrast, 3-year recurrence-free survival and overall survival rates among patients with recurrent/metastatic GIST were 49% and 82%, respectively. On multivariate analysis, predictors of worse outcomes included high mitotic rate and male sex for patients with locally advanced GIST, and age and lack of adjuvant TKI therapy were associated with adverse outcomes among patients with recurrent/metastatic GIST (all p < 0.05). CONCLUSIONS: Resection of advanced GIST can be safely accomplished with high rates of R0 resection. Among patients with advanced GIST, TKI therapy was underused. Barriers to the use of TKI therapy in this population should be explored.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".