Update on Cancer Screening Recommendations for Individuals with Li–Fraumeni Syndrome
Bibliographic record
Abstract
Li-Fraumeni syndrome (LFS) is an autosomal dominant cancer predisposition condition characterized by a high lifetime risk for a wide spectrum of malignancies associated with germline pathogenic/likely pathogenic variants in the TP53 tumor suppressor gene. Secondary malignant neoplasms are particularly common. Early cancer detection through surveillance enables early intervention and leads to improved clinical outcomes with reduced tumor-related mortality and treatment-related morbidity. Since the 2017 publication of LFS tumor surveillance guidelines from the inaugural American Association for Cancer Research Childhood Cancer Predisposition Workshop, understanding the genotype-phenotype relationships in LFS has evolved, and adaptations of the guidelines have been implemented in institutions worldwide. The "Toronto Protocol" remains the current standard for lifelong surveillance; however, as outlined in this perspective, modifications should be considered about the use of certain modalities to target organs in an age-dependent manner. The Working Group's recommendations have also been extended to include a more detailed outline for surveillance in the adult TP53 pathogenic/likely pathogenic variant carrier population, based on the recognition that early education of both practitioners and patients on what to expect after the transition from childhood/adolescence to young adulthood is important in preparing them for changes in surveillance strategies. In this perspective, we provide an up-to-date clinical overview of LFS and present our updated consensus tumor surveillance recommendations from the 2023 American Association for Cancer Research Childhood Cancer Predisposition Workshop.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| 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".