Radiomics in sarcoma trials: a complement to RECIST for patient assessment
Bibliographic record
Abstract
Radiological imaging has a critical role in the diagnosis of sarcomas and in evaluating therapy response assessment. The current gold standard for response assessment in solid tumors is the Response Evaluation Criteria in Solid Tumors, which evaluates changes in tumor size as a surrogate endpoint for therapeutic efficacy. However, tumors may undergo necrosis, changes in vascularization or become cystic in response to therapy, with no significant volume changes; thus, size assessments alone may not be adequate. Such morphological changes may give rise to radiographic phenotypes that are not easily detected by human operators. Fortunately, recent advances in high-performance computing and machine learning algorithms have enabled deep analysis of radiological images to extract features that can provide richer information about intensity, shape, size or volume, and texture of tumor phenotypes. There is growing evidence to suggest that these image-derived or “radiomic features” are sensitive to biological processes such as necrosis and glucose metabolism. Thus, radiomics could prove to be a critical tool for assessing treatment response and may present an integral complement to existing response criteria, opening new avenues for patient assessment in sarcoma trials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".