Learning Approach to Analyze Tumour Heterogeneity in DCE-MRI Data During Anti-cancer Treatment

  • Authors:

  • Alessandro Daducci;Umberto Castellani;Marco Cristani;Paolo Farace;Pasquina Marzola;Andrea Sbarbati;Vittorio Murino

  • Affiliations:

  • Department of Morphological-Biomedical Sciences, Section of Anatomy and Histology, University of Verona, Italy;VIPS lab, University of Verona, Italy;VIPS lab, University of Verona, Italy;Department of Morphological-Biomedical Sciences, Section of Anatomy and Histology, University of Verona, Italy;Department of Morphological-Biomedical Sciences, Section of Anatomy and Histology, University of Verona, Italy;Department of Morphological-Biomedical Sciences, Section of Anatomy and Histology, University of Verona, Italy;VIPS lab, University of Verona, Italy

  • Venue:
  • AIME '09 Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine
  • Year:
  • 2009

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Abstract

The paper proposes a learning approach to support medical researchers in the context of in-vivo cancer imaging, and specifically in the analysis of Dynamic Contrast-Enhanced MRI (DCE-MRI) data. Tumour heterogeneity is characterized by identifying regions with different vascular perfusion. The overall aim is to measure volume differences of such regions for two experimental groups: the treated group, to which an anticancer therapy is administered, and a control group. The proposed approach is based on a three-steps procedure: (i) robust features extraction from raw time-intensity curves, (ii) sample-regions identification manually traced by medical researchers on a small portion of input data, and (iii) overall segmentation by training a Support Vector Machine (SVM) to classify the MRI voxels according to the previously identified cancer areas. In this way a non-invasive method for the analysis of the treatment efficacy is obtained as shown by the promising results reported in our experiments.