Fast and sensitive dynamic oxygen‐enhanced MRI with a cycling gas challenge and independent component analysis
Bibliographic record
Abstract
Purpose There is a critical need for non‐invasive imaging biomarkers of tumor oxygenation to assist in patient stratification and development of hypoxia targeting therapies. Using a cycling gas challenge and independent component analysis (ICA), we sought to improve the sensitivity and speed of existing oxygen enhanced MRI (OE‐MRI) techniques to detect changes in oxygenation with dynamically acquired T1W signal intensity images (dOE‐MRI). Methods Mice were implanted with SCCVII, HCT‐116, BT‐474, or SKOV3 tumors in the dorsal subcutaneous region and imaged at 7T. T1W images were acquired during a respiratory challenge with alternating 2‐minute periods of air and 100% oxygen for three cycles. Data were analyzed with ICA and oxygenation maps were generated and compared to corresponding histology sections stained for hypoxia (pimonidazole) and blood vessels (CD31). Results Cycling air‐oxygen‐air gas challenges were well tolerated and ICA permitted extraction of the oxygen‐enhancing component in all imaged tumors from four different models. Comparison with synthetic response functions showed that dOE‐MRI does not require any a‐priori knowledge of the physiological response. The fraction of O2‐negative dOE‐MRI voxels that correlate inversely with the ICA gas‐cycling component correspond well with the histological hypoxic fraction in SCCVII tumors (r = 0.91, p = 0.0016) but did not correlate in HCT‐116 tumors (r = 0.13, p = 0.81). Conclusions Using ICA and adding a cycling gas challenge extends the sensitivity of OE‐MRI and allows the oxygenation status of tumors to be assessed in as little as six minutes. These findings support further development of OE‐MRI as a biomarker of tumor oxygenation.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".