Baseline surveillance in Li Fraumeni syndrome using whole-body MRI: A systematic review and meta-analysis.
Bibliographic record
Abstract
e22555 Background: Li-Fraumeni syndrome (LFS) is a cancer syndrome associated with early-onset neoplasias. Risk management through a routine cancer surveillance strategy is critical. Since patients with LFS are susceptible to radiation-induced cancers, the use of whole-body MRI (WBMRI) is recommended for regular cancer screening. However, the guidelines for clinical management and the evidence supporting the benefits of WBMRI surveillance in asymptomatic LFS patients are limited. Therefore, this systematic review and meta-analysis assessed the clinical utility of WBMRI in germline TP53 mutation carriers at baseline and at follow up. Methods: We systematically searched Pubmed, Cochrane, and Embase libraries for studies evaluating WBMRI as an early detection method for tumor screening in patients with LFS. We pooled the prevalence and presented the overall rate with 95% confidence intervals (CI) of the individuals found to have one or more investigable lesions, the lesions found to be new cancers among suspicious lesions, and the individuals with one or more new cancers among the total population. Statistical analysis was performed using R software version 4.3.1 and heterogeneity was evaluated with I2 statistics. Results: From 1,547 initial studies, 11 (10 observational cohort and 1 case control) comprising 703 patients (359 females [51%]; mean [SD] age, 29.5 [+/-25] years) were included from 7 different countries. Median follow-up of lesions ranged from 2 to 11 years. We found an estimated detection rate of 31% (95% CI 28-34, I2 55%) of any suspicious lesions in asymptomatic TP53 carriers who underwent baseline WBMRI was found. In total, 277 lesions that required clinical follow-up in 215 patients were identified. Cancer was confirmed in 46 lesions in 39 individuals. Most of the cancers found were Central Nervous System (CNS) tumors (n = 16), followed by sarcomas (n = 12) and breast cancer (n = 11). The estimated cancer diagnosis rate among suspicious lesions was 18% (95%CI 13-25, I2 5%). WBMRI detected 41 of 46 cancers in the early disease stage, with an overall detection rate of 6% (95% CI 5-8, I2 47%). Incidence rate was 2% per patient-round of WBMRI (95% CI 1-4, I2 0%), including baseline plus follow up. Conclusions: Our systematic review and meta-analysis supports that surveillance with WBMRI is effective in detecting cancers in patients with LFS. Additionally, WBMRI surveillance was successful in diagnosing almost 90% of cancers in early-disease stage, which increases the chances os cure in this young population.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.027 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".