Differentiation between Radiation Necrosis and Tumor Progression Using Chemical Exchange Saturation Transfer
Bibliographic record
Abstract
Abstract Purpose: Stereotactic radiosurgery (SRS) is a common treatment used in patients with brain metastases and is associated with high rates of local control, however, at the risk of radiation necrosis. It is difficult to differentiate radiation necrosis from tumor progression using conventional MRI, making it a major diagnostic dilemma for practitioners. This prospective study investigated whether chemical exchange saturation transfer (CEST) was able to differentiate these two conditions. Experimental Design: Sixteen patients with brain metastases who had been previously treated with SRS were included. Average time between SRS and evaluation was 12.6 months. Lesion type was determined by pathology in 9 patients and the other 7 were clinically followed. CEST imaging was performed on a 3T Philips scanner and the following CEST metrics were measured: amide proton transfer (APT), magnetization transfer (MT), magnetization transfer ratio (MTR), and area under the curve for CEST peaks corresponding to amide and nuclear Overhauser effect (NOE). Results: Five lesions were classified as progressing tumor and 11 were classified as radiation necrosis (using histopathologic confirmation and radiographic follow-up). The best separation was obtained by NOEMTR (NOEMTR,necrosis = 8.9 ± 0.9%, NOEMTR,progression = 12.6 ± 1.6%, P < 0.0001) and AmideMTR (AmideMTR,necrosis = 8.2 ± 1.0%, AmideMTR,progression = 12.0 ± 1.9%, P < 0.0001). MT (MTnecrosis = 4.7 ± 1.0%, MTprogression = 6.7 ± 1.7%, P = 0.009) and NOEAUC (NOEAUC,necrosis = 4.3 ± 2.0% Hz, NOEAUC,progression = 7.2 ± 1.9% Hz, P = 0.019) provided statistically significant separation but with higher P values. Conclusions: CEST was capable of differentiating radiation necrosis from tumor progression in brain metastases. Both NOEMTR and AmideMTR provided statistically significant separation of the two cohorts. However, APT was unable to differentiate the two groups. Clin Cancer Res; 23(14); 3667–75. ©2017 AACR.
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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 | 0.001 |
| 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".