Diagnostic Comparison of TI-RADS and a Nomogram for Thyroid Nodules in Northwestern China

Diagnostic Comparison of TI-RADS and a Nomogram for Thyroid Nodules in Northwestern China | 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 Diagnostic Comparison of TI-RADS and a Nomogram for Thyroid Nodules in Northwestern China Miao Tan, Wenhan Li, Jianhui Li, Jia Du, Xufeng Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8245786/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Objective The aims of this study were: ① to evaluate the diagnostic efficacy of six mainstream TI-RADS (Thyroid Imaging Reporting and Data System) classification systems (C-TIRADS, ACR-TIRADS, etc.) in the Northwestern Chinese population; and ② to identify risk factors for malignant thyroid nodules (TNs) using logistic regression based on clinical and ultrasound features, construct a quantifiable scoring Nomogram model, enable rapid and objective risk assessment, and assist in clinical decision-making. Methods A total of 2,047 patients with TNs (1,433 malignant and 614 benign) were enrolled from January 2018 to January 2024 at Shaanxi Provincial People’s Hospital. The nodules were divided into a training group (1,435 nodules) and a validation group (612 nodules) in a 7:3 ratio. Twelve characteristics were collected, including age, nodule size, margin, calcification, and the presence of suspicious lymph nodes. Independent risk factors were identified through univariate and multivariate logistic regression analyses to construct a Nomogram model. The model’s performance was evaluated using receiver operating characteristic (ROC) curves, accuracy, and other metrics, and compared with the six traditional TI-RADS systems. Results Ten independent risk factors were identified, including age, nodule size, and irregular margins. In the validation group, the Nomogram model achieved an accuracy of 78.4%, a sensitivity of 81.6%, a specificity of 71.7%, and an area under the ROC curve (AUC) of 0.849. The sensitivities of the six TI-RADS systems (C-TIRADS, ACR-TIRADS, EU-TIRADS, ATA Guidelines, Kwak-TIRADS, and AACE) for distinguishing benign and malignant nodules were 86.0%, 93.2%, 96.9%, 98.3%, 84.4%, and 98.1%, respectively; specificities were 55.6%, 34.8%, 25.3%, 22.2%, 57.1%, and 21.7%, respectively; accuracies were 76.1%, 74.3%, 73.7%, 73.7%, 75.8%, and 73.4%, respectively; and AUCs were 0.752, 0.661, 0.628, 0.617, 0.757, and 0.616, respectively, with no statistically significant differences among them. The Nomogram model significantly outperformed the traditional systems in measures such as AUC, Net Reclassification Improvement (NRI), Integrated Discrimination Improvement (IDI), Positive Likelihood Ratio (PLR), and Negative Likelihood Ratio (NLR) (P < 0.001). Conclusion The six traditional TI-RADS systems demonstrate similar but overall limited diagnostic efficacy in the Northwestern Chinese population. The Nomogram model, by integrating multidimensional features and applying a quantitative scoring approach, improves the accuracy and objectivity of malignancy risk assessment. Compared to traditional models, it offers better clinical utility, supports optimized decision-making, and helps reduce unnecessary invasive procedures. Thyroid nodule FNA Logistic regression analysis Nomogram Figures Figure 1 Figure 2 Figure 3 1. Introduction In recent years, the widespread clinical use of high-resolution ultrasound and increased public awareness of health examinations have significantly raised the detection rate of thyroid nodules (TNs). Ultrasound examinations indicate that the prevalence of TNs ≥ 0.5 cm in diameter among adults is approximately 20.43%, with 8%-16% of these being thyroid cancers[ 1 , 2 ]. As the preferred imaging modality for evaluating TNs, high-frequency ultrasound achieves a detection rate of 20%-76% due to its superior soft tissue resolution[ 3 ]. It not only confirms the presence of nodules but also enables detailed characterization of their sonographic features, including size, number, location, composition (solid or cystic), orientation, echogenicity, calcification, margin, capsule integrity, vascularity, and association with diffuse thyroid disease or invasion of adjacent tissues. Additionally, it facilitates the assessment of abnormal cervical lymph nodes in terms of size, number, location, and structural features [ 4 ].However, multicenter studies have demonstrated substantial heterogeneity in the ultrasound features of TNs, making it difficult to differentiate benign from malignant lesions based on single imaging parameters. Consequently, clinical practice increasingly relies on comprehensive systems that integrate multi-dimensional ultrasound findings to stratify malignancy risk. Fine-needle aspiration (FNA) biopsy is the gold standard for the preoperative diagnosis of TNs, helping to avoid over 50% of unnecessary thyroid surgeries and enhancing the detection rate of malignant nodules[ 5 ].Although the accuracy and safety of ultrasound-guided FNA have markedly improved[ 6 , 7 ],its invasive nature may still result in complications such as pain and bleeding, and may also provoke anxiety in patients[ 8 ]. Currently, FNA is mainly suggested for sepcific patients based on risk stratification systems for TNs. Traditional stratification models rely mainly on sonographic features; however, clinical indicators such as age, sex, nodule size, as well as thyroid function have also been identified as predictors of malignancy[ 9 – 13 ]. Large-scale, high-quality comparative studies between emerging risk stratification systems (e.g., Chinese Thyroid Imaging Reporting and Data System [C-TIRADS], and American College of Radiology Thyroid Imaging Reporting and Data System [ACR-TIRADS]) and traditional models remain limited. Furthermore, certain benign conditions (like asymptomatic subacute thyroiditis), can exhibit ultrasound features similar to those of malignant tumors, making it challenging for conventional models to accurately differentiate between benign and malignant lesions. Artificial intelligence-based diagnostic tools for ultrasound interpretation, such as S-Detect 2 and Automated Medical Computer-Aided Diagnosis for Ultrasonography of Thyroid nodules [AMCAD-UT], have been approved by the U.S. Food and Drug Administration, demonstrating diagnostic performance comparable to or even exceeding that of radiologists[ 14 – 16 ]. However, their high initial investment and ongoing subscription costs have hindered widespread adoption, resulting in a disparity between technological advancement and routine clinical utilization. In response to the above challenges, it is necessary to develop a multi-parameter model that integrates clinical data, biochemical indicators, as well as ultrasound features to enable rapid and accurate differentiation between benign and malignant TNs through intelligent assessment. Data from 2,047 patients were used to construct and validate the model. The objective is to optimize clinical decision-making, reduce unnecessary FNA procedures, and systematically compare the model’s diagnostic performance with six established risk stratification systems, thereby providing an innovative and standardized approach for the diagnosis and management of TNs. Therefore, this study aims to construct an intelligent evaluation model based on multi-parameter analysis to enable rapid and accurate differentiation between benign and malignant TNs, thereby optimizing clinical decision-making, reducing unnecessary FNA procedures, and providing a novel solution for the standardized diagnosis and management of TNs. Clinical, biochemical, and ultrasound data from 2,047 patients at our medical center were collected to develop and validate a Nomogram model. This model will be compared with several existing risk stratification systems, with the intention of enhancing predictive accuracy and ensuring practical applicability in clinical settings. 2. Materials and Methods 2.1 Study Subjects A total of 2,047 patients who underwent thyroid ultrasound and received corresponding pathological results via ultrasound-guided FNA or TN resection at Shaanxi Provincial People's Hospital between January 2018 and January 2024 were retrospectively enrolled, including 533 males and 1,514 females. Inclusion criteria were as follows: ① Underwent thyroid ultrasound before FNA or surgery, with complete and high-quality imaging data; ② Diagnosed with TNs classified as category 3, 4, or 5 according to the ACR-TIRADS; ③ Had definitive cytological or histological pathological results; ④ Completed relevant laboratory examinations; ⑤ Had no history of thyroid surgery. Exclusion criteria were as follows: ① Poor preoperative ultrasound image quality affecting diagnostic judgment; ② Incomplete clinical or pathological data after FNA or surgery; ③ Unclear pathological diagnosis. For each patient, one nodule was selected for analysis. In patients with a single nodule, that nodule was included. For patients with multiple nodules: If the TI-RADS classifications differed, the nodule with the highest classification was selected; If the TI-RADS classifications were the same, the largest nodule was selected. All selected nodules had corresponding postoperative histopathological results. This study was approved by the Ethics Committee of Shaanxi Provincial People's Hospital (approval number: 2025-017). 2.2 Clinical Data Collection Clinical and serological data were collected and recorded, including age, sex, marital status, and body mass index. Ultrasound imaging features included nodule size, location, margin, extracapsular invasion, halo sign, internal composition, echogenicity, calcification, aspect ratio, vascular pattern, presence of suspicious lymph nodes, and concomitant Hashimoto's thyroiditis. 2.3 Instruments and Methods (1) Equipment: A Mindray ultrasound system equipped with an L12-5 linear probe (5–12 MHz) was used. Patients were scanned in the supine position, with gain and focus settings adjusted according to the size and depth of the TNs. (2) Ultrasound Image Acquisition: Ultrasound physicians performed multi-planar scanning of the thyroid gland and nodules, carefully assessing two-dimensional and color Doppler sonographic features. These included nodule number, size, location, margin, shape, internal echogenicity, calcification, aspect ratio, presence of suspicious lymph nodes, and blood flow pattern. Two ultrasound physicians, each with over nine years of experience, retrospectively reviewed the images using a double-blind method and independently classified the nodules. In cases of disagreement, the final classification was determined through consensus after consultation between the two physicians. 2.4 FNA under ultrasound guidance Suspicious TNs were punctured under real-time ultrasound guidance. A disposable 21-gauge biopsy needle was used to perform multiple passes in different directions within the nodule to ensure adequate sample collection. The specimens were immediately fixed in 95% alcohol and sent to the Pathology Department of our medical center for cytological evaluation. Cytological findings were classified into six categories according to the 2017 Bethesda System for Reporting Thyroid Cytopathology[ 17 , 18 ]. Only nodules with definitive cytological or histopathological diagnoses were included in the study. 2.5 Statistical Analysis All experimental data in this study were statistically analyzed and visualized using SPSS (Version 26.0) and R software (Version 4.3.2). Variables with statistically significant differences in univariate regression analysis were included in multivariate logistic regression to identify independent predictors. Based on the regression coefficients, a Nomogram model was constructed using R software. The discriminative ability of the model was evaluated by plotting the area under the receiver operating characteristic (ROC) curve (AUC). The performance of the Nomogram model was compared with several established risk stratification systems, including C-TIRADS, ACR-TIRADS, Kwak Thyroid Imaging Reporting and Data System (Kwak-TIRADS), American Thyroid Association (ATA), European Thyroid Imaging Reporting and Data System (EU-TIRADS), and American Association of Clinical Endocrinologists (AACE). Additionally, we assessed the model's predictive accuracy, specificity, sensitivity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR). To further clarify the predictive advantages of this model, we calculated the Net Reclassification Index (NRI) to assess its ability to correctly reclassify cases compared to other models. Meanwhile, the Integrated Discrimination Improvement (IDI) [ 19 ]was calculated. A higher probability of correctly predicting positive outcomes in the positive group, coupled with a lower probability of false-positive predictions in the negative group, indicates superior performance of this model over others. Finally, the clinical utility of the model was evaluated using Decision Curve Analysis (DCA). A two-sided P value < 0.05 was considered statistically significant. 3. Results 3.1 Demographics and thyroid nodule characteristics A total of 2,047 nodules were included in this study, involving 533 male and 1,514 female patients, with a mean age of 47.37 ± 12.31 years. Among them, 1,433 nodules were malignant and 614 were benign, with an average nodule diameter of 1.28 ± 1.16 cm. The nodules were randomly divided into a training group (1,435 nodules) and a validation group (612 nodules) at a ratio of 7:3. There were no significant differences in baseline clinical data or ultrasound characteristics of the nodules between the two groups (Table 1 ). Table 1 Characteristics of Patients with Thyroid Nodules in the Training and Validation Sets Variable Total (n = 2047) Training set (n = 1435) Validation set (n = 612) P value Age 46.98 ± 12.28 46.85 ± 12.70 47.37 ± 12.31 0.49 b Gender 0.34 a Male 533 365 168 Female 1514 1070 444 Nodule size 1.28 ± 1.16 1.29 ± 1.16 1.26 ± 1.15 0.64 b BMI 24.2 ± 3.70 24.15 ± 3.67 24.33 ± 3.75 0.32 b Nodule Position 1 0.29 a Left lobe 915 633 282 Right lobe 1057 754 303 Isthmus 75 48 27 Nodule Position 2 Upper region 467 328 139 0.61 a Middle region 840 600 240 Lower region 671 458 213 Isthmus 69 49 20 Histology 0.13 a Benign 614 416 198 Malignant 1433 1019 414 Margin 0.97 a Regular 389 273 116 Irregular 1658 1162 496 Extrathyroidal extension 0.06 a Yes 357 266 91 No 1690 1169 521 Halo 0.28 a Absent 1844 1284 560 Complete 84 65 19 Incomplete 119 86 33 Composition 0.38 a Cystic or spongiform 10 8 2 Cystic and solid (Cystic ≥ 50%) 44 26 18 Cystic and solid (Solid ≥ 50%) 106 74 32 Solid 1887 1327 560 Calcification 0.21 a No calcification 657 458 199 Macrocalcification 204 155 49 Macro and microcalcification 114 83 31 Microcalcification 1072 739 333 Comet tail artifacts 0.84 a Yes 18 13 5 No/NA 2029 1422 607 Vascular distribution pattern 0.62 a Avascularity 823 572 251 Peripheral vascularity 498 359 139 Mainly central vascularity 480 329 151 Mixed vascularity 246 175 71 Suspicious LNM 0.52 a Yes 341 244 97 No 1706 1191 515 Aspect ratio >1 0.27 a Yes 1096 757 339 No 951 678 273 Complicated with thyroiditis 0.92 a Yes 322 225 97 No 1725 1210 515 a Using χ2 test for this statistic b Using two-sample t-test for this statistic BMI, Body Mass Index; LNM, Lymph Node Metastasis. 3.2 Univariate regression analysis Univariate analysis was performed on the training set to identify factors distinguishing benign from malignant nodules. As shown in Table 2 , twelve variables (age, sex, body mass index, nodule size, nodule location, margin, extracapsular extension, halo, calcification, suspicious lymph nodes, aspect ratio, and concomitant Hashimoto's thyroiditis) showed statistically significant differences between the benign and malignant groups (all P < 0.05). Table 2 Univariate and multivariate analysis of risk factors for thyroid cancer in the training group Variables Histology Univariate analysis Multivariate analysis Benign Malignant OR (95%CI) P value OR (95%CI) P value Gender Male 84 281 Reference Reference Female 332 738 0.66 (0.50–0.87) < 0.01* 0.75 (0.53–1.07) 0.12 BMI 23.75 ± 3.93 24.32 ± 3.55 1.04 (1.01–1.08) < 0.01* 1.04 (1.01–1.09) 0.04 Nodule size (mean ± SD) 1.65 ± 1.51 1.14 ± 0.95 0.70 (0.64–0.77) < 0.01* 0.77 (0.67–0.88) < 0.01* Nodule position 1 Left lobe 173 460 Reference Right lobe 235 519 0.83 (0.66–1.05) 0.12 Isthmus 8 40 1.88 (0.91–4.40) 0.11 Nodule position 2 Upper region 71 257 Reference Reference Middle region 193 407 0.58 (0.42–0.79) < 0.01* 0.53 (0.36–0.79) < 0.01* Lower region 139 319 0.63 (0.45–0.88) 0.01* 0.57 (0.38–0.84) < 0.01* Isthmus 13 36 0.77 (0.39–1.57) 0.44 0.41 (0.17–1.02) 0.04* Margin Regular 182 91 Reference Reference Irregular 234 928 7.93 (5.95–10.63) < 0.01* 3.28 (2.30–4.68) < 0.01* Extrathyroidal extension Yes 6 260 Reference Reference No 410 759 0.04 (0.02–0.09) < 0.01* 0.06 (0.02–0.12) < 0.01* Halo Absent 351 933 Reference Reference Complete 56 9 0.06 (0.03–0.12) < 0.01* 0.13 (0.05–0.31) < 0.01* Incomplete 9 77 3.22 (1.68–9.69) < 0.01* 2.93 (1.37–6.90) < 0.01* Composition Cystic or spongiform 4 4 Reference Cystic and solid (Cystic ≥ 50%) 22 4 0.18 (0.03–1.04) 0.06 Cystic and solid (Solid ≥ 50%) 42 32 0.76 (0.17–3.44) 0.72 Solid 348 979 2.81 (0.66–11.96) 0.15 Calcification No calcification 175 283 Reference Reference Macrocalcification 80 75 0.58 (0.40–0.84) < 0.01* 0.49 (0.30–0.79) < 0.01* Macro and microcalcification 21 62 1.83 (1.09–3.16) 0.03* 1.18 (0.60–2.36) 0.63 Microcalcification 140 599 2.66 (2.03–3.45) < 0.01* 1.61 (1.17–2.22) < 0.01* Vascular distribution pattern Avascularity 167 405 Reference Peripheral vascularity 99 260 1.08 (0.81–1.45) 0.60 Mainly central vascularity 93 236 1.05 (0.78–1.42) 0.77 Mixed vascularity 57 118 0.85 (0.60–1.23) 0.39 Suspicious LNM Yes 19 225 Reference Reference No 397 794 0.17 (0.10–0.27) < 0.01* 0.28 (0.16–0.49) 1 Yes 133 624 Reference Reference No 283 395 0.30 (0.23–0.38) < 0.01* 0.59 (0.43–0.80) < 0.01* Complicated with thyroiditis Yes 52 173 Reference Reference No 364 846 0.70 (0.50–0.97) 0.04* 0.71 (0.67–1.06) 0.10 * P value indicates significant difference. BMI, Body Mass Index; CI, Confidence Interval; SD, Standard Deviation. 3.3 Multivariate regression analysis Twelve variables (age, sex, nodule size, body mass index, nodule location, margin, extracapsular extension, halo, calcification, suspicious lymph nodes, aspect ratio, and concomitant Hashimoto's thyroiditis) were included in the logistic regression model. The analysis showed that ten of these variables (age, nodule size, nodule location, margin, extracapsular extension, halo, calcification, suspicious lymph nodes, aspect ratio, and concomitant Hashimoto's thyroiditis) were identified as independent predictors of malignant nodules (all P < 0.05). 3.4 Development of a Nomogram model Based on the results of the univariate and multivariate analyses, we used R software (R Studio) to construct a Nomogram model (Fig. 1 ).. The model assigns scores to each independent predictor according to their regression coefficients from the logistic regression analysis. Clinicians can sum these scores to calculate the probability of malignancy in TNs. The model's performance was evaluated by comparing its predictions with the final surgical pathology results, and calculating accuracy, specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV). In the training set (n = 1,435 nodules), 1,019 nodules were pathologically confirmed as malignant and 416 as benign. The model achieved an accuracy of 79.1%, sensitivity of 81.3%, specificity of 73.8%, PLR of 3.101, and NLR of 0.254 (Table 3 ). Table 3 Diagnostic performances of the nomogram and TI-RADS score model Nomogram Training cohort 0.791 0.738 0.813 3.101 0.254 Nomogram Validation cohort 0.784 0.717 0.816 2.887 0.256 ACR model Training cohort 0.766 0.406 0.913 1.537 0.215 ACR model Validation cohort 0.743 0.348 0.932 1.431 0.194 C model Training cohort 0.755 0.599 0.819 2.041 0.302 C model Validation cohort 0.761 0.556 0.860 1.935 0.252 Kwak model Training cohort 0.748 0.615 0.802 2.085 0.322 Kwak model Validation cohort 0.758 0.571 0.848 1.975 0.267 ATA model Training cohort 0.764 0.267 0.967 1.318 0.125 ATA model Validation cohort 0.737 0.222 0.983 1.264 0.076 EU model Training cohort 0.766 0.300 0.956 1.366 0.147 EU model Validation cohort 0.737 0.253 0.969 1.296 0.124 AACE model Training cohort 0.764 0.264 0.968 1.315 0.122 AACE model Validation cohort 0.734 0.217 0.981 1.253 0.089 3.5 Validation of the Nomogram model In the validation group, a total of 612 nodules were included, of which 414 were pathologically confirmed as malignant and 198 as benign. The model achieved a predictive accuracy of 78.4%, sensitivity of 81.6%, specificity of 71.7%, PLR of 2.887, and NLR of 0.256 (Table 3 ). Additionally, the ROC curve was used to assess the predictive performance of the Nomogram model (Fig. 2 ). Additionally, we compared the predictive performance of the Nomogram model with traditional models, including C-TIRADS, ACR-TIRADS, Kwak-TIRADS, ATA-TIRADS, EU-TIRADS, and AACE (Fig. 2 ). In both the training and validation groups, the AUC values for the Nomogram model were 0.849 and 0.838, respectively. The AUC values for the other models were as follows: 0.752 and 0.755 (C-TIRADS), 0.661 and 0.642 (ACR-TIRADS), 0.757 and 0.758 (Kwak-TIRADS), 0.617 and 0.603 (ATA-TIRADS), 0.628 and 0.611 (EU-TIRADS), and 0.616 and 0.599 (AACE). Furthermore, Table 3 presents the accuracy, specificity, sensitivity, PLR, and NLR of the Nomogram model compared with those of the other models. Results from the NRI and IDI analyses indicated that our Nomogram model outperformed the other models (Table 4 ). Table 4 Comparsion of diagnostic performance in Nomogram vs. 6 traditional guidelines IDI NRI(Categorical) NRI(Continuous) Nomogram vs. C TI-RADS 0.126 (0.1077–0.1443) 0.0941 [ 0.0459–0.1424 ] 0.7039 [ 0.5999–0.8079 ] Nomogram vs. ACR TI-RADS 0.1253 [ 0.1069–0.1436 ] 0.0893 [ 0.0411–0.1376 ] 0.6828 [ 0.5766–0.7891 ] Nomogram vs. Kwak TI-RADS 0.1166 [ 0.0974–0.1358 ] 0.095 [ 0.0466–0.1434 ] 0.6245 [ 0.5183–0.7306 ] Nomogram vs. ATA 0.2184 [ 0.1953–0.2416 ] 0.2787 [ 0.2251–0.3322 ] 0.8533 [ 0.7508–0.9558 ] Nomogram vs. EU TI-RADS 0.2027 [ 0.1801–0.2253 ] 0.2558 [ 0.2025–0.3091 ] 0.8039 [ 0.6999–0.908 ] Nomogram vs. AACE 0.2086 [ 0.1852–0.2319 ] 0.2801 [ 0.2262–0.334 ] 0.8426 [ 0.7398–0.9454 ] These results highlighted the superior predictive ability of our Nomogram model compared to other models. For calibration assessment, the calibration curves for the training and validation groups (Fig. 3 a and 3 b) showed strong agreement between predicted probabilities and actual outcomes, with deviations consistently within acceptable margins of error. Finally, the DCA demonstrated the satisfactory clinical utility of our model (Fig. 3 c). This study presented the sensitivity, specificity, accuracy, and AUC values for evaluating benign and malignant TNs using six TI-RADS evaluation systems (C-TIRADS, ACR-TIRADS, EU-TIRADS, ATA Guidelines, Kwak-TIRADS, and AACE), as shown in Table 3 . The specific data were as follows: (1) Sensitivity: 86.0%, 93.2%, 96.9%, 98.3%, 84.4%, and 98.1%, respectively; (2) Specificity: 55.6%, 34.8%, 25.3%, 22.2%, 57.1%, and 21.7%, respectively;(3) Accuracy: 76.1%, 74.3%, 73.7%, 73.7%, 75.8%, and 73.4%, respectively; (4) AUC: 0.752, 0.661, 0.628, 0.617, 0.757, and 0.616, respectively. 4. Discussion With the increasing prevalence of TNs and the widespread use of high-resolution ultrasound, the need for accurate, noninvasive malignancy risk assessment has become critical. Although FNA remains the diagnostic gold standard, its invasiveness, potential complications, and patient anxiety limit its universal application[ 20 ]. Therefore, improving preoperative risk stratification is essential to reduce unnecessary FNAs while maintaining diagnostic accuracy. In this study, we developed and validated a Nomogram model integrating clinical and sonographic features to predict malignancy in thyroid nodules. Our model demonstrated superior diagnostic performance compared to six established TI-RADS systems, highlighting its potential to optimize clinical decision-making and reduce unnecessary FNA procedures. Malignant thyroid nodules typically exhibit distinct ultrasound characteristics; however, certain benign conditions such as subacute thyroiditis or atrophic nodules may mimic malignant features, complicating differential diagnosis. In this context, the integration of multiple ultrasonographic and clinical parameters becomes essential. Our multivariate analysis identified ten independent predictors, including age, nodule size, margin, halo sign, calcification, aspect ratio, and the presence of suspicious lymph nodes. The resulting Nomogram offers a visual and quantifiable tool that allows clinicians to efficiently assess malignancy risk, enhancing both objectivity and ease of use. Among all variables, halo integrity and calcification type were the most influential predictors. The absence or disruption of a halo sign was strongly associated with malignancy, consistent with previous reports indicating its value in differentiating invasive tumors from benign encapsulated nodules[ 21 ]. Likewise, microcalcifications—representing psammoma bodies—showed high specificity for papillary carcinoma, confirming their diagnostic importance in risk stratification systems[ 22 , 23 ]. Suspicious cervical lymph nodes and an aspect ratio ≥ 1 were also strong malignancy indicators, reflecting tumor infiltration patterns and vertical growth tendencies of malignant nodules. The model was constructed using a large cohort of 2,047 patients, split into training and validation sets. The model achieved an AUC of 0.849 in the validation cohort, outperforming six widely used TI-RADS classification systems. Moreover, the Nomogram showed significant improvements in NRI and IDI compared to existing TI-RADS systems, supporting its superior ability to discriminate between benign and malignant nodules. Among the six TI-RADS systems evaluated in this study, all showed high sensitivity but limited specificity[ 24 ]. C-TIRADS, while straightforward and tailored to the Chinese population, does not incorporate nodule size in FNA recommendations, which may increase the rate of unnecessary biopsies. ACR-TIRADS effectively selects nodules appropriate for FNA biopsy while reducing unnecessary biopsies[ 25 , 26 ]. However, according to ACR scoring rules, some thyroid ultrasound images cannot be classified, such as when the entire thyroid or a single lobe exhibits diffuse heterogeneous echogenicity with multiple scattered punctate strong echoes[ 27 , 28 ]. ATA guidelines offer intuitive pattern-based categorization but are less applicable to nodules lacking typical malignant features.EU-TIRADS and AACE guidelines also showed high sensitivity but suboptimal specificity[ 29 ], with AACE providing particularly detailed descriptions of calcification and halo signs—features that contributed significantly to our Nomogram[ 1 ]. Compared with traditional TI-RADS systems, our Nomogram provides several advantages. Conventional systems primarily depend on imaging features and subjective scoring, often yielding variable inter-observer consistency and suboptimal specificity. In contrast, our model integrates multidimensional features and converts them into an intuitive quantitative scoring tool, improving objectivity and discrimination. Furthermore, the model was developed using data from the Northwestern Chinese population, which enhances its regional applicability and addresses population-specific heterogeneity in TN characteristics. Overall, this model demonstrates superior diagnostic performance and clinical practicality. By providing individualized malignancy probabilities, it assists clinicians in identifying high-risk nodules requiring FNA or surgery, while safely excluding benign cases from invasive procedures. Such quantitative and reproducible assessment tools can improve workflow efficiency and enhance patient management in daily practice. Despite these promising results, our study has several limitations. First, it was a single-center retrospective analysis, which may introduce selection bias and limit the generalizability of the findings. Second, ultrasound-based variables are inherently subjective and may vary among operators despite double-blind review. Third, we were unable to incorporate several potential risk factors such as family history of thyroid cancer, history of head and neck irradiation, blood flow patterns, and elastography scores. Future prospective multi-center studies incorporating these variables are warranted to further refine the model. Conclusion This study developed a Nomogram model that provides an objective, quantitative, intuitive, and concise tool to assist clinicians in evaluating the malignant risk of TNs. It helps guide clinical decision-making, reduces unnecessary FNA, enhances clinician efficiency, and minimizes unnecessary patient trauma. Overall, this model offers a novel approach for the effective management of thyroid malignant nodules. Declarations Acknowledgements We hereby extend our sincere gratitude to Doctors Wei Zhang from the Department of Pathology, Shaanxi Provincial People’s Hospital, for their invaluable support throughout this research. Author contributions MT: Conceptualization, Data curation, Formal analysis, Writing – original draft, Writing – review & editing. WL: Formal analysis,Investigation, Project administration,Resources,Supervision,Visualization, Writing – review & editing.JL: Conceptualization, Data curation, Formal Analysis, Validation, Writing – review & editing.ZL:Validation, Writing – original draft, Writing – review & editing. JD: Writing – review & editing. XZ: Data curation, Visualization, Writing – review & editing. Funding This work was financially supported by the Shaanxi Provincial Clinical Medical Research Center (S2021-0-ZC-LCZX-0002), the Scientific and Technological Talents Support Program Foundation of Shaanxi Provincial People ' s Hospital (2021LJ-07), the Key Research Project of Shaanxi Province (2019ZDLSF03-05), the Health Research and Innovation Capacity Enhancement Program Project of Shaanxi Province (2024TD-01). Data availability The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors. Ethics approval and consent to participate Samples of FNA or TN resection of thyroid nodules were acquired from Shaanxi Provincial People’s Hospital. 2,047 patients who underwent thyroid ultrasound and received corresponding pathological results via ultrasound-guided FNA or TN resection were included in the study sample between January 2018 and January 2024. This study was announced by the Ethical Committee of the Shaanxi Provincial People’s Hospital (reference number: 2025-017). The patients consented to use their tissue, clinical, and pathological information for the experimental research, and all signed an informed consent form. Clinical trial is not applicable. Consent for publication Our raw data, and manuscript did not contain any individual details, images, or videos. The authors used to number the cases to maintain confidentiality of patient data. Competing interests The authors declare no competing interests. References Gharib H, Papini E, Garber JR, AMERICAN ASSOCIATION OF CLINICAL ENDOCRINOLOGISTS, AMERICAN COLLEGE OF ENDOCRINOLOGY, AND ASSOCIAZIONE MEDICI ENDOCRINOLOGI MEDICAL GUIDELINES FOR CLINICAL PRACTICE FOR THE DIAGNOSIS AND MANAGEMENT OF THYROID NODULES, et al. –2016 UPDATE. Endocr Pract. 2016;22(5):622–39. http://doi.org/10.4158/ep161208.Gl . Li Y, Teng D, Ba J, et al. 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Clinical Application of Ultrasound-Guided Thyroid Fine Needle Aspiration Biopsy and Thinprep Cytology Test in Diagnosis of Thyroid Disease. Asian Pac J Cancer Prev. 2016;17(10):4689–92. http://doi.org/10.22034/apjcp.2016.17.10.4689 . Zawawi F, Mosli MH, Zawawi ST. Should ultrasound-guided fine needle aspiration be considered a first-line technique in assessing a thyroid nodule? Otolaryngol Pol. 2016;70(1):49–53. http://doi.org/10.5604/00306657.1193071 . Polyzos SA, Anastasilakis AD. Clinical complications following thyroid fine-needle biopsy: a systematic review. Clin Endocrinol (Oxf). 2009;71(2):157–65. http://doi.org/10.1111/j.1365-2265.2009.03522.x . Yang Z, Gao X, Yang L. Predictors and a prediction model for positive fine needle aspiration biopsy in C-TIRADS 4 thyroid nodules. Front Endocrinol (Lausanne). 2023;14:1154984. http://doi.org/10.3389/fendo.2023.1154984 . Hu T, Li Z, Peng C, et al. Nomogram to differentiate benign and malignant thyroid nodules in the American College of Radiology Thyroid Imaging Reporting and Data System level 5. Clin Endocrinol (Oxf). 2023;98(2):249–58. http://doi.org/10.1111/cen.14824 . Strieder DL, Cristo AP, Zanella AB, et al. Using an ultrasonography risk stratification system to enhance the thyroid fine needle aspiration performance. Eur J Radiol. 2022;150:110244. http://doi.org/10.1016/j.ejrad.2022.110244 . Tao Y, Yu Y, Wu T, et al. Deep learning for the diagnosis of suspicious thyroid nodules based on multimodal ultrasound images. Front Oncol. 2022;12:1012724. http://doi.org/10.3389/fonc.2022.1012724 . Xu S, Ni X, Zhou W, Zhan W, Zhang H. Development and validation of a novel diagnostic tool for predicting the malignancy probability of thyroid nodules: A retrospective study based on clinical, B-mode, color doppler and elastographic ultrasonographic characteristics. Front Endocrinol (Lausanne). 2022;13:966572. http://doi.org/10.3389/fendo.2022.966572 . Wildman-Tobriner B, Taghi-Zadeh E, Mazurowski MA. Artificial Intelligence (AI) Tools for Thyroid Nodules on Ultrasound, From the AJR Special Series on AI Applications. AJR Am J Roentgenol. 2022;219(4):1–8. http://doi.org/10.2214/ajr.22.27430 . Reverter JL, Vázquez F, Puig-Domingo M. Diagnostic Performance Evaluation of a Computer-Assisted Imaging Analysis System for Ultrasound Risk Stratification of Thyroid Nodules. AJR Am J Roentgenol. 2019;213(1):169–74. http://doi.org/10.2214/ajr.18.20740 . Han M, Ha EJ, Park JH. Computer-Aided Diagnostic System for Thyroid Nodules on Ultrasonography: Diagnostic Performance Based on the Thyroid Imaging Reporting and Data System Classification and Dichotomous Outcomes. AJNR Am J Neuroradiol. 2021;42(3):559–65. http://doi.org/10.3174/ajnr.A6922 . Ito Y, Amino N, Yokozawa T, et al. Ultrasonographic evaluation of thyroid nodules in 900 patients: comparison among ultrasonographic, cytological, and histological findings. Thyroid. 2007;17(12):1269–76. http://doi.org/10.1089/thy.2007.0014 . Cibas ES, Ali SZ. The 2017 Bethesda System for Reporting Thyroid Cytopathology. Thyroid. 2017;27(11):1341–6. http://doi.org/10.1089/thy.2017.0500 . Steyerberg EW, Vickers AJ, Cook NR, et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology. 2010;21(1):128–38. http://doi.org/10.1097/EDE.0b013e3181c30fb2 . Carpi A, Di Coscio G, Iervasi G, et al. Thyroid fine needle aspiration: How to improve clinicians’ confidence and performance with the technique. Cancer Lett. 2008;264(2):163–71. http://doi.org/https://doi.org/10.1016/j.canlet.2008.02.056 . Shi W, Zhang M, Tang W, Tang K. The Association Between the Thickness of the Hypoechoic Halo of Thyroid Nodules and Thyroid Cancer: A Retrospective Study. Acad Radiol. 2025;32(4):1906–17. http://doi.org/10.1016/j.acra.2024.12.009 . Khoo ML, Asa SL, Witterick IJ, Freeman JL. Thyroid calcification and its association with thyroid carcinoma. Head Neck. 2002;24(7):651–5. http://doi.org/10.1002/hed.10115 . Yin L, Zhang W, Bai W, He W. Relationship Between Morphologic Characteristics of Ultrasonic Calcification in Thyroid Nodules and Thyroid Carcinoma. Ultrasound Med Biol. 2020;46(1):20–5. http://doi.org/10.1016/j.ultrasmedbio.2019.09.005 . Kim DH, Kim SW, Basurrah MA, Lee J, Hwang SH. Diagnostic Performance of Six Ultrasound Risk Stratification Systems for Thyroid Nodules: A Systematic Review and Network Meta-Analysis. AJR Am J Roentgenol. 2023;220(6):791–803. http://doi.org/10.2214/ajr.22.28556 . Ha SM, Baek JH, Na DG, et al. Diagnostic Performance of Practice Guidelines for Thyroid Nodules: Thyroid Nodule Size versus Biopsy Rates. Radiology. 2019;291(1):92–9. http://doi.org/10.1148/radiol.2019181723 . Qi TY, Chen X, Liu H, et al. Comparison of thyroid nodule FNA rates recommended by ACR TI-RADS, Kwak TI-RADS and ATA guidelines. Eur J Radiol. 2022;148:110152. http://doi.org/10.1016/j.ejrad.2022.110152 . Tessler FN, Middleton WD, Grant EG, et al. White Paper of the ACR TI-RADS Committee. J Am Coll Radiol. 2017;14(5):587–95. http://doi.org/10.1016/j.jacr.2017.01.046 . ACR Thyroid Imaging, Reporting and Data System (TI-RADS). Tessler FN, Middleton WD, Grant EG. A User's Guide. Radiology. 2018;287(1):29–36. http://doi.org/10.1148/radiol.2017171240 . Thyroid Imaging Reporting and Data System (TI-RADS). Fu C, Cui Y, Li J, et al. Effect of the categorization method on the diagnostic performance of ultrasound risk stratification systems for thyroid nodules. Front Oncol. 2023;13:1073891. http://doi.org/10.3389/fonc.2023.1073891 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 Jan, 2026 Reviews received at journal 03 Jan, 2026 Reviews received at journal 27 Dec, 2025 Reviewers agreed at journal 25 Dec, 2025 Reviewers agreed at journal 20 Dec, 2025 Reviewers agreed at journal 19 Dec, 2025 Reviewers invited by journal 19 Dec, 2025 Editor invited by journal 17 Dec, 2025 Editor assigned by journal 09 Dec, 2025 Submission checks completed at journal 06 Dec, 2025 First submitted to journal 06 Dec, 2025 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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00:48:55","extension":"html","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":145321,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8245786/v1/b1374c3d98130d8df7e9ea33.html"},{"id":99190036,"identity":"582db539-9cea-4d1d-a05f-e6c920948ccf","added_by":"auto","created_at":"2025-12-30 00:48:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":113780,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting the risk of malignancy for thyroid nodules in TR4. All the points assigned on the top point scale for each factor are summed together to generate a total point score. The total point score is projected on the bottom scales to determine the overall survival rate in an individual.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8245786/v1/250be2ce42f20932a9bd6abb.png"},{"id":99190040,"identity":"434d3d66-f7dd-412b-9758-7fe7125faf82","added_by":"auto","created_at":"2025-12-30 00:48:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":159724,"visible":true,"origin":"","legend":"\u003cp\u003eThe receiver-operating characteristics (ROC) curve and area under the ROC curve (AUC) of the nomogram, C_TIRADS, ACR_TIRADS, Kawk_TIRADS, ATA, EU_TIRADS, AACE in the validation (A)and training cohorts(B).\u0026nbsp;\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8245786/v1/0835fbc285c846d6d8c834ef.png"},{"id":99190037,"identity":"63512cfa-4356-47d6-946b-b93d47141a41","added_by":"auto","created_at":"2025-12-30 00:48:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":98804,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram validation. Calibration curve showing nomogram‐predicted malignant nodules probabilities compared with the actual malignant nodules in the training (A) and validation (B) cohort. Decision curve analyses (DCAs) of the nomogram model for predicting malignant nodules (C).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8245786/v1/a22b1918f59278c85c129f54.png"},{"id":99323533,"identity":"9c3e90b7-d83f-4169-a4fb-39a3e1e670d1","added_by":"auto","created_at":"2025-12-31 16:45:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1563330,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8245786/v1/08a65d21-b878-40ff-9ea4-423951ce1796.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diagnostic Comparison of TI-RADS and a Nomogram for Thyroid Nodules in Northwestern China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn recent years, the widespread clinical use of high-resolution ultrasound and increased public awareness of health examinations have significantly raised the detection rate of thyroid nodules (TNs). Ultrasound examinations indicate that the prevalence of TNs\u0026thinsp;\u0026ge;\u0026thinsp;0.5 cm in diameter among adults is approximately 20.43%, with 8%-16% of these being thyroid cancers[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. As the preferred imaging modality for evaluating TNs, high-frequency ultrasound achieves a detection rate of 20%-76% due to its superior soft tissue resolution[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. It not only confirms the presence of nodules but also enables detailed characterization of their sonographic features, including size, number, location, composition (solid or cystic), orientation, echogenicity, calcification, margin, capsule integrity, vascularity, and association with diffuse thyroid disease or invasion of adjacent tissues. Additionally, it facilitates the assessment of abnormal cervical lymph nodes in terms of size, number, location, and structural features [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].However, multicenter studies have demonstrated substantial heterogeneity in the ultrasound features of TNs, making it difficult to differentiate benign from malignant lesions based on single imaging parameters. Consequently, clinical practice increasingly relies on comprehensive systems that integrate multi-dimensional ultrasound findings to stratify malignancy risk.\u003c/p\u003e \u003cp\u003eFine-needle aspiration (FNA) biopsy is the gold standard for the preoperative diagnosis of TNs, helping to avoid over 50% of unnecessary thyroid surgeries and enhancing the detection rate of malignant nodules[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].Although the accuracy and safety of ultrasound-guided FNA have markedly improved[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e],its invasive nature may still result in complications such as pain and bleeding, and may also provoke anxiety in patients[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Currently, FNA is mainly suggested for sepcific patients based on risk stratification systems for TNs. Traditional stratification models rely mainly on sonographic features; however, clinical indicators such as age, sex, nodule size, as well as thyroid function have also been identified as predictors of malignancy[\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Large-scale, high-quality comparative studies between emerging risk stratification systems (e.g., Chinese Thyroid Imaging Reporting and Data System [C-TIRADS], and American College of Radiology Thyroid Imaging Reporting and Data System [ACR-TIRADS]) and traditional models remain limited. Furthermore, certain benign conditions (like asymptomatic subacute thyroiditis), can exhibit ultrasound features similar to those of malignant tumors, making it challenging for conventional models to accurately differentiate between benign and malignant lesions.\u003c/p\u003e \u003cp\u003eArtificial intelligence-based diagnostic tools for ultrasound interpretation, such as S-Detect 2 and Automated Medical Computer-Aided Diagnosis for Ultrasonography of Thyroid nodules [AMCAD-UT], have been approved by the U.S. Food and Drug Administration, demonstrating diagnostic performance comparable to or even exceeding that of radiologists[\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, their high initial investment and ongoing subscription costs have hindered widespread adoption, resulting in a disparity between technological advancement and routine clinical utilization.\u003c/p\u003e \u003cp\u003eIn response to the above challenges, it is necessary to develop a multi-parameter model that integrates clinical data, biochemical indicators, as well as ultrasound features to enable rapid and accurate differentiation between benign and malignant TNs through intelligent assessment. Data from 2,047 patients were used to construct and validate the model. The objective is to optimize clinical decision-making, reduce unnecessary FNA procedures, and systematically compare the model\u0026rsquo;s diagnostic performance with six established risk stratification systems, thereby providing an innovative and standardized approach for the diagnosis and management of TNs.\u003c/p\u003e \u003cp\u003eTherefore, this study aims to construct an intelligent evaluation model based on multi-parameter analysis to enable rapid and accurate differentiation between benign and malignant TNs, thereby optimizing clinical decision-making, reducing unnecessary FNA procedures, and providing a novel solution for the standardized diagnosis and management of TNs. Clinical, biochemical, and ultrasound data from 2,047 patients at our medical center were collected to develop and validate a Nomogram model. This model will be compared with several existing risk stratification systems, with the intention of enhancing predictive accuracy and ensuring practical applicability in clinical settings.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Subjects\u003c/h2\u003e \u003cp\u003eA total of 2,047 patients who underwent thyroid ultrasound and received corresponding pathological results via ultrasound-guided FNA or TN resection at Shaanxi Provincial People's Hospital between January 2018 and January 2024 were retrospectively enrolled, including 533 males and 1,514 females.\u003c/p\u003e \u003cp\u003eInclusion criteria were as follows: ① Underwent thyroid ultrasound before FNA or surgery, with complete and high-quality imaging data; ② Diagnosed with TNs classified as category 3, 4, or 5 according to the ACR-TIRADS; ③ Had definitive cytological or histological pathological results; ④ Completed relevant laboratory examinations; ⑤ Had no history of thyroid surgery.\u003c/p\u003e \u003cp\u003eExclusion criteria were as follows: ① Poor preoperative ultrasound image quality affecting diagnostic judgment; ② Incomplete clinical or pathological data after FNA or surgery; ③ Unclear pathological diagnosis.\u003c/p\u003e \u003cp\u003eFor each patient, one nodule was selected for analysis. In patients with a single nodule, that nodule was included. For patients with multiple nodules: If the TI-RADS classifications differed, the nodule with the highest classification was selected; If the TI-RADS classifications were the same, the largest nodule was selected. All selected nodules had corresponding postoperative histopathological results.\u003c/p\u003e \u003cp\u003e This study was approved by the Ethics Committee of Shaanxi Provincial People's Hospital (approval number: 2025-017).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Clinical Data Collection\u003c/h2\u003e \u003cp\u003eClinical and serological data were collected and recorded, including age, sex, marital status, and body mass index. Ultrasound imaging features included nodule size, location, margin, extracapsular invasion, halo sign, internal composition, echogenicity, calcification, aspect ratio, vascular pattern, presence of suspicious lymph nodes, and concomitant Hashimoto's thyroiditis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Instruments and Methods\u003c/h2\u003e \u003cp\u003e(1) Equipment: A Mindray ultrasound system equipped with an L12-5 linear probe (5\u0026ndash;12 MHz) was used. Patients were scanned in the supine position, with gain and focus settings adjusted according to the size and depth of the TNs.\u003c/p\u003e \u003cp\u003e(2) Ultrasound Image Acquisition: Ultrasound physicians performed multi-planar scanning of the thyroid gland and nodules, carefully assessing two-dimensional and color Doppler sonographic features. These included nodule number, size, location, margin, shape, internal echogenicity, calcification, aspect ratio, presence of suspicious lymph nodes, and blood flow pattern.\u003c/p\u003e \u003cp\u003eTwo ultrasound physicians, each with over nine years of experience, retrospectively reviewed the images using a double-blind method and independently classified the nodules. In cases of disagreement, the final classification was determined through consensus after consultation between the two physicians.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 FNA under ultrasound guidance\u003c/h2\u003e \u003cp\u003eSuspicious TNs were punctured under real-time ultrasound guidance. A disposable 21-gauge biopsy needle was used to perform multiple passes in different directions within the nodule to ensure adequate sample collection. The specimens were immediately fixed in 95% alcohol and sent to the Pathology Department of our medical center for cytological evaluation. Cytological findings were classified into six categories according to the 2017 Bethesda System for Reporting Thyroid Cytopathology[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Only nodules with definitive cytological or histopathological diagnoses were included in the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e \u003cp\u003eAll experimental data in this study were statistically analyzed and visualized using SPSS (Version 26.0) and R software (Version 4.3.2). Variables with statistically significant differences in univariate regression analysis were included in multivariate logistic regression to identify independent predictors. Based on the regression coefficients, a Nomogram model was constructed using R software. The discriminative ability of the model was evaluated by plotting the area under the receiver operating characteristic (ROC) curve (AUC). The performance of the Nomogram model was compared with several established risk stratification systems, including C-TIRADS, ACR-TIRADS, Kwak Thyroid Imaging Reporting and Data System (Kwak-TIRADS), American Thyroid Association (ATA), European Thyroid Imaging Reporting and Data System (EU-TIRADS), and American Association of Clinical Endocrinologists (AACE). Additionally, we assessed the model's predictive accuracy, specificity, sensitivity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR).\u003c/p\u003e \u003cp\u003eTo further clarify the predictive advantages of this model, we calculated the Net Reclassification Index (NRI) to assess its ability to correctly reclassify cases compared to other models. Meanwhile, the Integrated Discrimination Improvement (IDI) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]was calculated. A higher probability of correctly predicting positive outcomes in the positive group, coupled with a lower probability of false-positive predictions in the negative group, indicates superior performance of this model over others. Finally, the clinical utility of the model was evaluated using Decision Curve Analysis (DCA). A two-sided \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Demographics and thyroid nodule characteristics\u003c/h2\u003e \u003cp\u003eA total of 2,047 nodules were included in this study, involving 533 male and 1,514 female patients, with a mean age of 47.37\u0026thinsp;\u0026plusmn;\u0026thinsp;12.31 years. Among them, 1,433 nodules were malignant and 614 were benign, with an average nodule diameter of 1.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16 cm. The nodules were randomly divided into a training group (1,435 nodules) and a validation group (612 nodules) at a ratio of 7:3. There were no significant differences in baseline clinical data or ultrasound characteristics of the nodules between the two groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\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\u003eCharacteristics of Patients with Thyroid Nodules in the Training and Validation Sets\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\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2047)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining set\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1435)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eValidation set\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;612)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.98\u0026thinsp;\u0026plusmn;\u0026thinsp;12.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.85\u0026thinsp;\u0026plusmn;\u0026thinsp;12.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.37\u0026thinsp;\u0026plusmn;\u0026thinsp;12.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.49\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\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 \u003cp\u003e0.34\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \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\" colname=\"c2\"\u003e \u003cp\u003e533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e168\u003c/p\u003e \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\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e444\u003c/p\u003e \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\u003eNodule size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.64\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.15\u0026thinsp;\u0026plusmn;\u0026thinsp;3.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.33\u0026thinsp;\u0026plusmn;\u0026thinsp;3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNodule Position 1\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 \u003cp\u003e0.29\u003csup\u003ea\u003c/sup\u003e\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\" colname=\"c2\"\u003e \u003cp\u003e915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e282\u003c/p\u003e \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\u003eRight lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e303\u003c/p\u003e \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\u003eIsthmus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \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\u003eNodule Position 2\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\u003eUpper region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.61\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e240\u003c/p\u003e \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\u003eLower region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e213\u003c/p\u003e \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\u003eIsthmus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \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\u003eHistology\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 \u003cp\u003e0.13\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenign\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e198\u003c/p\u003e \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\u003eMalignant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e414\u003c/p\u003e \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\u003eMargin\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 \u003cp\u003e0.97\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e116\u003c/p\u003e \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\u003eIrregular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e496\u003c/p\u003e \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\u003eExtrathyroidal extension\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 \u003cp\u003e0.06\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91\u003c/p\u003e \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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e521\u003c/p\u003e \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\u003eHalo\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 \u003cp\u003e0.28\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e560\u003c/p\u003e \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\u003eComplete\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \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\u003eIncomplete\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \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\u003eComposition\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 \u003cp\u003e0.38\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCystic or spongiform\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \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 and solid (Cystic\u0026thinsp;\u0026ge;\u0026thinsp;50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \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 and solid (Solid\u0026thinsp;\u0026ge;\u0026thinsp;50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \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\u003eSolid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e560\u003c/p\u003e \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\u003eCalcification\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 \u003cp\u003e0.21\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo calcification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e199\u003c/p\u003e \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\u003eMacrocalcification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49\u003c/p\u003e \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\u003eMacro and microcalcification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31\u003c/p\u003e \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\u003eMicrocalcification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e333\u003c/p\u003e \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\u003eComet tail artifacts\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 \u003cp\u003e0.84\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \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\u003eNo/NA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e607\u003c/p\u003e \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\u003eVascular distribution pattern\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 \u003cp\u003e0.62\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvascularity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e251\u003c/p\u003e \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\u003ePeripheral vascularity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e139\u003c/p\u003e \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\u003eMainly central vascularity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e151\u003c/p\u003e \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\u003eMixed vascularity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71\u003c/p\u003e \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\u003eSuspicious LNM\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 \u003cp\u003e0.52\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97\u003c/p\u003e \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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e515\u003c/p\u003e \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 \u0026gt;1\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 \u003cp\u003e0.27\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e339\u003c/p\u003e \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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e273\u003c/p\u003e \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\u003eComplicated with thyroiditis\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 \u003cp\u003e0.92\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97\u003c/p\u003e \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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003ea\u003c/sup\u003e Using χ2 test for this statistic\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003eb\u003c/sup\u003e Using two-sample t-test for this statistic\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eBMI, Body Mass Index; LNM, Lymph Node Metastasis.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Univariate regression analysis\u003c/h2\u003e \u003cp\u003eUnivariate analysis was performed on the training set to identify factors distinguishing benign from malignant nodules. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, twelve variables (age, sex, body mass index, nodule size, nodule location, margin, extracapsular extension, halo, calcification, suspicious lymph nodes, aspect ratio, and concomitant Hashimoto's thyroiditis) showed statistically significant differences between the benign and malignant groups (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\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 and multivariate analysis of risk factors for thyroid cancer in the training group\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eHistology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBenign\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMalignant\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 value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\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 \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\" colname=\"c2\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\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\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.66 (0.50\u0026ndash;0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.75 (0.53\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.75\u0026thinsp;\u0026plusmn;\u0026thinsp;3.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.32\u0026thinsp;\u0026plusmn;\u0026thinsp;3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04 (1.01\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.04 (1.01\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNodule size (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.70 (0.64\u0026ndash;0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.77 (0.67\u0026ndash;0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNodule position 1\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\" colname=\"c2\"\u003e \u003cp\u003e173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\" colname=\"c2\"\u003e \u003cp\u003e235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.83 (0.66\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.88 (0.91\u0026ndash;4.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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 position 2\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\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\u003eMiddle region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.58 (0.42\u0026ndash;0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.53 (0.36\u0026ndash;0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.63 (0.45\u0026ndash;0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.57 (0.38\u0026ndash;0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01*\u003c/p\u003e \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\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.77 (0.39\u0026ndash;1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.41 (0.17\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.04*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMargin\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\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\u003eIrregular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.93 (5.95\u0026ndash;10.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.28 (2.30\u0026ndash;4.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtrathyroidal extension\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04 (0.02\u0026ndash;0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06 (0.02\u0026ndash;0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHalo\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\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\u003eComplete\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06 (0.03\u0026ndash;0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.13 (0.05\u0026ndash;0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncomplete\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.22 (1.68\u0026ndash;9.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.93 (1.37\u0026ndash;6.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComposition\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCystic or spongiform\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003eCystic and solid (Cystic\u0026thinsp;\u0026ge;\u0026thinsp;50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.18 (0.03\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003eCystic and solid (Solid\u0026thinsp;\u0026ge;\u0026thinsp;50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.76 (0.17\u0026ndash;3.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003eSolid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.81 (0.66\u0026ndash;11.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003eCalcification\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo calcification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\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\u003eMacrocalcification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.58 (0.40\u0026ndash;0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.49 (0.30\u0026ndash;0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMacro and microcalcification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.83 (1.09\u0026ndash;3.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.18 (0.60\u0026ndash;2.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMicrocalcification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.66 (2.03\u0026ndash;3.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.61 (1.17\u0026ndash;2.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVascular distribution pattern\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvascularity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003ePeripheral vascularity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08 (0.81\u0026ndash;1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003eMainly central vascularity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05 (0.78\u0026ndash;1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003eMixed vascularity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85 (0.60\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003eSuspicious LNM\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.17 (0.10\u0026ndash;0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.28 (0.16\u0026ndash;0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspect ratio \u0026gt; 1\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30 (0.23\u0026ndash;0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.59 (0.43\u0026ndash;0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplicated with thyroiditis\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.70 (0.50\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.71 (0.67\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e value indicates significant difference.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eBMI, Body Mass Index; CI, Confidence Interval; SD, Standard Deviation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Multivariate regression analysis\u003c/h2\u003e \u003cp\u003eTwelve variables (age, sex, nodule size, body mass index, nodule location, margin, extracapsular extension, halo, calcification, suspicious lymph nodes, aspect ratio, and concomitant Hashimoto's thyroiditis) were included in the logistic regression model. The analysis showed that ten of these variables (age, nodule size, nodule location, margin, extracapsular extension, halo, calcification, suspicious lymph nodes, aspect ratio, and concomitant Hashimoto's thyroiditis) were identified as independent predictors of malignant nodules (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Development of a Nomogram model\u003c/h2\u003e \u003cp\u003eBased on the results of the univariate and multivariate analyses, we used R software (R Studio) to construct a Nomogram model (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).. The model assigns scores to each independent predictor according to their regression coefficients from the logistic regression analysis. Clinicians can sum these scores to calculate the probability of malignancy in TNs. The model's performance was evaluated by comparing its predictions with the final surgical pathology results, and calculating accuracy, specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the training set (n\u0026thinsp;=\u0026thinsp;1,435 nodules), 1,019 nodules were pathologically confirmed as malignant and 416 as benign. The model achieved an accuracy of 79.1%, sensitivity of 81.3%, specificity of 73.8%, PLR of 3.101, and NLR of 0.254 (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\u003eDiagnostic performances of the nomogram and TI-RADS score model\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNomogram Training cohort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.101\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNomogram Validation cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACR model Training cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACR model Validation cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC model Training cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC model Validation cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKwak model Training cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKwak model Validation cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATA model Training cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATA model Validation cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEU model Training cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEU model Validation cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAACE model Training cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAACE model Validation cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Validation of the Nomogram model\u003c/h2\u003e \u003cp\u003eIn the validation group, a total of 612 nodules were included, of which 414 were pathologically confirmed as malignant and 198 as benign. The model achieved a predictive accuracy of 78.4%, sensitivity of 81.6%, specificity of 71.7%, PLR of 2.887, and NLR of 0.256 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Additionally, the ROC curve was used to assess the predictive performance of the Nomogram model (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditionally, we compared the predictive performance of the Nomogram model with traditional models, including C-TIRADS, ACR-TIRADS, Kwak-TIRADS, ATA-TIRADS, EU-TIRADS, and AACE (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In both the training and validation groups, the AUC values for the Nomogram model were 0.849 and 0.838, respectively. The AUC values for the other models were as follows: 0.752 and 0.755 (C-TIRADS), 0.661 and 0.642 (ACR-TIRADS), 0.757 and 0.758 (Kwak-TIRADS), 0.617 and 0.603 (ATA-TIRADS), 0.628 and 0.611 (EU-TIRADS), and 0.616 and 0.599 (AACE). Furthermore, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the accuracy, specificity, sensitivity, PLR, and NLR of the Nomogram model compared with those of the other models. Results from the NRI and IDI analyses indicated that our Nomogram model outperformed the other models (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\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\u003eComparsion of diagnostic performance in Nomogram vs. 6 traditional guidelines\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIDI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNRI(Categorical)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNRI(Continuous)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNomogram vs. C TI-RADS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.126 (0.1077\u0026ndash;0.1443)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0941 [ 0.0459\u0026ndash;0.1424 ]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7039 [ 0.5999\u0026ndash;0.8079 ]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNomogram vs. ACR TI-RADS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1253 [ 0.1069\u0026ndash;0.1436 ]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0893 [ 0.0411\u0026ndash;0.1376 ]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6828 [ 0.5766\u0026ndash;0.7891 ]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNomogram vs. Kwak TI-RADS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1166 [ 0.0974\u0026ndash;0.1358 ]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.095 [ 0.0466\u0026ndash;0.1434 ]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6245 [ 0.5183\u0026ndash;0.7306 ]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNomogram vs. ATA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.2184 [ 0.1953\u0026ndash;0.2416 ]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2787 [ 0.2251\u0026ndash;0.3322 ]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8533 [ 0.7508\u0026ndash;0.9558 ]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNomogram vs. EU TI-RADS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.2027 [ 0.1801\u0026ndash;0.2253 ]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2558 [ 0.2025\u0026ndash;0.3091 ]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8039 [ 0.6999\u0026ndash;0.908 ]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNomogram vs. AACE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.2086 [ 0.1852\u0026ndash;0.2319 ]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2801 [ 0.2262\u0026ndash;0.334 ]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8426 [ 0.7398\u0026ndash;0.9454 ]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThese results highlighted the superior predictive ability of our Nomogram model compared to other models. For calibration assessment, the calibration curves for the training and validation groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb) showed strong agreement between predicted probabilities and actual outcomes, with deviations consistently within acceptable margins of error. Finally, the DCA demonstrated the satisfactory clinical utility of our model (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis study presented the sensitivity, specificity, accuracy, and AUC values for evaluating benign and malignant TNs using six TI-RADS evaluation systems (C-TIRADS, ACR-TIRADS, EU-TIRADS, ATA Guidelines, Kwak-TIRADS, and AACE), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The specific data were as follows: (1) Sensitivity: 86.0%, 93.2%, 96.9%, 98.3%, 84.4%, and 98.1%, respectively; (2) Specificity: 55.6%, 34.8%, 25.3%, 22.2%, 57.1%, and 21.7%, respectively;(3) Accuracy: 76.1%, 74.3%, 73.7%, 73.7%, 75.8%, and 73.4%, respectively; (4) AUC: 0.752, 0.661, 0.628, 0.617, 0.757, and 0.616, respectively.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eWith the increasing prevalence of TNs and the widespread use of high-resolution ultrasound, the need for accurate, noninvasive malignancy risk assessment has become critical. Although FNA remains the diagnostic gold standard, its invasiveness, potential complications, and patient anxiety limit its universal application[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Therefore, improving preoperative risk stratification is essential to reduce unnecessary FNAs while maintaining diagnostic accuracy.\u003c/p\u003e \u003cp\u003eIn this study, we developed and validated a Nomogram model integrating clinical and sonographic features to predict malignancy in thyroid nodules. Our model demonstrated superior diagnostic performance compared to six established TI-RADS systems, highlighting its potential to optimize clinical decision-making and reduce unnecessary FNA procedures.\u003c/p\u003e \u003cp\u003eMalignant thyroid nodules typically exhibit distinct ultrasound characteristics; however, certain benign conditions such as subacute thyroiditis or atrophic nodules may mimic malignant features, complicating differential diagnosis. In this context, the integration of multiple ultrasonographic and clinical parameters becomes essential. Our multivariate analysis identified ten independent predictors, including age, nodule size, margin, halo sign, calcification, aspect ratio, and the presence of suspicious lymph nodes. The resulting Nomogram offers a visual and quantifiable tool that allows clinicians to efficiently assess malignancy risk, enhancing both objectivity and ease of use.\u003c/p\u003e \u003cp\u003eAmong all variables, halo integrity and calcification type were the most influential predictors. The absence or disruption of a halo sign was strongly associated with malignancy, consistent with previous reports indicating its value in differentiating invasive tumors from benign encapsulated nodules[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Likewise, microcalcifications\u0026mdash;representing psammoma bodies\u0026mdash;showed high specificity for papillary carcinoma, confirming their diagnostic importance in risk stratification systems[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Suspicious cervical lymph nodes and an aspect ratio\u0026thinsp;\u0026ge;\u0026thinsp;1 were also strong malignancy indicators, reflecting tumor infiltration patterns and vertical growth tendencies of malignant nodules.\u003c/p\u003e \u003cp\u003eThe model was constructed using a large cohort of 2,047 patients, split into training and validation sets. The model achieved an AUC of 0.849 in the validation cohort, outperforming six widely used TI-RADS classification systems. Moreover, the Nomogram showed significant improvements in NRI and IDI compared to existing TI-RADS systems, supporting its superior ability to discriminate between benign and malignant nodules.\u003c/p\u003e \u003cp\u003eAmong the six TI-RADS systems evaluated in this study, all showed high sensitivity but limited specificity[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. C-TIRADS, while straightforward and tailored to the Chinese population, does not incorporate nodule size in FNA recommendations, which may increase the rate of unnecessary biopsies. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eACR-TIRADS\u003c/span\u003e effectively selects nodules appropriate for FNA biopsy while reducing unnecessary biopsies[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, according to ACR scoring rules, some thyroid ultrasound images cannot be classified, such as when the entire thyroid or a single lobe exhibits diffuse heterogeneous echogenicity with multiple scattered punctate strong echoes[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. ATA guidelines offer intuitive pattern-based categorization but are less applicable to nodules lacking typical malignant features.EU-TIRADS and AACE guidelines also showed high sensitivity but suboptimal specificity[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], with AACE providing particularly detailed descriptions of calcification and halo signs\u0026mdash;features that contributed significantly to our Nomogram[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCompared with traditional TI-RADS systems, our Nomogram provides several advantages. Conventional systems primarily depend on imaging features and subjective scoring, often yielding variable inter-observer consistency and suboptimal specificity. In contrast, our model integrates multidimensional features and converts them into an intuitive quantitative scoring tool, improving objectivity and discrimination. Furthermore, the model was developed using data from the Northwestern Chinese population, which enhances its regional applicability and addresses population-specific heterogeneity in TN characteristics.\u003c/p\u003e \u003cp\u003eOverall, this model demonstrates superior diagnostic performance and clinical practicality. By providing individualized malignancy probabilities, it assists clinicians in identifying high-risk nodules requiring FNA or surgery, while safely excluding benign cases from invasive procedures. Such quantitative and reproducible assessment tools can improve workflow efficiency and enhance patient management in daily practice.\u003c/p\u003e \u003cp\u003eDespite these promising results, our study has several limitations. First, it was a single-center retrospective analysis, which may introduce selection bias and limit the generalizability of the findings. Second, ultrasound-based variables are inherently subjective and may vary among operators despite double-blind review. Third, we were unable to incorporate several potential risk factors such as family history of thyroid cancer, history of head and neck irradiation, blood flow patterns, and elastography scores. Future prospective multi-center studies incorporating these variables are warranted to further refine the model.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study developed a Nomogram model that provides an objective, quantitative, intuitive, and concise tool to assist clinicians in evaluating the malignant risk of TNs. It helps guide clinical decision-making, reduces unnecessary FNA, enhances clinician efficiency, and minimizes unnecessary patient trauma. Overall, this model offers a novel approach for the effective management of thyroid malignant nodules.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe hereby extend our sincere gratitude to Doctors Wei Zhang from the Department of Pathology, Shaanxi Provincial People\u0026rsquo;s Hospital, for their invaluable support throughout this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMT: Conceptualization, Data curation, Formal analysis, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing. WL: Formal analysis,Investigation, Project administration,Resources,Supervision,Visualization, Writing \u0026ndash; review \u0026amp; editing.JL: Conceptualization, Data curation, Formal Analysis, Validation, Writing \u0026ndash; review \u0026amp; editing.ZL:Validation, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing. JD: Writing \u0026ndash; review \u0026amp; editing. XZ: Data curation, Visualization, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was financially supported by the\u0026nbsp;Shaanxi Provincial Clinical Medical Research Center\u0026nbsp;(S2021-0-ZC-LCZX-0002), the Scientific and Technological Talents Support Program Foundation of Shaanxi Provincial People \u0026apos; s Hospital (2021LJ-07), the Key Research Project of Shaanxi Province (2019ZDLSF03-05), the Health Research and Innovation Capacity Enhancement Program Project of Shaanxi Province (2024TD-01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSamples of FNA or TN resection of thyroid nodules were acquired from Shaanxi Provincial People\u0026rsquo;s Hospital.\u0026nbsp;2,047 patients who underwent thyroid ultrasound and received corresponding pathological results via ultrasound-guided FNA or TN resection\u0026nbsp;were included in the study sample\u0026nbsp;between January 2018 and January 2024. This study was announced by the Ethical Committee of the Shaanxi Provincial People\u0026rsquo;s Hospital (reference number:\u0026nbsp;2025-017). The patients consented to use their tissue, clinical, and pathological information for the experimental research, and all signed an informed consent form. Clinical trial is not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur raw data, and manuscript did not contain any individual details, images, or videos. The authors used to number the cases to maintain confidentiality of patient data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGharib H, Papini E, Garber JR, AMERICAN ASSOCIATION OF CLINICAL ENDOCRINOLOGISTS, AMERICAN COLLEGE OF ENDOCRINOLOGY, AND ASSOCIAZIONE MEDICI ENDOCRINOLOGI MEDICAL GUIDELINES FOR CLINICAL PRACTICE FOR THE DIAGNOSIS AND MANAGEMENT OF THYROID NODULES, et al. \u0026ndash;2016 UPDATE. 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Front Oncol. 2023;13:1073891. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.3389/fonc.2023.1073891\u003c/span\u003e\u003cspan address=\"10.3389/fonc.2023.1073891\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Thyroid nodule, FNA, Logistic regression analysis, Nomogram","lastPublishedDoi":"10.21203/rs.3.rs-8245786/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8245786/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThe aims of this study were: ① to evaluate the diagnostic efficacy of six mainstream TI-RADS (Thyroid Imaging Reporting and Data System) classification systems (C-TIRADS, ACR-TIRADS, etc.) in the Northwestern Chinese population; and ② to identify risk factors for malignant thyroid nodules (TNs) using logistic regression based on clinical and ultrasound features, construct a quantifiable scoring Nomogram model, enable rapid and objective risk assessment, and assist in clinical decision-making.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 2,047 patients with TNs (1,433 malignant and 614 benign) were enrolled from January 2018 to January 2024 at Shaanxi Provincial People\u0026rsquo;s Hospital. The nodules were divided into a training group (1,435 nodules) and a validation group (612 nodules) in a 7:3 ratio. Twelve characteristics were collected, including age, nodule size, margin, calcification, and the presence of suspicious lymph nodes. Independent risk factors were identified through univariate and multivariate logistic regression analyses to construct a Nomogram model. The model\u0026rsquo;s performance was evaluated using receiver operating characteristic (ROC) curves, accuracy, and other metrics, and compared with the six traditional TI-RADS systems.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTen independent risk factors were identified, including age, nodule size, and irregular margins. In the validation group, the Nomogram model achieved an accuracy of 78.4%, a sensitivity of 81.6%, a specificity of 71.7%, and an area under the ROC curve (AUC) of 0.849. The sensitivities of the six TI-RADS systems (C-TIRADS, ACR-TIRADS, EU-TIRADS, ATA Guidelines, Kwak-TIRADS, and AACE) for distinguishing benign and malignant nodules were 86.0%, 93.2%, 96.9%, 98.3%, 84.4%, and 98.1%, respectively; specificities were 55.6%, 34.8%, 25.3%, 22.2%, 57.1%, and 21.7%, respectively; accuracies were 76.1%, 74.3%, 73.7%, 73.7%, 75.8%, and 73.4%, respectively; and AUCs were 0.752, 0.661, 0.628, 0.617, 0.757, and 0.616, respectively, with no statistically significant differences among them. The Nomogram model significantly outperformed the traditional systems in measures such as AUC, Net Reclassification Improvement (NRI), Integrated Discrimination Improvement (IDI), Positive Likelihood Ratio (PLR), and Negative Likelihood Ratio (NLR) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe six traditional TI-RADS systems demonstrate similar but overall limited diagnostic efficacy in the Northwestern Chinese population. The Nomogram model, by integrating multidimensional features and applying a quantitative scoring approach, improves the accuracy and objectivity of malignancy risk assessment. Compared to traditional models, it offers better clinical utility, supports optimized decision-making, and helps reduce unnecessary invasive procedures.\u003c/p\u003e","manuscriptTitle":"Diagnostic Comparison of TI-RADS and a Nomogram for Thyroid Nodules in Northwestern China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-30 00:48:50","doi":"10.21203/rs.3.rs-8245786/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-07T09:22:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-03T23:39:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-27T17:14:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"20975844331230469809607518850489024741","date":"2025-12-25T16:19:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"238101047863853996207664317128824051024","date":"2025-12-20T12:57:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"67797238009874377463961679653900757525","date":"2025-12-19T18:29:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-19T17:29:50+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-17T09:48:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-09T10:24:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-06T14:37:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2025-12-06T14:33:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3ba2a32b-cfea-471b-adcf-7262b920f071","owner":[],"postedDate":"December 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-02T09:41:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-30 00:48:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8245786","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8245786","identity":"rs-8245786","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}