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Incorporating Radiomics into Machine Learning Models to Predict Outcomes of Neuroblastoma.

Gengbo Liu1, Mini Poon2, Matthew A Zapala2

  • 1Department of Computer Engineering and Sciences, Florida Institute of Technology, Melbourne, FL, USA.

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|March 3, 2022
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Summary

Machine learning models predict neuroblastoma outcomes from CT scans. Radiomics-based artificial neural networks show promise for non-invasive prognosis, aiding in patient management.

Keywords:
CTMachine learningNeural networkNeuroblastomaRadiomics

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Area of Science:

  • Oncology
  • Radiology
  • Machine Learning

Background:

  • Neuroblastoma is a common pediatric cancer requiring accurate prognostic tools.
  • Current prognostic methods can be invasive or lack precision.
  • Computed Tomography (CT) imaging offers a non-invasive data source.

Purpose of the Study:

  • To develop and compare machine learning (ML) models for non-invasively predicting neuroblastoma patient outcomes from CT images.
  • To evaluate the performance of different ML algorithms in predicting mortality, metastasis, differentiation, MKI, MYCN amplification, and IDRF.
  • To establish the utility of radiomic features extracted from CT scans for neuroblastoma prognosis.

Main Methods:

  • Retrospective analysis of CT images from 65 neuroblastoma patients.
  • Extraction of 105 radiomic features using the Pyradiomics library.
  • Implementation and comparison of multiple ML algorithms, including Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN), on CT-derived features and slices.

Main Results:

  • The radiomics-based ANN model demonstrated superior performance in predicting most investigated outcomes.
  • The elastic regression model achieved the best performance for classifying neuroblastic differentiation grade.
  • Area Under the Receiver Operating Characteristic Curve (ROC-AUC) was used to measure prediction accuracy across models.

Conclusions:

  • Non-invasive ML models utilizing CT radiomics can effectively predict various neuroblastoma outcomes.
  • ANN models show strong potential for prognostic applications in neuroblastoma.
  • This study provides a valuable comparison of ML approaches for medical imaging in pediatric oncology.