Phenotypic characterization of glioblastoma identified through shape descriptors
Bibliographic record
Abstract
This paper proposes quantitatively describing the shape of glioblastoma (GBM) tissue phenotypes as a set of shape features derived from segmentations, for the purposes of discriminating between GBM phenotypes and monitoring tumor progression. GBM patients were identified from the Cancer Genome Atlas, and quantitative MR imaging data were obtained from the Cancer Imaging Archive. Three GBM tissue phenotypes are considered including necrosis, active tumor and edema/invasion. Volumetric tissue segmentations are obtained from registered T1˗weighted (T1˗WI) postcontrast and fluid-attenuated inversion recovery (FLAIR) MRI modalities. Shape features are computed from respective tissue phenotype segmentations, and a Kruskal-Wallis test was employed to select features capable of classification with a significance level of p < 0.05. Several classifier models are employed to distinguish phenotypes, where a leave-one-out cross-validation was performed. Eight features were found statistically significant for classifying GBM phenotypes with p <0.05, orientation is uninformative. Quantitative evaluations show the SVM results in the highest classification accuracy of 87.50%, sensitivity of 94.59% and specificity of 92.77%. In summary, the shape descriptors proposed in this work show high performance in predicting GBM tissue phenotypes. They are thus closely linked to morphological characteristics of GBM phenotypes and could potentially be used in a computer assisted labeling system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".