Quantitative apparent diffusion coefficients and T2 relaxation times in characterizing contrast enhancing brain tumors and regions of peritumoral edema
Bibliographic record
Abstract
PURPOSE: To investigate the potential value and relationship of in vivo quantification of apparent diffusion coefficients (ADCs) and T2 relaxation times for characterizing brain tumor cellularity and tumor-related edema. MATERIALS AND METHODS: A total of 26 patients with newly diagnosed gliomas, meningiomas, or metastases underwent diffusion-weighted and six-echo multisection T2-preparation imaging. Regions of interest (ROIs) were drawn on conventional MR images to include tumor (as defined by contrast agent enhancement) and immediate and peripheral edema. Areas of necrosis were excluded. Median values of ADCs and T2 in the ROIs were calculated. RESULTS: ADCs for gliomas were similar to those for meningiomas or metastases in all regions. Tumor T2 values for gliomas (159.5+/-30.6 msec) were significantly higher than those for meningiomas or metastases (125.0+/-31.1 msec; P=0.005). Immediate-edema T2 values for meningiomas or metastases (226.0+/-44.1 msec) were significantly higher than those for gliomas (203.5+/-32.8 msec; P=0.033). Peripheral-edema T2 values for gliomas (219.5+/-41.9 msec) were similar to those for meningiomas or metastases (202.5+/-26.5 msec; P=0.377). Both immediate- and peritumoral-edema ADCs and T2 values were significantly higher than those in tumor for both tumor types. ADCs and T2 values from all regions correlated significantly for gliomas (r=0.95; P<0.0001) and for meningiomas or metastases (r=0.81; P<0.0001). CONCLUSION: The higher immediate-edema T2 values for nonglial tumors than for gliomas suggest tumor-related edema (vasogenic vs. infiltrated) can be further characterized by using T2 values. There were significant correlations between ADC and T2 values.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".