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Diagnostic support in pediatric craniopharyngioma using deep learning.

Giovanni Castiglioni1,2, Joaquín Vallejos3, Jhon Intriago1,2

  • 1Department of Electrical Engineering, Faculty of Physical and Mathematical Sciences, University of Chile, Santiago, Chile.

Child'S Nervous System : Chns : Official Journal of the International Society for Pediatric Neurosurgery
|April 22, 2024
PubMed
Summary

A new deep learning algorithm aids in classifying pediatric sellar-suprasellar tumors using MRI scans. Explainable AI (XAI) ensures diagnostic accuracy, showing promising results for radiological support.

Keywords:
ClassificationCraniopharyngiomaDeep learningMRI

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Area of Science:

  • Pediatric neuro-oncology
  • Radiology
  • Artificial Intelligence

Background:

  • Sellar-suprasellar tumors are a diverse group of neoplasms affecting pediatric patients.
  • Accurate radiological classification is crucial for diagnosis and treatment planning.

Purpose of the Study:

  • To develop a convolutional deep learning algorithm for classifying pediatric sellar-suprasellar tumors.
  • To provide radiological assistance in categorizing these tumors.

Main Methods:

  • Utilized T1w and T2w preoperative MRI scans from 226 Chilean pediatric patients.
  • Images were classified into three groups: healthy controls, craniopharyngioma, and other sellar/suprasellar tumors.
  • Applied deep learning and explainable artificial intelligence (XAI) techniques.

Main Results:

  • Achieved a positive predictive value (PPV) of 0.828±0.039 and a negative predictive value (NPV) of 0.919±0.063.
  • XAI identified that diagnostically relevant structures significantly influenced the algorithm's decision-making.

Conclusions:

  • This study represents the first application of deep learning and XAI for sellar-suprasellar tumor classification in this institution.
  • The developed algorithm shows promising results for radiological diagnostic support in pediatric neuro-oncology.