1.

Introduction

Thyroid nodules are very common, and ultrasound-guided fine-needle aspiration (FNA) cytology is widely accepted as an initial triage test to determine which patients require excision.^1,2 The FNA procedure is performed with a thin hollow needle (25 to 27 gauge) attached to a syringe, and the nodule is aspirated and prepared for cytologic evaluation. Though this approach has allowed clinicians to identify many malignant nodules (particularly papillary carcinoma) that required surgery, diagnosing follicular carcinoma using FNA has been problematic based on cytologic features alone.^3–5 This creates a problem for clinicians who must decide if surgery is needed to remove the thyroid nodule.

“Follicular neoplasm” is the term recommended by the Bethesda system for reporting thyroid cytopathology (BSRTC) when screening FNA samples containing moderate to high numbers of microfollicles.⁶ The histologic outcome of these nodules can include hyperplastic/adenomatous nodules, follicular neoplasm, and follicular variant of papillary carcinoma.⁷ Follicular neoplasm can be further subclassified into follicular thyroid adenoma (FTA) and Hürthle cell adenoma (HCA), which are benign, and follicular thyroid carcinoma (FTC) and Hürthle cell carcinoma (HCC), which are malignant. FTC is the second most common thyroid malignancy (papillary carcinoma being the most common) and consists of about 15% of all thyroid cancers.⁸ As capsular or vascular invasion is required on histology for a follicular cancer diagnosis, a cytologic interpretation of follicular neoplasm is, therefore, considered “indeterminate,” leading to clinical management conundrums. As the risk of malignancy for follicular neoplasm ranges from 10% to 40%,⁶ the vast majority of these patients with benign nodules will undergo unnecessary invasive surgery to exclude malignancy.

Thyroid surgery has associated clinical consequences. Many patients will be dependent on lifelong thyroid-replacement therapy, and while uncommon, surgical complications can occur, including iatrogenic hypoparathyroidism and vocal-cord paralysis.⁹ For these reasons, several molecular platforms have been developed and offered commercially to help triage “indeterminate” thyroid FNAs.¹⁰ However, they have limitations,¹¹ thus indicating the need for an alternative method that can provide better diagnostic accuracy (DA) and patient safety.

Raman spectroscopy (RS) is a well-established, nondestructive and noninvasive technique that provides intrinsic chemical information about the sample without requiring exogenous labels or stains.^12–15 In addition, it can reach subcellular spatial resolutions if implemented into a confocal microscope. Subtle differences in the chemical and structural compositions of healthy and diseased cells can lead to changes in peak intensities or positions in a Raman spectrum, allowing for identification of malignancy. Previously, we investigated the use of hyperspectral Raman microscopy on human thyroid nodules by using the average Raman spectrum over the entire cell and in association with multivariate statistical analysis, principal component analysis (PCA) and linear discriminant analysis (LDA).^16,17 We were able to differentiate benign follicular cells from papillary carcinoma cells with 97% DA.¹⁶ Also, medullary thyroid carcinoma nodules, whose FNA evaluation is still a challenge, were accurately discriminated from benign and papillary carcinoma with 97% and 99% accuracy, respectively.¹⁷

In this work, we collected clinical samples of thyroid nodules from patients with initial “follicular neoplasm” interpretation, meaning that their diagnoses were considered indeterminate by FNA cytology evaluation. Only patients who underwent surgery, so final pathological diagnoses could be achieved, were used in this study. In a similar fashion intended to simulate the FNA procedure, we performed hyperspectral Raman microscopy on single cells isolated from the nodule and used chemometric modeling, including support vector machine (SVM) and convolutional neural network (CNN), on the Raman spectral data to develop classification models. The DA of each model was evaluated.

2.

Materials and Methods

2.2.

Hyperspectral Raman Microscopy

Hyperspectral Raman images of individual cells were acquired using a previously published method and experimental setup^16,17 as shown in Fig. 1. Briefly, a home-built 785 nm Raman microscope with a line-shaped beam profile, from a master oscillator power amplifier fiber laser system (Sacher–laser), at the focal plane of the $60\times$ microscope objective is used. The cell sample is placed on the motorized translational stage of the microscope, which allows the sample to be scanned relative to the laser focus. The Raman signals generated from the line shaped focal region are collected by the microscope objective lens and are imaged by an achromatic lens onto the entrance-slit of the spectrometer and CCD detector. A typical Raman acquisition times per line is 50 s, leading to a full hyperspectral Raman image of a single cell (as shown in Fig. 1) within minutes by scanning the cell with a $1\text{-}\mu \mathrm{m}$ step size in the direction perpendicular to the excitation laser line.

Fig. 1

Line-scan hyperspectral Raman microscope setup: scanned area by a line profile laser allowing Raman imaging of single cells. Spherical lens (SL), cylindrical lens (CL), dichroic mirror (DM), longpass filter (LPF), and achromatic spherical lens (ASL).

3.

Results

Figure 2 shows the average Raman spectra of 61 FTA (blue line), 100 HCA (green line), 70 FTC (magenta line), and 161 HCC (red line) cells. The light gray shadows represent $\pm 1$ standard deviations (SD) over the average values.

Fig. 2

Average Raman spectra of (a) 61 FTA, (b) 100 HCA, (c) 70 FTC, and (d) 161 HCC cells. The gray shadows represent ±1 SD over the average values Raman spectrum of 117 single cells from MTC nodules, with the shaded region representing ±1 SD.

Figure 3(a) shows the first three-component coefficients that contribute the most data variance of the Raman spectral features from all four different subtypes used in this study. Figure 3(b) shows the cumulative variance versus the number of PCs used. The first three PCs captured 36% of the total variance in the data. PCA–LDA classification models were then created by using the principal components (PCs) as inputs for LDA, and then a 10-fold leave-$N$-out cross-validation was used to determine the classification parameters. Each model has an associated-CM; therefore, its associated-DA, which reflects the overall efficiency of the model and is expressed as a proportion of correctly classified subjects among all subjects, can then be calculated. Figure 3(c) shows a plot of the DA of the PCA–LDA model based on the number of PCs used as inputs for the LDA.

Fig. 3

(a) Plot of the first three PCs coefficients. (b) Plot of the cumulative variance as a proportion of the number of PCs. (c) DA of the PCA–LDA model versus the number of PCs used as inputs for the LDA.

The DA of the PCA–LDA model when using the first three PCs was only 59.5%. A higher DA using three PCs for the PCA–LDA model was previously achieved with different thyroid nodule subtypes.^16,17 This is an indication of the biochemical similarity of the follicular cells and, consequently, the challenge in differentiating the follicular subtypes in this study. In this paper, we focused on two AI-based methods for the extraction and classification of the Raman spectral features of the follicular cells: SVM and CNN.

Figure 4 summarizes the CM of each model, in which the probability of accurately diagnosing each follicular thyroid subtype is presented for both AI-based models. The overall DA of the SVM and CNN models, given by the ratio of correctly classified subjects among all subjects, is 80.4% and 80.9%, respectively.

Fig. 4

CM and its bar graph distribution of the probability of diagnosis for the (a) SVM and (b) CNN models.

Although discriminating each histologic subtype is the most ideal scenario, ultimately, an accurate technique that can distinguish benign from malignant nodules would avoid many unnecessary surgeries and overtreatment. Similar to the previous analyses, Fig. 5 summarizes the probability of accurately diagnosing malignant (including FTC and HCC) and benign (including FTA and HCA) cases.

Fig. 5

CM and its bar graph distribution of the probability of diagnosis for the (a) SVM and (b) CNN models for discriminating nonmalignant versus malignant follicular nodules subtypes.

The overall DA of the SVM and CNN model, given by the ratio of correctly classified subjects among all subjects, is 83.7 and 82.4%, respectively.

4.

Discussion

FNA cytology has been extremely helpful clinically in screening patients with thyroid nodules, particularly in identifying patients with papillary carcinoma with high sensitivity and specificity. However, follicular neoplasm is diagnostically challenging, as cytologic features alone cannot reliably provide definitive distinction between benign and malignant nodules. Therefore, an accurate minimally invasive methodology is needed to assist nodular thyroid management.

Much progress has been made in the application of ancillary techniques. Recently, some molecular testing platforms (e.g., Afirma GSC & Xpression Atlas, ThyroSeq v.3, and ThyGenX/ThyroMIR) have been developed and are frequently used in conjunction with FNA as an integral part to improve the cytologic evaluation of thyroid nodules.¹⁰ Molecular tests currently available for thyroid nodules are endorsed by the American Thyroid Association (ATA) for the clinical management of thyroid nodules but still have limited sensitivity and specificity for determining malignancy.

The cytologic interpretation of FTC and HCC is not straightforward. Cytologic evaluation alone cannot distinguish malignant FTC and HCC from their benign counterparts: FTA and HCA. In a recent review, Grani et al.¹⁸ discussed the challenges of indeterminate thyroid nodules, which also include specifically the challenges in diagnosing FTC and HCC.⁵

With the adoption of new histological classifications, efforts should be made to stimulate additional research in this area, as there is still a need for specific diagnostic and prognostic tools and effective evidence-based surveillance protocols. There has been a push towards developing a novel method that can be widely applied for medical diagnostics;¹⁵ i.e., the development of a cost-effective minimally invasive single test that can simply and accurately screen and diagnose a wide variety of illnesses and diseases early.

We recently reported using Raman spectral fingerprints to differentiate various thyroid nodules using PCA–LDA models with high sensitivity and specificity.^16,17 In fact, the spectral fingerprints of follicular neoplasm subtypes used in this study suggest that there is a higher similarity in their biochemical composition, as more PCs were necessary in this study to achieve higher accuracy, but the spectral feature extraction by the artificial intelligence models used here allowed for a more accurate classification. SVM is a method of discrimination commonly used for pattern recognition in machine learning technology. It is a method in which the data are represented as points in space, so any new data can be mapped into that same space and predicted to belong to a category based on which group they fit better in. CNN is a deep learning method composed of fully connected convolution layers, inspired by neural networks in the brain, in which the features can be automatically extracted and used for classification.

5.

Conclusion

This study shows that line scan hyperspectral Raman microscopy in combination chemometric, such as AI-based, analysis for cell classification is able to accurately differentiate malignant from benign “follicular neoplasm” nodules prior to surgery. To the best of our knowledge, our work is the first single cell, FNA-simulated, RS study on clinical patient specimens of follicular neoplasms. We believe this approach represent a proof-of-concept study and can be developed into an objective and accurate ancillary tool for analyzing cytology samples to improve diagnostic cytopathology and avoid unwarranted surgeries.

The objective of this study is to provide clinical guidance in managing “follicular neoplasm” and triage patients with a higher likelihood of cancer to surgery. Over the past decade, RS has demonstrated high sensitivity and overall DA when applied to cytologic specimens in various studies. In fact, the application of RS is not unique to thyroid cytopathology, as it has been widely proposed for several clinical applications, including a variety of cancers.¹⁵ The application of molecular testing for thyroid FNA specimens was accepted in 2015 by the ATA as a follow-up option for thyroid FNAs classified into one of the indeterminate categories.¹⁰ As we collect a larger cohort, we expect our classification model to improve, which can potentially lead to an alternative ancillary test in assisting the diagnosis and clinical management of thyroid nodules.