The European Organisation for Research and Treatment of Cancer, State of Science in radiation oncology and priorities for clinical trials meeting report
Bibliographic record
Abstract
BACKGROUND: New technologies and techniques in radiation oncology and imaging offer opportunities to enhance the benefit of loco-regional treatments, expand treatment to new patient populations such as those with oligometastatic disease and decrease normal tissue toxicity. Furthermore, novel agents have become available which may be combined with radiation therapy, and identification of radiation-related biomarkers can be studied to refine treatment prescriptions. Finally, the use of artificial intelligence (AI) capabilities may also improve treatment quality assurance or the ease with which radiation dosing is prescribed. All of these potential advances present both opportunities and challenges for academic clinical researchers. METHODS: Recently, the European Organisation for Research and Treatment of Cancer addressed these topics in a meeting of multiple stakeholders from Europe and North America. The following five themes radiobiology-based biomarkers, new technologies - particularly proton beam therapy, combination systemic and radiation therapy, management of oligometastatic disease and AI opportunities in radiation oncology were discussed in a State of Science format to define key controversies, unanswered questions and propose clinical trial priorities for development. CONCLUSIONS: Priorities for clinical trials implementing new science and technologies have been defined. Solutions to integrate the multidimensional complexity of data have been explored. New types of platforms and partnerships can support innovative approaches for clinical research in radiation oncology.
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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 | 0.092 | 0.079 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.019 | 0.009 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.021 | 0.015 |
| Insufficient payload (model declined to judge) | 0.026 | 0.015 |
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".