Adaptive magnetic resonance image guided radiation for intact localized prostate cancer how to optimally test a rapidly emerging technology
Bibliographic record
Abstract
Introduction: Prostate cancer is a common malignancy for which radiation therapy (RT) provides an excellent management option with high rates of control and low toxicity. Historically RT has been given with CT based image guidance. Recently, magnetic resonance (MR) imaging capabilities have been successfully integrated with RT delivery platforms, presenting an appealing, yet complex, expensive, and time-consuming method of adapting and guiding RT. The precise benefits of MR guidance for localized prostate cancer are unclear. We sought to summarize optimal strategies to test the benefits of MR guidance specifically in localized prostate cancer. Methods: A group of radiation oncologists, physicists, and statisticians were identified to collectively address this topic. Participants had a history of treating prostate cancer patients with the two commercially available MRI-guided RT devices. Participants also had a clinical focus on randomized trials in localized prostate cancer. The goal was to review both ongoing trials and present a conceptual focus on MRI-guided RT specifically in the definitive treatment of prostate cancer, along with developing and proposing novel trials for future consideration. Trial hypotheses, endpoints, and areas for improvement in localized prostate cancer that specifically leverage MR guided technology are presented. Results: Multiple prospective trials were found that explored the potential of adaptive MRI-guided radiotherapy in the definitive treatment of prostate cancer. Different primary areas of improvement that MR guidance may offer in prostate cancer were summarized. Eight clinical trial design strategies are presented that summarize options for clinical trials testing the potential benefits of MRI-guided RT. Conclusions: The number and scope of trials evaluating MRI-guided RT for localized prostate cancer is limited. Yet multiple promising opportunities to test this technology and potentially improve outcomes for men with prostate cancer undergoing definitive RT exist. Attention, in the form of multi-institutional randomized trials, is needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".