Role of color-coded virtual touch tissue imaging in suspected thyroid nodules

Abstract

BACKGROUND:

Conventional ultrasound (US) is the most widely used imaging test for thyroid nodule surveillance.

OBJECTIVE:

We used the color-coded virtual touch tissue imaging (VTI) in the Acoustic Radiation Force Impulse (ARFI) technique to assess the hardness of the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) TR3-5 nodules. The ability of color-coded VTI (CV) to discriminate between benign and malignant nodules was investigated.

METHODS:

In this retrospective study, US and CV were performed on 211 TR3-5 thyroid lesions in 181 consecutive patients. All nodules were operated on to obtain pathological results. A multivariate logistic regression model was chosen to integrate the data obtained from the US and CV.

RESULTS:

The area under the receiver operating characteristic (ROC) curve for the model was 0.945 (95% CI, 0.914 to 0.976). The cutoff value of predictive probability for diagnosing malignant thyroid nodules was 10.64%, the sensitivity was 94.43%, and the specificity was 83.12%. Through comparing with US and CV, respectively, it had been observed that the regression model had the best performance (all $P<$ 0.001). However, when the US was compared with CV, the difference was not significant ( $P=$ 0.3304).

CONCLUSIONS:

A combination of US and CV should be recommended for suspected malignant thyroid nodules in clinical practice.

Keywords

Thyroid nodule ACR TI-RADS acoustic radiation force impulse virtual touch tissue imaging

1. Introduction

The most common endocrine malignancy in thyroid cancer is rapidly increasing worldwide [1, 2, 3]. The most widely used imaging test for thyroid nodule surveillance is conventional ultrasound (US) [4, 5, 6]. In 2017, the American College of Radiology (ACR) launched a classification system-Thyroid Imaging Reporting and Data System (TI-RADS). This classification system classifies thyroid nodules into five classes based on a score, from TR1 to TR5, with a risk of malignancy of $\leqslant$ 2%, $\leqslant$ 2%, 5%, 5%–20%, and $>$ 20%, respectively [7, 8, 9, 10]. However, morphological images alone can be insufficient in accurately diagnosing nodule characteristics. Furthermore, the main reason for unnecessary fine-needle aspiration (FNA) or surgery is the overdiagnosis of “malignant” nodules in US screening. Therefore, we believe that it makes sense to use non-invasive methods to improve the accuracy of thyroid nodule diagnosis.

Acoustic Radiation Force Impulse (ARFI) is an elastography technique that assesses the elasticity of thyroid nodules and diagnoses malignancy [11, 12, 13, 14, 15]. Virtual Touch Tissue Quantification (VTQ) and Virtual Touch Tissue Imaging (VTI) represent the quantitative and qualitative techniques of ARFI. However, VTQ has some drawbacks, including the shear wave velocity shown as X.XX cm/s, limiting the use of VTQ. The result of X.XX cm/s may be due to the poor quality of the shear wave propagating in the tissue or the shear wave velocity exceeding the measured range. In contrast to quantitative VTQ, VTI is based on examining tissue displacement by acoustic radiation forces, allowing for qualitative tissue stiffness. VTI is usually displayed as grayscale or color-coded maps. However, only a few studies have addressed VTI’s diagnostic value in thyroid tumors, and all of these studies have been based on grayscale VTI [16, 17, 18, 19]. The human eye is very sensitive in detecting different colors but not so good in grayscale intensity.

This study focuses on TR3-5 category thyroid lesions because of the wide range of malignant probability, and the lesions were recommended for FNA. In addition, we aimed to assess color-coded VTI (CV) ability to diagnose malignancy in suspicious thyroid lesions.

2. Materials and methods

2.1 Subjects

The Ethics Committee approved the retrospective study and waived the requirement for informed consent because the data of all subjects were anonymous. This study analyzed 211 thyroid nodules in 181 patients from September 2015 to December 2019. We examined these patients sequentially for US and CV within one month before surgery. According to the ACR TI-RADS, all thyroid lesions were suspected of malignant and were noted as TR3, TR4, and TR5. The inclusion criteria were as follows: complete and clear US and CV images; complete information on pathological findings; no previous surgery or US-guided percutaneous thermotherapy. The exclusion criteria were as follows: (1) patients who only had pathological results of puncture but did not undergo surgical resection; (2) incomplete pathology results; and (3) patients with only preoperative US and VTI reports but no images.

2.2 Instruments

The US and CV examinations were performed using the Siemens Acuson S2000 (Siemens Medical Solutions Inc., CA, USA) with a 4–9 MHz linear transducer.

2.3 Image acquisition

The patient was instructed to assume a supine position with a total exposure of the anterior thyroid area, and the lesion was scanned, both in the longitudinal and transverse planes. All image acquisitions were made by an experienced radiologist (10 years of experience in thyroid imaging). Thyroid nodules were examined using the grayscale US and then by VTI. After obtaining clear images of the thyroid nodules in US mode, the VTI mode was turned on. In the VTI, the region of interest (ROI) should surround the lesion with sufficient normal thyroid tissue around the lesion. For large nodules whose volume exceeds the ROI limit, we placed the ROI at the junction of the nodule and the surrounding thyroid tissue. VTI has two display modes: grayscale and color-coded maps. In the grayscale display mode, the tissue in the dark areas of the VTI is harder than in the bright areas. The color-coded mode indicates the stiffness in hues, from low to high in purple, blue, green, orange, and red. In this study, in all cases, we performed only color-coded VTI imaging, not grayscale.

2.4 Image analysis

Two radiologists (both with more than five years of experience) reviewed US and CV images together. These two radiologists were blinded to the histopathology findings and were not the same radiologists who performed the examinations. When the two radiologists differed in the definition and diagnosis of image features, the final result was decided based on their consensus.

All US images of thyroid nodules were evaluated according to ACR TI-RADS, including composition (solid or almost completely solid, mixed solid and cystic, spongy, cystic or almost completely cystic), echogenicity (very hypoechoic, hypoechoic, hyperechoic or isoechoic, no echogenicity), shape (higher than wide, wider than high), margin (extrathyroidal extension, lobulated or unregular, indistinct, smooth), and echogenic foci (punctate echogenic foci, peripheral calcification, macrocalcifications, none or large comet-tail artifacts). Each feature was assigned a corresponding score, and the classification of the nodules was then determined based on the total score, as follows: 0 (TR1, benign), 2 (TR2, not suspicious), 3 (TR3, mildly suspicious), 4–6 (TR4, moderately suspicious), and 7 or more (TR5, highly suspicious). Next, each ultrasound feature with the same score was combined and then analyzed.

Figure 1.

Color-coded virtual touch tissue imaging scores of the thyroid lesions: a score 1; b score 2; c score 3; d score 4.

The lesion and surrounding thyroid tissue elasticity were evaluated according to the VTI color-coding map, ranging from purple or blue (soft) to red (hard). According to the four-point system recommended by Zhou et al., this study assigned a CV score to each lesion, as follows (Fig. 1): A score of 1 is defined when the predominant color of the lesion is green, blue, or purple, indicating a low stiffness of the lesion; A score of 2 is defined when the primary color of the lesion is orange, and the secondary color is green, indicating moderate hardness; A score of 3 is defined when the primary colors of the lesion are red and orange, and to a lesser extent other colors, indicating a high degree of hardness; A score of 4 is defined when the predominant colors of the lesion and surrounding tissues are red and orange, indicating high stiffness of the lesion and surrounding tissues. We defined the lesion as a score of 1–3 when the stiffness of the tissue surrounding the lesion was not high [20].

2.5 Interobserver variability

Thirty patients were randomly selected for inter-observer variability. Two radiologists with different experiences assessed the CV score who were blinded to patients’ clinical data results. The experience of two radiologists is more than five years and two years, respectively.

2.6 Statistical analysis

Statistical analysis was carried out with SPSS (version 26.0, SPSS, USA) and MedCalc (version 18.0, MedCalc, Mariakerke, Belgium) software. Measurement data were expressed as mean $\pm$ standard deviation and compared using the Student’s $t$ -test or Mann-Whitney U test. All count data were compared using the chi-square test or Fisher exact probability method. We used the scores corresponding to US features and CV scores as independent variables and the nodal benignity and malignancy determined by histopathology as dependent variables. Multivariate analysis was carried out on the variables with $p$ -values of $<$ 0.05 in the univariate analysis. Multivariate Logistic regression analysis was used to determine whether ultrasound features score and CV score were independent predictors of malignant lesions. The prediction model was built using the following logistic equation: Logit( $P$ ) $=$ X0 $+$ b1 $\times$ X1, where $P$ is the probability of malignant thyroid lesion, X0 is the constant, b1 is the regression coefficient of the predictor, and X1 is the predictor. Hosmer–Lemeshow goodness of fit test was used to assess the model fit (Hosmer-Lemeshow statistic $\geqslant$ 0.05). Receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC) was calculated to assess the specificity and sensitivity of distinguishing malignant thyroid lesions from benign lesions. The Kappa value was determined to assess the extent of consistency, with a Kappa value of $>$ 0.75 indicating high consistency and a Kappa value of $<$ 0.4 indicating weak consistency. $P<$ 0.05 was taken as statistically significant.

Table 1
Basic characteristics of patients with TR3, TR4, and TR5 nodules

Characteristic	Benign	Malignant	$P$ -value
Sex			0.222 ${}^{\text{a}}$
Male	24	5
Female	131	51
Age, y	47.1 $\pm$ 14.3	45.3 $\pm$ 13.8	0.432 ${}^{\text{b}}$
Diameter, mm	27.2 $\pm$ 15.5	15.1 $\pm$ 12.1	$<$ 0.001 ${}^{\text{b}}$

a $=$ Chi-square test. b $=$ $t$ -test.

3. Results

Histopathological examination of 211 nodules revealed 155 benign and 56 malignant nodules (Table 1). In the group of benign nodules, the diagnosis included nodular goiter ( $n=$ 123), adenoma ( $n=$ 25), subacute thyroiditis ( $n=$ 3), hyperthyroidism ( $n=$ 2) and Hashimoto’s nodule ( $n=$ 2). The 56 malignant diagnoses included 55 papillary carcinomas, one follicular carcinoma. There was a significant difference in the maximum diameter of the lesion between the benign and the malignant groups (27.2 $\pm$ 15.5 mm vs. 15.1 $\pm$ 12.1 mm, $P<$ 0.001). The gender and age were not significantly different between malignant and benign nodules ( $P=$ 0.222 and 0.4323).

Table 2
Comparison of qualitative characteristics obtained from the conventional US and CV between benign versus malignant thyroid nodules

Characteristics and classification	Pathological diagnosis		Malignancy rate (%)	$P$ value
	Benignity ( $n=$ 155)	Malignancy ( $n=$ 56)
Composition				0.732 ${}^{\text{b}}$
Mixed cystic and solid	9	2	18.2
Solid or almost solid	146	54	27.0
Echogenicity				$<$ 0.001 ${}^{\text{b}}$
Iso/Mix-echoic	71	4	5.3
Hypo-echoic	79	41	34.2
Very hypo-echoic	5	11	68.8
Shape(A/T)				$<$ 0.001 ${}^{\text{a}}$
$<$ 1	141	20	12.4
$>$ 1	14	36	72.0
Margin				$<$ 0.001 ${}^{\text{a}}$
Smooth or ill-defined	145	23	13.7
Uncertain	1	1	50.0
Lobulated or irregular	8	28	77.8
Extrathyroidal extension	1	4	80.0
Calcification				$<$ 0.001 ${}^{\text{a}}$
None or large comet-tail artifacts	132	21	13.7
Macrocalcification	12	7	36.8
Peripheral calcification	1	0	0.0
Micro-calcification	9	28	75.7
ACR TI-RADS				$<$ 0.001 ${}^{\text{a}}$
TR3	69	1	1.4
TR4	67	11	14.1
TR5	19	44	69.8
CV score				$<$ 0.001 ${}^{\text{a}}$
Score 1	28	1	3.4
Score 2	87	4	4.4
Score 3	25	28	52.8
Score 4	15	23	60.5

a $=$ Chi-square test. b $=$ Fisher exact test. US $=$ ultrasound; CV $=$ color-coded virtual touch tissue imaging; A/T $=$ anteroposterior/transverse diameter; ACR TI-RADS $=$ American college radiologist thyroid imaging report and data system.

Table 3

Multivariate logistic regression analysis based on US and CV

Factors	B	SE	Wald	Exp(B)	OR 95% CI		$P$ -value
					Lower limit	Upper limit
Echogenicity (X1)	0.942	0.482	3.818	2.565	0.997	6.600	0.051
Shape (X2)	0.450	0.183	6.014	1.568	1.094	2.245	0.014
Margin (X3)	0.925	0.251	13.552	2.523	1.541	4.130	$<$ 0.001
Calcification (X4)	0.574	0.192	8.903	1.775	1.218	2.588	0.003
CV score (X5)	0.906	0.295	9.455	2.475	1.389	4.410	0.002
Constant	$-$ 6.766	1.238	29.888	0.001	–	–	$<$ 0.001

CV $=$ color-coded virtual touch tissue imaging; CI $=$ confidence intervals.

Preoperative ultrasonographic features and CV scores were compared between benign and malignant nodules, as shown in Table 2. Univariate analysis revealed statistical differences in echogenicity, shape, margin, calcification, and CV between the two groups ( $P<$ 0.001). Multivariate analysis showed that shape score (X2), margin score (X3), calcification score (X4), and CV score (X5) were independent factors indicative of thyroid cancer (Table 3). The regression equation was Logit ( $P$ ) $=$ $-$ 6.766 $+$ 0.450 $\times$ X2 $+$ 0.925 $\times$ X3 $+$ 0.574 $\times$ X4 $+$ 0.906 $\times$ X5. The Hosmer and Lemeshow test was 0.343.

The discriminating capability of the model was excellent, as shown by the ROC curve, with an AUC of 0.945 (95% CI, 0.914 to 0.976). The cutoff value of predictive probability for diagnosing malignant thyroid nodules was 10.64%, the sensitivity was 94.43%, and the specificity was 83.12%. The regression model was found to have the best diagnostic performance by comparing with US and CV, respectively (all $P<$ 0.001). In addition, the difference was not significant between the US and CV (0.871 vs. 0.837, $P=$ 0.3304) (Table 4) (Fig. 2). Concerning the visual score of CV images, there is a high consistency between observers (Kappa $=$ 0.779, 95% CI $=$ 0.614-0.943).

Table 4

Comparison of ROC curve analysis to predict benign versus malignant thyroid nodules

Data ${}^{\text{a}}$	Threshold	Sensitivity	Specificity	AUC
US	$>$ TR4	78.57	87.74	0.871 ${}^{\text{b,c}}$
		(65.6,88.4)	(81.5,92.5)	(0.824, 0.919)
		[44/56]	[136/155]
CV	$>$ 2	91.07	74.19	0.837 ${}^{\text{c}}$
		(80.4,97.0)	(66.6, 80.9)	(0.781,0.893)
		[51/56]	[115/155]
US $+$ CV	$>$ 10.64%	96.43	83.12	0.945
		(87.7,99.6)	(76.2,88.7)	(0.914,0.976)
		[54/56]	[129/155]

ROC $=$ receiver operating characteristic; AUC $=$ the area under ROC curve; US $=$ ultrasound; CV $=$ color-coded virtual touch tissue imaging. a $=$ Data are percentages, with 95% confidence intervals in parentheses and raw data in brackets. b $=$ Compared with CV, $P=$ 0.3304. c $=$ Compared with US $+$ CV, $P<$ 0.05.

Figure 2.

Comparison of receiver operating characteristic curve analysis to predict benign versus malignant thyroid nodules. US $=$ ultrasound; CV $=$ color-coded virtual touch tissue imaging.

4. Discussion

The VTI technique assesses the stiffness of the tissue, is non-invasive, and can be used as a complement to the US examination. VTI is a grayscale or color-coded image that represents the hardness of the tissue. Two interpretative methods have been proposed to assess VTI of thyroid lesions: relative hardness based on VTI pattern classification and area ratio compared to the US. Several studies have shown that malignant lesions are greater in VTI than in the US, possibly due to invasion of the surrounding tissue by malignant lesions [21, 22]. Also, some studies have proposed a relative hardness criterion based on the VTI pattern classification according to the hue of the lesion [15].

In this study, a four-point scoring system based on the color-coded pattern of VTI was used to classify thyroid lesions. The scoring system takes into account both the hardness and area of the lesions. This study showed that the CV score of malignant nodules was mainly 3–4, while the CV score of benign nodules was mainly 1–2; the difference was statistically significant ( $P<$ 0.001). Most benign thyroid nodules consist of follicles of varying sizes with mainly colloidal components and a small interstitium, so they are soft. On the other hand, malignant thyroid nodules have more interstitium and contain blood vessels, fibers, and sand grains, so they are hard [8]. In addition, malignant nodules may infiltrate the surrounding tissues, which accounts for the increased hardness of the surrounding tissues. The AUC value of the CV was 0.837, the sensitivity was 91.07%, and the specificity was 74.19%. The advantage of VTI is that it has nothing to do with exerting pressure to be easily used without special training. Our results showed that a high degree of consistency could be obtained using CV between radiologists with different work experiences (Kappa $=$ 0.779, 95% CI $=$ 0.614–0.943).

However, there is also an overlap between benign and malignant thyroid nodules in VTI. This study showed that although the sensitivity of the CV score was high (91.07%), but the specificity was low (74.19%), there were 40 false-positive cases. Combined with the analysis of pathological changes, it may be that the nodular goiter has increased hardness due to repeated proliferation of nodular goiter, resulting in a large amount of fibrosis and calcification. In Hashimoto’s thyroiditis, the thyroid gland is infiltrated by many lymphocytes and plasma cells, follicles atrophy and disappearance, and extensive fibrosis, leading to increased hardness [23]. These nodules are prone to false-positive results diagnosed by CV alone. Therefore, elastography cannot replace the conventional US, and it must be based on the conventional US.

We included US features such as composition, echogenicity, shape, margin, echogenic foci, and CV as potential predictors of malignancy. The results showed that shape, margin, echogenic foci, and CV score were independent risk factors for malignant thyroid tumors. Shape, margin, and echogenic foci provide morphological information of the lesions. The CV score provides information on the hardness of the tumor and surrounding tissue. Based on the ultrasonic characteristics of ACR TI-RADS and CV score, the logistic regression model was established, and the method is simple. Our results showed that CV and US had moderate diagnostic values for thyroid nodules with AUCs of 0.837 and 0.871, respectively. However, in the regression model of combining the two diagnostic methods, the AUC reached 0.945, which is of high diagnostic value, significantly higher than that of the two alone. It further showed that the CV combined with the US could effectively improve the diagnostic value of lesions, consistent with other reports.

In the present study, the maximum diameter of benign lesions was significantly more than malignant lesions (27.2 $\pm$ 15.5 mm vs. 15.1 $\pm$ 12.1 mm, $P<$ 0.001). There may be several reasons for this. First, smaller nodules usually operate on a cytological, radiological, or clinical suspicion of a malignant tumor. Second, large nodules may be surgically removed, regardless of cytologic or radiologic findings, due to clinical pressure symptoms and cosmetic considerations.

The limitations of this study are as follows: First, the study was limited by retrospective nature. Although standard inspection and standardized image acquisition were used in this study, static images would affect the evaluation of ultrasonic signs. Second, only surgically resected thyroid nodules were included in the study. Therefore, the malignant rate and maximum diameter of thyroid nodules were higher than those of thyroid screening and examination in clinical work. Third, strain elastography and shear-wave elastography were not routinely performed in our study. Therefore, we could not compare the diagnostic performance of three types of elastography: strain elastography, shear wave elastography, and CV. Finally, this study took the results of postoperative histopathology as the gold standard, rather than the combination of surgery and puncture, and might not include all benign lesions.

5. Conclusion

US and CV have their advantages and limitations in the diagnosis of malignant thyroid lesions. The combination of US and CV can obtain more comprehensive information about the lesion, including morphology and hardness, and could effectively improve the diagnostic performance of suspicious thyroid lesions.

Footnotes

Acknowledgments

The authors gratefully acknowledge Wenxia Lin, Wenqiang Lin, Xiaohuan Zhu, and Xiao-E Huang from the Department of Ultrasound, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong Province, P.R. China.

Conflict of interest

The authors declare that they have no conflict of interest.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Role of color-coded virtual touch tissue imaging in suspected thyroid nodules

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSIONS:

Keywords

1. Introduction

2. Materials and methods

2.1 Subjects

2.2 Instruments

2.3 Image acquisition

2.4 Image analysis

2.6 Statistical analysis

Table 1 Basic characteristics of patients with TR3, TR4, and TR5 nodules

Table 2 Comparison of qualitative characteristics obtained from the conventional US and CV between benign versus malignant thyroid nodules

5. Conclusion

Footnotes

Acknowledgments

Conflict of interest

Funding

References

Table 1
Basic characteristics of patients with TR3, TR4, and TR5 nodules

Table 2
Comparison of qualitative characteristics obtained from the conventional US and CV between benign versus malignant thyroid nodules