Cancer Screening Recommendations and Clinical Management of Inherited Gastrointestinal Cancer Syndromes in Childhood
Bibliographic record
Abstract
Abstract Hereditary gastrointestinal cancer predisposition syndromes have been well characterized, but management strategies and surveillance remain a major challenge, especially in childhood. In October 2016, the American Association for Cancer Research organized the AACR Childhood Cancer Predisposition Workshop in which international experts in care of children with a hereditary risk of cancer met to define surveillance strategies and management of children with cancer predisposition syndromes. In this article, we review the current literature in polyposis syndromes that can be diagnosed in childhood and may be associated with an increased incidence of gastrointestinal neoplasms and other cancer types. These disorders include adenomatous polyposis syndromes (APC and MUTYH), juvenile polyposis coli (BMPR1A and SMAD4), Peutz–Jeghers Syndrome (STK11/LKB1), and PTEN hamartoma tumor syndrome (PHTS; PTEN), which can present with a more limited juvenile polyposis phenotype. Herein, the panel of experts provides recommendations for clinical diagnosis, approach to genetic testing, and focus on cancer surveillance recommendations when appropriate during the pediatric period. We also review current controversies on genetic evaluation of patients with hepatoblastoma and indications for surveillance for this tumor. Childhood cancer risks and surveillance associated with disorders involving the mismatch repair genes, including Lynch syndrome and constitutional mismatch repair deficiency (CMMRD), are discussed elsewhere in this series. Clin Cancer Res; 23(13); e107–e14. ©2017 AACR. See all articles in the online-only CCR Pediatric Oncology Series.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".