Diffusional kurtosis imaging for differentiating between high‐grade glioma and primary central nervous system lymphoma
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to assess the diagnostic accuracy of diffusion kurtosis magnetic resonance imaging parameters for differentiating high-grade gliomas (HGGs) from primary central nervous system lymphomas (PCNSLs). METHODS: Diffusion parameters, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (λ// ), radial diffusivity (λ⊥ ); and kurtosis parameters, including mean kurtosis (MK), axial kurtosis (K// ), and radial kurtosis (K⊥ ), were normalized to contralateral normal-appearing white matter (NAWMc) to decrease inter-individual and inter-regional changes across the entire brain, and then compared with the solid parts of 20 HGGs and 11 PCNSLs [median 95% confidence interval (CI), P < 0.004; 0.05/14], significance level, Kolmogorov-Smirnov test, Bonferroni correction]. RESULTS: FA, MD, λ// , and λ⊥ values were higher in HGGs than in PCNSLs, but not significantly [HGGs: 0.209 (95% CI, 0.134-0.338), 1.385 (95% CI, 1.05-1.710), 1.655 (95% CI, 1.30-2.060), 1.228 (95% CI, 0.932-1.480), respectively; PCNSLs: 0.143 (95% CI, 0.110-0.317), 1.070 (95% CI, 0.842-1.470), 1.260 (95% CI, 0.960-1.930), 1.010 (95% CI, 0.782-1.240)], respectively; P = 0.120, 0.010, 0.004, and 0.004, respectively). However, MK and K// were significantly higher in PCNSLs compared with HGGs [PCNSLs: 0.765 (95% CI, 0.697-0.890), 0.787 (95% CI, 0.615-1.030), respectively; HGGs: 0.531 (95% CI, 0.402-0.766), 0.532 (95% CI, 0.432-0.680], respectively; P = 0.001, 0.000, respectively); but not K⊥ [0.774 (95% CI, 0.681-0.899) for PCNSLs; 0.554 (95% CI, 0.389-0.954) for HGGs; P = 0.024]. CONCLUSION: There were significant differences in kurtosis parameters (MK and K// ) between HGGs and PCNSLs, while differences in diffusion parameters between them did not reach significance; hence, better separation was achieved with these parameters than with conventional diffusion imaging parameters. J. Magn. Reson. Imaging 2016;44:30-40.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".