Apparent transverse relaxation () on <scp>MRI</scp> as a method to differentiate treatment effect (pseudoprogression) versus progressive disease in chemoradiation for malignant glioma
Bibliographic record
Abstract
Abstract Introduction Pseudoprogression (psPD) is a transient post‐treatment imaging change that is commonly seen when treating glioma with chemotherapy and radiation. The use of apparent transverse relaxation rate ( ), which is calculated from a contrast‐free multi‐echo gradient echo Magnetic Resonance Imaging (MRI) sequence, may allow for quantitative identification of patients with suspected psPD. Methods We acquired a multi‐echo gradient echo sequence using a 3T‐Siemens Prisma MRI. The signal decay through the echoes was fitted to provide the coefficient. We segmented the T1‐gadolinium enhancing the image to provide a contrast enhancing lesion (CEL) and the FLAIR hyperintensity to provide a non‐enhancing lesion (NEL). These regions of interest were applied to the multi‐echo gradient echo to acquire a mean within the CEL and NEL. We additionally acquired ADC data to attempt to corroborate our findings. Results We found that patients who later exhibited PD exhibited a higher within the CEL as well as a higher ratio of CEL to NEL. Our data correctly distinguished pseudoprogression from treatment effect in 9/9 patients, while ADC corrected identified 7/9 patients using an absolute ADC of 1200 × 10−6 mm2/s. Conclusions Our method seems promising for the accurate identification of psPD, and the technique is amenable to evaluation in larger, multi‐centre patient cohorts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".