Automated differentiation of benign renal oncocytoma and chromophobe renal cell carcinoma on computed tomography using deep learning.

Journal: BJU international
PMID: 31901213

Abstract

OBJECTIVES: To develop and evaluate the feasibility of an objective method using artificial intelligence (AI) and image processing in a semi-automated fashion for tumour-to-cortex peak early-phase enhancement ratio (PEER) in order to differentiate CD117(+) oncocytoma from the chromophobe subtype of renal cell carcinoma (ChRCC) using convolutional neural networks (CNNs) on computed tomography imaging.

Authors

  • Amir Baghdadi
    a Department of Industrial and Systems Engineering , University at Buffalo, The State University of New York , Buffalo , NY , USA.
  • Naif A Aldhaam
    Roswell Park Comprehensive Cancer Center, NY, USA.
  • Ahmed S Elsayed
    1 A.T.L.A.S (Applied Technology Laboratory for Advanced Surgery) Program, Department of Urology, Roswell Park Comprehensive Cancer Center, Buffalo, New York.
  • Ahmed A Hussein
    Department of Urology, Applied Technology Laboratory for Advanced Surgery (ATLAS) Program at Roswell Park Cancer Institute, Buffalo, NY; Department of Urology, Cairo University, Cairo, Egypt.
  • Lora A Cavuoto
    Department of Urology, Roswell Park Cancer Institute, Buffalo, NY; Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, NY.
  • Eric Kauffman
    Department of Urology, Roswell Park Cancer Institute, Elm & Carlton Streets, Buffalo, NY, 14263, USA.
  • Khurshid A Guru

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