Update on Whole-Body MRI Surveillance for Pediatric Cancer Predisposition Syndromes
Bibliographic record
Abstract
Whole-body MRI (WBMRI) is an integral part of screening infants, children, and adolescents for presymptomatic neoplasms in certain cancer predisposition syndromes, which include Li-Fraumeni and constitutional mismatch repair deficiency syndromes, among others. The list of syndromes in which WBMRI adds value, as part of a comprehensive surveillance protocol, continues to evolve in response to new evidence, growing experience, and more widespread adoption. In July 2023, the AACR reconvened an international, multidisciplinary panel to revise and update recommendations stemming from the 2016 AACR Special Workshop on Childhood Cancer Predisposition. That initial meeting resulted in a series of publications in Clinical Cancer Research in 2017, including "Pediatric Cancer Predisposition Imaging: Focus on Whole-Body MRI." This 2024 review of WBMRI in cancer predisposition syndrome updates the 2017 WBMRI publication, the revised recommendations derived from the 2023 AACR Childhood Cancer Predisposition Workshop based on available data, societal guidelines, and expert opinion. Different aspects of acquiring and interpreting WBMRI, including diagnostic accuracy, are discussed. The application of WBMRI in resource-poor environments, as well as integration of whole-body imaging techniques with emerging technologies, such as cell-free DNA ("liquid biopsies") and artificial intelligence/machine learning, is also considered.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".