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Unsupervised Learning in PET Radiomics.

G Liu1, S-Y Huang2, B Franc2

  • 1School of Computing, Florida Institute of Technology, Melbourne, FL.

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|January 12, 2019
PubMed
Summary
This summary is machine-generated.

This study used unsupervised learning on radiomics data from 116 breast cancer patients. Wavelet-enhanced radiomics features improved patient and feature biclustering, aiding disease characteristic association.

Keywords:
Breast CancerPETRadiomicsUnsupervised ClusteringWorkflow

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Area of Science:

  • Medical Imaging
  • Oncology
  • Machine Learning

Background:

  • Radiomics analysis offers insights into tumor characteristics.
  • Unsupervised learning methods are valuable for identifying patient subgroups.
  • Integrating radiomics with machine learning can enhance breast cancer characterization.

Purpose of the Study:

  • To apply unsupervised learning for biclustering patients and radiomics features in breast cancer.
  • To associate identified biclusters with specific disease characteristics.
  • To evaluate the efficacy of radiomics features, particularly wavelet features, in biclustering and classification.

Main Methods:

  • Large-scale radiomics analysis was performed on data from 116 breast cancer patients.
  • Unsupervised learning techniques were employed for biclustering patients and features.
  • Radiomics features, including wavelet-derived features, were analyzed for their biclustering and classification performance.

Main Results:

  • Radiomics features, especially those derived using wavelets, demonstrated superior biclustering capabilities.
  • A subset of 172 radiomics features exhibited significant potential for accurate classification.
  • Biclustering successfully associated patient and feature groups with disease characteristics.

Conclusions:

  • Wavelet-enhanced radiomics features are effective for unsupervised biclustering in breast cancer.
  • Specific radiomics features can be leveraged for improved patient stratification and classification.
  • This approach facilitates a deeper understanding of breast cancer heterogeneity and its clinical implications.