Morphology-based prediction of cancer cell migration using an artificial neural network and a random decision forest.

Journal: Integrative biology : quantitative biosciences from nano to macro
Published Date: Dec 19, 2018

Abstract

Metastasis is the cause of death in most patients of breast cancer and other solid malignancies. Identification of cancer cells with highly migratory capability to metastasize relies on markers for epithelial-to-mesenchymal transition (EMT), a process increasing cell migration and metastasis. Marker-based approaches are limited by inconsistences among patients, types of cancer, and partial EMT states. Alternatively, we analyzed cancer cell migration behavior using computer vision. Using a microfluidic single-cell migration chip and high-content imaging, we extracted morphological features and recorded migratory direction and speed of breast cancer cells. By applying a Random Decision Forest (RDF) and an Artificial Neural Network (ANN), we achieved over 99% accuracy for cell movement direction prediction and 91% for speed prediction. Unprecedentedly, we identified highly motile cells and non-motile cells based on microscope images and a machine learning model, and pinpointed and validated morphological features determining cell migration, including not only known features related to cell polarization but also novel ones that can drive future mechanistic studies. Predicting cell movement by computer vision and machine learning establishes a ground-breaking approach to analyze cell migration and metastasis.

Authors

  • Zhixiong Zhang
    Department of Electrical Engineering and Computer Science, University of Michigan, 1301 Beal Avenue, Ann Arbor, MI 48109-2122, USA. zhangzx@umich.edu esyoon@umich.edu.
  • Lili Chen
    School of Mechatronics & Vehicle Engineering, Chongqing Jiaotong University, Chongqing 400074, China; School of Chongqing Key Laboratory of Urban Rail Transit Vehicle System Integration and Control, Chongqing Jiaotong University, Chongqing 400074, China.
  • Brock Humphries
  • Riley Brien
  • Max S Wicha
  • Kathryn E Luker
    Department of Radiology, Center for Molecular Imaging, University of Michigan, 109 Zina Pitcher Place, A526 BSRB, Ann Arbor, MI, 48109-2200, USA.
  • Gary D Luker
    Department of Radiology, Center for Molecular Imaging, University of Michigan, 109 Zina Pitcher Place, A526 BSRB, Ann Arbor, MI, 48109-2200, USA.
  • Yu-Chih Chen
  • Euisik Yoon

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