Hyperpolarized 13C lactate-to-bicarbonate signal ratio predicts brain metastases response to stereotactic radiosurgery
Bibliographic record
Abstract
Abstract Background Brain metastases (BM) are increasingly treated with stereotactic radiosurgery (SRS); however, up to 30% of BM recur locally. This work investigated whether hyperpolarized (HP) [1-13C]-pyruvate MRI can be used to predict SRS treatment response in patients with BM. Methods Eighteen patients with 44 BM were imaged with HP [1-13C]-pyruvate MRI prior to SRS. Treatment response was determined using the Response Assessment in Neuro-Oncology BM (RANO-BM) working group guidelines at 6-month follow-up. Fourteen parameters, including lesion [1-13C]-lactate to [13C]-bicarbonate, [1-13C]-lactate to [1-13C]-pyruvate and [13C]-bicarbonate to [1-13C]-pyruvate signal ratios, in addition to prognostic and dosimetric parameters, were analyzed using univariable and multivariable analysis. Results Univariable analysis identified lesion [1-13C]-lactate to [13C]-bicarbonate ratio (P = .0003), lesion [13C]-bicarbonate to [1-13C]-pyruvate ratio (P = .0118), lesion volume (P = .0264), and the number of involved organs with metastases including the brain (P = .0448) as significant predictors of treatment response. The lesion [1-13C]-lactate to [13C]-bicarbonate ratio was predictive of response with the best overall performance, achieving an AUCROC = 0.88, AUCPRC = 0.83, sensitivity = 67% (CI: 40%–87%), specificity = 97% (CI: 90%–100%), and positive predictive value (PPV) = 91% (CI: 73%–100%). Conclusions HP lesion [1-13C]-lactate to [13C]-bicarbonate ratio can predict SRS response with a high PPV.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".