CT-based assessment of sarcopenia for differentiating wild-type from mutant-type gastrointestinal stromal tumor
Bibliographic record
Abstract
Non-invasive prediction for KIT/PDGFRA status in GIST is a challenging problem. This study aims to evaluate whether CT based sarcopenia could differentiate KIT/PDGFRA wild-type gastrointestinal stromal tumor (wt-GIST) from the mutant-type GIST (mu-GIST), and to evaluate genetic features of GIST. A total of 174 patients with GIST (wt-GIST = 52) were retrospectively identified between January 2011 to October 2019. A sarcopenia nomogram was constructed by multivariate logistic regression. The performance of the nomogram was evaluated by discrimination, calibration curve, and decision curve. Genomic data was obtained from our own specimens and also from the open databases cBioPortal. Data was analyzed by R version 3.6.1 and clusterProfiler ( http://cbioportal.org/msk-impact ). There were significantly higher incidence (75.0% vs. 48.4%) and more severe sarcopenia in patients with wt-GIST than in patients with mu-GIST. Multivariate logistic regression analysis showed that sarcopenia score (fitted based on age, gender and skeletal muscle index), and muscle fat index were independent predictors for higher risk of wt-GIST (P < 0.05 for both the training and validation cohorts). Our sarcopenia nomogram achieved a promising efficiency with an AUC of 0.879 for the training cohort, and 0.9099 for the validation cohort with a satisfying consistency in the calibration curve. Favorable clinical usefulness was observed using decision curve analysis. The additional gene sequencing analysis based on both our data and the external data demonstrated aberrant signal pathways being closely associated with sarcopenia in the wt-GIST. Our study supported the use of CT-based assessment of sarcopenia in differentiating the wt-GIST from the mu-GIST preoperatively.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".