Work

Deep Learning-Assisted SERS of Fine-Needle Aspiration Washout Fluid for Preoperative Papillary Thyroid Carcinoma Diagnosis and Lymph Node Metastasis Prediction

Year: 2026

Type: article

Abstract: Background: Accurate preoperative assessment of papillary thyroid carcinoma (PTC) and cervical lymph node metastasis (LNM) is important. This study aimed to analyze molecular fingerprint in fine-needle aspiration (FNA) washout fluid using surface-enhanced Raman spectroscopy (SERS) and develop diagnostic models for nodule malignancy differentiation and LNM prediction integrating machine learning (ML) algorithms. Methods: A retrospective analysis was conducted on 444 FNA washout fluid samples. AgNPs served as the SERS substrate. Following spectral preprocessing, prominent differential peaks were statistically screened and subjected to biomolecular assignments. The dataset was partitioned into training, validation, and test sets (6:2:2) to construct five predictive models: SVM, RF, LightGBM, XGBoost, and CNN. Diagnostic performance was evaluated based on the area under the receiver operating characteristic curve (AUC), accuracy, precision, NPV, recall and F1-score. Results: Multiple significant differential peaks were identified, primarily reflecting alterations in nucleic acids, proteins, lipids, and the tryptophan-kynurenine metabolic pathway. For PTC diagnosis, PTCSpecNet model yielded a test set AUC of 0.991, cross-validation accuracy of 0.917, and recall of 0.979, while SVM achieved the highest test AUC (0.995) and LightGBM achieved the highest accuracy (0.978). For LNM prediction, LightGBM yielded the highest test AUC (0.916) and recall (0.909), whereas LymSpecNet model demonstrated competitive discriminative capacity (test set AUC = 0.913, cross-validation accuracy = 0.895) with an optimal clinical balance between precision (0.857) and recall (0.818). The fitting times for PTCSpecNet and LymSpecNet were 5.97 s and 8.52 s, respectively, while the estimated realistic clinical turnaround time is approximately 20-25 min, supporting rapid diagnostic feasibility. Conclusion: SERS analysis of FNA washout fluid combined with deep learning effectively captures the molecular fingerprint of the in situ thyroid nodule microenvironment, enabling high-sensitivity diagnosis of PTC and reliable prediction of LNM. Future prospective multi-center studies with external validation cohorts are warranted to further establish its broad clinical generalizability.

Source: International Journal of Nanomedicine

DOI: 10.2147/ijn.s632462