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A 3D Radiomics-Based Artificial Neural Network Model for Benign Versus Malignant Vertebral Compression Fracture

Natália S Chiari-Correia1, Marcello H Nogueira-Barbosa2,3,4, Rodolfo Dias Chiari-Correia5

  • 1Medical Artificial Intelligence Laboratory of the Ribeirão, Preto Medical School, University of São Paulo, 3900 Bandeirantes Avenue, Ribeirão Preto, SP, 14049-900, Brazil. natalia.chiari@alumni.usp.br.

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|May 30, 2023
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Summary

An artificial neural network model using 3D radiomic features accurately differentiates benign from malignant vertebral compression fractures (VCFs) on MRI. This AI tool shows excellent performance, aiding radiologists in VCF characterization.

Keywords:
Compression fracturesMagnetic resonance imageMedical image processingNeural network modelsSpine

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Vertebral compression fractures (VCFs) require accurate differentiation between benign and malignant causes.
  • Distinguishing between benign and malignant VCFs is crucial for appropriate patient management.
  • Current diagnostic methods may have limitations in definitively characterizing VCFs.

Purpose of the Study:

  • To develop and validate an artificial neural network (ANN) model for differentiating benign from malignant VCFs using 3D radiomic features from MRI.
  • To assess the diagnostic performance of the ANN model in a retrospective cohort.

Main Methods:

  • Retrospective analysis of sagittal T1-weighted lumbar spine MRIs from 91 patients with VCFs.
  • Three-dimensional segmentation of fractured vertebral bodies and extraction of radiomic features.
  • Training and validation of a multilayer perceptron neural network using a wrapper method for feature selection.
  • Evaluation using tenfold cross-validation and an independent test set.

Main Results:

  • The ANN model achieved excellent performance in differentiating benign from malignant VCFs.
  • Internal validation: ROC AUC of 0.98, accuracy 95%, sensitivity 93.5%, specificity 96.3%.
  • Independent test set validation: ROC AUC of 0.97, accuracy 93.3%, sensitivity 93.3%, specificity 93.3%.

Conclusions:

  • The proposed ANN model utilizing 3D radiomic features demonstrates high accuracy in distinguishing benign from malignant VCFs.
  • This AI-driven approach shows significant promise as an assistive tool for radiologists in VCF characterization.
  • The findings suggest a potential role for radiomics and AI in improving the diagnostic workflow for VCFs.