PPsNet: An improved deep learning model for microsatellite instability high prediction in colorectal cancer from whole slide images.

Journal: Computer methods and programs in biomedicine
PMID: 36057226

Abstract

BACKGROUND AND OBJECTIVE: Recent studies have shown that colorectal cancer (CRC) patients with microsatellite instability high (MSI-H) are more likely to benefit from immunotherapy. However, current MSI testing methods are not available for all patients due to the lack of available equipment and trained personnel, as well as the high cost of the assay. Here, we developed an improved deep learning model to predict MSI-H in CRC from whole slide images (WSIs).

Authors

  • Jingjiao Lou
    Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, Shandong, 250358, China.
  • Jiawen Xu
    Department of Pathology, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan, Shandong 250021, PR China.
  • Yuyan Zhang
    College of Life Science, Zhejiang Chinese Medical University, Hangzhou, China.
  • Yuhong Sun
    Department of Pathology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250117, PR China.
  • Aiju Fang
    Department of Pathology, Shandong Provincial Third Hospital, Shandong University, Jinan, Shandong 250132, PR China.
  • Jixuan Liu
    Key Laboratory of RF Circuits and Systems, Ministry of Education, Hangzhou Dianzi University, Hangzhou 310018, China.
  • Luis A J Mur
    Institute of Biological, Environmental and Rural Sciences (IBERS), Aberystwyth University, Aberystwyth, Wales SY23 3DZ, UK.
  • Bing Ji
    School of Control Science and Engineering, Shandong University, Jinan, Shandong, China.

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