Patterns of Growth of Tumors in Li-Fraumeni Syndrome by Imaging: A Case Series
Bibliographic record
Abstract
Although tumors of Li-Fraumeni syndrome (LFS) have a premalignant or dormant phase that could be exploited by early imaging detection, this has been underevaluated in the literature. We present a case series of patients with LFS followed by imaging over time to highlight patterns of growth of tumors and hotspots of missed tumors in this population. Clinical and imaging features were available for 29 tumors of 24 carriers of a germline TP53 pathogenic variant, developed between 1999 and 2023 were retrospectively reviewed in a single tertiary pediatric center. Imaging characteristics of tumors were evaluated with MRI, CT, and radiographs. Local invasion, time interval for developing primary cancer, and/or recurrent disease and metastasis, and factors that delayed the tumor diagnosis were assessed. In patients with multiple tumors the median time intervals for development of first, second, and third primary cancers were 45.9, 79.8, and 28.1 months, respectively. Hotspots of missed tumors included superficial soft tissues, areas close to bones, on the scalp, in tissues around the adrenal region and in small hypodense lesions on brain CT. In conclusion, the pattern of growth of tumors is variable and erratic in LFS patients with some tumors presenting with a dormant pattern.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".