The argument for screening programs in previvors with Li-Fraumeni syndrome
Bibliographic record
Abstract
INTRODUCTION: Li-Fraumeni syndrome (LFS) is a cancer predisposition syndrome caused by pathogenic/likely pathogenic germline TP53 variants. Core cancers include sarcomas, brain tumors, adrenocortical carcinoma and breast cancer. Surveillance with whole-body MRI (WBMRI) and other modalities is used for early cancer detection, regardless of the individual's personal cancer history. With the increasing use of diagnostic multigene panels in oncology, more diverse phenotypic presentations have emerged, and subsequent cascade testing identifies more asymptomatic cancer-free individuals - 'previvors.' AREAS COVERED: This review analyzes aspects of early cancer detection screening programs in asymptomatic germline TP53 variant carriers including current guidelines, specific founder variant populations, health economics and emerging strategies including liquid biopsies and wearable devices. A literature search with PubMed included publications in English until April 2025. EXPERT OPINION: Current guidelines recommend WBMRI in all LFS individuals, regardless of their prior cancer history, due to its demonstrated survival advantage. Guidelines for use of other modalities such as endoscopy, ultrasound or laboratory tests are less well-established. Therefore, longitudinal prospective studies including all these modalities and their cancer detection rates are needed. In the future, a one-size-fits-all approach toward surveillance will be replaced by more precise patient-centered tailor-made screening programs incorporating noninvasive methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".