Medical Imaging and Pediatric and Adolescent Hematologic Cancer Risk
Bibliographic record
Abstract
BACKGROUND: Assessing the risk of radiation-induced hematologic cancer from medical imaging in children and adolescents might support informed decisions on the use of imaging. METHODS: We followed a retrospective cohort of 3,724,623 children born between 1996 and 2016 in six U.S. health care systems and Ontario, Canada, until the earliest of cancer or benign-tumor diagnosis, death, end of health care coverage, an age of 21 years, or December 31, 2017. Radiation doses to active bone marrow from medical imaging were quantified. Associations between hematologic cancers and cumulative radiation exposure (vs. no exposure), with a lag of 6 months, were estimated with the use of continuous-time hazards models. RESULTS: During 35,715,325 person-years of follow-up (mean, 10.1 years per person), 2961 hematologic cancers were diagnosed, primarily lymphoid cancers (2349 [79.3%]), myeloid cancers or acute leukemia (460 [15.5%]), and histiocytic- or dendritic-cell cancers (129 [4.4%]). The mean (±SD) exposure among children exposed to at least 1 mGy was 14.0±23.1 mGy overall (for comparison, 13.7 mGy was the exposure from one computed tomographic [CT] scan of the head) and 24.5±36.4 mGy among children with hematologic cancer. Cancer risk increased with cumulative dose, with a relative risk (vs. no exposure) of 1.41 (95% confidence interval [CI], 1.11 to 1.78) for 1 to less than 5 mGy, 1.82 (95% CI, 1.33 to 2.43) for 15 to less than 20 mGy, and 3.59 (95% CI, 2.22 to 5.44) for 50 to less than 100 mGy. The cumulative radiation dose to bone marrow was associated with an increased risk of all hematologic cancers (excess relative risk per 100 mGy, 2.54 [95% CI, 1.70 to 3.51; P<0.001]; relative risk for 30 vs. 0 mGy, 1.76 [95% CI, 1.51 to 2.05]) and most tumor subtypes. The excess cumulative incidence of hematologic cancers by 21 years of age among children exposed to at least 30 mGy (mean, 57 mGy) was 25.6 per 10,000. We estimated that, in our cohort, 10.1% (95% CI, 5.8 to 14.2) of hematologic cancers may have been attributable to radiation exposure from medical imaging, with higher risks from the higher-dose medical-imaging tests such as CT. CONCLUSIONS: Our study suggests an association between exposure to radiation from medical imaging and a small but significantly increased risk of hematologic cancer among children and adolescents. (Funded by the National Cancer Institute and others.).
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".