Preoperative Identification of Papillary Thyroid Carcinoma Subtypes and Lymph Node Metastasis via Deep Learning-Assisted Surface-Enhanced Raman Spectroscopy.

Journal: ACS nano
Published Date: Jun 4, 2025

Abstract

Accurate preoperative diagnosis of papillary thyroid carcinoma (PTC) histological subtypes and lymph node metastasis is essential for formulating personalized treatment strategies. However, their preoperative diagnosis is challenged by the limited reliability of cytological identification of histological subtypes and the low accuracy of lymph node detection using ultrasound imaging. Herein, a deep learning-assisted surface-enhanced Raman scattering (SERS) chip is developed for the preoperative diagnosis of PTC histological subtypes and evaluation of lymph node metastasis, using fine-needle aspiration (FNA) samples. The convolutional neural network algorithm is used to analyze Raman spectral fingerprints, successfully distinguishing PTC subtypes and lymph node metastasis with an accuracy of 95.83%. Moreover, the deep learning-assisted SERS platform has been successfully employed to identify central cervical lymph node metastasis with an accuracy of 100%. This approach highlights the potential of personalized medicine, facilitating the development of individualized treatment strategies, reducing overtreatment, and mitigating recurrence risk.

Authors

  • Ze-Kai Hou
    Department of Urology, The Second Hospital of Tianjin Medical University, Tianjin 300060, China.
  • Jing Zhao
    Department of Pharmacy, Pharmacoepidemiology and Drug Safety Research Group, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway.
  • Mingjie Zhang
  • Wenjing Hou
    Department of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin 300060, China.
  • Yuanyuan Li
    Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, State Key Laboratory of Environmental Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Yang Yang
    Department of Gastrointestinal Surgery, The Third Hospital of Hebei Medical University, Shijiazhuang, China.
  • Yuanyuan Liu
    College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
  • Zhaoxiang Ye
    Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Huanhuxi Road, Hexi District, Tianjin 300060, China. Electronic address: yezhaoxiang@163.com.
  • Qiliang Cai
    Department of Urology, Tianjin Institute of Urology, Second Hospital of Tianjin Medical University, Tianjin, China. Electronic address: caiqiliang@tmu.edu.cn.
  • Xi Wei
    Department of Diagnostic and Therapeutic Ultrasonography, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China.
  • Dingbin Liu
    College of Chemistry, Research Center for Analytical Sciences, State Key Laboratory of Medicinal Chemical Biology, Tianjin Key Laboratory of Molecular Recognition and Biosensing, Nankai University, Tianjin 300071, China.
  • Cai Zhang
    State Key Laboratory of Natural Medicines, China Pharmaceutical University, No. 24 Tongjia Lane, Nanjing 210009, China.

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