The risk index as a basis for risk/benefit analyses and protocol optimization in diagnostic nuclear imaging
Bibliographic record
Abstract
BACKGROUND: Potential risk associated with low-dose radiation exposures is often expressed using the effective dose (E) quantity. Other risk-related quantities have been proposed as alternatives. The recently introduced risk index (RI) shares similarities with E but expands the metric to incorporate medical imaging-appropriate risks factors including patient-specific size, age, and sex. PURPOSE: The aim of this work is to examine the RI metric for quantifying stochastic radiation risk and demonstrate its applications in nuclear imaging. The advantages in this improved metric may help the field progress toward stratified risk consideration in the course of patient management, improve efforts for procedure optimization, and support an evolution in the science of radiation risk assessment. METHODS: In this study we describe, implement, and calculate RI for various diagnostic nuclear imaging scenarios using reference biokinetics published in ICRP Publication 128 for commonly utilized radiopharmaceuticals. All absorbed dose, E and RI calculations were performed using the freely available MIRDcalc nuclear medicine dosimetry software; the organ specific risk parameters used in the software are also benchmarked in this text. The resulting RI and E values are compared and various trends in RI values identified. RESULTS: E and RI coefficients were calculated for 3016 use cases. Notably RI values vary depending on patient characteristics. Overall, across the population, global trends in RI values can be identified. In general, RI values were 2.15 times higher for females than males, due to higher risk coefficients and activities being distributed in smaller reference masses. The pediatric patients showed higher RIs than adults, as younger patients generally receive higher absorbed doses per administered activity, and are more radiosensitive, and have a longer projected lifespan at risk. A compendium of E and RI values is also provided in table format to serve as a reference for the community. CONCLUSIONS: RI is a rational quantity that could be used for justification, risk communication and protocol optimization in medical imaging. It has some advantages when compared to the long-utilized E value with respect to personalization, since accounts for patient size, age, sex, and natural incidence of cancer risk.
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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 | 0.044 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".