A multimodal ultrasound approach: quantitative speed-of-sound, elastography, and B-mode imaging for predicting papillary thyroid carcinoma invasiveness

Introduction

Thyroid cancer is the most common endocrine malignancy, and its incidence rate increases progressively each year (1). Papillary thyroid carcinoma (PTC) is the predominant histological subtype, accounting for approximately 80% of all cases and primarily affecting individuals aged 45–54 years (2). Patients with PTC subjected to appropriate treatment generally exhibit favorable prognoses, with a 10-year survival rate reaching 90% (3,4). However, management strategies and surgical approaches must be individualized based on histological subtypes and aggressive features (5).

In recent years, the subclassification of PTC has undergone significant revisions. Non-invasive encapsulated follicular variant PTCs (EFVPTCs) have been redefined as noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP), highlighting their exceptionally low malignant potential and lack of metastatic or recurrent risk (6,7). In contrast, aggressive PTC may lead to extrathyroidal extension, lymph node metastasis, or recurrence, thereby complicating therapeutic interventions (8). Studies indicate that approximately 10% of patients with differentiated thyroid cancer present with metastasis at initial diagnosis. Additionally, up to 30% of patients experience local recurrence after surgical resection (9,10). Therefore, accurate identification of aggressive features in PTC is crucial for its treatment and management. Overtreatment can be avoided in low-risk patients, while early and intensive intervention should be implemented for high-risk patients. Quantitative speed-of-sound imaging (QSOS) maps the local speed-of-sound (SOS) in tissues, a property closely tied to density, elasticity, and composition that indicates pathological states (11,12). Unlike conventional ultrasound, which uses a fixed SOS (1,540 m/s), causing image degradation, QSOS employs multi-speed reconstruction by processing the same echo data repeatedly with different assumed sound speeds. The system then identifies the true local SOS by selecting the value that yields optimal image quality metrics, such as sharpness. Implemented on eSonic Image’s (Beijing, China) high-speed OmniSound^® platform, this process generates a color-coded QSOS map in real time. This technique provides quantitative, non-invasive tissue characterization that is unaffected by anisotropy and offers a novel basis for improved diagnostic screening, grading, and therapy monitoring.

Ultrasonography (US) serves as the primary diagnostic modality throughout the management of thyroid cancer due to its high sensitivity, noninvasiveness, and clinical utility in guiding treatment (13). Common US features assessed include nodule size, shape, margin, echogenicity, calcifications (such as microcalcifications or macrocalcifications), and hemodynamic characteristics. These features aid in distinguishing benign from malignant nodules (14). Based on these features, we document findings using the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS), a standardized system, in which a higher score indicates a greater probability of malignancy. If suspicious features are detected on US, such as hypoechogenicity, microcalcifications, or irregular margins, fine-needle aspiration biopsy (FNAB) is performed for cytological confirmation (14). Nodule size is associated with the risk of malignancy and invasiveness; therefore, it is used to guide treatment decisions, such as the extent of surgery (14,15). Although some studies suggest that tissue elastic modulus may be associated with tumor biological behavior, systematic analyses regarding the use of elastography in evaluating the aggressiveness of thyroid nodules remain limited (16,17). Furthermore, no relevant literature on the application of QSOS in thyroid diseases has been identified to date. The main objective of this study was to investigate the role of QSOS, elastography, and B-mode US in assessing the invasiveness of PTC. We present this article in accordance with the STARD reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1824/rc).

Methods

Study population

This single-center retrospective study was approved by the Ethics Committee of The Second Affiliated Hospital of Nanchang University (No. IIT-O-2025-317). This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study relied solely on conventional ultrasound examinations. It did not use protected health information and ensured complete de-identification of all data. The committee granted a waiver of informed consent because the research involved no more than minimal risk, and the waiver would not adversely affect the rights and welfare of the participants. A total of 194 patients with 212 thyroid nodules, who underwent or were scheduled to undergo thyroidectomy in the Department of Thyroid Surgery between April 2025 and June 2025, were initially enrolled. Preoperative evaluations included conventional US, elastography, and QSOS.

This study initially excluded patients with Hashimoto’s thyroiditis. Additionally, nine nodules were excluded due to their size exceeding the ultrasound sampling frame, which prevented complete inclusion. Another nine nodules, pathologically confirmed as benign, were also excluded. Moreover, 35 nodules from 15 patients with multiple nodules were excluded. Two cases of diffuse sclerosing variant PTC, without distinct nodules, were excluded, along with two cases pathologically diagnosed as follicular thyroid carcinoma. Ultimately, 157 nodules from 157 patients were included in the analysis. All nodules were pathologically confirmed as PTC. Among them: 118 nodules exhibited pathological evidence of invasiveness; 80 cases had confirmed lymph node metastasis; and 38 cases demonstrated thyroid capsular invasion with extension into adjacent structures (e.g., strap muscles, recurrent laryngeal nerve, trachea). The remaining 39 nodules were noninvasive, defined as tumors confined within the thyroid capsule without lymph node or distant metastasis. The cohort consisted of 42 males and 115 females, whose ages ranged from 17 to 70 years, with a mean age of 43.7 years.

Image acquisition

The examination was performed using the eSonic Image ePascal80 ultrasound imaging system equipped with an L16-4X linear array transducer. With the participant in a supine position and the neck fully exposed, conventional ultrasound and color Doppler ultrasound examinations were conducted first. The final assessment results based on conventional ultrasound were recorded according to the ACR TI-RADS (18).

To obtain clear images following standard ultrasound procedures, the same doctor carefully positioned the transducer on the skin overlying the thyroid nodule. Following picture stabilization, the sound speed measurement feature was activated by selecting the QSOS button on the screen. Measurements were then started by hitting the “Meas” button on the control panel. First, the complete thyroid nodule and a section of the nearby normal thyroid tissue were included in a suitable sampling box. A 2 mm measurement box was then placed. SOS1 was the sound speed measured inside the thyroid nodule, and SOS2 was the sound speed recorded at the same depth at the intersection of the nodule and normal thyroid tissue. As shown in Figure 1, the sound speed of normal thyroid tissue at the same depth was measured as SOS3. The difference value discussed in this article is obtained by subtracting SOS3 from SOS1.

Figure 1 Image showing the measurement of sound speed in thyroid nodules. In the figure, 1 indicates the measurement of SOS1, 2 indicates the measurement of SOS2, and 3 indicates the measurement of SOS3. Diam, diameter; QSOS, quantitative speed-of-sound; RSD, relative standard deviation; SOS, speed-of-sound.

After measuring sound speed, we used the same procedure to activate the elastography function. We used shear wave elastography (SWE) for the elastic test and elastosonographic examinations. The sampling box for elastography was adjusted to fully encompass the nodule while excluding normal thyroid tissue. Three parameters of the thyroid nodule were measured: maximum elasticity (Emax), mean elasticity (Emean), and elasticity ratio (Eratio) between the nodule and adjacent normal thyroid tissue. Both QSOS and SWE were performed by the same physician, with each parameter measured three times, and the average value was calculated for analysis.

QSOS

The most basic physical factor in ultrasonic diagnostics is acoustic impedance, and variations in acoustic impedance are the main cause of the different echo patterns seen in sonograms. The formula for acoustic impedance (Z) is Z = ρ × C, where ρ is tissue density, and C is the longitudinal ultrasonic wave propagation velocity in human tissues. Sound velocity is an intrinsic acoustic characteristic of biological tissues that is intimately related to tissue density, structure, and composition, although acoustic impedance and tissue density cannot be measured directly.

QSOS is a technique based on differences in ultrasound propagation speeds across various tissues. Utilizing the OmniSound^® plane-wave ultrafast platform, QSOS achieves frame rates exceeding 25,000 frames per second. This enables parallel, high-speed processing of extensive raw image data captured at different time points, which facilitates the reconstruction of precise tissue information images.

In conventional B-mode ultrasound imaging, image quality is often compromised by acoustic mismatch—a discrepancy between the assumed and actual SOS during beamforming. This mismatch leads to measurable degradation in resolution and contrast. Standard ultrasound reconstruction typically assumes a constant SOS (e.g., 1,540 m/s); however, significant variations exist among different tissue types (such as fat vs. muscle) and even within the same tissue across individuals. These variations can result in suboptimal image quality for certain tissues.

SOS estimation addresses this issue by reconstructing multiple images from the same echo data, each based on a different assumed sound velocity. The quality of each reconstructed image is evaluated to generate an SOS image quality factor curve (Figure 2). This curve, which plots image quality against assumed SOS, reveals that optimal image quality is achieved when the assumed SOS aligns most closely with the actual tissue value, thereby identifying the estimated SOS for the tissue (19).

Figure 2 SOS image quality factor curve. SOS, speed-of-sound.

Building on this principle, QSOS uses an ultrafast imaging platform to perform pixel-by-pixel SOS estimation across the entire field of view, encompassing over 100,000 sampling points. The resulting two-dimensional SOS data are color-coded and displayed as a quantitative map illustrating the spatial distribution of sound velocity in tissue. A schematic diagram of the QSOS principle is provided in Figure 3.

Figure 3 Schematic diagram of QSOS. QSOS, quantitative speed-of-sound.

Statistical analysis

Based on a review of relevant literature, we incorporated several conventional variables potentially associated with PTC, such as gender, age, nodule adjacency to the thyroid capsule, and nodule size (20). Additionally, parameters related to elasticity and QSOS measurements were included to investigate PTC invasiveness. Since research on QSOS in the context of PTC is currently lacking, this study was exploratory in nature and aimed to describe the associations between a predefined set of variables and the outcome. Therefore, the focus lay on evaluating the effects within a comprehensive conceptual model rather than selecting a single “best” model. Consequently, no formal model comparison or variable selection was performed.

Statistical analysis of the obtained data was performed using the software SPSS 23.0 (IBM Corp., Armonk, NY, USA). Categorical variables (gender, ACR TI-RADS classification, and PTC adjacent to the thyroid capsule) were expressed as n (%) and analyzed by Chi-squared tests (χ²). Among the measurement data, SOS3 and the difference value, which followed a normal distribution, were presented as mean ± standard deviation (SD) and analyzed using the independent t-test. The age, maximum diameter, SOS1, SOS2, Emean, Emax, and Eratio, which did not follow a normal distribution, were expressed as medians and interquartile ranges (IQRs) and analyzed using Mann-Whitney U tests accordingly. Logistic regression analysis was conducted to identify independent predictors of PTC invasiveness. A predictive model was constructed based on the regression coefficients of independent predictors, and a combined predictor was calculated. To assess model performance, a receiver operating characteristic (ROC) curve was plotted to determine the cutoff value corresponding to the parameter value at the maximum Youden index. The Youden index was calculated as follows:

Youdenindex=sensitivity+specificity−1[1]

A P value <0.05 was considered statistically significant. To evaluate the robustness of the model, five-fold cross-validation was performed using R (version 4.5.1; R Foundation for Statistical Computing, Vienna, Austria), and the ROC curve for each fold was plotted.

Results

Clinical characteristics and measured parameters

Table 1 presents the clinical characteristics and measured parameters of the invasive and non-invasive groups. No significant differences were found between the groups regarding gender, age, ACR TI-RADS classification, Emean, Eratio, SOS1, or SOS3. However, SOS2, maximum thyroid nodule diameter, Emax, and difference showed significant differences.

Specifically, the invasive group exhibited a larger maximum nodule diameter than the non-invasive group [9.5 (IQR, 7.1–12.3) vs. 6.2 (IQR, 5.2–8.5) mm]. Additionally, SOS2 in the invasive group was significantly lower than that in the non-invasive group [1,570.1 (IQR, 1,565.3–1,576.5) vs. 1,591.1 (IQR, 1,582.8–1,594.4) m/s]. The Emax was higher in the invasive group [52.7 (IQR, 41.1–66.0) vs. 45.9 (IQR, 35.7–58.1) kPa]. However, the difference was lower in the invasive group (6.5±13.2 vs. 11.6±11.9 m/s).

Logistic regression analysis

Logistic regression analysis, detailed in Table 2, revealed that SOS2, difference, and maximum diameter were independently associated with the aggressiveness of PTC. The specific results were as follows: SOS2 [odds ratio (OR): 0.857; 95% confidence interval (CI): 0.810–0.908; P<0.001]; maximum diameter (OR: 1.274; 95% CI: 1.070–1.517; P=0.007); and difference (OR: 0.945; 95% CI: 0.903–0.988; P=0.013).

Calculation of combined predictor and analysis of their diagnostic value

Table 2 demonstrates that the magnitude of the regression coefficients reflects the degree to which the aforementioned four indicators influence the invasiveness of thyroid nodules. Based on this, the formula for calculating the combined predictor value is derived as follows:

L=difference−4.2×maximumdiameter+2.7×SOS2[2]

ROC curves were plotted for the combined predictor, SOS2, difference, and maximum diameter. The results revealed that the difference low accuracy in determining the invasiveness of PTC, with an area under the curve (AUC) of <0.7. Maximum diameter demonstrated moderate accuracy (AUC =0.71), whereas SOS2 showed relatively high accuracy (AUC =0.86). Notably, the combined predictor achieved the highest diagnostic accuracy (AUC =0.91) in assessing thyroid cancer invasiveness (Table 3, Figure 4).

Figure 4 Multiple indicators ROC curve for predicting the aggressiveness of PTC. AUC, area under the curve; PTC, papillary thyroid carcinoma; ROC, receiver operating characteristic; SOS, speed-of-sound.

In summary, the diagnostic efficacy of these parameters in descending order was: combined predictor > SOS2 > maximum diameter > difference. Further analysis determined that the maximum Youden index for the combined predictor was 0.74, corresponding to a cutoff value of 4,251.6. Therefore, values greater than 4,251.6 indicate low invasiveness of PTC, whereas values below 4,251.6 suggest high invasiveness. At this cutoff, the sensitivity and specificity were 0.924 and 0.816, respectively.

Stability of the cross-validated model

Using five-fold cross-validation, the AUC values for the combined predictor were as follows: 0.914, 0.900, 0.892, 0.913, and 0.918. All five AUC values were above 0.8, indicating that the model exhibits excellent discriminatory ability. Moreover, the consistency of these values across folds suggests stable performance, and the model’s ability to maintain high AUC scores implies good generalization to unseen data (Figure 5).

Figure 5 The ROC curve of five-fold cross-validation. AUC, area under the curve; ROC, receiver operating characteristic.

Discussion

The global occurrence rate of PTC has shown a significant increase (21,22). However, the generally favorable prognosis of PTC has shifted clinical focus. Researchers are now aiming to identify potential indicators that predict adverse clinical courses in a subset of patients. These adverse outcomes include invasion of surrounding structures, development of occult and clinical metastases, and local recurrence. Currently, imaging examinations and FNAB serve as primary diagnostic tools, yet limitations persist in distinguishing benign from malignant nodules and assessing their aggressiveness (23). Consequently, exploring novel technologies to enhance diagnostic accuracy for early PTC detection and aggressiveness evaluation becomes particularly crucial.

Previous studies have reported that aggressive PTC cases tend to occur in older patients (24) and show male predominance (22). In our study, no statistically significant differences were observed in age or gender distribution between groups, potentially attributable to the relatively small sample size. The key findings demonstrate that larger maximum nodule diameter, lower SOS2, and smaller difference independently correlate with higher PTC aggressiveness. The association between tumor size and invasiveness aligns with Altiner et al.’s findings (25), showing that larger tumor dimensions increase the risk of central lymph node metastasis. In the invasive group, the SOS2 value was lower, and the difference between SOS1 and SOS2 was reduced. This disruption may originate from the aggressive growth pattern of the tumor. Such growth can alter the biomechanical properties of the tissue, thereby affecting the overall mechanical response observed in this study. Aggressive nodules grow rapidly and have a shorter growth duration; this shorter duration may not allow sufficient time for the development of perinodular fibrosis or calcification, leading to a decrease in SOS2. Furthermore, invasive nodules infiltrate the adjacent normal thyroid tissue, inducing fibrosis in this area. This fibrosis results in a relative increase in sound velocity, which in turn contributes to the reduction in the difference of sonic velocity between the nodules and surrounding tissue. Although the Emax was higher in the aggressive group compared to non-aggressive cases (though not an independent predictor), this reflects greater tissue stiffness in aggressive PTC. Elastography shows promising potential in thyroid nodule evaluation (26). Real-time quantitative measurement of tissue sound speed synchronized with B-mode imaging lays the foundation for clinical application of sound speed metrics as diagnostic biomarkers in medical US (27). Although QSOS remains unexplored in thyroid research, hepatic studies have demonstrated its value in improving diagnostic accuracy for hepatic steatosis (28) and detecting early steatohepatitis through dynamic monitoring (29). Additional applications in musculoskeletal research (30,31) further validate this technique’s utility. Our study integrated multiple imaging modalities—B-mode imaging, QSOS, and elastography—to comprehensively evaluate nodule characteristics, thereby addressing a research gap in thyroid nodule sound speed quantification and proposing novel methodologies for assessing PTC aggressiveness.

Stratified management of PTC is crucial for optimizing clinical decision-making. First, it helps to avoid overtreatment in low-risk patients. Moreover, with the continuous advancement of ultrasound diagnostic equipment and improved image resolution, the detection rate of small or micro-PTCs has increased. It is therefore essential to differentiate between indolent and aggressive subtypes to prevent overtreatment such as surgery or radioactive iodine (RAI) therapy for indolent tumors (32). Furthermore, patients younger than 20 years old generally have a favorable prognosis, and early identification of indolent PTC may reduce overtreatment, such as total thyroidectomy, in this population (33). Second, early and intensive intervention is warranted for high-risk patients. For instance, the tall cell variant (TCV) of PTC is inherently aggressive. Total thyroidectomy with prophylactic lymph node dissection is recommended, and RAI therapy is advised even for small tumors (34).

Although individual parameters (difference, maximum diameter, and SOS2) demonstrated predictive capability for PTC aggressiveness, their performance remained suboptimal (AUC <0.9). However, the combined predictive model achieved superior performance (AUC =0.91; sensitivity =0.924; specificity =0.816), suggesting potential clinical utility for risk stratification and personalized treatment plans, particularly in complex cases.

Several limitations warrant consideration. First, the retrospective nature of this study may have introduced selection bias, potentially affecting the generalizability of the findings. Second, the relatively small sample size and exclusive focus on PTC limit the extension of the results to other thyroid malignancies (e.g., follicular or medullary carcinoma). Finally, the absence of multicenter validation calls for further investigation to confirm external validity.

Conclusions

Our study is the first to use QSOS technology to examine PTC invasiveness. A combined predictor was created by integrating QSOS, elastography, and B-mode imaging of thyroid nodules to predict PTC invasiveness. This indicator demonstrated high accuracy, sensitivity, and specificity, providing clinicians with valuable reference for the early diagnosis and management of PTC. Although the current study has some limitations, it still provides a useful conceptual framework for clinicians. Future research should aim to validate these findings further, thereby improving the accuracy and reliability of assessing PTC invasiveness in clinical practice.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STARD reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1824/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1824/dss

Funding: This research was funded by the General Project of the Natural Science Foundation of Jiangxi Province (No. 20232BAB206119).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1824/coif). All authors report that the ultrasound imaging equipment used in this study was provided by eSonic Image Company. The authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This single-center retrospective study was approved by the Ethics Committee of The Second Affiliated Hospital of Nanchang University (No. IIT-O-2025-317). This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The committee granted a waiver of informed consent because the research involved no more than minimal risk, and the waiver would not adversely affect the rights and welfare of the subjects.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Klimaitė R, Kazokaitė M, Kondrotienė A, Daukšienė D, Sabaliauskaitė R, Žukauskaitė K, Žilaitienė B, Jarmalaitė S, Daukša A. The Role of TSHR, PTEN and RASSF1A Promoters' Methylation Status for Non-Invasive Detection of Papillary Thyroid Carcinoma. J Clin Med 2022;11:4917. [Crossref] [PubMed]
2. Chen Z, Wang JJ, Du JB, Li JF, Zheng RT, Yuan SM, Wu T, Guo DM, Zhai YX. Development and validation of a dynamic nomogram for predicting central lymph node metastasis in papillary thyroid carcinoma patients based on clinical and ultrasound features. Quant Imaging Med Surg 2025;15:1555-70. [Crossref] [PubMed]
3. Fugazzola L, Elisei R, Fuhrer D, Jarzab B, Leboulleux S, Newbold K, Smit J. 2019 European Thyroid Association Guidelines for the Treatment and Follow-Up of Advanced Radioiodine-Refractory Thyroid Cancer. Eur Thyroid J 2019;8:227-45. [Crossref] [PubMed]
4. Ito Y, Miyauchi A, Kihara M, Fukushima M, Higashiyama T, Miya A. Overall Survival of Papillary Thyroid Carcinoma Patients: A Single-Institution Long-Term Follow-Up of 5897 Patients. World J Surg 2018;42:615-22. [Crossref] [PubMed]
5. Sabán M, Tolaba N, Orlandi AM, Deutsch S, Pitoia F, Lowenstein A, et al. Non-invasive follicular neoplasm with papillary-like nuclear features: a challenging and infrequent entity in Argentina. Endocrine 2022;77:493-9. [Crossref] [PubMed]
6. Jeon MJ, Song DE, Jung CK, Kim WG, Kwon H, Lee YM, Sung TY, Yoon JH, Chung KW, Hong SJ, Baek JH, Lee JH, Kim TY, Shong YK, Kim WB. Impact of Reclassification on Thyroid Nodules with Architectural Atypia: From Non-Invasive Encapsulated Follicular Variant Papillary Thyroid Carcinomas to Non-Invasive Follicular Thyroid Neoplasm with Papillary-Like Nuclear Features. PLoS One 2016;11:e0167756. [Crossref] [PubMed]
7. Chung R, Guan H, Ponchiardi C, Cerda S, Marwaha N, Yilmaz OH, Pinjic E, McAneny D, Lee SL, Drake FT. Noninvasive Follicular Thyroid Neoplasm with Papillary-Like Nuclear Features: Epidemiology and Long-Term Outcomes in a Strictly Defined Cohort. Thyroid 2021;31:68-75. [Crossref] [PubMed]
8. Ivković I, Limani Z, Jakovčević A, Huić D, Prgomet D. Role of Matrix Metalloproteinases and Their Inhibitors in Locally Invasive Papillary Thyroid Cancer. Biomedicines 2022;10:3178. [Crossref] [PubMed]
9. Medas F, Canu GL, Boi F, Lai ML, Erdas E, Calò PG. Predictive Factors of Recurrence in Patients with Differentiated Thyroid Carcinoma: A Retrospective Analysis on 579 Patients. Cancers (Basel) 2019.
10. Kowalska A, Walczyk A, Kowalik A, Pałyga I, Trybek T, Kopczyński J, Kajor M, Chrapek M, Pięciak L, Chłopek M, Góźdź S, Kamiński G. Increase in Papillary Thyroid Cancer Incidence Is Accompanied by Changes in the Frequency of the BRAF V600E Mutation: A Single-Institution Study. Thyroid 2016;26:543-51. [Crossref] [PubMed]
11. Ophir J, Jaeger P. A ternary solution for independent acoustic impedance and speed of sound matching to biological tissues. Ultrason Imaging 1982;4:163-70. [Crossref] [PubMed]
12. Malik BH, Klock JC. Breast Cyst Fluid Analysis Correlations with Speed of Sound Using Transmission Ultrasound. Acad Radiol 2019;26:76-85. [Crossref] [PubMed]
13. Kouvaraki MA, Shapiro SE, Fornage BD, Edeiken-Monro BS, Sherman SI, Vassilopoulou-Sellin R, Lee JE, Evans DB. Role of preoperative ultrasonography in the surgical management of patients with thyroid cancer. Surgery 2003;134:946-54; discussion 954-5. [Crossref] [PubMed]
14. Kim DS, Kim JH, Na DG, Park SH, Kim E, Chang KH, Sohn CH, Choi YH. Sonographic features of follicular variant papillary thyroid carcinomas in comparison with conventional papillary thyroid carcinomas. J Ultrasound Med 2009;28:1685-92. [Crossref] [PubMed]
15. Li QS, Chen SH, Xiong HH, Xu XH, Li ZZ, Guo GQ. Papillary thyroid carcinoma on sonography. Clin Imaging 2010;34:121-6. [Crossref] [PubMed]
16. Cantisani V, De Silvestri A, Scotti V, Fresilli D, Tarsitano MG, Polti G, Guiban O, Polito E, Pacini P, Durante C, Grani G, Isidori AM, Giannetta E, Sorrenti S, Trimboli P, Catalano C, Cirocchi R, Lauro A, D'Andrea V. US-Elastography With Different Techniques for Thyroid Nodule Characterization: Systematic Review and Meta-analysis. Front Oncol 2022;12:845549. [Crossref] [PubMed]
17. Nell S, Kist JW, Debray TP, de Keizer B, van Oostenbrugge TJ, Borel Rinkes IH, Valk GD, Vriens MR. Qualitative elastography can replace thyroid nodule fine-needle aspiration in patients with soft thyroid nodules. A systematic review and meta-analysis. Eur J Radiol 2015;84:652-61. [Crossref] [PubMed]
18. Tessler FN, Middleton WD, Grant EG, Hoang JK, Berland LL, Teefey SA, Cronan JJ, Beland MD, Desser TS, Frates MC, Hammers LW, Hamper UM, Langer JE, Reading CC, Scoutt LM, Stavros AT. ACR Thyroid Imaging, Reporting and Data System (TI-RADS): White Paper of the ACR TI-RADS Committee. J Am Coll Radiol 2017;14:587-95. [Crossref] [PubMed]
19. Imbault M, Faccinetto A, Osmanski BF, Tissier A, Deffieux T, Gennisson JL, Vilgrain V, Tanter M. Robust sound speed estimation for ultrasound-based hepatic steatosis assessment. Phys Med Biol 2017;62:3582-98. [Crossref] [PubMed]
20. Haugen BR, Alexander EK, Bible KC, Doherty GM, Mandel SJ, Nikiforov YE, Pacini F, Randolph GW, Sawka AM, Schlumberger M, Schuff KG, Sherman SI, Sosa JA, Steward DL, Tuttle RM, Wartofsky L. 2015 American Thyroid Association Management Guidelines for Adult Patients with Thyroid Nodules and Differentiated Thyroid Cancer: The American Thyroid Association Guidelines Task Force on Thyroid Nodules and Differentiated Thyroid Cancer. Thyroid 2016;26:1-133. [Crossref] [PubMed]
21. Lim H, Devesa SS, Sosa JA, Check D, Kitahara CM. Trends in Thyroid Cancer Incidence and Mortality in the United States, 1974-2013. JAMA 2017;317:1338-48. [Crossref] [PubMed]
22. Rossi ED, Pantanowitz L, Hornick JL. A worldwide journey of thyroid cancer incidence centred on tumour histology. Lancet Diabetes Endocrinol 2021;9:193-4. [Crossref] [PubMed]
23. Seethala RR, Baloch ZW, Barletta JA, Khanafshar E, Mete O, Sadow PM. LiVolsi VA, Nikiforov YE, Tallini G, Thompson LD. Noninvasive follicular thyroid neoplasm with papillary-like nuclear features: a review for pathologists. Mod Pathol 2018;31:39-55. [Crossref] [PubMed]
24. Davies L, Welch HG. Increasing incidence of thyroid cancer in the United States, 1973-2002. JAMA 2006;295:2164-7. [Crossref] [PubMed]
25. Altiner S, Kozan R, Emral AC, Taneri F, Karamercan A. Effects of Patient and Tumor Characteristics on Central Lymph Node Metastasis in Papillary Thyroid Cancer: A Guide for Selective Node Dissection. Arch Iran Med 2022;25:730-6. [Crossref] [PubMed]
26. Kwak JY, Kim EK. Ultrasound elastography for thyroid nodules: recent advances. Ultrasonography 2014;33:75-82. [Crossref] [PubMed]
27. Xiao D, Torre P, Yu ACH. Real-Time Speed-of-Sound Estimation In Vivo via Steered Plane Wave Ultrasound. IEEE Trans Ultrason Ferroelectr Freq Control 2024;71:673-86. [Crossref] [PubMed]
28. Nam K, Torkzaban M, Halegoua-DeMarzio D, Wessner CE, Lyshchik A. Improving diagnostic accuracy of ultrasound texture features in detecting and quantifying hepatic steatosis using various beamforming sound speeds. Phys Med Biol 2023;
29. Kumagai H, Taniguchi N, Yokoyama K, Katsuyama K, Yamamoto H, Hara S, Hirota N, Itoh K, Yamagata T. The Speed of Sound in Rat Liver With Steatohepatitis: Ex Vivo Analysis Using Two Types of Ultrasound Systems. Ultrasound Med Biol 2019;45:2258-65. [Crossref] [PubMed]
30. Korta Martiartu N, Nakhostin D, Ruby L, Frauenfelder T, Rominger MB, Sanabria SJ. Speed of sound and shear wave speed for calf soft tissue composition and nonlinearity assessment. Quant Imaging Med Surg 2021;11:4149-61. [Crossref] [PubMed]
31. Roggen I, Louis O, Van Biervliet S, Van Daele S, Robberecht E, De Wachter E, Malfroot A, De Waele K, Gies I, Vanbesien J, De Schepper J. Quantitative bone ultrasound at the distal radius in adults with cystic fibrosis. Ultrasound Med Biol 2015;41:334-8. [Crossref] [PubMed]
32. Soares P, Sobrinho-Simões M. Cancer: Small papillary thyroid cancers--is BRAF of prognostic value? Nat Rev Endocrinol 2011;7:9-10. [Crossref] [PubMed]
33. Kuo SF, Chao TC, Hsueh C, Chuang WY, Yang CH, Lin JD. Prognosis and risk stratification in young papillary thyroid carcinoma patients. Endocr J 2008;55:269-75. [Crossref] [PubMed]
34. Beck AC, Rajan A, Landers S, Kelley S, Bellizzi AM, Lal G, Sugg SL, Howe JR, Chan CH, Weigel RJ. Expression of cancer stem cell markers in tall cell variant papillary thyroid cancer identifies a molecular profile predictive of recurrence in classic papillary thyroid cancer. Surgery 2022;171:245-51. [Crossref] [PubMed]