Chemical exchange saturation transfer for predicting response to stereotactic radiosurgery in human brain metastasis
Bibliographic record
Abstract
PURPOSE: The purpose of this work was to determine the predictive value of chemical exchange saturation transfer (CEST) metrics in brain metastases treated with stereotactic radiosurgery (SRS). METHODS: CEST spectra at a radiofrequency power of 0.52 µT were collected on a 3 Tesla (T) magnetic resonance imaging from 25 patients at three time points: pretreatment, 1 week, and 1 month post-treatment. Amide proton transfer-weighted images and maps of the amplitude and width of Lorentzian-shaped CEST peaks and the relaxation-compensated AREX metric were constructed at the offset frequencies of amide, amine, and relayed nuclear Overhauser effect (NOE) from aliphatic groups as well as the broad magnetization transfer effect. Pretreatment CEST metrics, as well as CEST metric changes at 1 week post-treatment, were compared to changes in tumor volume at 1 month. RESULTS: Significant (P < 0.05) 1-week predictive metrics included NOE peak amplitude (R = 0.69) in normal-appearing white matter (NAWM) and width (R = -0.55) in tumor. Baseline NOE in contralateral NAWM was negatively correlated (R = -0.69) with volume changes at 1 month. Metrics-defined outside tumor margins had higher correlation with volume changes than tumor regions of interest. CONCLUSION: CEST metrics, in particular, the NOE peak amplitude, can predict volume changes 1 month post-SRS. Magn Reson Med 78:1110-1120, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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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 | 0.002 |
| 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) | 0.001 | 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".