MO-FG-BRC-01: MR-Guided Radiation Therapy with Gadolinium Nanoparticles: From Chalkboard to First Clinical Trials
Bibliographic record
Abstract
Experimental research in medical physics has expanded the limits of our knowledge and provided novel imaging and therapy technologies for patients around the world. However, experimental efforts are challenging due to constraints in funding, space, time and other forms of institutional support. In this joint ESTRO-AAPM symposium, four exciting experimental projects from four different countries are highlighted. Each project is focused on a different aspect of radiation therapy. From the USA, we will hear about a new linear accelerator concept for more compact and efficient therapy devices. From Canada, we will learn about novel linear accelerator target design and the implications for imaging and therapy. From France, we will discover a mature translational effort to incorporate theranostic nanoparticles in MR-guided radiation therapy. From Germany, we will find out about a novel in-treatment imaging modality for particle therapy. These examples of high impact, experimental medical physics research are representative of the diversity of such efforts that are on-going around the globe. J. Robar, Research is supported through collaboration with Varian Medical Systems and Brainlab AGD. Westerly, This work is supported by the Department of Radiation Oncology at the University of Colorado School of Medicine. COI: NONEK. Parodi, Part of the presented work is supported by the DFG (German Research Foundation) Cluster of Excellence MAP (Munich-Centre for Advanced Photonics) and has been carried out in collaboration with IBA.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".