Classification of cancer cells using computational analysis of dynamic morphology.

Journal: Computer methods and programs in biomedicine
Published Date: Dec 7, 2017

Abstract

BACKGROUND AND OBJECTIVE: Detection of metastatic tumor cells is important for early diagnosis and staging of cancer. However, such cells are exceedingly difficult to detect from blood or biopsy samples at the disease onset. It is reported that cancer cells, and especially metastatic tumor cells, show very distinctive morphological behavior compared to their healthy counterparts on aptamer functionalized substrates. The ability to quickly analyze the data and quantify the cell morphology for an instant real-time feedback can certainly contribute to early cancer diagnosis. A supervised machine learning approach is presented for identification and classification of cancer cell gestures for early diagnosis.

Authors

  • Mohammad R Hasan
    Nano-Bio Lab, University of Texas at Arlington, Arlington, TX 76019, USA; Nanotechnology Research Center, University of Texas at Arlington, Arlington, TX 76019, USA; Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX 76019, USA.
  • Naeemul Hassan
    Department of Computer and Information Science, University of Mississippi, University, MS 38677, USA.
  • Rayan Khan
    Nano-Bio Lab, University of Texas at Arlington, Arlington, TX 76019, USA; Nanotechnology Research Center, University of Texas at Arlington, Arlington, TX 76019, USA; Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX 76019, USA.
  • Young-Tae Kim
    Department of Bioengineering, University of Texas at Arlington, Arlington, TX 76019, USA; Department of Urology, University of Texas Southwestern Medical Center at Dallas, Dallas, TX 75235, USA.
  • Samir M Iqbal
    Nano-Bio Lab, University of Texas at Arlington, Arlington, TX 76019, USA; Department of Electrical Engineering, University of Texas Rio Grande Valley, Edinburg, TX 78539, USA; School of Medicine, University of Texas Rio Grande Valley, Edinburg, TX 78539, USA. Electronic address: SMIQBAL@ieee.org.

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