Abstract IA08: Theragnostics & external beam radiation dose absorbed - Similarities, differences and thoughts for the future
Bibliographic record
Abstract
Abstract External Beam Radiotherapy (EBRT) and theragnostics share a key core characteristic – the use of radiation to exert its therapeutic benefit. However, the role radiation dose absorbed plays is significantly different between these two modalities in clinical practice. On the one hand it is a critical element of EBRT prescriptions (e.g. cGy), while on the other it is not even a requirement to document in theragnostic practices, where prescriptions are expressed as administered activities (e.g. GBq). Radiation dose absorbed by tumor and normal tissues is recognized to be variable across patients. It is acknowledged to be a critical metric in the safe use of radiopharmaceuticals and is required for all first in human studies and a priority for the development of any novel radiopharmaceutical. The process of individualized dosimetry is well described, enabling radiation dose absorbed estimates to regions of interest. There is an active debate currently as to when, in whom and how individualized dosimetry should be incorporated into clinical practice. The lack of high quality evidence to support radiation dose absorbed to patient outcomes, the lack of standardized methodologies to enable comparability of dose absorbed data across institutions, the burden on patients to return for dosimetry scans post treatment, the burden on the health care system to incorporate individualized dosimetry into treatment workflows and the lack of reimbursement codes for these activities are the main barriers cited against routine clinical use. In this session, we will review some of the theoretical and logistic considerations as well as evidence behind this debate. We will also take a broader view, of theragnostics as a rapidly growing modality, that patients and their oncology teams would need radiation absorbed dose information when considering “re-irradiation”, be it re-challenge with theragnostics, or to provide safety estimates when using different forms of radiation along patients’ cancer journey in the real world. Through a recently completed multi-center trial as a case study, we will review the processes involved in setting up individualized dosimetry across four academic institutions and our subsequent transition into standard of care. We will discuss the value of radiation dose information for our patients, at the systems and academic levels. Finally, we will consider radiation dose absorbed in the future of theragnostics - as a prognostic factor for response and toxicity, as a factor in understanding the mechanism of treatment resistance, as a strategy to create novel dosing approaches and as part of combined modality to strive for superior patient outcomes. We propose that we are at a critical time, where a collaborative strategy across theragnostic practices is critical. The adoption of the reporting of radiation dose absorbed as a quality metric, blending lessons learned across theragnostics and EBRT will set the stage for the future with excellent quality of care and accelerated discoveries for our patients. (458 words). Citation Format: Rebecca K.S. Theragnostics & external beam radiation dose absorbed - Similarities, differences and thoughts for the future. [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Targeted Therapies in Combination with Radiotherapy; 2025 Jan 26-29; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(2_Suppl):Abstract nr IA08
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.036 | 0.028 |
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".