Contrast-Enhanced Ultrasound-Augmented Risk Stratification for Fine-Needle Aspiration Selection in Thyroid Nodules

Contrast-Enhanced Ultrasound-Augmented Risk Stratification for Fine-Needle Aspiration Selection in Thyroid Nodules | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Contrast-Enhanced Ultrasound-Augmented Risk Stratification for Fine-Needle Aspiration Selection in Thyroid Nodules Yaoyun Liang, Yao Chen, Yimin Wang, Si Qin, Jinzhi Hu, Yuxiao Huang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9268669/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Objective To investigate the value of qualitative contrast-enhanced ultrasound (CEUS) features in avoiding unnecessary fine-needle aspiration (FNA) of thyroid nodules. Methods This retrospective study included 131 thyroid nodules from 129 patients who underwent both conventional ultrasound (US) and CEUS examinations with confirmed FNA or surgical pathology at the Sixth Affiliated Hospital of Sun Yat-sen University between November 2023 and January 2025. Eight qualitative CEUS features were analyzed using univariate and multivariate logistic regression, including enhancement direction, presence of ring enhancement, presence of persistent perfusion defects, margin sharpness in the washout phase, presence of hypoenhancement (area smaller than the nodule on grayscale imaging), presence of non-enhancing peripheral halo, capsular enhancement continuity, and washout timing relative to surrounding thyroid parenchyma. A predictive model integrating conventional US and qualitative CEUS features was constructed. Receiver operating characteristic (ROC) curves were used to compare the performance of the new model with Chinese Thyroid Imaging Reporting and Data System (C-TIRADS) and American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) in recommending FNA for thyroid nodules. Decision curve analysis (DCA) was performed to evaluate the clinical net benefit of each model in avoiding unnecessary FNA. Results Multivariate analysis identified hypoenhancement and persistent perfusion defects as independent CEUS predictors. The integrated model combining conventional US features (aspect ratio > 1, irregular or ill-defined margin or extrathyroidal extension, and punctate echogenic foci) and CEUS features achieved the highest area under the curve (AUC) of 0.867 (95% CI: 0.802–0.932), with sensitivity, specificity, and accuracy of 78.4%, 82.5%, and 80.2%, respectively, significantly outperforming both C-TIRADS and ACR TI-RADS (both P < 0.001). At a 50% risk threshold, the integrated model avoided 23.7% of unnecessary FNA procedures, whereas C-TIRADS and ACR TI-RADS avoided 0% and 8.4%, respectively. Conclusion An integrated model combining conventional US and qualitative CEUS features can effectively identify thyroid nodules requiring FNA, optimize FNA indications, and significantly reduce unnecessary invasive procedures, demonstrating valuable clinical applicability. Thyroid nodules Contrast-enhanced ultrasound Fine-needle aspiration Diagnostic performance Predictive model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Thyroid nodules are commonly encountered in clinical practice, with prevalence rates varying by region, reaching 50%–67% in the general population. ( 1 – 3 ) Despite the high incidence of thyroid nodule, its mortality remains low as most nodules are benign and require no intervention. Currently, clinical management relies primarily on risk stratification to guide fine-needle aspiration (FNA) decisions. This stratification is performed using ultrasound-based systems, such as the American College of Radiology TI-RADS (ACR TI-RADS) and the Chinese TI-RADS (C-TIRADS), ( 4 , 5 ) which evaluate nodule malignancy risk based on sonographic features. However, these conventional ultrasound (US)-based assessment systems exhibit high false-positive rates, ( 6 ) resulting in a considerable proportion of benign nodules being misclassified as suspicious for malignancy and subjected to unnecessary FNA. ( 7 ) Globally, this leads to overdiagnosis in 60% of cases, with unnecessary biopsies performed on benign lesions. ( 8 , 9 ) This overdiagnosis not only increases patients' psychological and economic burden but may also elevate the risk of puncture-related complications. ( 10 , 11 ) Therefore, there is an urgent need to develop more accurate diagnostic tools to optimize current thyroid nodule management strategies. ( 12 ) By visualizing microvascular perfusion in real-time, CEUS provides hemodynamic information beyond conventional ultrasound capabilities, offering unique advantages for thyroid nodule characterization. ( 13 ) Qualitative CEUS features, including ring enhancement, homogeneous perfusion, and boundary clarity, have been particularly valuable in identifying low-risk nodules that may avoid invasive procedures. ( 14 , 15 )Although studies have demonstrated the value of qualitative CEUS features in differentiating benign from malignant thyroid nodules, ( 1 , 16 , 17 ) systematic evidence for reducing unnecessary FNA remains limited. Accordingly, this study develops an integrated risk prediction model combining conventional ultrasound and CEUS features, and evaluates its potential to reduce unnecessary FNA in thyroid nodules. Materials and Methods Study Participants This retrospective study was conducted at the Sixth Affiliated Hospital of Sun Yat-sen University between November 2023 and January 2025. Patients with thyroid nodules who underwent both US and CEUS examinations, followed by pathological confirmation via FNA cytology or surgical resection histology, were consecutively enrolled. Inclusion criteria were: (i) preoperative conventional US indicating ACR-TIRADS 3–5 categories, with CEUS examination completed before puncture or surgery; (ii) complete dynamic CEUS images with adequate quality for retrospective analysis; (iii) definitive pathological diagnosis via surgical pathology or FNA cytology; (iv) interval between CEUS examination and pathological diagnosis ≤ 3 months; and (v) complete clinical data including patient demographics and nodule characteristics. Exclusion criteria were: (i) FNA puncture or ablation intervention performed before CEUS examination; (ii) FNA cytology results of Bethesda I/III/IV categories without surgical pathology confirmation; and (iii) poor CEUS image quality preventing accurate interpretation. This study was approved by the Medical Ethics Committee of the Sixth Affiliated Hospital of Sun Yat-sen University (Ethics No.: 2025ZSLYEC-608) and conducted in accordance with the Declaration of Helsinki. Informed consent was waived due to the retrospective nature of the study. Image Acquisition All ultrasound examinations were performed as part of routine clinical practice at our institution using a Siemens ACUSON Sequoia ultrasound system (Siemens Healthineers, Erlangen, Germany) with a 14L5 linear array transducer. Standard imaging protocol included scanning of bilateral thyroid lobes in multiple planes (transverse, longitudinal, and oblique), with documentation of nodule characteristics including composition, echogenicity, shape, margins, calcifications, and dimensions. Contrast-enhanced ultrasound was performed using contrast mode (4–9 MHz) when clinically indicated. SonoVue contrast agent (Bracco, Milan, Italy; 1.2–1.5 mL) was administered as an intravenous bolus via the antecubital vein, followed by a 5-mL saline flush. The target nodule and surrounding normal thyroid parenchyma were continuously visualized in the longitudinal plane for at least 60 seconds following contrast injection. Dynamic cine clips were stored in DICOM format in PACS. Image analysis Two physicians with more than 5 years of CEUS diagnostic experience independently evaluated the sonographic and CEUS features of all included patients, blinded to pathological outcomes. When opinions between the two physicians were inconsistent, final consensus was reached through discussion and consultation. Cohen's kappa coefficient was used to evaluate inter-observer agreement for each ultrasound feature. Conventional US features evaluated included: nodule composition (solid, cystic, or mixed), echogenicity (anechoic, hyperechoic, isoechoic, hypoechoic, or markedly hypoechoic), aspect ratio (> 1 or ≤ 1), margin characteristics (smooth, ill-defined, lobulated/irregular, or extrathyroidal extension) and calcification type. Each nodule was graded according to C-TIRADS and ACR TI-RADS criteria, and FNA indication was determined according to corresponding guidelines based on nodule maximum diameter. ( 4 , 5 ) CEUS features evaluated included: enhancement direction (scattered /centripetal or centrifugal/ indeterminate), presence of ring enhancement, presence of persistent perfusion defects, margin sharpness in the washout phase (indistinct vs sharp), presence of hypoenhancement (area smaller than the nodule on grayscale imaging), presence of non-enhancing peripheral halo, capsular enhancement continuity (continuous vs interrupted), and washout timing relative to surrounding thyroid parenchyma (earlier vs synchronous or delayed). Statistical Analysis All analyses were performed using SPSS 25.0 and R 4.4.2 (R Foundation for Statistical Computing, Vienna, Austria). Continuous variables were presented as mean ± SD or median (IQR) depending on normality (Kolmogorov-Smirnov test) and compared using independent samples t-test or Mann-Whitney U test, respectively. Categorical variables were compared using χ² test or Fisher's exact test. Multivariable logistic regression with stepwise selection (entry/removal threshold: α = 0.10) was performed to identify independent predictors of malignancy from conventional US and CEUS features. Model discrimination was assessed using area under the receiver operating characteristic curve (AUC) with 95% CI. The optimal cutoff was determined using Youden's index, with corresponding sensitivity, specificity, and accuracy calculated. DeLong's test was used to compare AUCs between the developed model and existing risk stratification systems (C-TIRADS and ACR TI-RADS). Model calibration was evaluated using calibration curves and the Hosmer-Lemeshow test. Internal validation was conducted via 10-fold cross-validation, and the final model was visualized as a nomogram. To evaluate the clinical utility of each predictive model in guiding FNA decision-making, decision curve analysis (DCA) was performed to quantify net benefit (NB) across a range of threshold probabilities ( \(\:{P}_{t}\) ). ( 18 ) The threshold probability represents the risk level above which FNA is recommended over observation, reflecting the balance between avoiding missed malignancies and reducing unnecessary biopsies. At a given threshold, the model's NB was calculated as: : ( 19 ) $$\:NB\left({P}_{t}\right)=\frac{TP\left({P}_{t}\right)}{N}-\frac{FP\left({P}_{t}\right)}{N}\times\:\frac{{P}_{t}}{1-{P}_{t}}$$ where \(\:TP\left({P}_{t}\right)\) and \(\:FP\left({P}_{t}\right)\:\) represent the number of true positives and false positives at threshold Pt, respectively, and N is the total sample size. To facilitate comparison between different models and curve presentation, the standardized net benefit (sNB) was further calculated: ( 20 ) $$\:sNB\left({P}_{t}\right)=\frac{N{B}_{model}\left({P}_{t}\right)}{prevalence}$$ An ideal model has an sNB of 1. Additionally, from the perspective of intervention reduction, the net reduction in false-positives (NRFP) was employed to quantify the proportion of truly avoided unnecessary FNA without increasing missed diagnoses. The formula is as follows: ( 19 , 21 , 22 ) $$\:NRFP\left({P}_{t}\right)=\frac{N{B}_{model}\left({P}_{t}\right)-N{B}_{all}\left({P}_{t}\right)}{{P}_{t}/(1-{P}_{t})}$$ The 95% confidence intervals for all metrics were estimated using the non-parametric bootstrap method with 1000 resampling iterations. Results Participant Characteristics A total of 131 thyroid nodules from 129 patients were included, comprising 57 benign and 74 malignant nodules (all papillary thyroid carcinoma, PTC). The patient selection process is illustrated in the flowchart (Fig. 1 ). The cohort comprised 98 women and 33 men (mean age, 43.3 ± 14.6 years; range, 16–84 years). Demographic and clinical characteristics are summarized as follows (Table 1 ). Although females predominated overall, the proportion of male patients was significantly higher in the malignant group (32.4% vs 15.8%, P = 0.030). Patient age (P = 0.250) and nodule location (P = 0.893) did not differ significantly between groups. Table 1 Patient Demographics and Nodule Characteristics Characteristic All Participants ( n = 131) Benign( n = 57) Malignant( n = 74) P Mean age (y) 43.3 ± 14.6 45.0 ± 14.4 42.0 ± 14.7 0.250 Sex 0.030 Female 98(74.8) 48(84.2) 50(67.6) Male 33(25.2) 9(15.8) 24(32.4) Nodule location 0.893 Left lobe 59(45.0) 27(47.4) 32(43.2) Right lobe 67(51.2) 28(49.1) 39(52.7) Isthmus 5(3.8) 2(3.5) 3(4.1) Diameter (mm) 0.040 ≤ 5mm 15(11.5) 7(12.3) 8(10.8) > 5mm, ≤ 10mm 62(47.3) 20(35.1) 42(56.8) > 10mm 54(41.2) 30(52.6) 24(32.4) C-TIRADS level < 0.001 TR 3 4(3.1) 4(7.0) 0(0) TR 4A 31(23.7) 27(47.4) 4(5.4) TR 4B 41(31.3) 14(24.6) 27(36.5) TR 4C 54(41.2) 12(21.1) 42(56.8) TR 5 1(0.8) 0(0) 1(1.4) ACR TI-RADS level < 0.001 TR 3 16 (12.2) 16(28.1) 0(0) TR 4 50(38.2) 29(50.9) 21(28.4) TR 5 65(49.6) 12(21.1) 53(71.6) Management of C-TIRADS 0.286 Follow-up 62(47.3) 30(52.6) 32(43.2) FNA 69(52.7) 27(47.4) 42(56.8) Management of ACR TI-RADS < 0.001 No follow-up and no FNA 35(26.7) 23(40.4) 12(16.2) Follow-up 52(39.7) 12(21.1) 40(54.1) FNA 44(33.6) 22(38.6) 22(29.7) Unless otherwise specified, data are numbers of nodules, with percentages in parentheses. Mean data are ± SDs. P < 0.05 indicates a significant difference. FNA, fine-needle aspiration; C-TIRADS, Chinese Thyroid Imaging Reporting and Data System; ACR TI-RADS, American College of Radiology Thyroid Imaging Reporting and Data System. C-TIRADS and ACR TI-RADS distributions differed significantly between benign and malignant groups (both P < 0.001). Malignancy rates increased progressively with C-TIRADS category (TR3: 0%, TR4A: 12.9%, TR4B: 65.9%, TR4C: 77.8%, TR5: 100%) and ACR TI-RADS category (TR3: 0%, TR4: 42.0%, TR5: 81.5%). Regarding FNA guidance, C-TIRADS would recommend biopsy for 47.4% (27/57) of benign nodules while deferring 43.2% (32/74) of malignancies to follow-up. According to ACR TI-RADS criteria, 22 of 57 benign nodules (38.6%) met criteria for FNA, while 52 of 74 malignant nodules (70.3%) did not meet FNA criteria . Univariate Analysis Univariate analysis identified several features that differed significantly between benign (n = 57) and malignant (n = 74) nodules (Table 2 ). On conventional US, malignant nodules more frequently demonstrated solid composition (100% vs 93.0%, P = 0.021), aspect ratio > 1 (48.7% vs 10.5%, P < 0.001), irregular or ill-defined margin or extrathyroidal extension (68.9% vs 33.3%, P < 0.001), and punctate echogenic foci (44.6% vs 15.8%, P < 0.001). On CEUS, malignant nodules more frequently showed hypoenhancement (area smaller than the nodule on grayscale imaging) (64.9% vs 24.6%, P < 0.001) and interupted capsular enhancement (25.7% vs 8.8%, P = 0.013), while benign nodules more frequently demonstrated ring enhancement (22.8% vs 0%, P < 0.001), persistent perfusion defects (31.6% vs 9.5%, P = 0.001), and sharp margin in the washout phase (24.6% vs 8.1%, P = 0.009), and synchronous or delayed washout timing relative to surrounding thyroid parenchyma (61.4% vs 32.4%, P = 0.001). The qualitative CEUS features are illustrated with representative images. (Fig. 2 ) Table 2 Univariate Analysis of Qualitative Features on Conventional US and CEUS Variable All Participants ( n = 131) Benign ( n = 57) Malignant ( n = 74) P Conventional US Cystic–solid/Solid 4/127 (3.1/97.0) 4/53 (7.0/93.0) 0/74 (0/100) 0.021 Markedly hypoechoic (no/yes) 117/14 (89.3/10.7) 50/7 (87.7/12.3) 67/7 (90.5/9.5) 0.604 Aspect ratio (≤ 1/>1) 89/42 (67.9/32.1) 51/6 (89.5/10.5) 38/36 (51.4/48.7) < 0.001 Smooth margin/Irregular or ill-defined margin or ETE 61/70 (46.6/53.4) 38/19 (66.7/33.3) 23/51 (31.1/68.9) < 0.001 Punctate echogenic foci(no/yes) 89/42 (67.9/32.1) 48/9 (84.2/15.8) 41/33 (55.4/44.6) < 0.001 Large comet-tail artifacts(no/yes) 129/2 (98.5/1.5) 55/2 (96.5/3.5) 74/0 (100/0) 0.104 CEUS Enhancement direction (scattered /centripetal or centrifugal/indeterminate) 56/70/5 (42.8/53.4/3.8) 29/25/3 (50.9/43.9/5.3) 27/45/2 (36.5/60.8/2.7) 0.146 Ring enhancement(no/yes) 118/13 (90.1/9.9) 44/13 (77.2/22.8) 74/0 (100/0) < 0.001 Persistent perfusion defects (no/yes) 106/25 (80.9/19.1) 39/18 (68.4/31.6) 67/7 (90.5/9.5) 0.001 Margin sharpness in the washout phase (indistinct vs sharp) 111/20 (84.7/15.3) 43/14 (75.4/24.6) 68/6 (91.9/8.1) 0.009 Hypoenhancement (no/yes) 69/62 (52.7/47.3) 43/14 (75.4/24.6) 26/48 (35.1/64.9) < 0.001 Non-enhancing peripheral halo (no/yes) 122/9 (93.1/6.9) 54/3 (94.7/5.3) 68/6 (91.9/8.1) 0.523 Capsular enhancement continuity (continuous vs interrupted) 107/24 (81.7/18.3) 52/5 (91.2/8.8) 55/19 (74.3/25.7) 0.013 Washout timing relative to surrounding thyroid parenchyma (earlier/synchronous or delayed) 72/59 (55.0/45.0) 22/35 (38.6/61.4) 50/24 (67.6/32.4) 0.001 Unless otherwise specified, data are numbers of nodules, with percentages in parentheses. P < 0.05 indicates a significant difference. US, ultrasound; CEUS, contrast-enhanced ultrasound; ETE, extrathyroidal extension. Interobserver Agreement Inter-observer agreement was good to almost perfect for all features (κ = 0.713–1.000; all P < 0.001), with the lowest for capsular enhancement continuity and highest for large comet-tail artifacts (Table S1 ). Multivariable Analyses and Model Construction Based on multivariate analysis results, two predictive models were constructed: Model 1 incorporated conventional US features (aspect ratio > 1, irregular or ill-defined margin or extrathyroidal extension, and punctate echogenic foci); Model 2 further integrated CEUS features (hypoenhancement and persistent perfusion defects) with Model 1. C-TIRADS management strategy and ACR TI-RADS management strategy were designated as Model 3 and Model 4 respectively, for comparative analysis (Table 3 ). Table 3 Multivariate Analysis Based on Qualitative Conventional US and CEUS Features Variable Model 1 (AIC,139.79) Model 2 (AIC,129.24) OR (95%CI) P OR (95%CI) P Conventional US Aspect ratio > 1 9.63(3.38–27.43) < 0.001 9.21(2.96–28.65) < 0.001 Irregular or ill-defined margin or ETE 3.73(1.59–8.76) 0.003 3.21(1.27–8.08) 0.013 Punctate echogenic foci 4.14(1.59–10.79) 0.004 3.19(1.15–8.86) 0.026 CEUS Hypoenhancement NA NA 4.15(1.63–10.56) 0.003 Persistent perfusion defects NA NA 0.29(0.09–0.89) 0.031 P < 0.05 indicates a statistically significant difference. Model 1 is conventional US features, model 2 is model 1 combined with CEUS features. AIC, Akaike information criterion; OR, odds ratio; CI, confidence interval; US, ultrasound; CEUS, contrast-enhanced ultrasound; ETE, extrathyroidal extension; NA = not applicable. Model Performance in FNA Recommendation ROC analysis demonstrated that Model 2 achieved the highest discriminative performance, with an AUC of 0.867 (95% CI: 0.802–0.932), a sensitivity of 78.4%, a specificity of 82.5%, and an accuracy of 80.2% at the Youden's index–derived optimal cutoff. Model 2 significantly outperformed both Model 3 (AUC = 0.547, 95% CI: 0.447–0.647) and Model 4 (AUC = 0.694, 95% CI: 0.605–0.785) in recommending FNA (both P < 0.001) (Fig. 3 , Table S2 ). Model 1 (conventional US features alone) also demonstrated good discriminative performance (AUC = 0.823, 95% CI: 0.749–0.896), with no statistically significant difference from Model 2 (P = 0.073). To mitigate potential model overfitting, internal validation was conducted using 10-fold cross-validation, which yielded an AUC of 0.822 (95% CI: 0.776–0.867) for Model 2, indicating acceptable model stability. The performance of each model in recommending FNA across different thyroid nodule categories is summarized in Table 4 . Model 2 maintained consistently high performance across different risk stratifications, with particularly strong discrimination in TR 4A (AUC = 0.935, 95% CI: 0.862–1.000) and TR 4B (AUC = 0.843, 95% CI: 0.708–0.977) subgroups. Table 4 Subgroup analysis of FNA recommendation performance across models in different thyroid nodule categories C-TIRADS Level AUC* Sensitivity Specificity Accuracy Youden Index TR 3–5 Model 1 0.823(0.749–0.896) 94.6 57.9 78.6 0.525 Model 2 0.867(0.802–0.932) 78.4 82.5 80.2 0.609 Model 3 0.547(0.447–0.647) 56.8 52.6 55.0 0.094 Model 4 0.694(0.605–0.785) 54.1 78.9 64.9 0.33 TR 4A Model 1 0.516(0.485–0.548) 100 3.2 14.3 0.032 Model 2 0.935(0.862-1.000) 100 87.1 88.6 0.871 Model 3 0.726(0.637–0.815) 100 45.2 51.4 0.452 Model 4 0.560(0.243–0.878) 25.0 83.9 77.1 0.089 TR 4B Model 1 0.656(0.472–0.840) 59.3 64.3 61.0 0.236 Model 2 0.843(0.708–0.977) 66.7 92.9 75.6 0.596 Model 3 0.601(0.437–0.764) 63.0 57.1 61.0 0.201 Model 4 0.696(0.538–0.854) 51.9 85.7 63.4 0.376 TR 4C-5 Model 1 0.684(0.528–0.840) 58.1 66.7 60.0 0.248 Model 2 0.727(0.569–0.885) 74.4 58.3 70.9 0.327 Model 3 0.501(0.337–0.665) 41.9 58.3 45.5 0.002 Model 4 0.592(0.421–0.763) 67.4 50.0 63.6 0.174 Unless otherwise indicated, data are are percentages. The TR 3 subgroup comprised only 4 cases, ROC-derived metrics were not reported due to insufficient sample size. Model 1 is conventional US features, model 2 is model 1 combined with CEUS features, model 3 is management of C-TIRADS, model 4 is management of ACR TI-RADS. *Data in parentheses are 95% CIs. ROC, receiver operating characteristic; AUC, area under the receiver operating characteristic curve; CI, confidence interval; US, ultrasound; CEUS, contrast-enhanced ultrasound; C-TIRADS, Chinese Thyroid Imaging Reporting and Data System; ACR TI-RADS, American College of Radiology Thyroid Imaging Reporting and Data System. FNA, fine-needle aspiration. Calibration analysis demonstrated good agreement between predicted and observed probabilities for all models (Figure S1 ). Both Model 1 (Brier score = 16.5, Hosmer-Lemeshow χ² = 8.280, P = 0.141) and Model 2 (Brier score = 14.1, Hosmer-Lemeshow χ² = 11.78, P = 0.161) exhibited superior calibration compared with C-TIRADS (Brier score = 24.4) and ACR TI-RADS (Brier score = 21.5). To facilitate clinical application of Model 2, a nomogram was constructed based on its constituent predictors, enabling clinicians to visually estimate the probability of malignancy and guide FNA decision-making for individual thyroid nodules (Fig. 4 ). Decision Curve Analysis Decision curve analysis demonstrated that Model 2 provided higher net benefit than Model 3 and Model 4 across clinically relevant threshold probabilities below 86% (Fig. 5 ). Given that a 50% risk threshold is commonly used to guide FNA decisions, ( 2 , 23 ) this study focused on this threshold as the primary analysis point. At this threshold, Model 2 avoided 23.7% of unnecessary FNA procedures without significantly increasing missed diagnoses, with a false-negative rate of 20.3%. In contrast, Model 3 failed to reduce unnecessary FNA by recommending biopsy for all at-risk nodules. Although Model 4 had a slightly lower false-negative rate (16.2%), it avoided only 8.4% of unnecessary FNA while maintaining a high false-positive rate of 59.7%, suggesting that its lower missed diagnosis rate came at the cost of substantial oversampling. At the optimal cutoff of Model 2 (risk threshold: 64%), the unnecessary FNA rate was reduced by 29.0%, at the cost of a false-negative rate of 21.6%. At this threshold, Model 3 yielded a false-negative rate of 100%, failing to identify any malignant nodules, whereas Model 4 demonstrated a false-negative rate of 46.0% while avoiding only 19.8% of unnecessary FNA procedures. In summary, at the most commonly used 50% decision threshold in clinical practice, Model 2 achieved more efficient FNA reduction while reasonably controlling missed diagnosis risk, with overall decision-making performance superior to current Model 3 and Model 4 strategies (Table S3 ). Discussion This study examined whether CEUS combined with conventional ultrasound features could improve malignancy prediction and FNA decision-making for thyroid nodules. A qualitative CEUS-augmented model (Model 2) developed from 131 consecutive nodules achieved an AUC of 0.867, significantly outperforming C-TIRADS and ACR TI-RADS. At the clinically established 50% decision threshold, Model 2 reduced unnecessary FNA procedures by 23.7% while maintaining an acceptable false-negative rate of 20.3%—a balance neither C-TIRADS nor ACR TI-RADS could achieve. These findings suggest that integrating CEUS into conventional US-based risk stratification meaningfully enhances FNA decision-making beyond what current guidelines offer. Our findings are consistent with prior studies demonstrating the added value of CEUS over conventional US alone. Wang et al. developed a nomogram integrating both modalities in 815 nodules, achieving AUCs of 0.947 and 0.957 in training and validation cohorts and reducing unnecessary FNA from 29% to 6.1–6.7%; ( 24 ) Borlea et al. similarly showed that adding CEUS to EU-TIRADS improved AUC from 0.707 to 0.840 in a prospective cohort of 157 nodules. ( 25 ) Wang et al., however, restricted enrollment to ACR TI-RADS 4–5 nodules with iso- or hyperenhancement on CEUS—a pre-selected subpopulation where CEUS adds the greatest incremental value—and applied a point-based scoring system, achieving AUCs of 0.921 and 0.900 across two cohorts.( 26 ) Our study, by contrast, enrolled an unselected consecutive series and applied multivariate logistic regression across both modalities, enabling joint feature weighting and better generalizability. We further benchmarked Model 2 against both C-TIRADS and ACR TI-RADS using decision-curve analysis, demonstrating superior net clinical benefit across a broad range of threshold probabilities. Model 2 provides incremental diagnostic value by combining the morphological features of grayscale US with the microvascular perfusion characteristics of CEUS. Perfusion signatures, such as hypoenhancement, heterogeneous perfusion, and disrupted capsular enhancement, are inaccessible to conventional US and have been established as strong independent predictors of malignancy. ( 16 , 27 , 28 ) This biological complementarity confers the greatest benefit in intermediate-risk categories, where Model 2 demonstrated its strongest discrimination (TR 4A: AUC = 0.935; TR 4B: AUC = 0.843). Hence, The model was further operationalized as a nomogram, enabling individualized malignancy probability estimation at the point of care without computational dependencies. In high-volume screening programs, Model 2 may help reduce avoidable procedures without compromising sensitivity, as decision-curve analysis shows greater net benefit than either guideline alone across relevant threshold probabilities. Several methodological strengths support the validity and practical relevance of our findings. First, rather than constructing additive scoring rules from univariably selected features—a common limitation in prior CEUS studies—we applied multivariate logistic regression, which allows simultaneous weighting of correlated predictors and more faithfully captures the joint discriminative structure of dual-modality data. Second, model performance was evaluated not only by AUC but also by decision-curve analysis benchmarked against both C-TIRADS and ACR TI-RADS. This approach moves beyond conventional accuracy metrics and directly addresses whether adopting the model would yield net clinical benefit across a range of real-world decision thresholds. Third, interobserver agreement for CEUS feature interpretation was formally assessed, quantifying the reproducibility of the imaging inputs on which the model relies and confirming that its application is not contingent on a single experienced reader. These considerations may strengthen the credibility of the conclusions drawn. This study has several limitations that should be acknowledged. First, as a single-center retrospective cohort, inherent selection and information biases cannot be fully excluded, and independent prospective validation is needed before the findings can be widely adopted. Second, the predominance of nodules measuring ≤ 10 mm in this cohort meant that a substantial proportion of malignant nodules fell below the size thresholds required for FNA recommendation under ACR TI-RADS, which may limit the generalizability of comparative performance findings between risk stratification systems. Finally, the exclusion of poor-quality CEUS images, though necessary to ensure analytical rigor, may lead to a modest overestimation of model performance relative to unselected real-world imaging conditions. In conclusion, CEUS-augmented risk stratification of thyroid nodules may offer a clinically meaningful complement to existing guideline-based systems, particularly in refining FNA decision-making for nodules that current scoring frameworks inadequately resolve. Prospective multicenter studies are needed to establish the external validity of these findings across diverse clinical settings and patient populations. Abbreviations Abbreviation Full Term ACR TI-RADS American College of Radiology Thyroid Imaging Reporting and Data System AIC Akaike Information Criterion AUC Area Under the Receiver Operating Characteristic Curve Brier Score Measure of forecast accuracy C-TIRADS Chinese Thyroid Imaging Reporting and Data System CEUS Contrast-Enhanced Ultrasound CI Confidence Interval DCA Decision Curve Analysis DICOM Digital Imaging and Communications in Medicine ETE Extrathyroidal Extension FNA Fine-Needle Aspiration IQR Interquartile Range NB Net Benefit NRFP Net Reduction in False-Positives OR Odds Ratio PACS Picture Archiving and Communication System PTC Papillary Thyroid Carcinoma ROC Receiver Operating Characteristic SD Standard Deviation sNB Standardized Net Benefit US Ultrasound Declarations Ethics approval and consent to participate This study was approved by the Medical Ethics Committee of the Sixth Affiliated Hospital of Sun Yat-sen University (Ethics No.: 2025ZSLYEC-608) and conducted in accordance with the Declaration of Helsinki. Informed consent was waived due to the retrospective nature of the study. Consent for publication Not applicable. This retrospective study analyzed existing clinical data and imaging records without containing any identifiable individual person's data. Availability of data and materials The data that support the findings of this study are not publicly available due to patient privacy protection under Chinese regulations and institutional policy. The raw data generated during this study include imaging data (ultrasound and CEUS images), clinical demographics, pathological results, and imaging measurements from 131 thyroid nodules obtained from 129 patients at the Sixth Affiliated Hospital of Sun Yat-sen University. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the Clinical Research '1010' Program of the Sixth Affiliated Hospital of Sun Yat-sen University (Grant No. 1010PY(2022)-27) and the Youth Program of the National Natural Science Foundation of China (Grant No. 82502360). Authors' contributions YYL: Data collection (equal), statistical analysis (lead), writing – original draft preparation (lead). YC: Conceptualization (equal), methodology (equal), image analysis (equal). YMW: Data collection (equal). SQ: Data collection (equal). JZH: Data collection (equal). YXH: Data collection (equal). YL: Data collection (equal). GJL: Supervision (lead). RC: Writing – review and editing (lead), conceptualization (equal), methodology (equal), image analysis (equal), statistical analysis (supporting). Acknowledgements Not applicable. References Solomon C, Petea-Balea DR, Dudea SM, Bene I, Silaghi CA, Lenghel ML. 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Technol Cancer Res Treat. 2024 Jan;23:15330338241297599. doi:10.1177/15330338241297599 Żyłka A, Dobruch-Sobczak K, Piotrzkowska-Wróblewska H, Jędrzejczyk M, Bakuła-Zalewska E, Góralski P, et al. The utility of contrast-enhanced ultrasound (CEUS) in assessing the risk of malignancy in thyroid nodules. Cancers. 2024 May 17;16(10):1911. doi:10.3390/cancers16101911 PubMed PMID: 38791990; PubMed Central PMCID: PMC11119249. Fan J, Tao L, Zhan W, Li W, Kuang L, Zhao Y, et al. Diagnostic value of qualitative and quantitative parameters of contrast-enhanced ultrasound for differentiating differentiated thyroid carcinomas from benign nodules. Front Endocrinol. 2024 Jan 4;14. doi:10.3389/fendo.2023.1240615 Vickers AJ, Van Calster B, Steyerberg EW. A simple, step-by-step guide to interpreting decision curve analysis. Diagn Progn Res. 2019 Dec;3(1):18. doi:10.1186/s41512-019-0064-7 Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. 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Cancers. 2022 Jan 30;14(3):717. doi:10.3390/cancers14030717 PubMed PMID: 35158985; PubMed Central PMCID: PMC8833686. Wang QG, Li M, Deng GX, Huang HQ, Qiu Q, Lin JJ. Development and validation of a nomogram based on conventional and contrast-enhanced ultrasound for differentiating malignant from benign thyroid nodules. Quant Imaging Med Surg. 2025 May 1;15(5):4641–54. doi:10.21037/qims-24-1796 PubMed PMID: 40384666; PubMed Central PMCID: PMC12082573. Borlea A, Moisa-Luca L, Popescu A, Bende F, Stoian D. Combining CEUS and ultrasound parameters in thyroid nodule and cancer diagnosis: a TIRADS-based evaluation. Front Endocrinol. 2024;15:1417449. doi:10.3389/fendo.2024.1417449 PubMed PMID: 38952390; PubMed Central PMCID: PMC11215041. Wang Y, Dong T, Nie F, Wang G, Liu T, Niu Q. Contrast-enhanced ultrasound in the differential diagnosis and risk stratification of ACR TI-RADS category 4 and 5 thyroid nodules with non-hypovascular. Front Oncol. 2021;11:662273. doi:10.3389/fonc.2021.662273 PubMed PMID: 34123819; PubMed Central PMCID: PMC8189148. Ding Y, Peng Y, Zhang J, Pan X, Huang X, Zhang CQ. Diagnostic value of contrast-enhanced ultrasound in the diagnosis of papillary thyroid microcarcinoma: a systematic review and meta-analysis. Medicine (Baltimore). 2024 Apr 12;103(15):e37768. doi:10.1097/MD.0000000000037768 PubMed PMID: 38608080; PubMed Central PMCID: PMC11018218. Mitchell JC, Parangi S. Angiogenesis in benign and malignant thyroid disease. Thyroid: Off J Am Thyroid Assoc. 2005 Jun;15(6):494–510. doi:10.1089/thy.2005.15.494 PubMed PMID: 16029116. Additional Declarations No competing interests reported. Supplementary Files OnlinesuppTableS1.docx OnlinesuppTableS2.docx OnlinesuppTableS3.docx OnlinesuppFigureS1.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 17 May, 2026 Reviewers agreed at journal 09 May, 2026 Reviews received at journal 03 May, 2026 Reviewers agreed at journal 28 Apr, 2026 Reviewers invited by journal 28 Apr, 2026 Editor invited by journal 03 Apr, 2026 Editor assigned by journal 02 Apr, 2026 Submission checks completed at journal 02 Apr, 2026 First submitted to journal 30 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9268669","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":635572506,"identity":"747be88a-469e-4a81-a9a7-cad94e32f626","order_by":0,"name":"Yaoyun Liang","email":"","orcid":"","institution":"Department of Ultrasonography, The Sixth Affiliated Hospital, Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Yaoyun","middleName":"","lastName":"Liang","suffix":""},{"id":635572507,"identity":"8315e68a-2b4e-4bf9-ba99-ce7f29d8a71c","order_by":1,"name":"Yao Chen","email":"","orcid":"","institution":"Department of Ultrasonography, The Sixth Affiliated Hospital, Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Yao","middleName":"","lastName":"Chen","suffix":""},{"id":635572508,"identity":"b07a82c6-9437-486d-9483-75c51038e5f9","order_by":2,"name":"Yimin Wang","email":"","orcid":"","institution":"Department of Ultrasonography, The Sixth Affiliated Hospital, Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Yimin","middleName":"","lastName":"Wang","suffix":""},{"id":635572509,"identity":"16a0e4f0-e47d-43a4-8431-742d2b465d3c","order_by":3,"name":"Si Qin","email":"","orcid":"","institution":"Department of Ultrasonography, The Sixth Affiliated Hospital, Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Si","middleName":"","lastName":"Qin","suffix":""},{"id":635572510,"identity":"b5a39066-ade6-4c5b-b7a5-2488a68e5d97","order_by":4,"name":"Jinzhi Hu","email":"","orcid":"","institution":"Department of Ultrasonography, The Sixth Affiliated Hospital, Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Jinzhi","middleName":"","lastName":"Hu","suffix":""},{"id":635572511,"identity":"6d77cf32-1a11-48f5-ba25-8ca9478510fa","order_by":5,"name":"Yuxiao Huang","email":"","orcid":"","institution":"Department of Ultrasonography, The Sixth Affiliated Hospital, Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Yuxiao","middleName":"","lastName":"Huang","suffix":""},{"id":635572512,"identity":"e9513a0a-612c-481e-95b6-e220a5365a2e","order_by":6,"name":"Yun Lin","email":"","orcid":"","institution":"Department of Ultrasonography, The Sixth Affiliated Hospital, Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Yun","middleName":"","lastName":"Lin","suffix":""},{"id":635572513,"identity":"67aece51-fb84-49d5-bf08-72f3355621bc","order_by":7,"name":"Guangjian Liu","email":"","orcid":"","institution":"Department of Ultrasonography, The Sixth Affiliated Hospital, Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Guangjian","middleName":"","lastName":"Liu","suffix":""},{"id":635572514,"identity":"880991bc-cbbf-4a0b-bfdd-238b741946c8","order_by":8,"name":"Rui Cui","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYFACxgbmHxUMDGzscJEEIrQwnAFqYSZeCwMDM2MbiCRWC/+M5LbPhfO2yfMxMzB/5vlzmIGfPceA4ecO3FokbiQ2z5657bZhGzMDmzRv22EGyZ43Boy9Z3BrMZBIbGbg3XabEaSFmbfhMIPBjRwDiFPxaplz274N5jB7YrQADb+dCNTCIM3DBrRFgoAWiTMPmxlnHLud3AZUJjm3LZ1H4syzgoO9eLTwt6c/ZvhQc9t2fnvz4Q9v/ljL8bcnb3zwE48WJMDYwMTDwMADYh4gSgNY0w+ilY6CUTAKRsFIAgDc/UnJv5lBegAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Ultrasonography, The Sixth Affiliated Hospital, Sun Yat-sen University","correspondingAuthor":true,"prefix":"","firstName":"Rui","middleName":"","lastName":"Cui","suffix":""}],"badges":[],"createdAt":"2026-03-30 14:53:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9268669/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9268669/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108955283,"identity":"a562f7fe-7343-4e4d-a3d2-b75aa4edd2ce","added_by":"auto","created_at":"2026-05-11 08:06:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":296682,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of patient selection. US, ultrasound; CEUS, contrast-enhanced ultrasound; FNA, fine-needle aspiration; n, number of thyroid nodules; pt, number of patients.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9268669/v1/b0a53a80ab2587de4d0335ad.png"},{"id":108977353,"identity":"8752c0dc-6ab6-44e2-98f8-2cc7536d5882","added_by":"auto","created_at":"2026-05-11 11:31:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3371955,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagrams and representative CEUS images of thyroid nodules. The left column illustrates two malignant features (A-B) and four benign features (C-F) as schematic diagrams. Each row (A-F) contains a grayscale ultrasound image (middle) and a single CEUS image (right) for each nodule. The maximum diameter of each nodule is reported. Arrows or dashed lines highlight specific areas corresponding to each enhancement pattern. \u003cstrong\u003e(A)\u003c/strong\u003e Hypoenhancement (area smaller than the nodule on grayscale imaging): 56-year-old male, right lobe, 8mm; cytopathology: PTC. \u003cstrong\u003e(B) \u003c/strong\u003eCapsular enhancement continuity (interrupted): 61-year-old female, left lobe, 10 mm; histopathology: PTC.\u003cstrong\u003e (C) \u003c/strong\u003eRing enhancement: 26-year-old female, left lobe, 26 mm; cytopathology: benign. \u003cstrong\u003e(D)\u003c/strong\u003e Persistent perfusion defects: 56-year-old female, left lobe, 15 mm; cytopathology: benign. \u003cstrong\u003e(E) \u003c/strong\u003eMargin sharpness in the washout phase (sharp): 35-year-old female, left lobe, 13 mm; cytopathology: benign.\u003cstrong\u003e (F) \u003c/strong\u003eWashout timing relative to surrounding thyroid parenchyma (synchronous): 75-year-old female, right lobe, 26 mm; cytopathology: benign.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9268669/v1/80c13b48a02c0c4ece60f078.png"},{"id":108978043,"identity":"112dd63d-10d7-4dae-9d1a-1986454bdce2","added_by":"auto","created_at":"2026-05-11 11:33:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":164065,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curves show models for recommending FNA of thyroid nodules. Model 2 performed best. Model 1 is conventional US features, model 2 is model 1 combined with CEUS features, model 3 is management of C-TIRADS, model 4 is management of ACR TI-RADS. AUC, area under the receiver operating characteristic curve; CI, confidence interval; US, ultrasound; CEUS, contrast-enhanced ultrasound; C-TIRADS, Chinese Thyroid Imaging Reporting and Data System; ACRTI-RADS, American College of Radiology Thyroid Imaging Reporting and Data System. FNA, fine-needle aspiration.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9268669/v1/ca0a653301a109ba1c0d4e36.png"},{"id":108978136,"identity":"b0f43fcf-88ff-44bb-9831-393c64b1e09d","added_by":"auto","created_at":"2026-05-11 11:34:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":113557,"visible":true,"origin":"","legend":"\u003cp\u003eThe nomogram of model 2 for estimating the malignancy probability of thyroid nodules. Model 2 is conventional US combined with CEUS features.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9268669/v1/54bc54f430757cbb2d847b38.png"},{"id":108977623,"identity":"e8ea42d5-93ef-4880-983c-1a52202cee19","added_by":"auto","created_at":"2026-05-11 11:32:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":163324,"visible":true,"origin":"","legend":"\u003cp\u003eGraph shows the standardized net benefit of each model in recommending FNA for thyroid nodules based on the probability of malignancy. All two built models had higher standardized net benefits than model 3 and model 4. \u003cem\u003eAll \u003c/em\u003eindicates FNA to all thyroid nodules model; model 1 is conventional US features; model 2 is model 1 combined with CEUS features; model 3 is management of C-TIRADS; model 4 is management of ACR TI-RADS; \u003cem\u003enone \u003c/em\u003eindicates no FNA to all thyroid nodules model. FNA, fine-needle aspiration; US, ultrasound; CEUS, contrast-enhanced ultrasound; C-TIRADS, Chinese Thyroid Imaging Reporting and Data System; ACRTI-RADS, American College of Radiology Thyroid Imaging Reporting and Data System.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9268669/v1/7b201cfe5c44b1fec47aa0b0.png"},{"id":108979785,"identity":"ef0b96ce-fd57-46b2-813c-98e9fb93c7d4","added_by":"auto","created_at":"2026-05-11 12:01:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4416969,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9268669/v1/a25dc699-5d5d-4166-b396-7504e9efd5ba.pdf"},{"id":108977780,"identity":"b9c20ce0-d87e-4283-b02a-f8c68fd89fb3","added_by":"auto","created_at":"2026-05-11 11:32:52","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16816,"visible":true,"origin":"","legend":"","description":"","filename":"OnlinesuppTableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9268669/v1/137ef7c1cf79cbcac9732ed7.docx"},{"id":108978017,"identity":"66f4d6cc-bce3-4739-9f10-abc490790ccd","added_by":"auto","created_at":"2026-05-11 11:33:43","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":15395,"visible":true,"origin":"","legend":"","description":"","filename":"OnlinesuppTableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-9268669/v1/017ea1e136eaeec910869922.docx"},{"id":108978135,"identity":"b2f67527-6e89-4ae4-82e2-a6770c23d360","added_by":"auto","created_at":"2026-05-11 11:34:16","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":18251,"visible":true,"origin":"","legend":"","description":"","filename":"OnlinesuppTableS3.docx","url":"https://assets-eu.researchsquare.com/files/rs-9268669/v1/9cae8b1586e9ce534ae4b2da.docx"},{"id":108977486,"identity":"c08a7986-35a3-45f3-82c1-72928460337a","added_by":"auto","created_at":"2026-05-11 11:31:53","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":902630,"visible":true,"origin":"","legend":"","description":"","filename":"OnlinesuppFigureS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9268669/v1/b865db081a38608e1a96f40c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Contrast-Enhanced Ultrasound-Augmented Risk Stratification for Fine-Needle Aspiration Selection in Thyroid Nodules","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThyroid nodules are commonly encountered in clinical practice, with prevalence rates varying by region, reaching 50%\u0026ndash;67% in the general population. (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Despite the high incidence of thyroid nodule, its mortality remains low as most nodules are benign and require no intervention. Currently, clinical management relies primarily on risk stratification to guide fine-needle aspiration (FNA) decisions. This stratification is performed using ultrasound-based systems, such as the American College of Radiology TI-RADS (ACR TI-RADS) and the Chinese TI-RADS (C-TIRADS), (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) which evaluate nodule malignancy risk based on sonographic features. However, these conventional ultrasound (US)-based assessment systems exhibit high false-positive rates, (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) resulting in a considerable proportion of benign nodules being misclassified as suspicious for malignancy and subjected to unnecessary FNA. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) Globally, this leads to overdiagnosis in 60% of cases, with unnecessary biopsies performed on benign lesions. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) This overdiagnosis not only increases patients' psychological and economic burden but may also elevate the risk of puncture-related complications. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eTherefore, there is an urgent need to develop more accurate diagnostic tools to optimize current thyroid nodule management strategies. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) By visualizing microvascular perfusion in real-time, CEUS provides hemodynamic information beyond conventional ultrasound capabilities, offering unique advantages for thyroid nodule characterization. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) Qualitative CEUS features, including ring enhancement, homogeneous perfusion, and boundary clarity, have been particularly valuable in identifying low-risk nodules that may avoid invasive procedures. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)Although studies have demonstrated the value of qualitative CEUS features in differentiating benign from malignant thyroid nodules, (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) systematic evidence for reducing unnecessary FNA remains limited. Accordingly, this study develops an integrated risk prediction model combining conventional ultrasound and CEUS features, and evaluates its potential to reduce unnecessary FNA in thyroid nodules.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Participants\u003c/h2\u003e \u003cp\u003eThis retrospective study was conducted at the Sixth Affiliated Hospital of Sun Yat-sen University between November 2023 and January 2025. Patients with thyroid nodules who underwent both US and CEUS examinations, followed by pathological confirmation via FNA cytology or surgical resection histology, were consecutively enrolled.\u003c/p\u003e \u003cp\u003eInclusion criteria were: (i) preoperative conventional US indicating ACR-TIRADS 3\u0026ndash;5 categories, with CEUS examination completed before puncture or surgery; (ii) complete dynamic CEUS images with adequate quality for retrospective analysis; (iii) definitive pathological diagnosis via surgical pathology or FNA cytology; (iv) interval between CEUS examination and pathological diagnosis\u0026thinsp;\u0026le;\u0026thinsp;3 months; and (v) complete clinical data including patient demographics and nodule characteristics.\u003c/p\u003e \u003cp\u003eExclusion criteria were: (i) FNA puncture or ablation intervention performed before CEUS examination; (ii) FNA cytology results of Bethesda I/III/IV categories without surgical pathology confirmation; and (iii) poor CEUS image quality preventing accurate interpretation.\u003c/p\u003e \u003cp\u003eThis study was approved by the Medical Ethics Committee of the Sixth Affiliated Hospital of Sun Yat-sen University (Ethics No.: 2025ZSLYEC-608) and conducted in accordance with the Declaration of Helsinki. Informed consent was waived due to the retrospective nature of the study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eImage Acquisition\u003c/h3\u003e\n\u003cp\u003eAll ultrasound examinations were performed as part of routine clinical practice at our institution using a Siemens ACUSON Sequoia ultrasound system (Siemens Healthineers, Erlangen, Germany) with a 14L5 linear array transducer. Standard imaging protocol included scanning of bilateral thyroid lobes in multiple planes (transverse, longitudinal, and oblique), with documentation of nodule characteristics including composition, echogenicity, shape, margins, calcifications, and dimensions. Contrast-enhanced ultrasound was performed using contrast mode (4\u0026ndash;9 MHz) when clinically indicated. SonoVue contrast agent (Bracco, Milan, Italy; 1.2\u0026ndash;1.5 mL) was administered as an intravenous bolus via the antecubital vein, followed by a 5-mL saline flush. The target nodule and surrounding normal thyroid parenchyma were continuously visualized in the longitudinal plane for at least 60 seconds following contrast injection. Dynamic cine clips were stored in DICOM format in PACS.\u003c/p\u003e\n\u003ch3\u003eImage analysis\u003c/h3\u003e\n\u003cp\u003eTwo physicians with more than 5 years of CEUS diagnostic experience independently evaluated the sonographic and CEUS features of all included patients, blinded to pathological outcomes. When opinions between the two physicians were inconsistent, final consensus was reached through discussion and consultation. Cohen's kappa coefficient was used to evaluate inter-observer agreement for each ultrasound feature.\u003c/p\u003e \u003cp\u003eConventional US features evaluated included: nodule composition (solid, cystic, or mixed), echogenicity (anechoic, hyperechoic, isoechoic, hypoechoic, or markedly hypoechoic), aspect ratio (\u0026gt;\u0026thinsp;1 or \u0026le;\u0026thinsp;1), margin characteristics (smooth, ill-defined, lobulated/irregular, or extrathyroidal extension) and calcification type. Each nodule was graded according to C-TIRADS and ACR TI-RADS criteria, and FNA indication was determined according to corresponding guidelines based on nodule maximum diameter. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eCEUS features evaluated included: enhancement direction (scattered /centripetal or centrifugal/ indeterminate), presence of ring enhancement, presence of persistent perfusion defects, margin sharpness in the washout phase (indistinct vs sharp), presence of hypoenhancement (area smaller than the nodule on grayscale imaging), presence of non-enhancing peripheral halo, capsular enhancement continuity (continuous vs interrupted), and washout timing relative to surrounding thyroid parenchyma (earlier vs synchronous or delayed).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll analyses were performed using SPSS 25.0 and R 4.4.2 (R Foundation for Statistical Computing, Vienna, Austria). Continuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median (IQR) depending on normality (Kolmogorov-Smirnov test) and compared using independent samples t-test or Mann-Whitney U test, respectively. Categorical variables were compared using χ\u0026sup2; test or Fisher's exact test.\u003c/p\u003e \u003cp\u003eMultivariable logistic regression with stepwise selection (entry/removal threshold: α\u0026thinsp;=\u0026thinsp;0.10) was performed to identify independent predictors of malignancy from conventional US and CEUS features. Model discrimination was assessed using area under the receiver operating characteristic curve (AUC) with 95% CI. The optimal cutoff was determined using Youden's index, with corresponding sensitivity, specificity, and accuracy calculated. DeLong's test was used to compare AUCs between the developed model and existing risk stratification systems (C-TIRADS and ACR TI-RADS). Model calibration was evaluated using calibration curves and the Hosmer-Lemeshow test. Internal validation was conducted via 10-fold cross-validation, and the final model was visualized as a nomogram.\u003c/p\u003e \u003cp\u003eTo evaluate the clinical utility of each predictive model in guiding FNA decision-making, decision curve analysis (DCA) was performed to quantify net benefit (NB) across a range of threshold probabilities (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{t}\\)\u003c/span\u003e\u003c/span\u003e). (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) The threshold probability represents the risk level above which FNA is recommended over observation, reflecting the balance between avoiding missed malignancies and reducing unnecessary biopsies. At a given threshold, the model's NB was calculated as: : (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:NB\\left({P}_{t}\\right)=\\frac{TP\\left({P}_{t}\\right)}{N}-\\frac{FP\\left({P}_{t}\\right)}{N}\\times\\:\\frac{{P}_{t}}{1-{P}_{t}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:TP\\left({P}_{t}\\right)\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:FP\\left({P}_{t}\\right)\\:\\)\u003c/span\u003e\u003c/span\u003erepresent the number of true positives and false positives at threshold Pt, respectively, and N is the total sample size. To facilitate comparison between different models and curve presentation, the standardized net benefit (sNB) was further calculated: (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:sNB\\left({P}_{t}\\right)=\\frac{N{B}_{model}\\left({P}_{t}\\right)}{prevalence}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAn ideal model has an sNB of 1. Additionally, from the perspective of intervention reduction, the net reduction in false-positives (NRFP) was employed to quantify the proportion of truly avoided unnecessary FNA without increasing missed diagnoses. The formula is as follows: (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:NRFP\\left({P}_{t}\\right)=\\frac{N{B}_{model}\\left({P}_{t}\\right)-N{B}_{all}\\left({P}_{t}\\right)}{{P}_{t}/(1-{P}_{t})}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe 95% confidence intervals for all metrics were estimated using the non-parametric bootstrap method with 1000 resampling iterations.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eParticipant Characteristics\u003c/h2\u003e \u003cp\u003eA total of 131 thyroid nodules from 129 patients were included, comprising 57 benign and 74 malignant nodules (all papillary thyroid carcinoma, PTC). The patient selection process is illustrated in the flowchart (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The cohort comprised 98 women and 33 men (mean age, 43.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.6 years; range, 16\u0026ndash;84 years). Demographic and clinical characteristics are summarized as follows (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Although females predominated overall, the proportion of male patients was significantly higher in the malignant group (32.4% vs 15.8%, P\u0026thinsp;=\u0026thinsp;0.030). Patient age (P\u0026thinsp;=\u0026thinsp;0.250) and nodule location (P\u0026thinsp;=\u0026thinsp;0.893) did not differ significantly between groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient Demographics and Nodule Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAll Participants (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;131)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eBenign(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;57)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMalignant(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;74)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean age (y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e43.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e45.0\u0026thinsp;\u0026plusmn;\u0026thinsp;14.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42.0\u0026thinsp;\u0026plusmn;\u0026thinsp;14.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e98(74.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e48(84.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50(67.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e33(25.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e9(15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24(32.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNodule location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e59(45.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e27(47.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32(43.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e67(51.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e28(49.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39(52.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsthmus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e5(3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2(3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3(4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiameter (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e15(11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e7(12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8(10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5mm, \u0026le;\u0026thinsp;10mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e62(47.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e20(35.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42(56.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e54(41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e30(52.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24(32.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-TIRADS level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTR 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e4(3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e4(7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTR 4A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e31(23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e27(47.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4(5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTR 4B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e41(31.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e14(24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27(36.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTR 4C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e54(41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e12(21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42(56.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTR 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1(0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1(1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACR TI-RADS level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTR 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e16 (12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e16(28.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTR 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e50(38.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e29(50.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21(28.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTR 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e65(49.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e12(21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53(71.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eManagement of C-TIRADS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e62(47.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e30(52.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32(43.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e69(52.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e27(47.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42(56.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eManagement of ACR TI-RADS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo follow-up and no FNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e35(26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e23(40.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12(16.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e52(39.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e12(21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40(54.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e44(33.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e22(38.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22(29.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eUnless otherwise specified, data are numbers of nodules, with percentages in parentheses. Mean data are \u0026plusmn;\u0026thinsp;SDs. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates a significant difference.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eFNA, fine-needle aspiration; C-TIRADS, Chinese Thyroid Imaging Reporting and Data System; ACR TI-RADS, American College of Radiology Thyroid Imaging Reporting and Data System.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eC-TIRADS and ACR TI-RADS distributions differed significantly between benign and malignant groups (both P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Malignancy rates increased progressively with C-TIRADS category (TR3: 0%, TR4A: 12.9%, TR4B: 65.9%, TR4C: 77.8%, TR5: 100%) and ACR TI-RADS category (TR3: 0%, TR4: 42.0%, TR5: 81.5%). Regarding FNA guidance, C-TIRADS would recommend biopsy for 47.4% (27/57) of benign nodules while deferring 43.2% (32/74) of malignancies to follow-up. According to ACR TI-RADS criteria, 22 of 57 benign nodules (38.6%) met criteria for FNA, while 52 of 74 malignant nodules (70.3%) did not meet FNA criteria .\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eUnivariate Analysis\u003c/h3\u003e\n\u003cp\u003eUnivariate analysis identified several features that differed significantly between benign (n\u0026thinsp;=\u0026thinsp;57) and malignant (n\u0026thinsp;=\u0026thinsp;74) nodules (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). On conventional US, malignant nodules more frequently demonstrated solid composition (100% vs 93.0%, P\u0026thinsp;=\u0026thinsp;0.021), aspect ratio\u0026thinsp;\u0026gt;\u0026thinsp;1 (48.7% vs 10.5%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), irregular or ill-defined margin or extrathyroidal extension (68.9% vs 33.3%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and punctate echogenic foci (44.6% vs 15.8%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). On CEUS, malignant nodules more frequently showed hypoenhancement (area smaller than the nodule on grayscale imaging) (64.9% vs 24.6%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and interupted capsular enhancement (25.7% vs 8.8%, P\u0026thinsp;=\u0026thinsp;0.013), while benign nodules more frequently demonstrated ring enhancement (22.8% vs 0%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), persistent perfusion defects (31.6% vs 9.5%, P\u0026thinsp;=\u0026thinsp;0.001), and sharp margin in the washout phase (24.6% vs 8.1%, P\u0026thinsp;=\u0026thinsp;0.009), and synchronous or delayed washout timing relative to surrounding thyroid parenchyma (61.4% vs 32.4%, P\u0026thinsp;=\u0026thinsp;0.001). The qualitative CEUS features are illustrated with representative images. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate Analysis of Qualitative Features on Conventional US and CEUS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll Participants\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;131)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBenign\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;57)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMalignant\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;74)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConventional US\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCystic\u0026ndash;solid/Solid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4/127\u003c/p\u003e \u003cp\u003e(3.1/97.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4/53\u003c/p\u003e \u003cp\u003e(7.0/93.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0/74\u003c/p\u003e \u003cp\u003e(0/100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarkedly hypoechoic (no/yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117/14\u003c/p\u003e \u003cp\u003e(89.3/10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50/7\u003c/p\u003e \u003cp\u003e(87.7/12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67/7\u003c/p\u003e \u003cp\u003e(90.5/9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspect ratio (\u0026le;\u0026thinsp;1/\u0026gt;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89/42\u003c/p\u003e \u003cp\u003e(67.9/32.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51/6\u003c/p\u003e \u003cp\u003e(89.5/10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38/36\u003c/p\u003e \u003cp\u003e(51.4/48.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmooth margin/Irregular or ill-defined margin or ETE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61/70\u003c/p\u003e \u003cp\u003e(46.6/53.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38/19\u003c/p\u003e \u003cp\u003e(66.7/33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23/51\u003c/p\u003e \u003cp\u003e(31.1/68.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePunctate echogenic foci(no/yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89/42\u003c/p\u003e \u003cp\u003e(67.9/32.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48/9\u003c/p\u003e \u003cp\u003e(84.2/15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41/33\u003c/p\u003e \u003cp\u003e(55.4/44.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLarge comet-tail artifacts(no/yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129/2\u003c/p\u003e \u003cp\u003e(98.5/1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55/2\u003c/p\u003e \u003cp\u003e(96.5/3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74/0\u003c/p\u003e \u003cp\u003e(100/0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnhancement direction (scattered /centripetal or centrifugal/indeterminate)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56/70/5 (42.8/53.4/3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29/25/3\u003c/p\u003e \u003cp\u003e(50.9/43.9/5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27/45/2\u003c/p\u003e \u003cp\u003e(36.5/60.8/2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRing enhancement(no/yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e118/13\u003c/p\u003e \u003cp\u003e(90.1/9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44/13\u003c/p\u003e \u003cp\u003e(77.2/22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74/0\u003c/p\u003e \u003cp\u003e(100/0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersistent perfusion defects (no/yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106/25\u003c/p\u003e \u003cp\u003e(80.9/19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39/18\u003c/p\u003e \u003cp\u003e(68.4/31.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67/7\u003c/p\u003e \u003cp\u003e(90.5/9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMargin sharpness in the washout phase (indistinct vs sharp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111/20\u003c/p\u003e \u003cp\u003e(84.7/15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43/14\u003c/p\u003e \u003cp\u003e(75.4/24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68/6\u003c/p\u003e \u003cp\u003e(91.9/8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypoenhancement (no/yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69/62\u003c/p\u003e \u003cp\u003e(52.7/47.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43/14\u003c/p\u003e \u003cp\u003e(75.4/24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26/48\u003c/p\u003e \u003cp\u003e(35.1/64.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-enhancing peripheral halo (no/yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e122/9\u003c/p\u003e \u003cp\u003e(93.1/6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54/3\u003c/p\u003e \u003cp\u003e(94.7/5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68/6\u003c/p\u003e \u003cp\u003e(91.9/8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.523\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCapsular enhancement continuity (continuous vs interrupted)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107/24\u003c/p\u003e \u003cp\u003e(81.7/18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52/5\u003c/p\u003e \u003cp\u003e(91.2/8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55/19\u003c/p\u003e \u003cp\u003e(74.3/25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWashout timing relative to surrounding thyroid parenchyma (earlier/synchronous or delayed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72/59\u003c/p\u003e \u003cp\u003e(55.0/45.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22/35\u003c/p\u003e \u003cp\u003e(38.6/61.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50/24\u003c/p\u003e \u003cp\u003e(67.6/32.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eUnless otherwise specified, data are numbers of nodules, with percentages in parentheses. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates a significant difference.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eUS, ultrasound; CEUS, contrast-enhanced ultrasound; ETE, extrathyroidal extension.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eInterobserver Agreement\u003c/h3\u003e\n\u003cp\u003eInter-observer agreement was good to almost perfect for all features (κ\u0026thinsp;=\u0026thinsp;0.713\u0026ndash;1.000; all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with the lowest for capsular enhancement continuity and highest for large comet-tail artifacts (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMultivariable Analyses and Model Construction\u003c/h2\u003e \u003cp\u003eBased on multivariate analysis results, two predictive models were constructed: Model 1 incorporated conventional US features (aspect ratio\u0026thinsp;\u0026gt;\u0026thinsp;1, irregular or ill-defined margin or extrathyroidal extension, and punctate echogenic foci); Model 2 further integrated CEUS features (hypoenhancement and persistent perfusion defects) with Model 1. C-TIRADS management strategy and ACR TI-RADS management strategy were designated as Model 3 and Model 4 respectively, for comparative analysis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate Analysis Based on Qualitative Conventional US and CEUS Features\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1 (AIC,139.79)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 2 (AIC,129.24)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConventional US\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspect ratio\u0026thinsp;\u0026gt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.63(3.38\u0026ndash;27.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.21(2.96\u0026ndash;28.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIrregular or ill-defined margin or ETE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.73(1.59\u0026ndash;8.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.21(1.27\u0026ndash;8.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePunctate echogenic foci\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.14(1.59\u0026ndash;10.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.19(1.15\u0026ndash;8.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypoenhancement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.15(1.63\u0026ndash;10.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersistent perfusion defects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29(0.09\u0026ndash;0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates a statistically significant difference. Model 1 is conventional US features, model 2 is model 1 combined with CEUS features.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAIC, Akaike information criterion; OR, odds ratio; CI, confidence interval; US, ultrasound; CEUS, contrast-enhanced ultrasound; ETE, extrathyroidal extension; NA\u0026thinsp;=\u0026thinsp;not applicable.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eModel Performance in FNA Recommendation\u003c/h2\u003e \u003cp\u003eROC analysis demonstrated that Model 2 achieved the highest discriminative performance, with an AUC of 0.867 (95% CI: 0.802\u0026ndash;0.932), a sensitivity of 78.4%, a specificity of 82.5%, and an accuracy of 80.2% at the Youden's index\u0026ndash;derived optimal cutoff. Model 2 significantly outperformed both Model 3 (AUC\u0026thinsp;=\u0026thinsp;0.547, 95% CI: 0.447\u0026ndash;0.647) and Model 4 (AUC\u0026thinsp;=\u0026thinsp;0.694, 95% CI: 0.605\u0026ndash;0.785) in recommending FNA (both P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Model 1 (conventional US features alone) also demonstrated good discriminative performance (AUC\u0026thinsp;=\u0026thinsp;0.823, 95% CI: 0.749\u0026ndash;0.896), with no statistically significant difference from Model 2 (P\u0026thinsp;=\u0026thinsp;0.073). To mitigate potential model overfitting, internal validation was conducted using 10-fold cross-validation, which yielded an AUC of 0.822 (95% CI: 0.776\u0026ndash;0.867) for Model 2, indicating acceptable model stability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe performance of each model in recommending FNA across different thyroid nodule categories is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Model 2 maintained consistently high performance across different risk stratifications, with particularly strong discrimination in TR 4A (AUC\u0026thinsp;=\u0026thinsp;0.935, 95% CI: 0.862\u0026ndash;1.000) and TR 4B (AUC\u0026thinsp;=\u0026thinsp;0.843, 95% CI: 0.708\u0026ndash;0.977) subgroups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSubgroup analysis of FNA recommendation performance across models in different thyroid nodule categories\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-TIRADS Level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYouden Index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTR 3\u0026ndash;5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.823(0.749\u0026ndash;0.896)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.867(0.802\u0026ndash;0.932)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.609\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.547(0.447\u0026ndash;0.647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.694(0.605\u0026ndash;0.785)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTR 4A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.516(0.485\u0026ndash;0.548)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.935(0.862-1.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.726(0.637\u0026ndash;0.815)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.560(0.243\u0026ndash;0.878)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTR 4B\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.656(0.472\u0026ndash;0.840)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.843(0.708\u0026ndash;0.977)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.596\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.601(0.437\u0026ndash;0.764)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.696(0.538\u0026ndash;0.854)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.376\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTR 4C-5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.684(0.528\u0026ndash;0.840)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.248\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.727(0.569\u0026ndash;0.885)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.501(0.337\u0026ndash;0.665)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.592(0.421\u0026ndash;0.763)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eUnless otherwise indicated, data are are percentages. The TR 3 subgroup comprised only 4 cases, ROC-derived metrics were not reported due to insufficient sample size. Model 1 is conventional US features, model 2 is model 1 combined with CEUS features, model 3 is management of C-TIRADS, model 4 is management of ACR TI-RADS.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e*Data in parentheses are 95% CIs.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eROC, receiver operating characteristic; AUC, area under the receiver operating characteristic curve; CI, confidence interval; US, ultrasound; CEUS, contrast-enhanced ultrasound; C-TIRADS, Chinese Thyroid Imaging Reporting and Data System; ACR TI-RADS, American College of Radiology Thyroid Imaging Reporting and Data System. FNA, fine-needle aspiration.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCalibration analysis demonstrated good agreement between predicted and observed probabilities for all models (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Both Model 1 (Brier score\u0026thinsp;=\u0026thinsp;16.5, Hosmer-Lemeshow χ\u0026sup2; = 8.280, P\u0026thinsp;=\u0026thinsp;0.141) and Model 2 (Brier score\u0026thinsp;=\u0026thinsp;14.1, Hosmer-Lemeshow χ\u0026sup2; = 11.78, P\u0026thinsp;=\u0026thinsp;0.161) exhibited superior calibration compared with C-TIRADS (Brier score\u0026thinsp;=\u0026thinsp;24.4) and ACR TI-RADS (Brier score\u0026thinsp;=\u0026thinsp;21.5).\u003c/p\u003e \u003cp\u003eTo facilitate clinical application of Model 2, a nomogram was constructed based on its constituent predictors, enabling clinicians to visually estimate the probability of malignancy and guide FNA decision-making for individual thyroid nodules (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDecision Curve Analysis\u003c/h2\u003e \u003cp\u003eDecision curve analysis demonstrated that Model 2 provided higher net benefit than Model 3 and Model 4 across clinically relevant threshold probabilities below 86% (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Given that a 50% risk threshold is commonly used to guide FNA decisions, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) this study focused on this threshold as the primary analysis point. At this threshold, Model 2 avoided 23.7% of unnecessary FNA procedures without significantly increasing missed diagnoses, with a false-negative rate of 20.3%. In contrast, Model 3 failed to reduce unnecessary FNA by recommending biopsy for all at-risk nodules. Although Model 4 had a slightly lower false-negative rate (16.2%), it avoided only 8.4% of unnecessary FNA while maintaining a high false-positive rate of 59.7%, suggesting that its lower missed diagnosis rate came at the cost of substantial oversampling.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt the optimal cutoff of Model 2 (risk threshold: 64%), the unnecessary FNA rate was reduced by 29.0%, at the cost of a false-negative rate of 21.6%. At this threshold, Model 3 yielded a false-negative rate of 100%, failing to identify any malignant nodules, whereas Model 4 demonstrated a false-negative rate of 46.0% while avoiding only 19.8% of unnecessary FNA procedures. In summary, at the most commonly used 50% decision threshold in clinical practice, Model 2 achieved more efficient FNA reduction while reasonably controlling missed diagnosis risk, with overall decision-making performance superior to current Model 3 and Model 4 strategies (Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined whether CEUS combined with conventional ultrasound features could improve malignancy prediction and FNA decision-making for thyroid nodules. A qualitative CEUS-augmented model (Model 2) developed from 131 consecutive nodules achieved an AUC of 0.867, significantly outperforming C-TIRADS and ACR TI-RADS. At the clinically established 50% decision threshold, Model 2 reduced unnecessary FNA procedures by 23.7% while maintaining an acceptable false-negative rate of 20.3%\u0026mdash;a balance neither C-TIRADS nor ACR TI-RADS could achieve. These findings suggest that integrating CEUS into conventional US-based risk stratification meaningfully enhances FNA decision-making beyond what current guidelines offer.\u003c/p\u003e \u003cp\u003eOur findings are consistent with prior studies demonstrating the added value of CEUS over conventional US alone. Wang et al. developed a nomogram integrating both modalities in 815 nodules, achieving AUCs of 0.947 and 0.957 in training and validation cohorts and reducing unnecessary FNA from 29% to 6.1\u0026ndash;6.7%; (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) Borlea et al. similarly showed that adding CEUS to EU-TIRADS improved AUC from 0.707 to 0.840 in a prospective cohort of 157 nodules. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) Wang et al., however, restricted enrollment to ACR TI-RADS 4\u0026ndash;5 nodules with iso- or hyperenhancement on CEUS\u0026mdash;a pre-selected subpopulation where CEUS adds the greatest incremental value\u0026mdash;and applied a point-based scoring system, achieving AUCs of 0.921 and 0.900 across two cohorts.(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) Our study, by contrast, enrolled an unselected consecutive series and applied multivariate logistic regression across both modalities, enabling joint feature weighting and better generalizability. We further benchmarked Model 2 against both C-TIRADS and ACR TI-RADS using decision-curve analysis, demonstrating superior net clinical benefit across a broad range of threshold probabilities.\u003c/p\u003e \u003cp\u003eModel 2 provides incremental diagnostic value by combining the morphological features of grayscale US with the microvascular perfusion characteristics of CEUS. Perfusion signatures, such as hypoenhancement, heterogeneous perfusion, and disrupted capsular enhancement, are inaccessible to conventional US and have been established as strong independent predictors of malignancy. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) This biological complementarity confers the greatest benefit in intermediate-risk categories, where Model 2 demonstrated its strongest discrimination (TR 4A: AUC\u0026thinsp;=\u0026thinsp;0.935; TR 4B: AUC\u0026thinsp;=\u0026thinsp;0.843). Hence, The model was further operationalized as a nomogram, enabling individualized malignancy probability estimation at the point of care without computational dependencies. In high-volume screening programs, Model 2 may help reduce avoidable procedures without compromising sensitivity, as decision-curve analysis shows greater net benefit than either guideline alone across relevant threshold probabilities.\u003c/p\u003e \u003cp\u003eSeveral methodological strengths support the validity and practical relevance of our findings. First, rather than constructing additive scoring rules from univariably selected features\u0026mdash;a common limitation in prior CEUS studies\u0026mdash;we applied multivariate logistic regression, which allows simultaneous weighting of correlated predictors and more faithfully captures the joint discriminative structure of dual-modality data. Second, model performance was evaluated not only by AUC but also by decision-curve analysis benchmarked against both C-TIRADS and ACR TI-RADS. This approach moves beyond conventional accuracy metrics and directly addresses whether adopting the model would yield net clinical benefit across a range of real-world decision thresholds. Third, interobserver agreement for CEUS feature interpretation was formally assessed, quantifying the reproducibility of the imaging inputs on which the model relies and confirming that its application is not contingent on a single experienced reader. These considerations may strengthen the credibility of the conclusions drawn.\u003c/p\u003e \u003cp\u003eThis study has several limitations that should be acknowledged. First, as a single-center retrospective cohort, inherent selection and information biases cannot be fully excluded, and independent prospective validation is needed before the findings can be widely adopted. Second, the predominance of nodules measuring\u0026thinsp;\u0026le;\u0026thinsp;10 mm in this cohort meant that a substantial proportion of malignant nodules fell below the size thresholds required for FNA recommendation under ACR TI-RADS, which may limit the generalizability of comparative performance findings between risk stratification systems. Finally, the exclusion of poor-quality CEUS images, though necessary to ensure analytical rigor, may lead to a modest overestimation of model performance relative to unselected real-world imaging conditions.\u003c/p\u003e \u003cp\u003eIn conclusion, CEUS-augmented risk stratification of thyroid nodules may offer a clinically meaningful complement to existing guideline-based systems, particularly in refining FNA decision-making for nodules that current scoring frameworks inadequately resolve. Prospective multicenter studies are needed to establish the external validity of these findings across diverse clinical settings and patient populations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 488px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFull Term\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eACR TI-RADS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 488px;\"\u003e\n \u003cp\u003eAmerican College of Radiology Thyroid Imaging Reporting and Data System\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 488px;\"\u003e\n \u003cp\u003eAkaike Information Criterion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 488px;\"\u003e\n \u003cp\u003eArea Under the Receiver Operating Characteristic Curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eBrier Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 488px;\"\u003e\n \u003cp\u003eMeasure of forecast accuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eC-TIRADS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 488px;\"\u003e\n \u003cp\u003eChinese Thyroid Imaging Reporting and Data System\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eCEUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 488px;\"\u003e\n \u003cp\u003eContrast-Enhanced Ultrasound\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 488px;\"\u003e\n \u003cp\u003eConfidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 488px;\"\u003e\n \u003cp\u003eDecision Curve Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eDICOM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 488px;\"\u003e\n \u003cp\u003eDigital Imaging and Communications in Medicine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eETE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 488px;\"\u003e\n \u003cp\u003eExtrathyroidal Extension\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eFNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 488px;\"\u003e\n \u003cp\u003eFine-Needle Aspiration\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eIQR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 488px;\"\u003e\n \u003cp\u003eInterquartile Range\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eNB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 488px;\"\u003e\n \u003cp\u003eNet Benefit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eNRFP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 488px;\"\u003e\n \u003cp\u003eNet Reduction in False-Positives\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 488px;\"\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003ePACS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 488px;\"\u003e\n \u003cp\u003ePicture Archiving and Communication System\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003ePTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 488px;\"\u003e\n \u003cp\u003ePapillary Thyroid Carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 488px;\"\u003e\n \u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 488px;\"\u003e\n \u003cp\u003eStandard Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003esNB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 488px;\"\u003e\n \u003cp\u003eStandardized Net Benefit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 488px;\"\u003e\n \u003cp\u003eUltrasound\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Medical Ethics Committee of the Sixth Affiliated Hospital of Sun Yat-sen University (Ethics No.: 2025ZSLYEC-608) and conducted in accordance with the Declaration of Helsinki. Informed consent was waived due to the retrospective nature of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This retrospective study analyzed existing clinical data and imaging records without containing any identifiable individual person\u0026apos;s data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are not publicly available due to patient privacy protection under Chinese regulations and institutional policy. The raw data generated during this study include imaging data (ultrasound and CEUS images), clinical demographics, pathological results, and imaging measurements from 131 thyroid nodules obtained from 129 patients at the Sixth Affiliated Hospital of Sun Yat-sen University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Clinical Research \u0026apos;1010\u0026apos; Program of the Sixth Affiliated Hospital of Sun Yat-sen University (Grant No. 1010PY(2022)-27) and the Youth Program of the National Natural Science Foundation of China (Grant No. 82502360).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors\u0026apos; contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYYL: Data collection (equal), statistical analysis (lead), writing \u0026ndash; original draft preparation (lead). YC: Conceptualization (equal), methodology (equal), image analysis (equal). YMW: Data collection (equal). SQ: Data collection (equal). JZH: Data collection (equal). YXH: Data collection (equal). YL: Data collection (equal). GJL: Supervision (lead). RC: Writing \u0026ndash; review and editing (lead), conceptualization (equal), methodology (equal), image analysis (equal), statistical analysis (supporting).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSolomon C, Petea-Balea DR, Dudea SM, Bene I, Silaghi CA, Lenghel ML. Role of ultrasound elastography and contrast-enhanced ultrasound (CEUS) in diagnosis and management of malignant thyroid nodules-an update. Diagn (basel Switz). 2025 Mar 1;15(5):599. doi:10.3390/diagnostics15050599 PubMed PMID: 40075847; PubMed Central PMCID: PMC11898416.\u003c/li\u003e\n\u003cli\u003eXiao F, Li JM, Han ZY, Liu FY, Yu J, Xie MX, et al. Multimodality US versus Thyroid Imaging Reporting and Data System Criteria in Recommending Fine-Needle Aspiration of Thyroid Nodules. Radiology. 2023 Jun;307(5):e221408. doi:10.1148/radiol.221408 PubMed PMID: 37367448.\u003c/li\u003e\n\u003cli\u003eZamora EA, Khare S, Cassaro S. Thyroid nodule. In: Statpearls [internet] [Internet]. StatPearls Publishing; 2023 [cited 2025 Oct 8]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK535422/ PubMed PMID: 30571043.\u003c/li\u003e\n\u003cli\u003eTessler FN, Middleton WD, Grant EG, Hoang JK, Berland LL, Teefey SA, et al. ACR Thyroid Imaging, Reporting and Data System (TI-RADS): White Paper of the ACR TI-RADS Committee. J Am Coll Radiol. 2017 May;14(5):587\u0026ndash;95. doi:10.1016/j.jacr.2017.01.046 PubMed PMID: 28372962.\u003c/li\u003e\n\u003cli\u003eZhou J, Yin L, Wei X, Zhang S, Song Y, Luo B, et al. 2020 chinese guidelines for ultrasound malignancy risk stratification of thyroid nodules: the C-TIRADS. Endocrine. 2020 Nov;70(2):256\u0026ndash;79. doi:10.1007/s12020-020-02441-y PubMed PMID: 32827126.\u003c/li\u003e\n\u003cli\u003eShi M, Nong D, Xin M, Lin L. Accuracy of ultrasound diagnosis of benign and malignant thyroid nodules: a systematic review and meta-analysis. Int J Clin Pract. 2022;2022:5056082. doi:10.1155/2022/5056082 PubMed PMID: 36160289; PubMed Central PMCID: PMC9489364.\u003c/li\u003e\n\u003cli\u003eLiu X, Xie L, Ye X, Cui Y, He N, Hu L. Evaluation of ultrasound elastography combined with chi-square automatic interactive detector in reducing unnecessary fine-needle aspiration on TIRADS 4 thyroid nodules. Front Oncol. 2022;12:823411. doi:10.3389/fonc.2022.823411 PubMed PMID: 35251988; PubMed Central PMCID: PMC8889496.\u003c/li\u003e\n\u003cli\u003eLi M, Zheng R, Dal Maso L, Zhang S, Wei W, Vaccarella S. Mapping overdiagnosis of thyroid cancer in China. Lancet, Diabetes Endocrinol. 2021 Jun;9(6):330\u0026ndash;2. doi:10.1016/S2213-8587(21)00083-8 PubMed PMID: 33891886.\u003c/li\u003e\n\u003cli\u003eKim PH, Suh CH, Baek JH, Chung SR, Choi YJ, Lee JH. Unnecessary thyroid nodule biopsy rates under four ultrasound risk stratification systems: a systematic review and meta-analysis. Eur Radio. 2021 May;31(5):2877\u0026ndash;85. doi:10.1007/s00330-020-07384-6 PubMed PMID: 33057762.\u003c/li\u003e\n\u003cli\u003eJia MJ, Wang S, Li Y, Liu XN, Jiang F, Li HL. Global burden of thyroid cancer among adolescents and young adults, 1990-2021, and projections to 2050: an analysis based on the GBD 2021. Front Endocrinol. 2025 Apr 14;16. doi:10.3389/fendo.2025.1503144\u003c/li\u003e\n\u003cli\u003ePark JY, Choi W, Hong AR, Yoon JH, Kim HK, Kang HC. A comprehensive assessment of the harms of fine-needle aspiration biopsy for thyroid nodules: a systematic review. Endocrinol Metab (Seoul Korea). 2023 Feb;38(1):104\u0026ndash;16. doi:10.3803/EnM.2023.1669 PubMed PMID: 36891657; PubMed Central PMCID: PMC10008658.\u003c/li\u003e\n\u003cli\u003eUppal N, Collins R, James B. Thyroid nodules: Global, economic, and personal burdens. Front Endocrinol (Lausanne). 2023;14:1113977. doi:10.3389/fendo.2023.1113977 PubMed PMID: 36755911; PubMed Central PMCID: PMC9899850.\u003c/li\u003e\n\u003cli\u003eAbou Ali AN, Fittipaldi A, Rocha-Neves J, Ruaro B, Benedetto F, Al Ghadban Z, et al. Clinical applications of contrast-enhanced ultrasound in vascular surgery: state-of-the-art narrative and pictorial review. Jvs-vasc Insights. 2025 Jan 1;3:100254. doi:10.1016/j.jvsvi.2025.100254\u003c/li\u003e\n\u003cli\u003eRadzina M, Ratniece M, Putrins DS, Saule L, Cantisani V. Performance of Contrast-Enhanced Ultrasound in Thyroid Nodules: Review of Current State and Future Perspectives. Cancers. 2021 Oct 30;13(21):5469. doi:10.3390/cancers13215469 PubMed PMID: 34771632; PubMed Central PMCID: PMC8582579.\u003c/li\u003e\n\u003cli\u003eYuan X, Wen H, Huang M, Xie X, Yi Z, Li S. A clinical retrospective study on the qualitative value of multimodal ultrasonography for ACR-TIRADS 4 thyroid nodules ranging from 1 cm to 1.5 cm. Technol Cancer Res Treat. 2024 Jan;23:15330338241297599. doi:10.1177/15330338241297599\u003c/li\u003e\n\u003cli\u003eŻyłka A, Dobruch-Sobczak K, Piotrzkowska-Wr\u0026oacute;blewska H, Jędrzejczyk M, Bakuła-Zalewska E, G\u0026oacute;ralski P, et al. The utility of contrast-enhanced ultrasound (CEUS) in assessing the risk of malignancy in thyroid nodules. Cancers. 2024 May 17;16(10):1911. doi:10.3390/cancers16101911 PubMed PMID: 38791990; PubMed Central PMCID: PMC11119249.\u003c/li\u003e\n\u003cli\u003eFan J, Tao L, Zhan W, Li W, Kuang L, Zhao Y, et al. Diagnostic value of qualitative and quantitative parameters of contrast-enhanced ultrasound for differentiating differentiated thyroid carcinomas from benign nodules. Front Endocrinol. 2024 Jan 4;14. doi:10.3389/fendo.2023.1240615\u003c/li\u003e\n\u003cli\u003eVickers AJ, Van Calster B, Steyerberg EW. A simple, step-by-step guide to interpreting decision curve analysis. Diagn Progn Res. 2019 Dec;3(1):18. doi:10.1186/s41512-019-0064-7\u003c/li\u003e\n\u003cli\u003eVickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making. 2006 Nov;26(6):565\u0026ndash;74. doi:10.1177/0272989X06295361\u003c/li\u003e\n\u003cli\u003eKerr KF, Brown MD, Zhu K, Janes H. Assessing the Clinical Impact of Risk Prediction Models With Decision Curves: Guidance for Correct Interpretation and Appropriate Use. JCO. 2016 Jul 20;34(21):2534\u0026ndash;40. doi:10.1200/JCO.2015.65.5654\u003c/li\u003e\n\u003cli\u003ePecoraro M, Catanzaro G, Conte F, Besharat ZM, Messina E, Laschena L, et al. Prospective validation study of a novel integrated pathway based on clinical features, magnetic resonance imaging biomarkers, and MicroRNAs for early detection of prostate cancer. Eur Urol Oncol. 2024 Feb;7(1):73\u0026ndash;82. doi:10.1016/j.euo.2023.05.008\u003c/li\u003e\n\u003cli\u003eAmendola RL, Miller MA, Kaupp SM, Cleary RJ, Damron TA, Mann KA. Modification to mirels scoring system location component improves fracture prediction for metastatic disease of the proximal femur. BMC Musculoskelet Disord. 2023 Jan 24;24(1):65. doi:10.1186/s12891-023-06182-7\u003c/li\u003e\n\u003cli\u003eRago T, Vitti P. Risk Stratification of Thyroid Nodules: From Ultrasound Features to TIRADS. Cancers. 2022 Jan 30;14(3):717. doi:10.3390/cancers14030717 PubMed PMID: 35158985; PubMed Central PMCID: PMC8833686.\u003c/li\u003e\n\u003cli\u003eWang QG, Li M, Deng GX, Huang HQ, Qiu Q, Lin JJ. Development and validation of a nomogram based on conventional and contrast-enhanced ultrasound for differentiating malignant from benign thyroid nodules. Quant Imaging Med Surg. 2025 May 1;15(5):4641\u0026ndash;54. doi:10.21037/qims-24-1796 PubMed PMID: 40384666; PubMed Central PMCID: PMC12082573.\u003c/li\u003e\n\u003cli\u003eBorlea A, Moisa-Luca L, Popescu A, Bende F, Stoian D. Combining CEUS and ultrasound parameters in thyroid nodule and cancer diagnosis: a TIRADS-based evaluation. Front Endocrinol. 2024;15:1417449. doi:10.3389/fendo.2024.1417449 PubMed PMID: 38952390; PubMed Central PMCID: PMC11215041.\u003c/li\u003e\n\u003cli\u003eWang Y, Dong T, Nie F, Wang G, Liu T, Niu Q. Contrast-enhanced ultrasound in the differential diagnosis and risk stratification of ACR TI-RADS category 4 and 5 thyroid nodules with non-hypovascular. Front Oncol. 2021;11:662273. doi:10.3389/fonc.2021.662273 PubMed PMID: 34123819; PubMed Central PMCID: PMC8189148.\u003c/li\u003e\n\u003cli\u003eDing Y, Peng Y, Zhang J, Pan X, Huang X, Zhang CQ. Diagnostic value of contrast-enhanced ultrasound in the diagnosis of papillary thyroid microcarcinoma: a systematic review and meta-analysis. Medicine (Baltimore). 2024 Apr 12;103(15):e37768. doi:10.1097/MD.0000000000037768 PubMed PMID: 38608080; PubMed Central PMCID: PMC11018218.\u003c/li\u003e\n\u003cli\u003eMitchell JC, Parangi S. Angiogenesis in benign and malignant thyroid disease. Thyroid: Off J Am Thyroid Assoc. 2005 Jun;15(6):494\u0026ndash;510. doi:10.1089/thy.2005.15.494 PubMed PMID: 16029116.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Thyroid nodules, Contrast-enhanced ultrasound, Fine-needle aspiration, Diagnostic performance, Predictive model","lastPublishedDoi":"10.21203/rs.3.rs-9268669/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9268669/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo investigate the value of qualitative contrast-enhanced ultrasound (CEUS) features in avoiding unnecessary fine-needle aspiration (FNA) of thyroid nodules.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective study included 131 thyroid nodules from 129 patients who underwent both conventional ultrasound (US) and CEUS examinations with confirmed FNA or surgical pathology at the Sixth Affiliated Hospital of Sun Yat-sen University between November 2023 and January 2025. Eight qualitative CEUS features were analyzed using univariate and multivariate logistic regression, including enhancement direction, presence of ring enhancement, presence of persistent perfusion defects, margin sharpness in the washout phase, presence of hypoenhancement (area smaller than the nodule on grayscale imaging), presence of non-enhancing peripheral halo, capsular enhancement continuity, and washout timing relative to surrounding thyroid parenchyma. A predictive model integrating conventional US and qualitative CEUS features was constructed. Receiver operating characteristic (ROC) curves were used to compare the performance of the new model with Chinese Thyroid Imaging Reporting and Data System (C-TIRADS) and American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) in recommending FNA for thyroid nodules. Decision curve analysis (DCA) was performed to evaluate the clinical net benefit of each model in avoiding unnecessary FNA.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMultivariate analysis identified hypoenhancement and persistent perfusion defects as independent CEUS predictors. The integrated model combining conventional US features (aspect ratio\u0026thinsp;\u0026gt;\u0026thinsp;1, irregular or ill-defined margin or extrathyroidal extension, and punctate echogenic foci) and CEUS features achieved the highest area under the curve (AUC) of 0.867 (95% CI: 0.802\u0026ndash;0.932), with sensitivity, specificity, and accuracy of 78.4%, 82.5%, and 80.2%, respectively, significantly outperforming both C-TIRADS and ACR TI-RADS (both P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). At a 50% risk threshold, the integrated model avoided 23.7% of unnecessary FNA procedures, whereas C-TIRADS and ACR TI-RADS avoided 0% and 8.4%, respectively.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAn integrated model combining conventional US and qualitative CEUS features can effectively identify thyroid nodules requiring FNA, optimize FNA indications, and significantly reduce unnecessary invasive procedures, demonstrating valuable clinical applicability.\u003c/p\u003e","manuscriptTitle":"Contrast-Enhanced Ultrasound-Augmented Risk Stratification for Fine-Needle Aspiration Selection in Thyroid Nodules","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 08:06:36","doi":"10.21203/rs.3.rs-9268669/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-17T07:19:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"145386200173203715924387644804796345877","date":"2026-05-09T12:57:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-03T07:24:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"66394662491836793986592073979202434729","date":"2026-04-29T01:06:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-28T18:30:07+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-03T07:02:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-02T11:48:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-02T11:47:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Imaging","date":"2026-03-30T14:47:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0180a96d-1189-4e99-9278-29e801f7ce96","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-17T07:19:11+00:00","index":56,"fulltext":""},{"type":"reviewerAgreed","content":"145386200173203715924387644804796345877","date":"2026-05-09T12:57:03+00:00","index":55,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-03T07:24:24+00:00","index":37,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T08:06:36+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 08:06:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9268669","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9268669","identity":"rs-9268669","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}