Overview of the First NRG Oncology–National Cancer Institute Workshop on Dosimetry of Systemic Radiopharmaceutical Therapy
Bibliographic record
Abstract
In 2018, the National Cancer Institute and NRG Oncology partnered for the first time to host a joint workshop on systemic radiopharmaceutical therapy (RPT) to specifically address dosimetry issues and strategies for future clinical trials. The workshop focused on current dosimetric approaches for clinical trials, strategies under development that would optimize dose reporting, and future desired or optimized approaches for novel emerging radionuclides and carriers in development. In this article, we review the main approaches that are applied clinically to calculate the absorbed dose. These include absorbed doses calculated over a variety of spatial scales, including whole body, organ, suborgan, and voxel, the last 3 of which are achievable within the MIRD schema (S value) and can be calculated with analytic methods or Monte Carlo methods, the latter in most circumstances. This article will also contrast currently available methods and tools with those used in the past, to propose a pathway whereby dosimetry helps the field by optimizing the biologic effect of the treatment and trial design in the drug approval process to reduce financial and logistical costs. We also briefly discuss the dosimetric equivalent of biomarkers to help bring a precision medicine approach to RPT implementation when merited by evidence collected during early-phase trial investigations. Advances in the methodology and related tools have made dosimetry the optimum biomarker for RPT.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".