Non-invasive screening of bladder cancer using digital microfluidics and FLIM technology combined with deep learning.

Journal: Journal of biophotonics
PMID: 38938144

Abstract

Non-invasive screening for bladder cancer is crucial for treatment and postoperative follow-up. This study combines digital microfluidics (DMF) technology with fluorescence lifetime imaging microscopy (FLIM) for urine analysis and introduces a novel non-invasive bladder cancer screening technique. Initially, the DMF was utilized to perform preliminary screening and enrichment of urine exfoliated cells from 54 participants, followed by cell staining and FLIM analysis to assess the viscosity of the intracellular microenvironment. Subsequently, a deep learning residual convolutional neural network was employed to automatically classify FLIM images, achieving a three-class prediction of high-risk (malignant), low-risk (benign), and minimal risk (normal) categories. The results demonstrated a high consistency with pathological diagnosis, with an accuracy of 91% and a precision of 93%. Notably, the method is sensitive for both high-grade and low-grade bladder cancer cases. This highly accurate non-invasive screening method presents a promising approach for bladder cancer screening with significant clinical application potential.

Authors

  • Wenhua Su
    Department of Optical Science and Engineering, Shanghai Engineering Research Center of Ultra-Precision Optical Manufacturing, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), School of Information Science and Technology, Fudan University, Shanghai, China.
  • Chenyang Xu
    Department of Genetics of Dingli Clinical Medical School, Wenzhou Central Hospital, Wenzhou 325000, China.
  • Jinzhong Hu
    Department of Urology, Huashan Hospital, Fudan University, Shanghai, China.
  • Qiushu Chen
    Department of Optical Science and Engineering, Shanghai Engineering Research Center of Ultra-Precision Optical Manufacturing, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), School of Information Science and Technology, Fudan University, Shanghai, China.
  • Yuwei Yang
    Department of Chemistry, New York University, New York, NY, 10003, USA.
  • Mingmei Ji
    Department of Optical Science and Engineering, Shanghai Engineering Research Center of Ultra-Precision Optical Manufacturing, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), School of Information Science and Technology, Fudan University, Shanghai, China.
  • Yiyan Fei
    Shanghai Engineering Research Center of Ultra-precision Optical Manufacturing, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Green Photoelectron Platform, Department of Optical Science and Engineering, Fudan University, 220 Handan Road, Shanghai, 200433, China.
  • Jiong Ma
    Institute of Biomedical Engineering and Technology, Academy for Engineer and Technology, Fudan University, 220 Handan Road, Shanghai, 200433, China. jiongma@fudan.edu.cn.
  • Haowen Jiang
    Department of Urology, Huashan Hospital, Fudan University, Shanghai, China. urology_hs@163.com.
  • Lan Mi
    Shanghai Engineering Research Center of Ultra-precision Optical Manufacturing, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Green Photoelectron Platform, Department of Optical Science and Engineering, Fudan University, 220 Handan Road, Shanghai, 200433, China. lanmi@fudan.edu.cn.

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