Metastatic brain cancer: prediction of response to whole-brain helical tomotherapy with simultaneous intralesional boost for metastatic disease using quantitative MR imaging features
Bibliographic record
Abstract
The sequential application of whole brain radiotherapy (WBRT) and more targeted stereotactic radiosurgery (SRS) is frequently used to treat metastatic brain tumors. However, SRS has side effects related to necrosis and edema, and requires separate and relatively invasive localization procedures. Helical tomotherapy (HT) allows for a SRS-type simultaneous infield boost (SIB) of multiple brain metastases, synchronously with WBRT and without separate stereotactic procedures. However, some patients’ tumors may not respond to HT+SIB, and would be more appropriately treated with radiosurgery or conventional surgery despite the additional risks and side effects. As a first step toward a broader objective of developing a means for response prediction to HT+SIB, the goal of this study was to investigate whether quantitative measurements of tumor size and appearance (including first- and second-order texture features) on a magnetic resonance imaging (MRI) scan acquired prior to treatment could be used to differentiate responder and nonresponder patient groups after HT+SIB treatment of metastatic disease of the brain. Our results demonstrated that smaller lesions may respond better to this form of therapy; measures of appearance provided limited added value over measures of size for response prediction. With further validation on a larger data set, this approach may lead to a means for prediction of individual patient response based on pre-treatment MRI, supporting appropriate therapy selection for patients with metastatic brain cancer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".