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

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.

Introduction

The global incidence of thyroid cancer has escalated rapidly, making it one of the most prevalent endocrine malignancies worldwide, with papillary thyroid carcinoma (PTC) representing the vast majority of diagnosed cases. Although PTC typically exhibits an indolent clinical course, it possesses a strong propensity for cervical lymph node metastasis (LNM).¹ The 2025 American Thyroid Association (ATA) guidelines have formally integrated non-surgical paradigms, such as active surveillance (AS) and radiofrequency ablation (RFA), into the management framework for low-risk differentiated thyroid cancer.² Within this evolving clinical landscape, accurate preoperative diagnosis of PTC and early prediction of LNM are essential for formulating personalized treatment strategies.

However, their preoperative diagnosis is challenged by the technical constraints of current diagnostic modalities. First, conventional high-resolution ultrasound (US) is inherently limited by acoustic shadowing by the thyroid and trachea, operator dependence, and subtle sonographic manifestations of early nodal disease, yielding a suboptimal sensitivity for preoperative evaluation of nodule nature and LNM.^3,4 Second, fine-needle aspiration (FNA) cytology frequently encounters diagnostic bottlenecks within the morphological “grey zone” of Bethesda categories III–IV, which accounts for approximately 20% to 30% of cases and often mandates unnecessary diagnostic thyroidectomy.^5,6

Liquid biopsy offers a promising strategy to circumvent these diagnostic challenges by profiling diverse biomarkers directly derived from the tumor and its metabolic macroenvironment.⁷ In this study, we introduce fine-needle aspiration (FNA) washout fluids—obtained by rinsing the aspiration needle with normal saline—as a high-value diagnostic matrix. As a specimen harvested directly from the tumor in situ microenvironment, FNA washouts contain cell clusters and biomarkers derived from extracellular and intracellular environments, effectively bypassing the complex background interference and high signal dilution characteristic of peripheral blood, thereby serving as an ideal molecular reservoir for capturing fingerprints related to oncogenesis and metastatic progression.⁸

Surface-enhanced Raman spectroscopy (SERS) provides an ideal biophysical platform to decode the complex biochemical signatures contained within FNA washout fluids. Utilizing the localized surface plasmon resonance of noble metal nanostructures, SERS amplifies the inherently weak Raman signals of bio-analytes by 10⁶–10¹⁴ times, achieving ultra-sensitive detection down to the single-molecule level. This label-free strategy is non-destructive, high-throughput, and rapid, facilitating the comprehensive capture of sample molecular fingerprints in their native states.^9,10 Consequently, SERS liquid assays have demonstrated compelling diagnostic efficacy across diverse malignancies through the analysis of peripheral biofluids.^11,12 Nonetheless, the high-dimensional spectral datasets generated by SERS are extremely intricate, and conventional univariate or linear analytical methods struggle to fully exploit the deeply hidden multi-omic correlations. The integration of machine learning (ML), particularly deep learning algorithms, offers a robust resolution to this analytical bottleneck.¹³ Advanced artificial intelligence (AI) algorithms can automatically learn and extract multi-scale, non-linear feature patterns embedded within complex spectra, effectively mapping them to specific disease states. Thus, the deep fusion of Raman spectroscopy and AI accelerates the clinical translation of label-free SERS liquid biopsies toward precise cancer management.

In this study, we enrolled 444 FNA washout fluid samples from thyroid nodules and built five distinct classification models, including support vector machine (SVM), random forest (RF), light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost), and a convolutional neural network (CNN) (Figure 1). Our primary objective was to develop an AI-assisted, label-free SERS liquid biopsy platform utilizing thyroid FNA washout fluids to achieve rapid, cost-effective, and highly accurate thyroid cancer diagnosis, thereby serving as an intelligent, high-efficiency clinical decision support tool for clinical practice.

Figure 1.

Schematic workflow of the deep learning-assisted label-free SERS liquid biopsy platform for preoperative thyroid nodule diagnosis and lymph node metastasis prediction using fine-needle aspiration washout fluid.

Materials and Methods

Clinical Sample Collection and Storage

A total of 444 patients who underwent FNA cytology were retrospectively included in this study. For patients with multiple nodules, only the primary nodule with the largest lesion diameter that met the clinical indication for FNA was included. No repeated FNA procedures from the same patient were included. All samples were categorized based on cytopathological and postoperative histopathological results: (1) Benign group (n=207): lesions definitively diagnosed as benign via FNA cytopathology combined with molecular gene testing; (2) Malignant group (PTC, n=237): cytologically definitive PTC lesions (Bethesda category VI) that were subsequently confirmed as PTC by postoperative histopathology. The PTC cohort was further subdivided into a non-metastatic group (LNM−, n=122) and a metastatic group (LNM+, n=115) based on postoperative nodal histopathology. All patients in the PTC cohort underwent at least thyroid lobectomy with isthmusectomy combined with ipsilateral central lymph node dissection (CLND), with total thyroidectomy and/or therapeutic lateral neck dissection performed when clinically indicated. Accordingly, the LNM+ group encompassed histopathologically confirmed cervical nodal metastases in the central compartment, lateral compartment, or both, with a threshold of at least one positive lymph node (≥ 1). For patients in the LNM− group, a minimum of 5 central lymph nodes were dissected and confirmed negative to ensure the adequacy of nodal staging and minimize false-negative classifications. Patients with poor FNA washout sample quality, apparent blood contamination, or incomplete clinicopathological records were excluded.

Under ultrasound guidance, target thyroid nodules were aspirated using a standard 23G needle by two dedicated interventional thyroid surgeons (each possessing over 10 years of clinical experience) following a standardized institutional protocol. Following the collection of specimens required for routine cytology and molecular testing, the residual materials inside the aspiration needle were harvested by rinsing the inner lumen with 2 mL of sterile normal saline (0.9% NaCl). Each nodule was punctured three times, with 10 standardized flushes performed per puncture to ensure volumetric and operational consistency across all 444 procedures. The resulting washout fluids were then aliquoted into sterile Eppendorf tubes and transferred to an ultra-low temperature freezer at −80 °C for long-term storage (storage duration ranged from 1 to 6 months). All specimens underwent strictly a single freeze–thaw cycle prior to analysis to prevent macromolecular degradation from repeated freezing and thawing.

The study complied with the Declaration of Helsinki and was approved by the EthicsCommittee of China-Japan Union Hospital of Jilin University (No. 2026031905). All enrolled patients were adults (≥18 years of age) with full decision-making capacity. Written informed consent was obtained directly from each patient prior to sample collection.

Preparation of Silver Nanoparticles (AgNPs)

Citrate-stabilized AgNPs colloids were synthesized according to a modified classical chemical reduction method to serve as the enhancement substrates. Briefly, 100 mL of an aqueous silver nitrate (AgNO3, 1 mM) solution was brought to a vigorous boil on a magnetic stirrer (650 r/min). Under rapid agitation, 8 mL of a 1 wt% sodium citrate solution was swiftly introduced into the boiling system. The reaction mixture was maintained at a continuous boil for 40 min to ensure uniform nanoparticle growth. After the heating source was removed, the colloidal suspension was allowed to cool naturally to room temperature under continuous stirring. To obtain a concentrated nanoparticle suspension, the AgNPs colloids were concentrated by centrifugation at 7000 r/min for 7 min. The supernatant was subsequently discarded, and the precipitated AgNPs were re-dispersed in 100 μL of deionized water and stored at 4 °C away from light for further experimental use. The morphological features and surface characteristics of the synthesized AgNPs were examined using a field-emission scanning electron microscope (SEM). The quantitative particle-size distribution was determined by measuring individual nanoparticles (N=128) from SEM micrographs using digital image analysis and fitted with a Gaussian distribution function. To evaluate colloidal stability and batch reproducibility, Zeta potential measurements were conducted across five independent synthetic batches using dynamic light scattering.

SERS Spectral Acquisition and Raman Data Processing

Prior to spectral interrogation, the frozen FNA washout samples were thawed completely at room temperature for 10 min, then centrifuged at 3000×g for 15 min to precipitate intact cells and cellular debris, and the supernatant carefully aspirated for analysis. Equal volumes of the FNA washout supernatant and the concentrated AgNPs colloid were thoroughly mixed and 5 μL droplet of this mixture was dropped onto a clean hydrophobic substrate stage. Across the entire cohort, spectral measurements were performed using five quality-controlled synthetic batches of AgNPs. All samples were anonymized with random identification codes and measured in a randomized sequence across experimental days, daily sessions, and AgNP batches. SERS measurements were performed using an iRaman@785 plus portable Raman spectrometer. To mitigate outliers and random fluctuations, 100 single spectra were recorded for each sample droplet, and their mathematical average was calculated to represent the final characteristic signal of the sample. Prior to formal detection, wavenumber calibration was executed using a standard Rhodamine B solution. The parameters were set as follows: an excitation wavelength of 785 nm, a laser power of 20 mW, an integration time of 5 s, and a spectral acquisition window range 500–1800 cm⁻¹. All measurements were conducted in a dark room to eliminate stray environmental light, and the system dark current was subtracted before each acquisition. The technical operator performing sample preparation and spectral acquisition was completely blinded to all clinical information. A total of 444 independent spectra were successfully compiled.

The raw spectra were subjected to sequential preprocessing, in which the iteratively reweighted penalized least squares (airPLS) method was adopted for baseline correction to eliminate the auto-fluorescence background, and a Savitzky-Golay filter was applied for denoising. Subsequently, to eliminate potential confounding from overall sample concentration or laser intensity fluctuations, each individual baseline-corrected spectrum was standardized using vector normalization (L2 norm) across the fingerprint region. The average spectra and corresponding average difference spectrum were plotted for the Benign versus PTC and LNM- versus LNM+ groups. A point-by-point statistical test was conducted across the standardized wavenumber grid to map highly discriminative regions. Prior to intergroup comparison, data normality and homoscedasticity at each continuous wavenumber channel were systematically evaluated using the Shapiro–Wilk test and Levene’s test, respectively. Intergroup spectral differences were then adaptively evaluated based on the distributional properties: (1) two-sample Student’s t-test for normally distributed variables with equal variances; (2) Welch’s t-test for normally distributed variables with unequal variances; and (3) two-sided Mann–Whitney U-test for non-normally distributed data. To strictly control family-wise false positives arising from thousands of point-by-point spectral comparisons, multiple-testing correction was conducted using the Benjamini-Hochberg (BH) false discovery rate (FDR) procedure, with statistical significance defined as an FDR-adjusted P-value (q-value) < 0.05. Candidate differential peaks within continuous significant regions were identified using complementary peak-detection strategies. All candidate peaks were ranked by standardized effect size (Cohen’s d, computed using the pooled sample standard deviation), and a spatial deduplication filter (distance threshold = 5 cm⁻¹) was applied to eliminate redundant collinear bands, the top 10 peaks with the highest effect sizes were retained as core characteristic peaks.

Machine Learning Classification Model

A multi-strategy paradigm was employed for spectral feature selection. Initially, an ANOVA F-test was deployed to evaluate the variance of each spectral channel across diagnostic categories, retaining the top 800 most discriminative univariate features. Subsequently, a Pearson correlation filter (| r |>0.95) was applied to remove highly collinear and redundant variables. Finally, a random forest-based spatially dispersed feature selection strategy was introduced. The entire 500–1800 cm⁻¹ interval was segmented into 8 uniform wavenumber sub-regions, and features were selected proportionally from each bin based on Random Forest (RF) Gini importance (Mean Decrease in Impurity) to yield a comprehensive feature subset. The entire selection workflow was fitted strictly on the training set to prevent data leakage, evaluating different candidate feature numbers to select the configuration with optimal validation performance, which was ultimately determined as 100 features for PTC diagnosis and 200 features for LNM prediction.

The dataset was partitioned into training, validation, and test sets at a 6:2:2 ratio at the patient level. The exact sample sizes and class distributions for each subset are provided in Table S1. For the dual tasks of PTC diagnosis and LNM prediction, five distinct modeling frameworks were evaluated: SVM, RF, LightGBM, XGBoost, and CNN. The CNN architecture comprised two sequential convolutional blocks. The first block featured a Conv1D convolutional layer configured with 16 filters (kernel size = 5), and the second block utilized 32 filters (kernel size = 3). Following each convolutional layer, a batch normalization layer, a rectified linear unit (ReLU) activation function, and a MaxPooling1D layer were sequentially appended. A Dropout layer was employed between the dense fully connected layers, effectively mitigating overfitting by randomly setting a fraction of input units to 0 (dropout rate of 0.5 and 0.3 for the respective blocks). The network was optimized using the Adam optimizer with an initial learning rate of 0.001, combined with an early stopping mechanism (patience = 20 epochs) over a maximum budget of 200 epochs to safeguard generalization capability. During model development on the training set, internal stability and computational efficiency were evaluated via five-fold cross-validation accuracy and training time (computational fitting time in seconds). Final diagnostic efficacy was comprehensively verified on the independent test set using multiple metrics, including the area under the receiver operating characteristic curve (AUC), accuracy, precision, negative predictive value (NPV), recall, specificity, and F1-score. The flowchart for model construction is shown in Figure S1.

Statistical Analysis

Conventional statistical analyses were performed using SPSS 23.0 (IBM Corp., Armonk, NY, USA). Continuous variables were expressed as mean ± standard deviation and compared between groups using the Student’s t test when normally distributed. Categorical variables were summarized as counts and percentages and compared using the chi-square test. A P value < 0.05 was considered statistically significant. For the point-by-point spectral comparisons, multiple-testing correction was performed using the Benjamini-Hochberg false discovery rate procedure, as detailed in the “SERS Spectral Acquisition and Raman Data Processing” section.

Results

Clinical Characteristics

The baseline clinical characteristics of the enrolled patients are summarized in Tables 1 and 2. In the primary diagnostic cohort (Table 1), PTC patients were younger than benign patients (44.06 ± 11.26 vs 49.92 ± 11.34 years, P < 0.001). Importantly, tumor size did not differ significantly between benign and malignant nodules (0.74 ± 0.39 cm vs 0.81 ± 0.54 cm, P = 0.107). In the metastatic prediction cohort (Table 2), LNM+ patients had significantly larger primary tumors than LNM− patients (0.93 ± 0.56 cm vs 0.70 ± 0.49 cm, P = 0.002), while TI-RADS distributions were comparable (P = 0.273).

Table 1.

Clinical Characteristics of Patients in Benign and PTC

Characteristics	Benign (n=207)	PTC (n=237)	t/χ2	P
Age (years)	49.92 ± 11.34	44.06 ± 11.26	5.45	< 0.001
Gender [n(%)]			9.40	0.002
Male	34(16.4%)	16(6.8%)
Female	173(83.6%)	221(93.2%)
Tumor size (cm)	0.74 ± 0.39	0.81 ± 0.54	−1.61	0.107
TI-RADS [n(%)]			8.14	0.016
3	7 (3.4%)	0 (0.0%)
4	15 (7.2%)	18 (7.6%)
5	185 (89.4%)	219 (92.4%)

Notes: Bold values indicate statistically significant differences (P < 0.05).

Table 2.

Clinical Characteristics of Patients in LNM- and LNM+

Characteristics	LNM- (n=122)	LNM+ (n=115)	t/χ2	P
Age (years)	46.70 ± 11.29	41.26 ± 10.58	3.83	< 0.001
Gender [n(%)]			16.06	< 0.001
Male	0 (0.0)	16 (13.9)
Female	122 (100.0)	99 (86.1)
Tumor size (cm)	0.70 ± 0.49	0.93 ± 0.56	−3.22	0.002
TI-RADS [n(%)]			1.20	0.273
4	12 (9.8)	6 (5.2)
5	110 (90.2)	109 (94.8)

Notes: Bold values indicate statistically significant differences (P < 0.05).

Characterization of AgNPs

AgNPs were synthesized via the classical sodium citrate reduction method to serve as the SERS-active substrates. As illustrated by the scanning electron microscopy (SEM) characterization in Figure 2A, the engineered AgNPs exhibited a highly uniform morphology characterized by regular spherical geometries and favorable spatial dispersion with negligible aggregation. Quantitative particle-size analysis based on SEM micrographs revealed an average spherical diameter of 48.6 ± 5.4 nm with a unimodal Gaussian distribution (Figure 2B), confirming high morphological homogeneity. Furthermore, Zeta potential evaluations across five independent synthetic batches yielded a consistent surface potential of +44.19 ± 11.37 mV (Figure 2C), verifying satisfactory colloidal stability and batch-to-batch synthesis reproducibility.

Figure 2.

Physicochemical characterization of the synthesized silver nanoparticles (AgNPs). (A) Scanning electron microscopy (SEM) image of synthesized AgNPs. (B) Quantitative particle-size distribution histogram based on SEM measurements (N=128, mean ± SD: 48.6 ± 5.4 nm) fitted with a Gaussian distribution curve. (C) Quantitative Zeta potential measurements across five independent synthetic batches (mean ± SD: +44.19 ± 11.37).

Comparative Analysis of SERS Spectra from FNA Fluid Samples

The average SERS spectra and average difference spectrum of the Benign and PTC groups are shown in Figure 3A. The average spectrum for each group was calculated by averaging the mean spectra of all samples within the group. The peak positions in the 500–1800 cm⁻¹ fingerprint region were largely consistent between the two groups, but the PTC group exhibited significantly enhanced intensities at multiple characteristic peaks. A total of 10 core differential feature peaks were identified, specifically at 549, 906, 1021, 1173, 1186, 1334, 1374, 1546, 1735, and 1765 cm⁻¹. The violin plots (Figure 3B) indicated that the intergroup differences for all characteristic peaks reached statistical significance, with standardized effect sizes (Cohen’s d) ranging from 0.866 to 1.029, demonstrating that these SERS molecular fingerprints possess robust discriminative power and analytical robustness.

Figure 3.

SERS analysis of FNA washout fluid for differentiating benign thyroid nodules from papillary thyroid carcinoma (PTC). (A) Averaged SERS spectra of thyroid malignant and benign samples, as well as the averaged difference spectrum (malignant-benign). Solid lines represent the mean spectra, and the shaded regions denote ± 1 standard deviation (SD). (B) Violin plots mapping the intensity distributions and intergroup statistical divergence of the 10 core characteristic peaks. ↑, relative increase in the malignant group; ↓, relative decrease; ***, P < 0.001.(C) Intensity heatmap illustrating the consistent expression configurations of the 10 core characteristic peaks across all individual malignant and benign clinical cohorts. (D) Pearson correlation heatmap elucidating the synergistic co-regulation networks among core vibrational bands (green borders delineate strong correlation clusters with ∣r∣>0.6).

These prominent vibrational bands are primarily attributed to nucleic acids, protein, lipids, and metabolites involved in the tryptophan-kynurenine metabolic pathway. The main SERS band positions and their potential assignment to biochemical components are listed in Table 3. The intensity heatmap (Figure 3C) provides a direct visualization of the consistent expression of these feature bands across the entire clinical cohort. Furthermore, a Pearson correlation heatmap (Figure 3D) elucidated that the PTC group exhibited a pronounced signal synergistic elevation (r > 0.6) among the bands at 1173 cm⁻¹ (DNA/RNA bases), 1186 cm⁻¹ (DNA/RNA bases), 1374 cm⁻¹ (thymine), and 1765 cm⁻¹ (lipid). Concurrently, a coordinated intensity increase (r>0.6) was captured among the 906 cm⁻¹ (amino acids), 1021 cm⁻¹ (phenylalanine), and 1546 cm⁻¹ (protein secondary structures) bands. In contrast, the bands at 549 cm⁻¹ (kynurenine) and 1334 cm⁻¹ (tryptophan) displayed a significant negative correlation (r= −0.6).

Table 3.

Peak Positions and Tentative Assignments of Major SERS Bands for PTC Diagnosis

Raman shift (cm−1)	Assignment	Refs.
549	Kynurenine	[14,15]
906	CH3 bending vibration (amino acids)	[16,17]
1021	Phenylalanine ring breathing vibration (phenylalanine)	[18,19]
1173	C-C stretching of cytosine/guanine (DNA/RNA bases)	[20]
1186	Ring stretching of cytosine/guanine (DNA/RNA bases)	[20]
1334	C-H deformation of indole ring (tryptophan)	[14,15]
1374	Methyl vibration (thymine)	[20,21]
1546	Amide III band (protein secondary structure)	[17,22]
1735	C=O ester stretching vibration (lipids)	[23]
1765	C=O stretching (lipids)	[23]

Figure 4A displays the average SERS spectra and average difference spectrum of the LNM− and LNM+ cohorts. Compared to the LNM−, the LNM+ cohort exhibited marked relative intensity elevations at 549, 566, 843, 906, 922, 1000, 1129, 1255, and 1429 cm⁻¹, whereas only the band at 1232 cm⁻¹ manifested a relative decrease in intensity. The corresponding violin plots (Figure 4B) demonstrated that the intergroup differences for all candidate feature bands achieved high statistical significance, with Cohen’s d values ranging from 0.567 to 1.396. The comprehensive vibrational assignments and biomolecular annotations for these metastasis-associated features are detailed in Table 4. To elucidate the underlying molecular dynamics accompanying metastatic progression, we examined the co-regulation patterns using peak intensity heatmaps and Pearson correlation matrices (Figures 4C and D). Notably, the bands at 549 cm⁻¹ and 566 cm⁻¹, both assigned to kynurenine, demonstrated an exceptionally strong positive correlation (r=0.87). Additionally, the collagen-associated feature at 922 cm⁻¹ (collagen) exhibited a robust positive correlation with the protein secondary structure at 1255 cm⁻¹ (protein secondary structure) and the tyrosine band at 843 cm⁻¹ (tyrosine phosphorylation), reflecting coordinated extracellular matrix remodeling and proteomic alterations during nodal metastasis.

Figure 4.

SERS analysis of FNA washout fluid for predicting lymph node metastasis (LNM) in PTC patients. (A) Average SERS spectra of the LNM-negative and LNM-positive samples, as well as the average difference spectrum (LNM+ - LNM-). Solid lines represent the mean spectra, and the shaded regions denote ± 1 standard deviation (SD). (B) Violin plots mapping the intensity distributions and intergroup statistical divergence of the 10 core metastasis-associated characteristic peaks. ↑, relative increase in the LNM+ group; ↓, relative decrease; ***, P < 0.001. (C) Intensity heatmap illustrating the consistent expression configurations of the 10 core metastasis-associated characteristic peaks between LNM- and LNM+ groups. (D) Pearson correlation heatmap elucidating the synergistic co-regulation networks among core vibrational bands during metastatic progression (green borders delineate strong correlation clusters with ∣r∣>0.6).

Table 4.

Peak Positions and Tentative Assignments of Major SERS Bands for LNM Prediction

Raman shift (cm−1)	Assignment	Refs.
549	Kynurenine	[14,15]
566	Kynurenine	[14,15]
843	Tyrosine	[24]
906	CH3 bending vibration (amino acids)	[16,17]
922	C-C skeletal stretching vibration (proline, collagen)	[11,17,22]
1000	Phenylalanine ring breathing vibration (phenylalanine)	[18,19,22]
1129	C-C skeletal vibration (lipids)	[19,25]
1232	Amide III band (protein secondary structure)	[22]
1255	Amide III band (protein secondary structure)	[22]
1429	CH2 deformation vibration (lipids)	[16,19]

Classification Models for PTC Diagnosis

In this study, a subset of 100 core Raman features uniformly distributed across the 500–1800 cm⁻¹ fingerprint region was extracted via the multi-strategy feature selection paradigm. Feature importance analysis ranking the top 20 most heavily weighted variables revealed that spectral features situated around the 1200–1300 cm⁻¹ region exerted a predominant feature contribution in driving the classification process (Figure 5A and B). As illustrated in Figure 5C, all five machine learning models demonstrated strong discriminative performance in differentiating benign from malignant nodules, yielding high AUC values on the independent test set. Specifically, SVM achieved the highest test AUC of 0.995, while LightGBM yielded the highest accuracy of 0.978. Meanwhile, the CNN-based deep learning framework, PTCSpecNet, demonstrated competitive diagnostic performance, achieving a test set AUC of 0.991 and a five-fold cross-validation accuracy of 0.917. From a clinical perspective prioritizing diagnostic sensitivity to minimize missed malignancies, PTCSpecNet yielded the highest recall of 0.979, misclassifying only 1 false negative out of the 47 malignant test samples (Figure 5D). Furthermore, regarding computational efficiency, PTCSpecNet required only 5.97s, reflecting favorable computational fitting efficiency during model development. The performance comparisons of all models are presented in Table 5.

Figure 5.

Performance evaluation of models for PTC diagnosis. (A) Horizontal bar chart ranking the top 20 most heavily weighted Raman shifts contributing to the classification process, evaluated using the Random Forest (RF) algorithm (Gini importance). (B) Random Forest-derived feature importance distribution profile across the entire 500–1800 cm⁻¹ fingerprint region. (C) Receiver operating characteristic (ROC) curves for the five classification models on the independent test set. (D) Confusion matrix displaying the classification performance using the five classification models.

Table 5.

Performance Comparisons of Five Machine Learning Models for PTC Diagnosis

Metrics Model	Training Set		Test Set
	CV Accuracy	Training Time (s)	AUC (95% CI)	Accuracy (95% CI)	Precision (95% CI)	NPV (95% CI)	Recall (95% CI)	Specificity (95% CI)	F1-Score (95% CI)
SVM	0.893	0.01	0.995 (0.948–0.999)	0.956 (0.891–0.983)	0.957 (0.858–0.988)	0.953 (0.845–0.987)	0.957 (0.858–0.988)	0.953 (0.845–0.987)	0.957 (0.916–0.999)
RF	0.854	0.49	0.969 (0.904–0.991)	0.933 (0.862–0.969)	0.936 (0.828–0.978)	0.930 (0.814–0.976)	0.936 (0.828–0.978)	0.930 (0.814–0.976)	0.936 (0.885–0.987)
LightGBM	0.884	0.70	0.982 (0.919–0.996)	0.978 (0.923–0.994)	1.000 (0.921–1.000)	0.956 (0.852–0.988)	0.957 (0.858–0.988)	1.000 (0.918–1.000)	0.978 (0.948–0.999)
XGBoost	0.850	0.44	0.966 (0.900–0.989)	0.889 (0.807–0.939)	0.863 (0.743–0.932)	0.923 (0.797–0.974)	0.936 (0.828–0.978)	0.837 (0.700–0.919)	0.898 (0.835–0.961)
PTCSpecNet	0.917	5.97	0.991 (0.926–0.999)	0.956 (0.891–0.983)	0.939 (0.835–0.979)	0.976 (0.874–0.996)	0.979 (0.889–0.996)	0.930 (0.814–0.976)	0.958 (0.918–0.999)

Notes: Precision, recall, and F1-score were evaluated using standard binary classification with “Malignant” designated as the positive class.

Abbreviations: CV Accuracy, cross-validation accuracy; 95% CI, 95% confidence interval; NPV, negative predictive value.

Classification Models for LNM Prediction

A total of 200 LNM-associated Raman features were isolated. The top 20 core variables indicated that spectral features located within the 540–580 cm⁻¹ fingerprint region possessed a predominant feature contribution in classification accuracy (Figure 6A and B). As shown in Figure 6C, all predictive models exhibited excellent discriminative performance, with test set AUC values exceeding 0.89 across all configurations. The confusion matrix (Figure 6D) further verified their robust classification accuracy. Regarding generalization capability and stability, the CNN-based deep learning model, LymSpecNet, demonstrated a modest competitive edge, yielding an high cross-validation accuracy of 0.895, which led all traditional machine learning classifiers. Crucially, LymSpecNet achieved an optimal balance between precision (0.857) and recall (0.818), whereas LightGBM yielded the highest recall (0.909) but with a lower precision (0.800), which may increase false-positive identifications in risk assessment, while conversely, SVM demonstrated high precision (0.882) but lower recall (0.682), carrying a higher likelihood of underestimating occult nodal disease. Furthermore, regarding computational efficiency, compared with the highly time-consuming training runs of LightGBM (66.22s) and XGBoost (51.48s), LymSpecNet required only 8.52s, objectively reflecting computational fitting differences across models during training. The performance comparisons of all models are presented in Table 6.

Figure 6.

Performance evaluation of models for LNM prediction in PTC patients. (A) Horizontal bar chart ranking the top 20 most heavily weighted Raman shifts contributing to the metastatic risk prediction, evaluated using the Random Forest (RF) algorithm (Gini importance). (B) Random Forest-derived feature importance distribution profile across the entire 500–1800 cm⁻¹ fingerprint region. (C) ROC curves for the five predictive models on the independent test set. (D) Confusion matrix displaying the classification performance using the five predictive models.

Table 6.

Performance Comparisons of Five Machine Learning Models for LNM Prediction

Metrics Model	Training Set		Test Set
	CV Accuracy	Training Time (s)	AUC (95% CI)	Accuracy (95% CI)	Precision (95% CI)	NPV (95% CI)	Recall (95% CI)	Specificity (95% CI)	F1-Score (95% CI)
SVM	0.845	0.02	0.913 (0.775–0.970)	0.809 (0.675–0.896)	0.882 (0.657–0.967)	0.767 (0.591–0.882)	0.682 (0.473–0.836)	0.920 (0.750–0.978)	0.769 (0.623–0.916)
RF	0.796	0.61	0.897 (0.765–0.958)	0.851 (0.723–0.926)	0.895 (0.686–0.971)	0.821 (0.644–0.921)	0.773 (0.566–0.899)	0.920 (0.750–0.978)	0.829 (0.705–0.954)
LightGBM	0.824	66.22	0.916 (0.779–0.971)	0.851 (0.723–0.926)	0.800 (0.609–0.911)	0.909 (0.722–0.975)	0.909 (0.722–0.975)	0.800 (0.609–0.911)	0.851 (0.742–0.960)
XGBoost	0.831	51.48	0.902 (0.761–0.964)	0.809 (0.675–0.896)	0.783 (0.581–0.903)	0.833 (0.641–0.933)	0.818 (0.615–0.927)	0.800 (0.609–0.911)	0.800 (0.672–0.928)
LymSpecNet	0.895	8.52	0.913 (0.775–0.970)	0.851 (0.723–0.926)	0.857 (0.654–0.950)	0.846 (0.665–0.939)	0.818 (0.615–0.927)	0.880 (0.700–0.958)	0.837 (0.718–0.956)

Notes: Precision, recall, and F1-score were evaluated using standard binary classification with “LNM+” designated as the positive class.

Abbreviations: CV Accuracy, cross-validation accuracy; 95% CI, 95% confidence interval; NPV, negative predictive value.

Discussion

Overall Interpretation of Findings

In recent years, the clinical management of thyroid cancer has undergone a profound paradigm shift from a “one-size-fits-all” surgical approach toward risk-stratified, individualized treatment. However, the limitations of current preoperative diagnostic modalities remain a primary challenge, frequently constraining surgeons from formulating precise therapeutic strategies. This study systematically evaluated the clinical utility of a label-free SERS liquid assay utilizing FNA washout fluids, coupled with multiple machine learning frameworks, for preoperative PTC diagnosis and LNM prediction. By examining a large-scale clinical cohort of 444 FNA washout fluid samples, we successfully captured multiple key SERS feature bands closely associated with malignant transformation and metastatic progression and constructed multiple machine learning models, which exhibited promising diagnostic efficacy. These findings indicate that AI-assisted SERS analysis can effectively probe the molecular fingerprints of the tumor in situ microenvironment, offering a robust strategy to overcome current preoperative diagnostic bottlenecks.

Biological Significance of Differential SERS Peaks

By exploiting the fundamental principle of SERS to capture the intrinsic vibrational energy levels of chemical bonds, we elucidated the complex metabolic reprogramming and molecular dynamics driving PTC oncogenesis and cervical lymph node metastasis. In the PTC cohort, the signal synergistic elevation of nucleic acid-associated bands (eg, 1173, 1186, and 1374 cm⁻¹) may imply highly active DNA replication and transcription activities within the malignant microenvironment. Concurrently, the coordinated intensity increases observed at 906, 1021, and 1546 cm⁻¹ are compatible with altered protein synthesis and amino acid dynamics. Within the LNM+ cohort, significant spectral shifts observed at 922 cm⁻¹ and 1255 cm⁻¹, combined with the synergistic enhancement of the 843 cm⁻¹, are consistent with putative changes in extracellular matrix composition and phosphorylation-associated cellular processes.^17,22,24 This molecular signature aligns with the classic pathological mechanisms required for tumors cells to breach physical tissue barriers during metastasis.²⁶

Furthermore, this study also uncovered SERS spectral features of the tryptophan-kynurenine metabolic axis. During the initial stages of PTC development, a prominent elevation of the kynurenine band (549 cm⁻¹) was captured alongside a significant negative correlation with the tryptophan band (1334 cm⁻¹). Crucially, the marked, concurrent up-regulation of the 549 cm⁻¹ and 566 cm⁻¹ emerged as the most critical spectral hallmark distinguishing the LNM+ group from LNM− cases. This localized metabolic acceleration raises the possibility that tryptophan degradation is highly active in primary thyroid oncogenesis and becomes further amplified during metastatic progression. This observation is consistent with prior SERS-based investigations monitoring accelerated tryptophan metabolism in aggressive pancreatic malignancies.¹⁴ Nonetheless, because no direct metabolomic profiling, enzymatic quantification, or immunohistochemical analyses were conducted in this study, these spectral observations remain correlative hypotheses. Comprehensive multi-omics and orthogonal biochemical investigations will be required to validate these putative metabolic alterations in thyroid tumorigenesis and nodal.

Performance Analysis of the Classification Models

Although SERS initially successfully reveal characteristic biomolecular fingerprints, manual interpretation of such high-dimensional, highly complex spectral datasets is inherently prone to subjective bias and cannot fulfill the high time-efficiency demands of routine clinical workflows.²⁷ Consequently, advanced ML algorithms are required to objectively decode non-linear spectral patterns. In the PTC diagnostic task, all evaluated classifiers demonstrated excellent discriminative capacity, maintaining test set AUC values within a high tier of 0.966 to 0.995. This consistently strong performance validates the combination of SERS and machine learning as an effective tool for precise thyroid nodule differentiation. Compared with traditional shallow ML classifiers, which rely heavily on hand-crafted features and may struggle with tumor heterogeneity, the CNN-based deep learning framework (PTCSpecNet) demonstrated improved discrimination and stability to some extent. PTCSpecNet achieved a solid five-fold cross-validation accuracy of 0.917, indicating its capability to filter out baseline instrumental noise and matrix background interference, thereby exhibiting robust internal classification stability within this cohort. Notably, handling cytologically indeterminate nodules remains a major clinical dilemma and minimizing the false-negative rate is the foremost clinical priority to formulate precise diagnostic and treatment strategies. PTCSpecNet aligned well with this clinical priority by delivering a recall of 0.979, yielding only 1 false-negative result among 47 independent malignant test samples. While this indicates a favorable conceptual basis for auxiliary nodule evaluation, whether such spectral platforms can assist in resolving the morphological “grey zone” of Bethesda III–IV categories and ultimately help avoid unnecessary diagnostic surgeries represents a potential future clinical avenue that requires dedicated validation in cytologically indeterminate cohorts.

Importantly, our strategy of utilizing FNA washout fluids provides clear technical advantages over conventional peripheral blood serum SERS assays. By directly sampling the localized fluid environment of the nodule, our FNA washout assay successfully avoids these complex systemic backgrounds, isolating concentrated, tumor-specific molecular information. Consequently, PTCSpecNet compared favorably with previously reported serum-based SERS diagnostic studies (whose reported accuracies typically range between 80% and 90%^28–30). This difference likely reflects the advantage of in situ sampling in bypassing the complex background signals and systemic interference characteristic of peripheral blood. Although direct head-to-head trials under unified instrumental conditions will be valuable to formally validate this comparison, our findings demonstrate the compelling potential of FNA washout fluid over peripheral biofluids. Furthermore, compared to early exploratory attempts utilizing FNA washouts (18 malignant and 18 benign), this study significantly improved overall diagnostic accuracy to 95.6% (vs 88.1%³¹) through an expanded patient cohort and standardized spectral acquisition protocols. This low-cost, high-efficiency platform holds clear promise as a clinical adjunct to routine cytopathology.

LNM represents an important component in contemporary multidimensional risk stratification systems for PTC. It serves as a vital clinical compass for selecting active surveillance, radiofrequency ablation, or defining the extent of surgical resection. The five LNM prediction models demonstrated strong discriminative performance on the test partition. Among them, the deep learning-driven LymSpecNet framework showed comparatively favorable clinical adaptability, performing competitively among all classifiers, with a test set accuracy of 85.1% and a cross-validation accuracy of 0.895. Crucially, LymSpecNet achieved an favorable balance between precision (0.857) and recall (0.818), closely aligning with the core clinical imperative of preventing missed metastatic diagnoses (avoiding under-treatment) while minimizing excessive lymph node dissections (avoiding over-treatment). Nonetheless, translating this predictive balance into reduced prophylactic central neck dissections remains a clinical hypothesis that must be confirmed in prospective surgical decision-making trials. In the domain of FNA-based SERS diagnostics, Hou et al³² successfully utilized FNA cell pellet to demonstrate the feasibility of predicting LNM within smaller pilot cohorts (10 LNM− vs 13 LNM+ cases). Our study advances this paradigm by utilizing the FNA washout fluid, a matrix that is easier to handle and requires no complex cellular extraction or pre-analytical processing. By expanding the clinical evaluation to 237 histopathologically confirmed PTC cases and implementing strict independent test set validation, we have confirmed the robustness, clinical reproducibility, and scalable potential of this liquid biopsy strategy across a much larger population.

Notably, in an actual clinical point-of-care scenario using freshly aspirated washout fluid, the physical diagnostic workflow comprises three successive stages: (1) pre-analytical processing, where freshly collected washout fluid is centrifuged to precipitate cellular debris and harvest the supernatant (~10–15 min); (2) spectral acquisition, involving automated Raman interrogation across 100 accumulations at 5 s integration time (500 s ≈ 8.3 min); and (3) algorithmic inference, where the trained neural network executes instantaneous forward prediction (< 0.02 s per spectrum). Consequently, the realistic total sample-to-result turnaround time is estimated at approximately 20–25 minutes. This timeframe aligns well with routine outpatient biopsy workflows, confirming the genuine practical feasibility of this platform for rapid preoperative decision support.

Strengths, Limitations, and Future Directions

This study leverages SERS of FNA washout fluids combined with deep learning algorithms to simultaneously achieve accurate preoperative diagnosis of PTC and prediction of LNM. By capturing the comprehensive molecular fingerprints of the tumor in situ microenvironment and converting discarded samples from routine clinical procedures into high-value molecular information carriers, this approach avoids the complex systemic backgrounds associated with peripheral blood testing while seamlessly integrating into existing diagnostic workflows. Moreover, Compared with molecular testing, SERS offers lower cost per sample, faster turnaround, and broader applicability in centers where molecular testing is not routinely available. This low-cost, high-efficiency platform holds clear promise as a clinical adjunct to routine cytopathology.

However, several limitations of this study should be noted. First, this was a single-center, retrospective investigation; hence, the performance and generalizability of the PTCSpecNet and LymSpecNet models across diverse patient populations and different spectrometer platforms require further validation. Additionally, the performance of the model in the most challenging subgroup of Bethesda III–IV nodules has not been specifically validated, and the actual clinical utility of reducing overtreatment or guiding surgical extent remains unproven in this retrospective setting. Future research will focus on initiating prospective, multi-center clinical trials to provide robust external validation.

Conclusion

In summary, this study successfully developed an exploratory intelligent diagnostic platform by merging label-free SERS liquid biopsies of FNA washout fluids with advanced deep learning architectures. The specialized deep neural networks, PTCSpecNet and LymSpecNet, demonstrated high sensitivity, favorable internal classification stability, and rapid processing speeds for both PTC differentiation and LNM prediction within this single-center cohort. Moving forward, this low-cost, high-efficiency molecular diagnostic framework holds potential as a complementary adjunct to conventional cytopathology; however, prospective multi-center external validation remains essential before it can be considered for routine clinical implementation or used to directly inform surgical strategies.

Funding Statement

The present study was financially supported by Jilin Provincial Science and Technology Development Plan Project (No.20260203159SF) and the National Natural Science Foundation of China (No. 42406182, 62605405).

Abbreviations

AI, Artificial intelligence; AUC, Area under curve; CNN, Convolutional neural network; FNA, Fine-needle aspiration; LightGBM, Light gradient boosting machine; LNM, Lymph node metastasis; ML, Machine learning; PTC, Papillary thyroid carcinoma; RF, Random forest; SERS, Surface-enhanced Raman spectroscopy; SVM, Support vector machine; XGBoost, eXtreme gradient boosting.

Data Sharing Statement

The raw datasets analyzed during the current study are available from the corresponding author (Prof. Hui Sun, s_h@jlu.edu.cn) upon reasonable scientific request.

Ethics Statement

The study was approved by the Ethics Committee of China-Japan Union Hospital of Jilin University (No.2026031905).

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

The authors Rundong He, Ping Sun, Le Zhou, Danheng Gao, Rui Du, Meng Luo, Chong Chen, Yiming Guo, Qihan Zhang, Hui Sun and Haoran Meng have no conflict of interest to disclose, and no other funding or financial relationship.