AI-assisted SERS imaging method for label-free and rapid discrimination of clinical lymphoma.

Journal: Journal of nanobiotechnology
PMID: 40241186

Abstract

BACKGROUND: Lymphoma is a malignant tumor of the immune system and its incidence is increasing year after year, causing a major threat to people's health. Conventional diagnosis of lymphoma basically depends on histological images consuming long-time and tedious manipulations (e.g., 7-15 days) and large-field view (e.g., > 1000 × 1000 μm). Artificial intelligence has recently revolutionized cancer diagnosis by training pathological image databases via deep learning. Current approaches, however, remain dependent on analyzing wide-field pathological images to detect distinct nuclear, cytologic, and histomorphologic traits for diagnostic categorization, limiting their applicability to minimally invasive lesion.

Authors

  • Haiting Cao
    Suzhou Key Laboratory of Nanotechnology and Biomedicine, Institute of Functional Nano & Soft Materials (FUNSOM), and Collaborative Innovation Center of Suzhou Nano Science and Technology (NANO-CIC), Soochow University, Suzhou, 215123, Jiangsu, China.
  • Xiaofeng Wu
    The Three Departments of Medicine, Dayu County Peoples Hospital, Ganzhou, Jiangxi 341500, China.
  • Huayi Shi
    Laboratory of Nanoscale Biochemical Analysis, Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Institute of Functional Nano & Soft Materials (FUNSOM), and Collaborative Innovation Center of Suzhou Nano Science and Technology (NANO-CIC) , Soochow University , Suzhou , Jiangsu 215123 , China.
  • Binbin Chu
    Suzhou Key Laboratory of Nanotechnology and Biomedicine, Institute of Functional Nano & Soft Materials (FUNSOM), and Collaborative Innovation Center of Suzhou Nano Science and Technology (NANO-CIC), Soochow University, Suzhou, 215123, Jiangsu, China. bbchu@suda.edu.cn.
  • Yao He
    School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China.
  • Houyu Wang
    Laboratory of Nanoscale Biochemical Analysis, Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices, Institute of Functional Nano & Soft Materials (FUNSOM), and Collaborative Innovation Center of Suzhou Nano Science and Technology (NANO-CIC) , Soochow University , Suzhou , Jiangsu 215123 , China.
  • Fenglin Dong
    Department of Ultrasound, First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, China. Electronic address: 13771978973@163.com.

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