MRI for biology‐guided radiation therapy: Are we there yet? A summary of the 2024 ISMRM member‐initiated session
Bibliographic record
Abstract
Abstract At the 2024 ISMRM Annual Meeting in Singapore, a member‐initiated session on MRI for biology‐guided radiation therapy (RT), endorsed by the ISMRM MR in RT Study Group, was successfully organized. The session convened a diverse group of global experts in quantitative MRI for RT, who presented the latest research on the technical development and clinical translation of various quantitative MRI techniques for biology‐guided RT planning and delivery. The session highlighted clinical needs and a variety of MRI techniques, including MR spectroscopic imaging, oxygen‐enhanced MRI, four‐dimensional MR fingerprinting, dynamic contrast‐enhanced MRI, and 129 Xe MRI. Additionally, technical aspects and challenges for clinical translation of quantitative MRI into biology‐guided RT were presented, both in the context of RT planning and adaptation. This article summarizes the progress made in this emerging field, identifies key challenges that need to be addressed, and outlines areas for future research. These insights are crucial for the integration of quantitative MRI techniques into RT clinical practice, ultimately aiming to improve patient outcomes through more personalized RT approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".