massNet: integrated processing and classification of spatially resolved mass spectrometry data using deep learning for rapid tumor delineation.

Journal: Bioinformatics (Oxford, England)
Published Date: Mar 28, 2022

Abstract

MOTIVATION: Mass spectrometry imaging (MSI) provides rich biochemical information in a label-free manner and therefore holds promise to substantially impact current practice in disease diagnosis. However, the complex nature of MSI data poses computational challenges in its analysis. The complexity of the data arises from its large size, high-dimensionality and spectral nonlinearity. Preprocessing, including peak picking, has been used to reduce raw data complexity; however, peak picking is sensitive to parameter selection that, perhaps prematurely, shapes the downstream analysis for tissue classification and ensuing biological interpretation.

Authors

  • Walid M Abdelmoula
    Surgical Molecular Imaging Laboratory, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
  • Sylwia A Stopka
    Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
  • Elizabeth C Randall
    Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
  • Michael Regan
    Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
  • Jeffrey N Agar
    Department of Chemistry and Chemical Biology, Northeastern University, 412 TF (140 The Fenway), Boston, MA, 02111, USA.
  • Jann N Sarkaria
    Department of Radiation Oncology, Mayo Clinic, 200 First St SW, Rochester, MN, 55902, USA.
  • William M Wells
  • Tina Kapur
    Harvard Medical School, Boston MA, USA.
  • Nathalie Y R Agar
    Surgical Molecular Imaging Laboratory, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA. Nathalie_Agar@dfci.harvard.edu.

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