Deep learning for quantitative MRI brain tumor analysis
Bibliographic record
Abstract
ABSTRACT The infiltrative nature of malignant gliomas results in active tumor spreading into the peritumoral edema, which is not visible in conventional magnetic resonance imaging (cMRI) even after contrast injection. MR relaxometry (qMRI) measures relaxation rates dependent on tissue properties, and can offer additional contrast mechanisms to highlight the non-enhancing infiltrative tumor. The aim of this study is to investigate if qMRI data provides additional information compared to cMRI sequences (T1w, T1wGd, T2w, FLAIR), when considering deep learning-based brain tumor (1) detection and (2) segmentation. A total of 23 patients with histologically confirmed malignant glioma were retrospectively included in the study. Quantitative MR imaging was used to obtain R 1 (1/T1), R 2 (1/T2) and proton density maps pre- and post-gadolinium contrast injection. Conventional MR imaging was also performed. A 2D CNN detection model and a 2D U-Net were trained on transversal slices (n=528) using either cMRI or a combination of qMRI pre- and post-contrast data for tumor detection and segmentation, respectively. Moreover, trends in quantitative R 1 and R 2 rates of regions identified as relevant for tumor detection by model explainability methods were qualitatively analyzed. Tumor detection and segmentation performance for models trained with a combination of qMRI pre- and post-contrast was the highest (detection MCC=0.72, segmentation Dice=0.90), however, improvements were not statistically significant compared to cMRI (detection MCC=0.67, segmentation Dice=0.90). The analysis of the relaxation rates of the relevant regions identified using model explainability methods showed no differences between models trained on cMRI or qMRI. Relevant regions which fell outside the annotation showed changes in relaxation rates after contrast injection similar to those within the annotation, when looking at majority of the individual cases. A similar trend could not be seen when looking at relaxation trends over all the dataset. In conclusion, models trained on qMRI data obtain similar performance to those trained on cMRI data, with the advantage of quantitatively measuring brain tissue properties within the scan time (11.8 minutes for qMRI with and without contrast, and 12.2 minutes for cMRI). Moreover, when considering individual patients, regions identified by model explainability methods as relevant for tumor detection outside the manual annotation of the tumor showed changes in quantitative relaxation rates after contrast injection similar to regions within the annotation, suggestive of infiltrative tumor in the peritumoral edema.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".