Risk Prediction Model for Elderly Differentiated Thyroid Cancer Based on Combined Sleep Quality Assessment and Multimodal Ultrasound

Risk Prediction Model for Elderly Differentiated Thyroid Cancer Based on Combined Sleep Quality Assessment and Multimodal Ultrasound | 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 Risk Prediction Model for Elderly Differentiated Thyroid Cancer Based on Combined Sleep Quality Assessment and Multimodal Ultrasound Xudan Lou, Na Yi, Yingchun Liu, Yuanyuan Xu, Jieyuzhen Qiu, Xiaoming Tao, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6028524/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: To explore the differential diagnosis for benign and malignant thyroid nodules and the diagnostic value of sleep quality, to construct and validate a risk prediction model, providing the basis for clinical treatment decision for elderly thyroid cancer. Methods: Clinical data, Pittsburgh Sleep Quality Index (PSQI), and multimodal ultrasound were collected from elderly patients undergoing fine needle aspiration biopsy or thyroid surgery in our department of endocrinology and general surgery. Postoperative pathological served as the gold standard, binary logistic regression identified significant risk factors, and the receiver-operating characteristic (ROC) curves was plotted to construct and validate the prediction model. Results: Among 763 enrolled patients (566 benign and 197 malignant), multivariate analysis revealed independent risk factors: TPOAB positive, daytime dysfunction, PSQI > 7, irregular nodule shape, calcification, blood flow, high elasticity scores, and low contrast enhancement. The area under the curve (AUC) for the combined model was 0.860, significantly higher than models using multimodal ultrasound alone (AUC = 0.824) or multimodal ultrasound with TPOAB (AUC = 0.831), p < 0.05. The nomogram-based prediction model demonstrated excellent discrimination, calibration, and clinical utility in internal and external validation. Conclusions: Integrating sleep quality assessment with multimodal ultrasound assisted in the differentiation of thyroid nodules in the elderly, thus may improve the preoperative diagnostic levels. Risk prediction model in a nomogram format provided an intuitive and reliable tool for clinical decision-making. Prediction Model Elderly Thyroid Cancer Sleep Quality Multimodal Ultrasound Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction According to the National Cancer Center, the mortality and disability-adjusted life years (DALY) of thyroid cancer reached the peak in the elderly. From 2006 to 2016, the incidence and mortality rates of thyroid cancer in elderly women in China have both significantly increased [1]. Although there were numerous clinical differentiation methods for thyroid cancer, none of them can predict the nature of nodules alone. Previously, thyroid ultrasound imaging often followed the 2011 Kwak criteria and the 2015 ATA guidelines, but the classification system did not match the current medical situation in China. In 2020, the Chinese-TIRADS (C-TIRADS) based on a counting method was developed [2]. However, our preliminary research results showed the positive rate of C-TIRADS guidance was 50.3% for fine-needle aspiration (FNA) [3]. Even FNA had inherent problems with undiagnosed rates and high false negative rates. Surgery was performed on patients with unclear or suspected malignancy, and pathology confirmed only 15-30% to be malignant, indicating that many patients have undergone unnecessary surgery [4]. Non-selective biopsies of newly developed nodules may lead to a harmful epidemic in the diagnosis of thyroid cancer, while excessively conservative biopsies may result in missed diagnosis [5]. New evidence supported the use of risk assessment to select patients for FNA according to clinical data, lifestyle factors, ultrasound characteristics, and other comprehensive analyses to determine individualized risks and guide subsequent treatment [6,7]. Of note, a large prospective research followed 140,000 women for an average of 11 years, showed that postmenopausal non-obese women with higher insomnia scores had an increased risk of thyroid cancer [8]. Therefore, the adverse consequences of sleep interruption have attracted widespread attention and debate. Sleep disorders may promote the occurrence of tumors, disrupt the rhythm and delay the expression time of cancer-related genes, make susceptible individuals more prone to DNA damage, as well as reduce the efficiency of cell repair [9]. Circadian rhythm disorders caused by jet lag, shift work, or sleep disorders, were all known risk factors for cancer [10]. Recently, the relationship between dysfunction of the circadian clock mechanism and thyroid cancer has been proposed. Rhythm disorders can alter the function of the HPA axis, potentially leading to peripheral clock disturbance and the occurrence of thyroid cancer [11]. Our previous research indicated that poor sleep quality (PSQI > 7), prolonged sleep latency and daytime dysfunction were independent risk factors for elderly differentiated thyroid cancer. In this study, clinical data, sleep quality scores, and multimodal ultrasound were collected from patients scheduled for thyroid fine-needle aspiration or thyroid surgery in our endocrinology and general surgery departments. Postoperative pathological diagnoses were used as the gold standard, and significant indicators were selected using binary logistic regression. A risk prediction model was constructed and validated using ROC curve analysis to explore methods for differentiating between benign and malignant nodules and to assess the diagnostic value of sleep quality. The results aimed to provide guidance for clinical decision-making in the diagnosis and treatment of elderly thyroid cancer. Materials and methods Patient enrollment and study protocol According to the sample size calculation formula of the prediction model published in BMJ in 2020 [12], 763 patients scheduled for thyroid fine-needle aspiration or thyroid surgery in our endocrinology and general surgery departments, aged ≥ 60 years, were enrolled. The exclusion criteria included the following: (a) patients with neurological disorders or severe mental illness; (b) patients with obstructive sleep apnea syndrome or irritable bowel syndrome; (c) patients with periodic limb movement disorder or restless legs syndrome; (d) patients with severe cardiovascular disease, malignant tumors, or liver and kidney dysfunction; (e) patients with a history of head and neck trauma or radiation therapy; (f) patients taking long-term sedatives, melatonin, or psychotropic drugs; (g) patients taking medications such as amiodarone, glucocorticoids, or somatostatin that affect thyroid function; (h) patients undergoing cognitive behavioral therapy or participating in other clinical studies; (i) patients with thyroid dysfunction and those who refuse to cooperate. This study was approved by the Ethics Committee of Huadong Hospital Affiliated to Fudan University, Shanghai, China (Ethics Number: 2018K065). Written informed consent was obtained from all subjects prior to the study. The general study protocol was shown in Fig. 1. Clinical data collection Clinical data was collected from elderly patients with thyroid nodules who were scheduled for fine-needle aspiration (FNA) or thyroid surgery in our hospital, including gender, age, BMI, duration of thyroid nodule, systolic and diastolic blood pressure (measured in a sitting position, average of 2 measurements), educational level, occupation status, smoking and alcohol history, family history of thyroid cancer, as well as liver and kidney function, blood glucose, lipid profile, glycosylated hemoglobin, uric acid, thyroid function, and autoantibodies. Pittsburgh Sleep Quality Index The scale was used to evaluate sleep status over the past month and consisted of seven components: sleep quality, sleep latency, sleep duration, sleep efficiency, sleep disorder, sleep medication, and daytime dysfunction. The score range for each question was 0-3 points, with a total score of 0-21 points. The higher the score, the worse the sleep quality, PSQI > 7 defined as poor sleep quality [13], with sensitivity and specificity of 0.983 and 0.902, respectively. Thyroid Nodule Evaluation Multimodal ultrasonography referred to the combined use of two or more ultrasound examination methods for making certification and recognition more accurate. Elastography [14] and contrast-enhanced ultrasound [15] have certain value in the diagnosis of thyroid nodules, but usually required comprehensive interpretation with morphological features of the nodules. Thus, the evaluation was performed through high-resolution ultrasonography, elastography, and contrast-enhanced ultrasound by the same experienced ultrasound physician. Model Construction and Validation The risk prediction model was established based on the coefficients and relevant relationships, and presented in the form of a nomogram. The evaluation of the model was using ROC curves, calibration curves, and clinical decision curves. To test the reproducibility of the model and prevent overfitting, internal validation was performed using the bootstrap method with 1000 resamples. Meanwhile, collected data from another hospital was used for external validation to further estimate the portability and generalization of the model. Statistical analysis The data were analyzed using SPSS 24.0 software. For normally distributed continuous variables, t-tests were used for comparisons between two groups, and the results were expressed as mean ± standard deviation. For non-normally distributed variables, non-parametric tests were used, and the results were expressed as median. The chi-square test was for the comparisons of categorical variables, and trend tests were performed for ordinal variables. Binary logistic regression was to analyze the relationship between categorical variables and risk factors. ROC curves were constructed, and AUC comparisons were made using MedCalc software. R language (version 3.6.3) was adopted to plot the nomogram, calibration plot, and clinical decision curve. P < 0.05 was considered statistically significant. Results Study Information A total of 854 elderly patients with thyroid nodules who were scheduled for fine-needle aspiration (FNA) and thyroid surgery in the Endocrinology and General Surgery departments of our hospital were collected. According to the inclusion and exclusion criteria, 91 patients were excluded. Finally, 763 individuals participated in this study, aged between 60 and 87 years, including 177 males and 586 females. According to the criteria for puncture and postoperative pathological diagnosis, the benign nodule group consisted of 566 people. The malignant nodule group consisted of 197 people, including 185 cases of papillary carcinoma, 12 cases of follicular carcinoma, and no medullary carcinoma or undifferentiated carcinoma. Clinical Characteristics of the Patients Among the clinical characteristics of elderly patients with thyroid nodules, only a family history of thyroid cancer was found to be associated with malignant nodules ( p 0.05). All participants had been living in Shanghai, China for two years or more, with a median urinary iodine reference level of 138.4μg/L [16]. There were no significant differences ( p > 0.05) in biochemical indicators such as liver and kidney function, blood lipids, blood glucose, glycated hemoglobin, and blood uric acid in the comparison of benign and malignant thyroid nodules. FT3, FT4, and TSH did not show differences between the two groups. Even in the subgroup analysis of TSH, there was still no statistical significance, perhaps it was related to the thyroid function of the enrolled patients within the normal range. Only the autoantibody TPOAB had a higher malignant rate in the positive group ( p < 0.05). Assessment of Sleep Quality in Patients with Thyroid Nodules There were significant differences in the comparison of sleep duration, daytime dysfunction and PSQI scores between the benign and malignant nodule groups among elderly patients ( p < 0.05). That was to say, shorter sleep duration, poorer daytime function, and higher PSQI scores were associated with malignancy. Both groups had a PSQI score above 7, indicating sleep quality was generally poor in elderly individuals. Subgroup analysis revealed a higher incidence of malignant nodules in patients with poor sleep quality ( p 0.05), seen in Table 1. Table 1. Relationship between sleep quality and thyroid nodules Characteristic NO. Benign Malignant P value Sleep quality score (no./%) 0.097 0 98（12.84） 78（10.22） 20（2.62） 1 352（46.13） 258（33.81） 94（12.32） 2 244（31.98） 173（22.68） 71（9.31） 3 69（9.05） 57（7.47） 12（1.57） Sleep latency time (no./%) 0.057 ≤15 minutes 90（11.80） 65（8.52） 25（3.28） 16-30 minutes 277（36.30） 221（28.96） 56（7.34） 31-60 minutes 240（31.45） 167（21.89） 73（9.57） ≥ 60 minutes 156（20.45） 113（14.81） 43（5.63） Sleep latency score (no./%) 0.203 0 32（4.19） 25（3.28） 7（0.92） 1 263（34.47） 203（26.61） 60（7.86） 2 314（41.16） 234（30.67） 80（10.48） 3 154（20.18） 104（13.63） 50（6.55） Sleep duration score (no./%) 0.028 0（> 7 hours） 99（12.98） 84（11.01） 15（1.97） 1（6-7 hours） 255（33.42） 191（25.03） 64（8.39） 2（5-6 hours） 285（37.35） 208（27.26） 77（10.09） 3（< 5 hours） 124（16.25） 83（10.88） 41（5.37） Sleep efficiency score (no./%) 0.067 0 181（23.72） 151（19.79） 30（3.93） 1 246（32.24） 170（22.28） 76（9.96） 2 200（26.21） 140（18.35） 60（7.87） 3 136（17.83） 105（13.76） 31（4.06） Sleep disorder score (no./%) 0.073 0 89（11.66） 71（9.31） 18（2.36） 1 248（32.50） 189（24.77） 59（7.73） 2 287（37.62） 212（27.78） 75（9.83） 3 139（18.22） 94（12.32） 45（5.90） Sleep medication score (no./%) 0.305 0 262（34.34） 225（29.49） 37（4.85） 1 231（30.28） 156（20.45） 75（9.83） 2 184（24.12） 122（15.98） 62（8.13） 3 86（11.26） 63（8.26） 23（3.01） Daytime dysfunction score (no./%) 0.031 0 136（17.82） 125（16.38） 11（1.44） 1 236（30.93） 182（23.86） 54（7.08） 2 245（32.11） 154（20.18） 91（11.93） 3 146（19.14） 105（13.76） 41（5.37） Disease duration (years) 7.13±4.92 6.97±4.38 7.59±5.23 0.418 PSQI score 8.78±2.46 8.51±2.52 9.58±2.08 0.000 PSQI (no./%) 0.000 0-7 256（33.55） 202（26.47） 54（7.08） > 7 507（66.45） 364（47.71） 143（18.74） Multimodal Ultrasound Evaluation of Thyroid Nodules Characteristics of multimodal ultrasound such as the nodule number, morphology, margin, calcification, blood flow, elastography, and ultrasonic contrast between benign and malignant groups were statistically differences ( p < 0.05). Correlation analysis showed single nodular, irregular morphology, unclear margins, coarse or micro-calcification, blood flow, higher elasticity score and low enhancement were associated with a higher risk of malignancy ( p 0.05). Refer to Table 2 for more details. Table 2. Relationship between Multimodal Ultrasound Characteristics and Thyroid Nodules Characteristic NO. Benign Malignant P value Maximum diameter (mm) 12.64±9.16 12.19±8.54 15.09±10.80 0.059 Nodule number (no./%) Solitary 221 (28.97) 149 (19.53) 72 (9.44) 0.004 Multiple 542 (71.03) 417 (54.65) 125 (16.38) Morphology (no/%) Regular 535 (70.12) 446 (58.45) 89 (11.67) 0.000 Irregular 228 (29.88) 120 (15.73) 108 (14.15) Margin (no./%) Clear 578 (75.75) 461 (60.42) 117 (15.33) 0.000 Unclear 185 (24.25) 105 (13.76) 80 (10.49) Echo (no./%) Hypo echo 614 (80.47) 464 (60.81) 150 (19.66) 0.130 Non-hypo echo 149 (19.53) 102 (13.37) 47 (6.16) Calcification (no./%) No 469 (61.47) 386 (50.59) 83 (10.88) 0.000 Coarse 190 (14.90) 120 (15.73) 70 (9.17) Micron 104 (13.63) 60 (7.86) 44 (5.77) Blood flow (no./%) No 531 (69.60) 422 (55.31) 109 (14.29) 0.000 Yes 232 (30.40) 144 (18.87) 88 (11.53) Elasticity score 3.82±0.96 3.39±0.93 4.32±0.73 0.000 Elasticity score (no./%) 0.000 2 52 (6.82) 52 (6.82) 0 (0) 3 305 (39.97) 266 (34.86) 39 (5.11) 4 223 (29.23) 149 (19.53) 74 (9.70) 5 183 (23.98) 99 (12.97) 84 (11.01) Ultrasonic contrast (no./%) High enhanced 430 (56.36) 363 (47.58) 67 (8.78) 0.035 Low enhanced 333 (43.64) 203 (26.60) 130 (17.04) Uniform perfusion 402 (52.69) 302 (39.58) 100 (13.11) 0.141 Uneven perfusion 361 (47.31) 264 (34.60) 97 (12.71) Logistic Regression Analysis Considering the lower risk of outcome events as the continuous independent variable changes by one unit, this study discretized the continuous variables into multiple categorical variables for analysis. When these variables were included in a multivariate logistic regression analysis, eight independent risk factors for malignant thyroid nodules were ultimately identified, p < 0.05. See Table 3 for details. Fig. 2 revealed ROC curves for the predicted probabilities, the AUC of curve 1 was 0.860 (95% CI 0.841-0.896) for the comprehensive analysis of all independent risk factors, with a sensitivity of 82.5% and a specificity of 74.1%. AUC for the individual multimodal ultrasound (curve 2) was 0.824 (95% CI 0.790-0.858), with a sensitivity of 69.5% and a specificity of 80.5%. AUC for the combination of multimodal ultrasound and TPOAB (curve 3) was 0.831 (95% CI 0.798-0.864), with a sensitivity of 67.4% and a specificity of 83.2%. Results showed significant differences only when curve 1 compared with curve 2 or curve 3, p < 0.05, indicating that combination of sleep quality assessment can improve the diagnostic accuracy of malignant thyroid nodules in elderly patients. Table 3. Univariate and Multivariate Logistic Regression Single factor analysis Multiple-factor analysis OR 95%CI P value OR 95%CI P value Family history of thyroid cancer 2.429 1.146-5.145 0.021 2.300 0.773-6.843 0.134 TPOAB positive 1.468 1.038-2.077 0.030 1.772 1.115-2.814 0.015 Sleep duration score 1.296 1.081-1.555 0.005 1.230 0.951-1.590 0.114 Daytime dysfunction score 1.707 1.430-2.037 0.000 1.832 1.420-2.363 0.000 PSQI score > 7 2.133 1.486-3.063 0.000 1.584 1.009-2.761 0.003 Nodule number (solitary) 1.667 1.177-2.361 0.004 1.989 0.960-3.140 0.105 Morphology (irregular) 4.459 3.149-6.313 0.000 3.601 2.231-5.812 0.000 Margin (unclear) 2.946 2.059-4.215 0.000 1.230 0.740-2.046 0.425 Calcification 2.055 1.645-2.567 0.000 1.576 1.182-2.102 0.002 Blood flow 2.358 1.666-3.337 0.000 1.700 1.081-2.673 0.022 Elasticity score 2.553 2.080-3.133 0.000 2.419 1.898-3.083 0.000 Contrast enhancement (low) 3.470 2.467-4.880 0.000 3.787 2.460-5.832 0.000 Model Performance Based on the results of logistic regression analysis, a risk prediction model for elderly thyroid cancer was constructed using the R language [17]. The model represents the relationships between variables in an intuitive way using a nomogram [18,19]. The nomogram can be used as follows: draw a vertical line for each variable value, locate the variable score on the first line, and sum up all the scores to obtain the total score. Read the thyroid cancer risk probability on the total score axis vertically (Fig. 3). The accuracy of the prediction model was 0.825, and the AUC was 0.860 (Fig. 4A). The calibration ability was evaluated using Hosmer-Lemeshow goodness-of-fit test, result showed X 2 = 4.218, p = 0.821. The predicted probability was very close to the actual probability (y = x), indicating high calibration of the model (Fig. 4B). A decision curve analysis (DCA) was conducted to evaluate the clinical utility (Fig. 4C). The horizontal line (None) represented all samples being negative, where no interventions were performed and the net benefit was zero. The diagonal line (All) represented all samples being positive, where all individuals received interventions, and the net benefit was a negatively sloping line [20]. The curves for model_1 and model_2 were both above the two extreme curves, and model_1 had a higher net benefit within a larger threshold probability range, suggesting good clinical utility of the prediction model. Verification Study Adopting Bootstrap method with 1000 iterations for internal validation, Kappa statistic was used to measure the stability of the predictive model. The results showed an accuracy of 0.818 and a Kappa value of 0.486, furthermore, C-index was 0.804, indicating good discrimination and accuracy of the model in the internal validation. The study used elderly patients undergoing thyroid fine needle aspiration biopsy and surgery at Shanghai North Hospital as an external validation cohort. A total of 221 patients were collected between January 2018 and January 2024, 198 patients were ultimately enrolled based on the inclusion and exclusion criteria. There were no statistical differences between the predictive variables of the validation cohort and the development cohort (Table 4). The AUC was 0.787, with a sensitivity of 72.9% and specificity of 74.0% (Fig. 5A). The Hosmer-Lemeshow test showed X 2 = 5.855, p = 0.406 (Fig. 5B). The DCA plot indicated that the predictive model consistently outperformed the two extreme curves within the threshold probability range of 0.1-0.7 (Fig. 5C). These results demonstrated that the predictive model had similar discrimination, calibration, and clinical utility in the external validation. Table 4. Clinical data, sleep quality, and multimodal ultrasound features of the validation Characteristic Total number Benign Malignant TPOAB (no./%) Negative 156 (78.79) 122 (61.62) 34 (17.17) Positive 42 (21.21) 30 (15.15) 12 (6.06) Daytime dysfunction score (no./%) 0 19 (9.60) 16 (8.08) 3 (1.52) 1 58 (29.29) 54 (27.27) 4 (2.02) 2 87 (43.94) 59 (29.80) 28 (14.14) 3 34 (17.17) 23 (11.62) 11 (5.55) PSQI (no./%) 0-7 49 (24.75) 35 (17.68) 14 (7.07) > 7 149 (75.25) 117 (59.09) 32 (16.16) Morphology (no./%) Regular 135 (68.18) 113 (57.07) 22 (11.11) Irregular 63 (31.82) 39 (19.70) 24 (12.12) Calcification (no./%) No 97 (48.99) 77 (38.89) 20 (10.10) Coarse 66 (33.33) 49 (24.75) 17 (8.58) Micron 35 (17.68) 26 (13.13) 9 (4.55) Blood flow (no./%) No 122 (61.62) 97 (48.99) 25 (12.63) Yes 76 (38.38) 55 (27.78) 21 (10.60) Elasticity score (no./%) 2 15 (7.57) 15 (7.58) 0 (0) 3 65 (32.83) 56 (28.28) 9 (4.54) 4 69 (34.85) 48 (24.24) 21 (10.61) 5 49 (24.75) 33 (16.67) 16 (8.08) Ultrasonic Contrast (no./%) High enhanced 103 (52.02) 92 (46.46) 11 (5.56) Low enhanced 95 (47.98) 60 (30.30) 35 (17.68) Discussion Since the 1970s, thyroid nodules have been detected in over 60% of the general population through ultrasound imaging. However, most of these nodules are benign and do not require further treatment, making accurate screening for malignant nodules a major challenge in the management of thyroid disease. FNA is the most widely used cytological examination method and can differentiate approximately 75-80% of nodules. In a study, when both malignant tumors and suspicious malignant tumors were classified as positive, the sensitivity of FNA alone was 90.7% and the specificity was 85.2% [21], which affects the routine use of FNA as a frontline screening tool. A previous large-sample study showed that repeated FNA of atypical lesions or follicular lesions of undetermined significance (AUS/FLUS) does not improve the detection accuracy of malignant tumors [22]. To benefit elderly patients with truly malignant nodules during surgery, improve the quality life of the elderly population, and allocate medical resources effectively, we evaluated the individualized risk of participants through clinical data, sleep quality assessment and multimodal ultrasound, then constructed an economical, convenient and operable prediction model to guide FNA decision. According to the research flow chart, a total of 763 patients were enrolled, 566 with benign nodules and 197 with malignant ones. All the malignant nodules were differentiated, and the malignant rate was 25.82%. Univariate analysis showed that family history, TPOAB positive, sleep duration score, daytime dysfunction score, PSQI > 7, solitary nodule, irregular shape, unclear margins, nodule calcification, blood flow, elastic score, and low contrast enhancement may be associated with an increased risk of malignant nodules. Further logistic regression analysis identified eight independent risk factors for thyroid cancer as shown in Table 3. Patients with Hashimoto's thyroiditis (HT) usually have elevated levels of thyroid antibodies in serum, or positive for TPOAB alone, accounting for 97% of literature reports [23]. In 1955, Dailey first proposed a close relationship between HT and papillary thyroid carcinoma (PTC) [24]. In a retrospective analysis of 8,524 cases of thyroid surgery in China, it was found that the risk of PTC in HT patients was significantly increased [25], which was consistent with our research results. Sleep deprivation prolonged exposure to nighttime light, affecting melatonin and TSH secretion, leading to circadian rhythm disruption. In 2007, the International Agency for Research on Cancer classified staying up late, including night shift work involving circadian rhythm disorders as a Group 2A carcinogen [26]. A prospective cohort study targeting female nurses, after 26 years of follow-up, found evidence that night shift work, extreme sleep duration and sleep difficulties collectively affected the risk of thyroid cancer [27]. Another large-scale prospective study of 460,000 participants showed that exposure to artificial light at night greatly increased the incidence of thyroid cancer [28]. According to clinical researches, 57% of elderly people aged 60 and above in China experienced some degree of sleep disorders, with most sleeping for about 5 hours and frequent waking at night. The duration of light sleep was much longer than that of deep sleep, making them a group with severe sleep disorders. A recent study used genome-wide association studies (GWAS) published in the FinnGen and UK Biobank databases to explore the causal relationship between sleep characteristics and thyroid cancer risk based on Mendelian randomization. The results revealed that shortened sleep time was associated with thyroid cancer [29], indicating the importance of adequate sleep in preventing thyroid cancer. These prior studies aligned with our findings linking circadian disruption to thyroid carcinogenesis. The ultrasound risk stratification system and thyroid biopsy threshold for thyroid nodules varied in different versions of the guidelines, including C-TIRADS may not reliably predict nodule properties and guide FNA. Our study used multimodal ultrasound combined with PSQI to plot ROC curves. The AUC obtained from the integrated factors was 0.860 (95% CI 0.841-0.896), with a sensitivity of 82.5% and specificity of 74.1%, higher than that of the other two curves, p < 0.05 (Fig. 2). The results showed sleep quality assessment and multimodal ultrasound may improve the diagnostic level of malignant nodules, providing clinical reference value for FNA. Based on the above statistical analysis, a risk prediction model for elderly thyroid cancer was constructed and presented in the form of a nomogram (Fig. 3). The combined model achieved an AUC of 0.860, outperforming single-modality approaches and showing good discrimination. Calibration curve aligned closely with ideal predictions (Hosmer-Lemeshow, p = 0.821), and DCA confirmed clinical utility (Fig. 4A-C). External validation yielded an AUC of 0.787, demonstrating repeatability and generalizability (Fig. 5A-C). Researchers both domestic and international have emphasized the importance of developing multivariate prediction algorithms to determine the cumulative risk of malignant tumors for this common clinical problem. Raza et al. used a multivariate stepwise regression model to predict the malignancy rate of thyroid nodules in patients based on factors such as patient age, solid/nodule calcification, and FNA cytology examination [30]. Tuttle applied Bayesian analysis modeling and found that male gender, nodules larger than 4 centimeters, and glandular features could be systematically integrated into clinical decision-making, thereby reducing the probability of surgery for follicular tumor patients [31]. Alexander et al. used prospective cohorts for Bayesian classification and constructed and cross-validated a clinically relevant prognostic assessment tool [32]. Another study constructed a multivariate logistic regression model with all ultrasound features for 1500 patients from Shanghai and Fujian, incorporating variable weights and combination patterns to predict PTC, FTC, and MTC [33]. Therefore, significant progress has been made in understanding the clinical significance of thyroid nodules and insufficient evaluation of potential harms. Various clinical models can estimate the thresholds for thyroid nodule biopsies to some extent, balancing the diagnostic value of thyroid cancer and the potential risk of missed diagnosis. However, the choice of the model needs to consider specific patients, population preferences, regional characteristics, operational conditions, etc. Predictive models that combine clinical, biochemical, and radiological features can support clinical doctors in reducing unnecessary invasive surgeries for thyroid nodule patients [34]. This study focused on constructing a prediction model for elderly thyroid cancer, highlighting the synergistic value of sleep quality assessment and multimodal ultrasound. It not only provided personalized risk of malignancy but also allowed real-time evaluation of suspicious nodules, promoting clinical decision-making and patients education. In cases where FNA examination was limited, such as poor patient health, difficulty in puncturing small nodules, inadequate tissue obtained for diagnosis, or uncertain diagnosis, the model also demonstrated its advantages. Limitations First, a major limitation of this study was the lack of prospective validation of the model, as well as the small size and limited sample of the validation cohort, which were derived from a single-center dataset. While we followed the recommended minimum of 10 events per predictive variable, it was necessary to validate the model in a larger patient population. Second, the malignancy rate of thyroid nodules in this study was 25.82%. The selection of patients who underwent thyroid nodule puncture or surgery inevitably led to a significantly higher proportion, which may not objectively reflect the incidence of thyroid cancer in the elderly population. Third, the questionnaire used in this study was subjective, and recall bias may exist. Declarations Acknowledgments We were grateful to all the patients who were willing to participate in this study. Author contributions Xudan Lou was the major contributor in writing the manuscript. Na Yi and Yuanyuan Xu collected the data information of the patients. Yingchun Liu was responsible for the Multimodal ultrasound examination. Jieyuzhen Qiu made statistical analysis. Xiaoming Tao and Zhijun Bao designed and funded the study. All authors read and approved the final manuscript. Funding The clinical special project of Shanghai Municipal Health Commission (202240258). Conflict of interest The authors declare that there are no conficts of interest regarding the publication of this paper. Ethics approval The study was carried out in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki) and approved by the Ethics Committee of our institute. References Shi Z, Lin J, Wu Y, et al. 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J Am Soc Cytopathol, 2017, 6(6): 217-222. Valderrabano P, Khazai L, Thompson ZJ, et al. Cancer Risk Stratification of Indeterminate Thyroid Nodules: A Cytological Approach[J]. Thyroid, 2017, 27(10): 1277-1284. Luo J, Sands M, Wactawski-Wende J, et al. Sleep disturbance and incidence of thyroid cancer in postmenopausal women the Women’s Health Initiative[J]. Am J Epidemiol, 2013, 177(1): 42-49. Koritala BSC, Porter KI, Arshad OA, et al. Night shift schedule causes circadian dysregulation of DNA repair genes and elevated DNA damage in humans[J]. J Pineal Res, 2021, 70(3): e12726. Malaguarnera R, Ledda C, Filippello A, et al. Thyroid Cancer and Circadian Clock Disruption[J]. Cancers, 2020, 12(11). Mogavero MP, DelRosso LM, Fanfulla F, et al. Sleep disorders and cancer: state of the art and future perspectives[J]. Sleep Med Rev, 2021, 56: 101409. Riley RD, Ensor J, Snell KIE, et al. Calculating the sample size required for developing a clinical prediction model[J]. BMJ, 2020, 368: m441. Xudan L, Haidong W,Yanyuan T, et al. Alterations of Sleep Quality and Circadian Rhythm Genes Expression in Elderly Thyroid Nodule Patients and Risks Associated with Thyroid Malignancy[J]. Sci Rep, 2021, 11(1): 13682. Cosgrove D, Barr R, Bojunga J, et al. WFUMB Guidelines and Recommendations on the Clinical Use of Ultrasound Elastography: Part 4. Thyroid[J]. Ultrasound Med Biol, 2017, 43(1): 4-26. Sidhu PS, Cantisani V, Dietrich CF, et al. The EFSUMB Guidelines and Recom mendations for the Clinical Practice of Contrast-Enhanced Ultrasound CEUS in Non-Hepatic Applications: Update 2017 (Long Version)[J]. Ultraschall Med, 2018, 39(2): e2-e44. Jiajie Z, Jingzhe Z, Shurong Z, et al. Comprehensive Evaluation of Iodine Nutrition and Dietary Iodine Intake Status of Shanghai Residents[J]. Shanghai Preventive Medicine, 2017, 29(6): 417-422. Au EH, Francis A, Bernier-Jean A, et al. Prediction modeling-part 1: regression modeling[J]. Kidney Int, 2020, 97(5): 877-884. Utsumi T, Kamiya N, Kaga M, et al. Development of novel nomograms to predict renal functional outcomes after laparoscopic adrenalectomy in patients with primary aldosteronism[J]. World J Urol, 2017, 35(10): 1577-1583. Gafita A, Calais J, Grogan TR, et al. Nomograms to predict outcomes after 177Lu-PSMA therapy in men with metastatic castration-resistant prostate cancer: an international, multicentre, retrospective study[J]. Lancet Oncol, 2021, 22(8): 1115-1125. Vickers AJ, van Calster B, Steyerberg EW. A simple, step-by-step guide to interpreting decision curve analysis[J]. Diagn Progn Res, 2019, 3: 18. Mao Z, Ding Y, Wen L, et al. Combined fine-needle aspiration and selective intraoperative frozen section to optimize prediction of malignant thyroid nodules: A retrospective cohort study of more than 3000 patients[J]. Front Endocrinol, 2023, 14: 1091200. Marin F, Murillo R, Diego C, et al. The impact of repeat fine-needle aspiration in thyroid nodules categorized as atypia of undetermined significance or follicular lesion of undetermined significance: A single center experience[J]. Diagn Cytopathol, 2021, 49(3): 412-417. Kotani T, Ohtaki S. Clinical application of recombinant thyroid peroxidase[J]. Nihon Naibunpi Gakkai Zasshi, 1993, 69(11): 1123-1128. Dailey ME, Lindsay S, Skahen R. Relation of thyroid neoplasms to Hashimoto disease of the thyroid gland[J]. AMA Arch Surg, 1955, 70(2): 291-297. Zhang Y, Dai J, Wu T，et al. The study of the coexistence of Hashimoto's thyroiditis with papillary thyroid carcinoma[J]. J Cancer Res Clin, 2014, 140(6): 1021-1026. Straif K, Baan R, Grosse Y. Carcinogenicity of shift-work, painting, and fire-fighting[J]. Lancet Oncol, 2007, 8(12): 1065-1066. Papantoniou K, Konrad P, Haghayegh S, et al. Rotating Night Shift Work, Sleep, and Thyroid Cancer Risk in the Nurses’ Health Study 2[J]. Cancer, 2023, 15(23). Zhang D, Jones RR, James P, et al. Associations Between Artificial Light at Night and Risk for Thyroid Cancer: A large US cohort study[J]. Cancer, 2021, 127(9): 1448-1458. Zong L, Liu G, He H, et al. Causal association of sleep traits with the risk of thyroid cancer: A mendelian randomization study[J]. BMC Cancer, 2024, 24(1): 605. Raza SN, Shah MD, Palme CE, et al. Risk factors for well-differentiated thyroid carcinoma in patients with thyroid nodular disease[J]. Otolaryng Head Neck, 2008, 139(1): 21-26. Tuttle RM, Lemar H, Burch HB. Clinical features associated with an increased risk of thyroid malignancy in patients with follicular neoplasia by fine-needle aspiration[J]. Thyroid, 1998, 8(5): 377-383. Stojadinovic A, Peoples GE, Libutti SK, et al. Development of a clinical decision model for thyroid nodules[J]. BMC Surg, 2009, 9: 12. Jiang S, Xie Q, Li N, et al. Modified Models for Predicting Malignancy Using Ultrasound Characters Have High Accuracy in Thyroid Nodules With Small Size[J]. Front Mol Biosci, 2021, 8: 752417. Witczak J, Taylor P, Chai J, et al. Predicting malignancy in thyroid nodules: feasibility of a predictive model integrating clinical, biochemical, and ultrasound characteristics[J]. Thyroid Res, 2016, 9: 4. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6028524","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":416933181,"identity":"130b8d79-20df-4452-8c66-f6bce89e7d56","order_by":0,"name":"Xudan Lou","email":"","orcid":"","institution":"Huadong Hospital Affiliated to Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Xudan","middleName":"","lastName":"Lou","suffix":""},{"id":416933182,"identity":"0d6e35fc-3847-41c2-8f9b-e0e65ccbd710","order_by":1,"name":"Na Yi","email":"","orcid":"","institution":"Huadong Hospital Affiliated to Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Na","middleName":"","lastName":"Yi","suffix":""},{"id":416933184,"identity":"626b248b-0596-4d04-bd89-0a5be9478475","order_by":2,"name":"Yingchun Liu","email":"","orcid":"","institution":"Huadong Hospital Affiliated to Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Yingchun","middleName":"","lastName":"Liu","suffix":""},{"id":416933186,"identity":"f531f15f-6ee9-4e24-81c5-7040682d28eb","order_by":3,"name":"Yuanyuan Xu","email":"","orcid":"","institution":"Huadong Hospital Affiliated to Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Yuanyuan","middleName":"","lastName":"Xu","suffix":""},{"id":416933188,"identity":"6295a221-e9b1-4554-b936-22f8aec7b073","order_by":4,"name":"Jieyuzhen Qiu","email":"","orcid":"","institution":"Huadong Hospital Affiliated to Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Jieyuzhen","middleName":"","lastName":"Qiu","suffix":""},{"id":416933189,"identity":"03ca09b6-a830-41d9-971f-c1abf8b9c257","order_by":5,"name":"Xiaoming Tao","email":"","orcid":"","institution":"Huadong Hospital Affiliated to Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoming","middleName":"","lastName":"Tao","suffix":""},{"id":416933191,"identity":"119bfb16-0616-4239-97e6-ec6ca22cf2a0","order_by":6,"name":"Zhijun Bao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIie3RMUsDMRTA8VcCuSV460mh/QqRgxO5Wr9KQuC6iVCQGw+ETAW/gB8iq9sLgXYpdT3o0u4OFkFwEWNv1Lve6JD/GPLjkTyAUOgfRglBFOWEURLZXQnkeJp0kbOIStyti1EcLRRf9yGjmKV2r116vsAs6UUoAY6CFtLUoiitzseXQOyWwfS2gwgUbOKJXNZWzy6eK6pyBmreQfzzk2bK9qDdwCDLhgxQVq1kUKHg7odkd1a7G4PxxwnixwjRPB88kX4KPUGoJ9h8coKbmTKOpldPXLWS8ePL++Hzq1nlG97n12b1sK9fy2kr+aPjanj/+6FQKBT63Tfmpl7uGrCzWQAAAABJRU5ErkJggg==","orcid":"","institution":"Huadong Hospital Affiliated to Fudan University","correspondingAuthor":true,"prefix":"","firstName":"Zhijun","middleName":"","lastName":"Bao","suffix":""}],"badges":[],"createdAt":"2025-02-14 08:08:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6028524/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6028524/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76688130,"identity":"157bf779-0d28-40dd-be87-9148419b8b68","added_by":"auto","created_at":"2025-02-19 16:31:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":236319,"visible":true,"origin":"","legend":"\u003cp\u003eThe study flowchart.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6028524/v1/c8ce69aeb9b1e5684be31a96.png"},{"id":76688132,"identity":"441a756c-c4ba-4bf3-88fb-8bf7e32d68b5","added_by":"auto","created_at":"2025-02-19 16:31:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":203356,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the area under ROC curves. Curve 1: the comprehensive analysis of all independent risk factors. Curve 2: the individual multimodal ultrasound. Curve 3: the combination of multimodal ultrasound and TPOAB. Curve 1 \u003cem\u003evs\u003c/em\u003e Curve 2, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05; Curve 1 \u003cem\u003evs\u003c/em\u003eCurve 3, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05; Curve 2 \u003cem\u003evs\u003c/em\u003e Curve 3,\u003cem\u003e p\u003c/em\u003e \u0026gt; 0.05.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6028524/v1/2fc3c8f783dbf082c7ddde9b.png"},{"id":76688134,"identity":"e8233def-4416-45b0-9669-546ab1fd2f4a","added_by":"auto","created_at":"2025-02-19 16:31:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":247479,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram of the risk prediction model.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6028524/v1/80a467292a4815d5ad7c4400.png"},{"id":76689195,"identity":"417fe604-b171-465d-9b11-dda891f3c979","added_by":"auto","created_at":"2025-02-19 16:39:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":587908,"visible":true,"origin":"","legend":"\u003cp\u003eModel performance. (A) ROC curve, (B) Calibration curve and (C) Clinical decision curve to evaluate discrimination, calibration ability and clinical utility of the risk prediction model, respectively. Model_1: sleep quality combined with multimodal ultrasound. Model_2: individual multimodal ultrasound.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6028524/v1/f7d5f56b9ae40e22956ad377.png"},{"id":76688131,"identity":"a50c9db6-06af-4524-9abf-ff9ec25729b4","added_by":"auto","created_at":"2025-02-19 16:31:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":508353,"visible":true,"origin":"","legend":"\u003cp\u003eExternal validation of the risk prediction model. (A) ROC curve, (B) Calibration curve and (C) Clinical decision curve to evaluate the discrimination, calibration ability and clinical utility of the validation cohort, respectively.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6028524/v1/739f119763ed2564ff3f436f.png"},{"id":77669403,"identity":"510c0697-9d10-4846-a7d7-97b59692eda5","added_by":"auto","created_at":"2025-03-04 06:39:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2898774,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6028524/v1/59448366-d9a4-4607-ab3e-b45717b61a06.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Risk Prediction Model for Elderly Differentiated Thyroid Cancer Based on Combined Sleep Quality Assessment and Multimodal Ultrasound","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAccording to the National Cancer Center, the mortality and disability-adjusted life years (DALY) of thyroid cancer reached the peak in the elderly. From 2006 to 2016, the incidence and mortality rates of thyroid cancer in elderly women in China have both significantly increased\u003csup\u003e\u0026nbsp;\u003c/sup\u003e[1]. Although there were numerous clinical differentiation methods for thyroid cancer, none of them can predict the nature of nodules alone. Previously, thyroid ultrasound imaging often followed the 2011 Kwak criteria and the 2015 ATA guidelines, but the classification system did not match the current medical situation in China. In 2020, the Chinese-TIRADS (C-TIRADS) based on a counting method was developed [2]. However, our preliminary research results showed the positive rate of C-TIRADS guidance was 50.3% for fine-needle aspiration (FNA) [3]. Even FNA had inherent problems with undiagnosed rates and high false negative rates. Surgery was performed on patients with unclear or suspected malignancy, and pathology confirmed only 15-30% to be malignant, indicating that many patients have undergone unnecessary surgery [4].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNon-selective biopsies of newly developed nodules may lead to a harmful epidemic in the diagnosis of thyroid cancer, while excessively conservative biopsies may result in missed diagnosis [5]. New evidence supported the use of risk assessment to select patients for FNA according to clinical data, lifestyle factors, ultrasound characteristics, and other comprehensive analyses to determine individualized risks and guide subsequent treatment [6,7]. Of note, a large prospective research followed 140,000 women for an average of 11 years, showed that postmenopausal non-obese women with higher insomnia scores had an increased risk of thyroid cancer [8]. Therefore, the adverse consequences of sleep interruption have attracted widespread attention and debate.\u0026nbsp;Sleep disorders may promote the occurrence of tumors, disrupt the rhythm and delay the expression time of cancer-related genes, make susceptible individuals more prone to DNA damage, as well as reduce the efficiency of cell repair [9]. Circadian rhythm disorders caused by jet lag, shift work, or sleep disorders, were all known risk factors for cancer [10].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRecently, the relationship between dysfunction of the circadian clock mechanism and thyroid cancer has been proposed. Rhythm disorders can alter the function of the HPA axis, potentially leading to peripheral clock disturbance and the occurrence of thyroid cancer [11]. Our previous research indicated that poor sleep quality (PSQI \u0026gt; 7), prolonged sleep latency and daytime dysfunction were independent risk factors for elderly differentiated thyroid cancer. In this study, clinical data, sleep quality scores, and multimodal ultrasound were collected from patients scheduled for thyroid fine-needle aspiration or thyroid surgery in our endocrinology and general surgery departments. Postoperative pathological diagnoses were used as the gold standard, and significant indicators were selected using binary logistic regression. A risk prediction model was constructed and validated using ROC curve analysis to explore methods for differentiating between benign and malignant nodules and to assess the diagnostic value of sleep quality. The results aimed to provide guidance for clinical decision-making in the diagnosis and treatment of elderly thyroid cancer.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003ePatient enrollment and study protocol\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the sample size calculation formula of the prediction model published in BMJ in 2020 [12], 763 patients scheduled for thyroid fine-needle aspiration or thyroid surgery in our endocrinology and general surgery departments, aged\u0026nbsp;\u003cstrong\u003e\u0026ge;\u003c/strong\u003e 60 years, were enrolled. The exclusion criteria included the following:\u0026nbsp;(a) patients with neurological disorders or severe mental illness; (b) patients with obstructive sleep apnea syndrome or irritable bowel syndrome; (c) patients with periodic limb movement disorder or restless legs syndrome; (d) patients with severe cardiovascular disease, malignant tumors, or liver and kidney dysfunction; (e) patients with a history of head and neck trauma or radiation therapy; (f) patients taking long-term sedatives, melatonin, or psychotropic drugs; (g) patients taking medications such as amiodarone, glucocorticoids, or somatostatin that affect thyroid function; (h) patients undergoing cognitive behavioral therapy or participating in other clinical studies; (i) patients with thyroid dysfunction and those who refuse to cooperate.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Huadong Hospital Affiliated to Fudan University, Shanghai, China (Ethics Number: 2018K065). Written informed consent was obtained from all subjects prior to the study. The general study protocol was shown in Fig. 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical data collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical data was collected from elderly patients with thyroid nodules who were scheduled for fine-needle aspiration (FNA) or thyroid surgery in our hospital, including gender, age, BMI, duration of thyroid nodule, systolic and diastolic blood pressure (measured in a sitting position, average of 2 measurements), educational level, occupation status, smoking and alcohol history, family history of thyroid cancer, as well as liver and kidney function, blood glucose, lipid profile, glycosylated hemoglobin, uric acid, thyroid function, and autoantibodies.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003ePittsburgh Sleep Quality Index\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe scale was used to evaluate sleep status over the past month and consisted of seven components: sleep quality, sleep latency, sleep duration, sleep efficiency, sleep disorder, sleep medication, and daytime dysfunction. The score range for each question was 0-3 points, with a total score of 0-21 points. The higher the score, the worse the sleep quality, PSQI \u0026gt; 7 defined as poor sleep quality [13], with sensitivity and specificity of 0.983 and 0.902, respectively.\u0026nbsp;\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eThyroid Nodule Evaluation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; Multimodal ultrasonography referred to the combined use of two or more ultrasound examination methods for making certification and recognition more accurate. Elastography [14] and contrast-enhanced ultrasound [15] have certain value in the diagnosis of thyroid nodules, but usually required comprehensive interpretation with morphological features of the nodules. Thus, the evaluation was performed through high-resolution ultrasonography, elastography, and contrast-enhanced ultrasound by the same experienced ultrasound physician.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eModel Construction and Validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe risk prediction model was established based on the coefficients and relevant relationships, and presented in the form of a nomogram. The evaluation of the model was using ROC curves, calibration curves, and clinical decision curves. To test the reproducibility of the model and prevent overfitting, internal validation was performed using the bootstrap method with 1000 resamples. Meanwhile, collected data from another hospital was used for external validation to further estimate the portability and generalization of the model.\u0026nbsp;\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data were analyzed using SPSS 24.0 software. For normally distributed continuous variables, t-tests were used for comparisons between two groups, and the results were expressed as mean \u0026plusmn; standard deviation. For non-normally distributed variables, non-parametric tests were used, and the results were expressed as median. The chi-square test was for the comparisons of categorical variables, and trend tests were performed for ordinal variables. Binary logistic regression was to analyze the relationship between categorical variables and risk factors. ROC curves were constructed, and AUC comparisons were made using MedCalc software. R language (version 3.6.3) was adopted to plot the nomogram, calibration plot, and clinical decision curve. \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e\n"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eStudy Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 854 elderly patients with thyroid nodules who were scheduled for fine-needle aspiration (FNA) and thyroid surgery in the Endocrinology and General Surgery departments of our hospital were collected. According to the inclusion and exclusion criteria, 91 patients were excluded. Finally, 763 individuals participated in this study, aged between 60 and 87 years, including 177 males and 586 females. According to the criteria for puncture and postoperative pathological diagnosis, the benign nodule group consisted of 566 people. The malignant nodule group consisted of 197 people, including 185 cases of papillary carcinoma, 12 cases of follicular carcinoma, and no medullary carcinoma or undifferentiated carcinoma.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Characteristics of the Patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the clinical characteristics of elderly patients with thyroid nodules, only a family history of thyroid cancer was found to be associated with malignant nodules (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). There were no significant differences in gender, age, BMI, course of disease, blood pressure, education level, and smoking and drinking history between benign and malignant nodules (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05). All participants had been living in Shanghai, China for two years or more, with a median urinary iodine reference level of 138.4\u0026mu;g/L [16].\u003c/p\u003e\n\u003cp\u003eThere were no significant differences (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05) in biochemical indicators such as liver and kidney function, blood lipids, blood glucose, glycated hemoglobin, and blood uric acid in the comparison of benign and malignant thyroid nodules. FT3, FT4, and TSH did not show differences between the two groups. Even in the subgroup analysis of TSH, there was still no statistical significance, perhaps it was related to the thyroid function of the enrolled patients within the normal range. Only the autoantibody TPOAB had a higher malignant rate in the positive group (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of Sleep Quality in Patients with Thyroid Nodules\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere were significant differences\u0026nbsp;in the comparison of\u0026nbsp;sleep duration, daytime dysfunction and PSQI scores between the benign and malignant nodule groups among elderly patients (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). That was to say, shorter sleep duration, poorer daytime function, and higher PSQI scores were associated with malignancy. Both groups had a PSQI score above 7, indicating sleep quality was generally poor in elderly individuals. Subgroup analysis revealed a higher incidence of malignant nodules in patients with poor sleep quality (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). However, the remaining components and disease duration showed no differences between the two groups (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05), seen in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Relationship between sleep quality and thyroid nodules\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"560\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNO.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBenign\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMalignant\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eSleep quality score (no./%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e98（12.84）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e78（10.22）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e20（2.62）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e352（46.13）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e258（33.81）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e94（12.32）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e244（31.98）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e173（22.68）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e71（9.31）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e69（9.05）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e57（7.47）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e12（1.57）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eSleep latency time (no./%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026le;15 minutes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e90（11.80）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e65（8.52）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e25（3.28）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e16-30 minutes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e277（36.30）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e221（28.96）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e56（7.34）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e31-60 minutes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e240（31.45）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e167（21.89）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e73（9.57）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026ge; 60 minutes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e156（20.45）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e113（14.81）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e43（5.63）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eSleep latency score (no./%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e32（4.19）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e25（3.28）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e7（0.92）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e263（34.47）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e203（26.61）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e60（7.86）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e314（41.16）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e234（30.67）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e80（10.48）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e154（20.18）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e104（13.63）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e50（6.55）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eSleep duration score (no./%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e0（\u0026gt; 7 hours）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e99（12.98）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e84（11.01）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e15（1.97）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e1（6-7 hours）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e255（33.42）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e191（25.03）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e64（8.39）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e2（5-6 hours）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e285（37.35）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e208（27.26）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e77（10.09）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e3（\u0026lt; 5 hours）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e124（16.25）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e83（10.88）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e41（5.37）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eSleep efficiency score (no./%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e181（23.72）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e151（19.79）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e30（3.93）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e246（32.24）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e170（22.28）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e76（9.96）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e200（26.21）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e140（18.35）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e60（7.87）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e136（17.83）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e105（13.76）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e31（4.06）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eSleep disorder score (no./%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e89（11.66）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e71（9.31）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e18（2.36）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e248（32.50）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e189（24.77）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e59（7.73）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e287（37.62）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e212（27.78）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e75（9.83）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e139（18.22）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e94（12.32）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e45（5.90）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eSleep medication score (no./%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.305\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e262（34.34）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e225（29.49）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e37（4.85）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e231（30.28）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e156（20.45）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e75（9.83）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e184（24.12）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e122（15.98）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e62（8.13）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e86（11.26）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e63（8.26）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e23（3.01）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eDaytime dysfunction score (no./%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e136（17.82）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e125（16.38）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e11（1.44）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e236（30.93）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e182（23.86）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e54（7.08）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e245（32.11）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e154（20.18）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e91（11.93）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e146（19.14）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e105（13.76）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e41（5.37）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003eDisease duration (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e7.13\u0026plusmn;4.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e6.97\u0026plusmn;4.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e7.59\u0026plusmn;5.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.418\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003ePSQI score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e8.78\u0026plusmn;2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e8.51\u0026plusmn;2.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e9.58\u0026plusmn;2.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003ePSQI (no./%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e0-7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e256（33.55）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e202（26.47）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e54（7.08）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026gt; 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e507（66.45）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e364（47.71）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e143（18.74）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eMultimodal Ultrasound Evaluation of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eThyroid Nodules\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCharacteristics of multimodal ultrasound such as the nodule number, morphology, margin, calcification, blood flow, elastography, and ultrasonic contrast between benign and malignant groups were statistically differences (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). \u0026nbsp;Correlation analysis showed single nodular, irregular morphology, unclear margins, coarse or micro-calcification, blood flow, higher elasticity score and low enhancement were associated with a higher risk of malignancy (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). Other features had no significance in distinguishing (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05). Refer to Table 2 for more details.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Relationship between Multimodal Ultrasound Characteristics and Thyroid Nodules\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"570\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNO.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBenign\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMalignant\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eMaximum diameter (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e12.64\u0026plusmn;9.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e12.19\u0026plusmn;8.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e15.09\u0026plusmn;10.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eNodule number (no./%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eSolitary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e221 (28.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e149 (19.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e72 (9.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eMultiple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e542 (71.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e417 (54.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e125 (16.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eMorphology (no/%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eRegular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e535 (70.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e446 (58.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e89 (11.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eIrregular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e228 (29.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e120 (15.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e108 (14.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eMargin (no./%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eClear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e578 (75.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e461 (60.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e117 (15.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eUnclear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e185 (24.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e105 (13.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e80 (10.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eEcho (no./%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eHypo echo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e614 (80.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e464 (60.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e150 (19.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eNon-hypo echo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e149 (19.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e102 (13.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e47 (6.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eCalcification (no./%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e469 (61.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e386 (50.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e83 (10.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eCoarse\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e190 (14.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e120 (15.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e70 (9.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eMicron\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e104 (13.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e60 (7.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e44 (5.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eBlood flow (no./%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e531 (69.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e422 (55.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e109 (14.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e232 (30.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e144 (18.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e88 (11.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eElasticity score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e3.82\u0026plusmn;0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e3.39\u0026plusmn;0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e4.32\u0026plusmn;0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eElasticity score (no./%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e52 (6.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e52 (6.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e305 (39.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e266 (34.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e39 (5.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e223 (29.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e149 (19.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e74 (9.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e183 (23.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e99 (12.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e84 (11.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eUltrasonic contrast (no./%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eHigh enhanced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e430 (56.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e363 (47.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e67 (8.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eLow enhanced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e333 (43.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e203 (26.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e130 (17.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eUniform perfusion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e402 (52.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e302 (39.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e100 (13.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eUneven perfusion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e361 (47.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e264 (34.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e97 (12.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eLogistic Regression Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsidering the lower risk of outcome events as the continuous independent variable changes by one unit, this study discretized the continuous variables into multiple categorical variables for analysis. When these variables were included in a multivariate logistic regression analysis, eight independent risk factors for malignant thyroid nodules were ultimately identified, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05. See Table 3 for details.\u003c/p\u003e\n\u003cp\u003eFig. 2 revealed ROC curves for the predicted probabilities, the AUC of curve 1 was 0.860 (95% CI 0.841-0.896) for the comprehensive analysis of all independent risk factors, with a sensitivity of 82.5% and a specificity of 74.1%. AUC for the individual multimodal ultrasound (curve 2) was 0.824 (95% CI 0.790-0.858), with a sensitivity of 69.5% and a specificity of 80.5%. AUC for the combination of multimodal ultrasound and TPOAB (curve 3) was 0.831 (95% CI 0.798-0.864), with a sensitivity of 67.4% and a specificity of 83.2%. Results showed significant differences only when curve 1 compared with curve 2 or curve 3, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, indicating that combination of sleep quality assessment can improve the diagnostic accuracy of malignant thyroid nodules in elderly patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Univariate and Multivariate Logistic Regression\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 140px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSingle factor analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 221px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultiple-factor analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eFamily history of thyroid cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e2.429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e1.146-5.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e2.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.773-6.843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eTPOAB positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e1.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e1.038-2.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1.772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e1.115-2.814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eSleep duration score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e1.296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e1.081-1.555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.951-1.590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eDaytime dysfunction score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e1.707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e1.430-2.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1.832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e1.420-2.363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003ePSQI score \u0026gt; 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e2.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e1.486-3.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e1.009-2.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eNodule number (solitary)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e1.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e1.177-2.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.960-3.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eMorphology (irregular)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e4.459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e3.149-6.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e3.601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e2.231-5.812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eMargin (unclear)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e2.946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e2.059-4.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.740-2.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.425\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eCalcification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e2.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e1.645-2.567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1.576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e1.182-2.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eBlood flow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e2.358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e1.666-3.337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1.700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e1.081-2.673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eElasticity score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e2.553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e2.080-3.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e2.419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e1.898-3.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eContrast enhancement (low)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e3.470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e2.467-4.880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e3.787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e2.460-5.832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eModel Performance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the results of logistic regression analysis, a risk prediction model for elderly thyroid cancer was constructed using the R language [17]. The model represents the relationships between variables in an intuitive way using a nomogram [18,19]. The nomogram can be used as follows: draw a vertical line for each variable value, locate the variable score on the first line, and sum up all the scores to obtain the total score. Read the thyroid cancer risk probability on the total score axis vertically (Fig. 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe accuracy of the prediction model was 0.825, and the AUC was 0.860 (Fig. 4A). The calibration ability was evaluated using Hosmer-Lemeshow goodness-of-fit test, result showed X\u003csup\u003e2\u003c/sup\u003e = 4.218, \u003cem\u003ep\u003c/em\u003e = 0.821. The predicted probability was very close to the actual probability (y = x), indicating high calibration of the model (Fig. 4B). A decision curve analysis (DCA) was conducted to evaluate the clinical utility (Fig. 4C). The horizontal line (None) represented all samples being negative, where no interventions were performed and the net benefit was zero. The diagonal line (All) represented all samples being positive, where all individuals received interventions, and the net benefit was a negatively sloping line [20]. The curves for model_1 and model_2 were both above the two extreme curves, and model_1 had a higher net benefit within a larger threshold probability range, suggesting good clinical utility of the prediction model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVerification Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdopting Bootstrap method with 1000 iterations for internal validation, Kappa statistic was used to measure the stability of the predictive model. The results showed an accuracy of 0.818 and a Kappa value of 0.486, furthermore, C-index was 0.804, indicating good discrimination and accuracy of the model in the internal validation.\u003c/p\u003e\n\u003cp\u003eThe study used elderly patients undergoing thyroid fine needle aspiration biopsy and surgery at Shanghai North Hospital as an external validation cohort. A total of 221 patients were collected between January 2018 and January 2024, 198 patients were ultimately enrolled based on the inclusion and exclusion criteria. There were no statistical differences between the predictive variables of the validation cohort and the development cohort (Table 4). The AUC was 0.787, with a sensitivity of 72.9% and specificity of 74.0% (Fig. 5A). The Hosmer-Lemeshow test showed X\u003csup\u003e2\u003c/sup\u003e = 5.855, \u003cem\u003ep\u003c/em\u003e = 0.406 (Fig. 5B). The DCA plot indicated that the predictive model consistently outperformed the two extreme curves within the threshold probability range of 0.1-0.7 (Fig. 5C). These results demonstrated that the predictive model had similar discrimination, calibration, and clinical utility in the external validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Clinical data, sleep quality, and multimodal ultrasound features of the validation\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"539\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal number\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBenign\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMalignant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eTPOAB (no./%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e156 (78.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 130px;\"\u003e\n \u003cp\u003e122 (61.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e34 (17.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e42 (21.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 130px;\"\u003e\n \u003cp\u003e30 (15.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e12 (6.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eDaytime dysfunction score (no./%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e19 (9.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 130px;\"\u003e\n \u003cp\u003e16 (8.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e3 (1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e58 (29.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 130px;\"\u003e\n \u003cp\u003e54 (27.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e4 (2.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e87 (43.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 130px;\"\u003e\n \u003cp\u003e59 (29.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e28 (14.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e34 (17.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 130px;\"\u003e\n \u003cp\u003e23 (11.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e11 (5.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003ePSQI (no./%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e0-7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e49 (24.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 130px;\"\u003e\n \u003cp\u003e35 (17.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e14 (7.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026gt; 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e149 (75.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 130px;\"\u003e\n \u003cp\u003e117 (59.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e32 (16.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eMorphology (no./%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eRegular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e135 (68.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 130px;\"\u003e\n \u003cp\u003e113 (57.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e22 (11.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eIrregular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e63 (31.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 130px;\"\u003e\n \u003cp\u003e39 (19.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e24 (12.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eCalcification (no./%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e97 (48.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 130px;\"\u003e\n \u003cp\u003e77 (38.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e20 (10.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eCoarse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e66 (33.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 130px;\"\u003e\n \u003cp\u003e49 (24.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e17 (8.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eMicron\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e35 (17.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 130px;\"\u003e\n \u003cp\u003e26 (13.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e9 (4.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eBlood flow (no./%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e122 (61.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 130px;\"\u003e\n \u003cp\u003e97 (48.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e25 (12.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e76 (38.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 130px;\"\u003e\n \u003cp\u003e55 (27.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e21 (10.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eElasticity score (no./%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e15 (7.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 130px;\"\u003e\n \u003cp\u003e15 (7.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e65 (32.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 130px;\"\u003e\n \u003cp\u003e56 (28.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e9 (4.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e69 (34.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 130px;\"\u003e\n \u003cp\u003e48 (24.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e21 (10.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e49 (24.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 130px;\"\u003e\n \u003cp\u003e33 (16.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e16 (8.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eUltrasonic Contrast (no./%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eHigh enhanced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e103 (52.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 130px;\"\u003e\n \u003cp\u003e92 (46.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e11 (5.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 168px;\"\u003e\n \u003cp\u003eLow enhanced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e95 (47.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 130px;\"\u003e\n \u003cp\u003e60 (30.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e35 (17.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eSince the 1970s, thyroid nodules have been detected in over 60% of the general population through ultrasound imaging. However, most of these nodules are benign and do not require further treatment, making accurate screening for malignant nodules a major challenge in the management of thyroid disease. FNA is the most widely used cytological examination method and can differentiate approximately 75-80% of nodules. In a study, when both malignant tumors and suspicious malignant tumors were classified as positive, the sensitivity of FNA alone was 90.7% and the specificity was 85.2% [21], which affects the routine use of FNA as a frontline screening tool. A previous large-sample study showed that repeated FNA of atypical lesions or follicular lesions of undetermined significance (AUS/FLUS) does not improve the detection accuracy of malignant tumors [22]. To benefit elderly patients with truly malignant nodules during surgery, improve the quality life of the elderly population, and allocate medical resources effectively, we evaluated the individualized risk of participants through clinical data, sleep quality assessment and multimodal ultrasound, then constructed an economical, convenient and operable prediction model to guide FNA decision.\u003c/p\u003e\n\u003cp\u003eAccording to the research flow chart, a total of 763 patients were enrolled, 566 with benign nodules and 197 with malignant ones. All the malignant nodules were differentiated, and the malignant rate was 25.82%. Univariate analysis showed that family history, TPOAB positive, sleep duration score, daytime dysfunction score, PSQI \u0026gt; 7, solitary nodule, irregular shape, unclear margins, nodule calcification, blood flow, elastic score, and low contrast enhancement may be associated with an increased risk of malignant nodules. Further logistic regression analysis identified eight independent risk factors for thyroid cancer as shown in Table 3. Patients with Hashimoto\u0026apos;s thyroiditis (HT) usually have elevated levels of thyroid antibodies in serum, or positive for TPOAB alone, accounting for 97% of literature reports [23]. In 1955, Dailey first proposed a close relationship between HT and papillary thyroid carcinoma (PTC) [24]. In a retrospective analysis of 8,524 cases of thyroid surgery in China, it was found that the risk of PTC in HT patients was significantly increased [25], which was consistent with our research results.\u003c/p\u003e\n\u003cp\u003eSleep deprivation prolonged exposure to nighttime light, affecting melatonin and TSH secretion, leading to circadian rhythm disruption. In 2007, the International Agency for Research on Cancer classified staying up late, including night shift work involving circadian rhythm disorders as a Group 2A carcinogen [26]. A prospective cohort study targeting female nurses, after 26 years of follow-up, found evidence that night shift work, extreme sleep duration and sleep difficulties collectively affected the risk of thyroid cancer [27]. Another large-scale prospective study of 460,000 participants showed that exposure to artificial light at night greatly increased the incidence of thyroid cancer [28]. According to clinical researches, 57% of elderly people aged 60 and above in China experienced some degree of sleep disorders, with most sleeping for about 5 hours and frequent waking at night. The duration of light sleep was much longer than that of deep sleep, making them a group with severe sleep disorders. A recent study used genome-wide association studies (GWAS) published in the FinnGen and UK Biobank databases to explore the causal relationship between sleep characteristics and thyroid cancer risk based on Mendelian randomization. The results revealed that shortened sleep time was associated with thyroid cancer [29], indicating the importance of adequate sleep in preventing thyroid cancer. These prior studies aligned with our findings linking circadian disruption to thyroid carcinogenesis.\u003c/p\u003e\n\u003cp\u003eThe ultrasound risk stratification system and thyroid biopsy threshold for thyroid nodules varied in different versions of the guidelines, including C-TIRADS may not reliably predict nodule properties and guide FNA. Our study used multimodal ultrasound combined with PSQI to plot ROC curves. The AUC obtained from the integrated factors was 0.860 (95% CI 0.841-0.896), with a sensitivity of 82.5% and specificity of 74.1%, higher than that of the other two curves, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 (Fig. 2). The results showed sleep quality assessment and multimodal ultrasound may improve the diagnostic level of malignant nodules, providing clinical reference value for FNA. Based on the above statistical analysis, a risk prediction model for elderly thyroid cancer was constructed and presented in the form of a nomogram (Fig. 3). The combined model achieved an AUC of 0.860, outperforming single-modality approaches and showing good discrimination. Calibration curve aligned closely with ideal predictions (Hosmer-Lemeshow, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.821), and DCA confirmed clinical utility (Fig. 4A-C). External validation yielded an AUC of 0.787, demonstrating repeatability and generalizability (Fig. 5A-C).\u003c/p\u003e\n\u003cp\u003eResearchers both domestic and international have emphasized the importance of developing multivariate prediction algorithms to determine the cumulative risk of malignant tumors for this common clinical problem. Raza et al. used a multivariate stepwise regression model to predict the malignancy rate of thyroid nodules in patients based on factors such as patient age, solid/nodule calcification, and FNA cytology examination [30]. Tuttle applied Bayesian analysis modeling and found that male gender, nodules larger than 4 centimeters, and glandular features could be systematically integrated into clinical decision-making, thereby reducing the probability of surgery for follicular tumor patients [31]. Alexander et al. used prospective cohorts for Bayesian classification and constructed and cross-validated a clinically relevant prognostic assessment tool [32]. Another study constructed a multivariate logistic regression model with all ultrasound features for 1500 patients from Shanghai and Fujian, incorporating variable weights and combination patterns to predict PTC, FTC, and MTC [33]. Therefore, significant progress has been made in understanding the clinical significance of thyroid nodules and insufficient evaluation of potential harms. Various clinical models can estimate the thresholds for thyroid nodule biopsies to some extent, balancing the diagnostic value of thyroid cancer and the potential risk of missed diagnosis. However, the choice of the model needs to consider specific patients, population preferences, regional characteristics, operational conditions, etc. Predictive models that combine clinical, biochemical, and radiological features can support clinical doctors in reducing unnecessary invasive surgeries for thyroid nodule patients [34].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study focused on constructing a prediction model for elderly thyroid cancer, highlighting the synergistic value of sleep quality assessment and multimodal ultrasound. It not only provided personalized risk of malignancy but also allowed real-time evaluation of suspicious nodules, promoting clinical decision-making and patients education. In cases where FNA examination was limited, such as poor patient health, difficulty in puncturing small nodules, inadequate tissue obtained for diagnosis, or uncertain diagnosis, the model also demonstrated its advantages.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst, a major limitation of this study was the lack of prospective validation of the model, as well as the small size and limited sample of the validation cohort, which were derived from a single-center dataset. While we followed the recommended minimum of 10 events per predictive variable, it was necessary to validate the model in a larger patient population. Second, the malignancy rate of thyroid nodules in this study was 25.82%. The selection of patients who underwent thyroid nodule puncture or surgery inevitably led to a significantly higher proportion, which may not objectively reflect the incidence of thyroid cancer in the elderly population. Third, the questionnaire used in this study was subjective, and recall bias may exist.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003eWe were grateful to all the patients who were willing to participate in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003eXudan Lou was the major contributor in writing the manuscript. Na Yi and Yuanyuan Xu collected the data information of the patients. Yingchun Liu was responsible for the Multimodal ultrasound examination. Jieyuzhen Qiu made statistical analysis. Xiaoming Tao and Zhijun Bao designed and funded the study. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThe clinical special project of Shanghai Municipal Health Commission (202240258).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e The authors declare that there are no conficts of interest regarding the publication of this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003eThe study was carried out in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki) and approved by the Ethics Committee of our institute.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eShi Z, Lin J, Wu Y, et al. Burden of cancer and changing cancer spectrum among older adults in China: Trends and projections to 2030[J]. 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Cancer, 2021, 127(9): 1448-1458.\u003c/li\u003e\n\u003cli\u003eZong L, Liu G, He H, et al. Causal association of sleep traits with the risk of thyroid cancer: A mendelian randomization study[J]. BMC Cancer, 2024, 24(1): 605.\u003c/li\u003e\n\u003cli\u003eRaza SN, Shah MD, Palme CE, et al. Risk factors for well-differentiated thyroid carcinoma in patients with thyroid nodular disease[J]. Otolaryng Head Neck, 2008, 139(1): 21-26.\u003c/li\u003e\n\u003cli\u003eTuttle RM, Lemar H, Burch HB. Clinical features associated with an increased risk of thyroid malignancy in patients with follicular neoplasia by fine-needle aspiration[J]. Thyroid, 1998, 8(5): 377-383.\u003c/li\u003e\n\u003cli\u003eStojadinovic A, Peoples GE, Libutti SK, et al. Development of a clinical decision model for thyroid nodules[J]. BMC Surg, 2009, 9: 12.\u003c/li\u003e\n\u003cli\u003eJiang S, Xie Q, Li N, et al. Modified Models for Predicting Malignancy Using Ultrasound Characters Have High Accuracy in Thyroid Nodules With Small Size[J]. Front Mol Biosci, 2021, 8: 752417.\u003c/li\u003e\n\u003cli\u003eWitczak J, Taylor P, Chai J, et al. Predicting malignancy in thyroid nodules: feasibility of a predictive model integrating clinical, biochemical, and ultrasound characteristics[J]. Thyroid Res, 2016, 9: 4.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Prediction Model, Elderly Thyroid Cancer, Sleep Quality, Multimodal Ultrasound","lastPublishedDoi":"10.21203/rs.3.rs-6028524/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6028524/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e To explore the differential diagnosis for benign and malignant thyroid nodules and the diagnostic value of sleep quality, to construct and validate a risk prediction model, providing the basis for clinical treatment decision for elderly thyroid cancer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Clinical data, Pittsburgh Sleep Quality Index (PSQI), and multimodal ultrasound were collected from elderly patients undergoing fine needle aspiration biopsy or thyroid surgery in our department of endocrinology and general surgery. Postoperative pathological served as the gold standard, binary logistic regression identified significant risk factors, and the receiver-operating characteristic (ROC) curves was plotted to construct and validate the prediction model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Among 763 enrolled patients (566 benign and 197 malignant), multivariate analysis revealed independent risk factors: TPOAB positive, daytime dysfunction, PSQI \u0026gt; 7, irregular nodule shape, calcification, blood flow, high elasticity scores, and low contrast enhancement. The area under the curve (AUC) for the combined model was 0.860, significantly higher than models using multimodal ultrasound alone (AUC = 0.824) or multimodal ultrasound with TPOAB (AUC = 0.831), \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05. The nomogram-based prediction model demonstrated excellent discrimination, calibration, and clinical utility in internal and external validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eIntegrating sleep quality assessment with multimodal ultrasound assisted in the differentiation of thyroid nodules in the elderly, thus may improve the preoperative diagnostic levels. Risk prediction model in a nomogram format provided an intuitive and reliable tool for clinical decision-making.\u003c/p\u003e","manuscriptTitle":"Risk Prediction Model for Elderly Differentiated Thyroid Cancer Based on Combined Sleep Quality Assessment and Multimodal Ultrasound","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-19 16:31:46","doi":"10.21203/rs.3.rs-6028524/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bfac2f1b-3cc0-40c0-8650-66f138010711","owner":[],"postedDate":"February 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-04T06:39:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-19 16:31:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6028524","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6028524","identity":"rs-6028524","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}