Is pulsed saturation transfer sufficient for differentiating radiation necrosis from tumor progression in brain metastases?
Bibliographic record
Abstract
Abstract Background Stereotactic radiosurgery (SRS) for the treatment of brain metastases delivers a high dose of radiation with excellent local control but comes with the risk of radiation necrosis (RN), which can be difficult to distinguish from tumor progression (TP). Magnetization transfer (MT) and chemical exchange saturation transfer (CEST) are promising techniques for distinguishing RN from TP in brain metastases. Previous studies used a 2D continuous-wave (ie, block radiofrequency [RF] saturation) MT/CEST approach. The purpose of this study is to investigate a 3D pulsed saturation MT/CEST approach with perfusion MRI for distinguishing RN from TP in brain metastases. Methods The study included 73 patients scanned with MT/CEST MRI previously treated with SRS or fractionated SRS who developed enhancing lesions with uncertain diagnoses of RN or TP. Perfusion MRI was acquired in 49 of 73 patients. Clinical outcomes were determined by at least 6 months of follow-up or via pathologic confirmation (in 20% of the lesions). Results Univariable logistic regression resulted in significant variables of the quantitative MT parameter 1/(RA·T2A), with 5.9 ± 2.7 for RN and 6.5 ± 2.9 for TP. The highest AUC of 75% was obtained using a multivariable logistic regression model for MT/CEST parameters, which included the CEST parameters of AREXAmide,0.625µT (P = .013), AREXNOE,0.625µT (P = .008), 1/(RA·T2A) (P = .004), and T1 (P = .004). The perfusion rCBV parameter did not reach significance. Conclusions Pulsed saturation transfer was sufficient for achieving a multivariable AUC of 75% for differentiating between RN and TP in brain metastases, but had lower AUCs compared to previous studies that used a block RF approach.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".