A Decision-Support Tool for Renal Mass Classification.

Journal: Journal of digital imaging
Published Date: Dec 1, 2018

Abstract

We investigate the viability of statistical relational machine learning algorithms for the task of identifying malignancy of renal masses using radiomics-based imaging features. Features characterizing the texture, signal intensity, and other relevant metrics of the renal mass were extracted from multiphase contrast-enhanced computed tomography images. The recently developed formalism of relational functional gradient boosting (RFGB) was used to learn human-interpretable models for classification. Experimental results demonstrate that RFGB outperforms many standard machine learning approaches as well as the current diagnostic gold standard of visual qualification by radiologists.

Authors

  • Gautam Kunapuli
    UtopiaCompression Corporation, 11150 W Olympic Blvd. Suite #820, Los Angeles, CA, 90064, USA. gautam@utopiacompression.com.
  • Bino A Varghese
    Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.
  • Priya Ganapathy
    UtopiaCompression Corporation, 11150 W Olympic Blvd. Suite #820, Los Angeles, CA, 90064, USA.
  • Bhushan Desai
    Department of Radiology, Keck School of Medicine, University of Southern California, 1500 San Pablo Street, 2nd Floor, Los Angeles, CA, 90033, USA.
  • Steven Cen
    Department of Radiology, Keck School of Medicine, University of Southern California, 1500 San Pablo Street, 2nd Floor, Los Angeles, CA, 90033, USA.
  • Manju Aron
    Department of Pathology, Keck School of Medicine, University of Southern California, 2011 Zonal Avenue, Los Angeles, CA, 90033, USA.
  • Inderbir Gill
    USC Institute of Urology, Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
  • Vinay Duddalwar
    Department of Radiology, Keck School of Medicine, University of Southern California, 1500 San Pablo Street, 2nd Floor, Los Angeles, CA, 90033, USA.

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