DNA Methylation-Based Risk Stratification and Classification of Pediatric Thyroid Carcinoma

In pediatric thyroid carcinoma patients, DNA methylation assays accurately predict tumor invasiveness and driver mutations. Our findings highlight the clinical value of DNA methylation profiling for risk stratification and classification of pediatric thyroid cancers.

Authors

Institutions

Publication Details

Journal
Clinical Cancer Research
Published
2026-02-17
DOI
https://doi.org/10.1158/1078-0432.ccr-25-2109
Primary Topic
Thyroid Cancer Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

DNA Methylation-Based Risk Stratification and Classification of Pediatric Thyroid Carcinoma

Aime T. Franco, Julio Ricarte Filho, Amber Isaza, Kyle Hinkle et al.
Clinical Cancer Research
Thyroid Cancer Diagnosis and Treatment
article

DNA Methylation-Based Risk Stratification and Classification of Pediatric Thyroid Carcinoma

Aime T. Franco, Julio Ricarte Filho, Amber Isaza, Kyle Hinkle, Marilyn M. Li, Wanding Zhou, Jenny Z. Li, Feng Xu, Andrew J. Bauer
article en

Abstract

In pediatric thyroid carcinoma patients, DNA methylation assays accurately predict tumor invasiveness and driver mutations. Our findings highlight the clinical value of DNA methylation profiling for risk stratification and classification of pediatric thyroid cancers.

Clinical Cancer Research
Children's Hospital of Philadelphia (US), University of Pennsylvania (US)
Good health and well-being
Openalex Percentile: Top 55%
Thyroid Cancer Diagnosis and Treatment
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.