Translation of PET radiotracers for cancer imaging: recommendations from the National Cancer Imaging Translational Accelerator (NCITA) consensus meeting
Bibliographic record
Abstract
Abstract Background The clinical translation of positron emission tomography (PET) radiotracers for cancer management presents complex challenges. We have developed consensus-based recommendations for preclinical and clinical assessment of novel and established radiotracers, applied to image different cancer types, to improve the standardisation of translational methodologies and accelerate clinical implementation. Methods A consensus process was developed using the RAND/UCLA Appropriateness Method (RAM) to gather insights from a multidisciplinary panel of 38 key stakeholders on the appropriateness of preclinical and clinical methodologies and stakeholder engagement for PET radiotracer translation. Panellists independently completed a consensus survey of 57 questions, rating each on a 9-point Likert scale. Subsequently, panellists attended a consensus meeting to discuss survey outcomes and readjust scores independently if desired. Survey items with median scores ≥ 7 were considered ‘required/appropriate’, ≤ 3 ‘not required/inappropriate’, and 4–6 indicated ‘uncertainty remained’. Consensus was determined as ~ 70% participant agreement on whether the item was ‘required/appropriate’ or ‘not required/not appropriate’. Results Consensus was achieved for 38 of 57 (67%) survey questions related to preclinical and clinical methodologies, and stakeholder engagement. For evaluating established radiotracers in new cancer types, in vitro and preclinical studies were considered unnecessary, clinical pharmacokinetic studies were considered appropriate, and clinical dosimetry and biodistribution studies were considered unnecessary, if sufficient previous data existed. There was ‘agreement without consensus’ that clinical repeatability and reproducibility studies are required while ‘uncertainty remained’ regarding the need for comparison studies. For novel radiotracers, in vitro and preclinical studies, such as dosimetry and/or biodistribution studies and tumour histological assessment were considered appropriate, as well as comprehensive clinical validation. Conversely, preclinical reproducibility studies were considered unnecessary and ‘uncertainties remained’ regarding preclinical pharmacokinetic and repeatability evaluation. Other consensus areas included standardisation of clinical study protocols, streamlined regulatory frameworks and patient and public involvement. While a centralised UK clinical imaging research infrastructure and open access federated data repository were considered necessary, there was ‘agreement without consensus’ regarding the requirement for a centralised UK preclinical imaging infrastructure. Conclusions We provide consensus-based recommendations, emphasising streamlined methodologies and regulatory frameworks, together with active stakeholder engagement, for improving PET radiotracer standardisation, reproducibility and clinical implementation in oncology.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.133 | 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".