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A Decision-Support Tool for Renal Mass Classification

  • Gautam Kunapuli
  • Bino A. Varghese
  • Priya Ganapathy
  • Bhushan Desai
  • Steven Cen
  • Manju Aron
  • Inderbir Gill
  • Vinay Duddalwar
Article

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.

Keywords

Renal mass Multiphase CT Radiomics Statistical relational learning Clinical decision support 

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Copyright information

© Society for Imaging Informatics in Medicine 2018

Authors and Affiliations

  1. 1.UtopiaCompression CorporationLos AngelesUSA
  2. 2.Department of Radiology, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesUSA
  3. 3.Department of Pathology, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesUSA
  4. 4.Institute of Urology, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesUSA

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