Detecting and classifying the mechanics of cancer and non-cancer cells by machine learning algorithm.

Journal: Nanotechnology
Published Date: Jul 28, 2025

Abstract

The global burden of cancer has increased in recent years, posing a major public health challenge. Generally, cancer cells are mutate from normal cells and have distinctive mechanical specifications. Despite significant progress in precision medicine, accurately distinguishing cancer cells remains challenging due to the inherent complexities in characterizing single-cell surface properties. In this study, we utilized atomic force microscopy (AFM) to obtain the mechanical properties of hepatic cells, hepatoma cells, gastric cells, and gastric cancer cells. Then, machine learning techniques were used to identify and classify the cancer and non-cancer cells through AFM-based mechanical characteristics. After computational training, the accuracy of classification and screening of four kinds of cells reached 98%, with an area under the receiver operating characteristic curve (AUC-ROC) value of 97.98%. Consequently, we successfully identified digestive system cancer cells and highlighted the valuable role of digital pathology in tumor cell diagnosis. This study provides an objective basis and a new research method for the diagnosis of hepatic cancer and gastric cancer, enriching the tumor cell detection scheme.

Authors

  • Yuxi Huang
    International Research Centre for Nano Handling and Manufacturing of China, Changchun University of Science and Technology, Changchun 130022, China; Ministry of Education Key Laboratory for Cross-Scale Micro and Nano Manufacturing, Changchun University of Science and Technology, Changchun 130022, China.
  • Chuanzhi Liu
    Changchun University of Science and Technology, 7089 Weixing Road, Changchun 130022, China, Changchun, Jilin, 130022, CHINA.
  • Fan Yang
    School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou, China.
  • Jian Liang
    Cloud and Smart Industries Group, Tencent, Beijing, China.
  • James James Cardwell Crabbe
    University of Oxford, Oxford, Oxford, England, OX1 2JD, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND.
  • Guicai Song
    College of Physics, Changchun University of Science and Technology, Changchun 130022, China. Electronic address: songcust@163.com.
  • Zuobin Wang
    International Research Centre for Nano Handling and Manufacturing of China, Changchun University of Science and Technology, Changchun, 130022, China; JR3CN & IRAC, University of Bedfordshire, Luton, LU1 3JU, UK.

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