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

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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.