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Radiomics model and deep learning model based on T1WI image for acute lymphoblastic leukemia identification.

Q Cai1, H Tang2, W Wei1

  • 1Department of Radiology, The Affiliated Children's Hospital of Xiangya School of Medicine, Central South University (Hunan Children's Hospital), Changsha, China.

Clinical Radiology
|May 25, 2024
PubMed
Summary
This summary is machine-generated.

This study developed precise radiomics and deep learning models to detect acute lymphoblastic leukemia (ALL) using T1WI MRI. Both models showed high diagnostic efficacy, with deep learning slightly outperforming radiomics.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Oncology

Background:

  • Acute lymphoblastic leukemia (ALL) diagnosis in children often relies on imaging.
  • Developing accurate, non-invasive detection methods is crucial for early intervention.
  • T1-weighted imaging (T1WI) offers potential for quantitative analysis.

Purpose of the Study:

  • To develop and compare highly precise radiomics and deep learning models for ALL detection.
  • To evaluate the diagnostic efficacy of these models using T1WI brain MRI.
  • To assess the accuracy, sensitivity, and specificity of the developed models.

Main Methods:

  • Utilized 604 T1WI brain MRI scans from children with ALL and healthy controls.
  • Extracted radiomics features and trained a support vector machine model.
  • Developed a deep learning model using the Efficientnet-B3 network.
  • Validated both models on a separate testing cohort, calculating AUC, accuracy, sensitivity, and specificity.

Main Results:

  • The deep learning model achieved a higher Area Under the Curve (AUC) of 0.981 compared to the radiomics model's 0.962.
  • Deep learning model demonstrated superior accuracy (0.9344) and specificity (0.9737).
  • Radiomics model showed higher sensitivity (0.9565) and negative predictive value (0.9714).

Conclusions:

  • Both radiomics and deep learning models exhibit high diagnostic efficacy for ALL detection using T1WI MRI.
  • Deep learning models show a slight advantage in overall performance, particularly in specificity.
  • These AI-driven approaches hold promise for improving the accuracy and efficiency of ALL diagnosis.