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Background <p>Artificial intelligence (AI) and radiomics are increasingly applied in pediatric neuroradiology to enhance diagnostic precision. However, their clinical implementation remains limited due to methodological variability and lack of standardization.</p> Objective <p>To systematically evaluate the diagnostic applications, performance, and methodological quality of artificial intelligence models, including both radiomics/machine learning and deep learning approaches, in pediatric brain tumor imaging.</p> Methods <p>A systematic review was conducted in accordance with PRISMA 2020 guidelines, searching PubMed, Scopus, and Web of Science (2010–2025). Eligible studies included patients aged 0–18 years with brain tumors, applied AI to neuroimaging for diagnostic classification, molecular characterization, or integral image processing tasks, and reported performance metrics. Methodological quality was assessed using the Newcastle–Ottawa Scale (NOS) and the Checklist for Artificial Intelligence in Medical Imaging (CLAIM).</p> Results <p>From 638 records, 24 studies were included. Most studies used MRI (96%) and machine learning models based on radiomics (88%), with a smaller proportion employing deep learning (29%). The primary diagnostic tasks were tumor classification (50%) and molecular subtype prediction (33%). Reported AUCs for diagnostic tasks ranged from 0.73 to 0.98 (median: 0.91). Based on the NOS, 19 studies (79%) were rated as low risk of bias (scores 8–9), though only 3 studies (12.5%) performed external validation on independent cohorts.</p> Conclusion <p>AI and radiomics demonstrate high diagnostic accuracy for pediatric brain tumor characterization. Nonetheless, the lack of prospective design and critically low rate of external validation limits generalizability. Standardized, multicenter studies are needed to support broader clinical adoption.</p>

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Artificial intelligence and radiomics for pediatric brain tumor classification and molecular characterization: a systematic review

  • 期刊论文

  • 发表日期：

    2026年3月27日

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  • Jheremy S. Reyes,
  • David F. Estupiñan-Pepinosa,
  • Sofia I. Leal-Giraldo,
  • María F. Cordoba-Gallego,
  • María P Silva-López,
  • Juan S. Aguirre-Patiño,
  • Laura S. Gordillo-Iriarte,
  • Cristian S. Cabezas,
  • Raul F. Vega-Alvear,
  • Luis M. Navarro-Ramirez

摘要

Background

Artificial intelligence (AI) and radiomics are increasingly applied in pediatric neuroradiology to enhance diagnostic precision. However, their clinical implementation remains limited due to methodological variability and lack of standardization.

Objective

To systematically evaluate the diagnostic applications, performance, and methodological quality of artificial intelligence models, including both radiomics/machine learning and deep learning approaches, in pediatric brain tumor imaging.

Methods

A systematic review was conducted in accordance with PRISMA 2020 guidelines, searching PubMed, Scopus, and Web of Science (2010–2025). Eligible studies included patients aged 0–18 years with brain tumors, applied AI to neuroimaging for diagnostic classification, molecular characterization, or integral image processing tasks, and reported performance metrics. Methodological quality was assessed using the Newcastle–Ottawa Scale (NOS) and the Checklist for Artificial Intelligence in Medical Imaging (CLAIM).

Results

From 638 records, 24 studies were included. Most studies used MRI (96%) and machine learning models based on radiomics (88%), with a smaller proportion employing deep learning (29%). The primary diagnostic tasks were tumor classification (50%) and molecular subtype prediction (33%). Reported AUCs for diagnostic tasks ranged from 0.73 to 0.98 (median: 0.91). Based on the NOS, 19 studies (79%) were rated as low risk of bias (scores 8–9), though only 3 studies (12.5%) performed external validation on independent cohorts.

Conclusion

AI and radiomics demonstrate high diagnostic accuracy for pediatric brain tumor characterization. Nonetheless, the lack of prospective design and critically low rate of external validation limits generalizability. Standardized, multicenter studies are needed to support broader clinical adoption.