Update on Cancer Screening in Children with Syndromes of Bone Lesions, Hereditary Leiomyomatosis and Renal Cell Carcinoma Syndrome, and Other Rare Syndromes
Bibliographic record
Abstract
The management of children with syndromes associated with an increased risk of benign and malignant neoplasms is a complex challenge for health care professionals. The 2023 American Association for Cancer Research Childhood Cancer Predisposition Workshop provided updated consensus guidelines on cancer surveillance in these syndromes, aiming to improve early detection and intervention and reduce morbidity associated with such neoplasms. In this article, we review several of the rare conditions discussed in this workshop. Ollier disease and Maffucci syndrome are enchondromatoses (disorders featuring benign bone lesions) with up to 50% risk of malignancy, including chondrosarcoma. These patients require surveillance with baseline whole-body MRI and routine monitoring of potential malignant transformation of bony lesions. Hereditary multiple osteochondromas carry a lower risk of chondrosarcoma (<6%) but still require lifelong surveillance and baseline imaging. Related syndromes of benign bone lesions are also described. Hereditary leiomyomatosis and renal cell carcinoma syndrome, associated with fumarate hydratase pathogenic variants, is discussed in detail. Surveillance for renal cell carcinoma in pediatric age is recommended, as well as prompt intervention when a lesion is detected. Schinzel-Giedion syndrome and Rubinstein-Taybi syndrome are described for their associated malignancies and other complications, as well as expert consensus on the need for childhood cancer surveillance. Clinical recommendations, including imaging modalities and frequency of screenings, are proposed and are tailored to each syndrome's age-specific tumor risk profile. In all syndromes, patients and their families should be educated about the potential malignancy risk and advised to seek medical care for rapid growth of a mass, persistent pain, or other unexplained symptoms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".