Predicting Post Neoadjuvant Axillary Response Using a Novel Convolutional Neural Network Algorithm.

Journal: Annals of surgical oncology
Published Date: Oct 1, 2018

Abstract

OBJECTIVES: In the postneoadjuvant chemotherapy (NAC) setting, conventional radiographic complete response (rCR) is a poor predictor of pathologic complete response (pCR) of the axilla. We developed a convolutional neural network (CNN) algorithm to better predict post-NAC axillary response using a breast MRI dataset.

Authors

  • Richard Ha
    Department of Radiology, Columbia University Medical Center, New York, NY.
  • Peter Chang
    Department of Urology, Beth Israel Deaconess Medical Center, Boston, MA, USA.
  • Jenika Karcich
    Department of Radiology, Columbia University Medical Center, New York, New York 10032.
  • Simukayi Mutasa
    Department of Radiology, Columbia University Medical Center, New York, New York 10032.
  • Eduardo Pascual Van Sant
    Department of Radiology, Columbia University Medical Center, New York, NY, USA.
  • Eileen Connolly
    Division of Radiation Oncology, Columbia University Medical Center, New York, NY, USA.
  • Christine Chin
    Division of Radiation Oncology, Columbia University Medical Center, New York, NY, USA.
  • Bret Taback
    Department of Surgery, Columbia University Medical Center, New York, NY, USA.
  • Michael Z Liu
    Department of Radiology, Columbia University Medical Center, New York, NY.
  • Sachin Jambawalikar
    Department of Radiology, Columbia University Medical Center, New York, NY.

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