WE-E-BRA-03: MR Functional Imaging to Guide Radiotherapy: Opportunities and Challenges in the Clinic
Bibliographic record
Abstract
Human tumors are characterized by an abnormal vascular network that develops because of unregulated angiogenesis. This contributes to abnormalities of the tumor microenvironment like hypoxia, acidosis and high interstitial fluid pressure that influence treatment response and patient survival. There is an important clinical need to develop new minimally invasive tools for characterizing the tumor microenvironment at diagnosis, and monitoring changes during treatment with radiotherapy, chemotherapy or new biologically targeted drugs. MR-based imaging approaches offer exciting possibilities that have yet to be fully exploited. Dynamic contrast enhanced (DCE) MR allows the functional characteristics of the tumor vasculature to be interrogated serially over time. Studies in human cancers have shown substantial differences in DCE MR parameters between tumor and normal muscle in keeping with higher blood flow and vascular permeability. DCE MR has been shown to correlate with response to radiotherapy or drugs that specifically target the tumor vasculature. However, despite important advances, MR functional imaging has not been adopted in routine clinical practice, in part because of a lack of consensus on optimal imaging techniques, analysis methods and reporting metrics. Further refinement and standardization is required founded on interdisciplinary collaboration among clinicians, medical imagers, biologists, physicists and mathematicians to make these techniques robust and clinically applicable. LEARNING OBJECTIVES: 1. Discuss the clinical use of MR functional imaging in patients receiving radiotherapy and the challenges to wide-spread clinical utilization. 2. Discuss the value of MR functional imaging as a predictor of clinical outcome in patients receiving radiotherapy, and a means of monitoring biologic response over a course of fractionated treatment. 3. Understand the role of MR functional imaging in the evaluation of new treatment strategies comprised of radiotherapy and drugs that specifically target the tumor vasculature.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.031 | 0.031 |
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".