Key Ultrasonographic Features, Fine-Needle Aspiration, and Nodule Location in the Preoperative Differentiation of Benign and Malignant Thyroid Nodules: A Retrospective Study

Key Ultrasonographic Features, Fine-Needle Aspiration, and Nodule Location in the Preoperative Differentiation of Benign and Malignant Thyroid Nodules: A Retrospective Study | 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 Key Ultrasonographic Features, Fine-Needle Aspiration, and Nodule Location in the Preoperative Differentiation of Benign and Malignant Thyroid Nodules: A Retrospective Study Yuanguang Tian, Kaikai Zhai, Honghong Wu, Zheng Wang, Haiyi Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7975029/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 Background This study aimed to explore clinical and ultrasonographic factors associated with malignant thyroid nodules and to develop a diagnostic prediction model integrating key ultrasound features, fine-needle aspiration (FNA), and nodule location for preoperative risk stratification. Methods We retrospectively analyzed 186 patients with thyroid nodules confirmed by surgery and pathology. Clinical data, ultrasound features, and FNA results were compared between benign and malignant groups. Variables with statistical significance in univariate analysis were entered into binary logistic regression to identify independent predictors. A combined diagnostic model was constructed and validated using receiver operating characteristic (ROC) curves. Results Among the 186 patients, 53 (28.5%) had benign and 133 (71.5%) malignant nodules. Solid composition, aspect ratio ≥ 1, calcification, and upper-pole location were independent risk factors for malignancy (P < 0.05). The combined model integrating key ultrasound features, FNA, and nodule location achieved an AUC of 0.867 with 87.2% sensitivity and 77.4% specificity, significantly outperforming any single method. Conclusions The combined model markedly improves preoperative differentiation between benign and malignant thyroid nodules and may serve as a valuable tool for personalized management. Thyroid Nodule Thyroid Cancer Anatomic Location Ultrasonography Fine-Needle Aspiration Introduction Thyroid cancer, the most common endocrine malignancy, has a preoperative diagnostic accuracy that directly influences both surgical planning and patient acceptance of the proposed procedure[ 1 ]. Thyroid nodules are a common clinical finding, with an ultrasound detection rate as high as 70%, yet only 5–10% are malignant[ 2 ]. Ultrasound-guided fine-needle aspiration (US-FNA) is currently regarded as the reference standard for preoperative assessment of thyroid nodules; however, approximately 30% of cases still fail to yield a definitive diagnosis[ 3 ]. Studies have indicated that clinical factors such as nodule location, ultrasound features, and patient age are all associated with the risk of malignancy.[ 4 , 5 ]. This study conducted a retrospective analysis of clinical data from 186 patients with thyroid nodules to systematically evaluate the independent predictive value of nodule location, ultrasonographic features, and clinical indicators, and to develop a combined diagnostic model based on key ultrasound features, fine-needle aspiration cytology, and nodule location. Materials and Methods Baseline Characteristics With the approval of our institutional ethics committee, clinical data were collected from 186 eligible patients with thyroid nodules who presented to Tongling People's Hospital between January 2019 and May 2025. The cohort comprised 47 males and 139 females (male-to-female ratio 1:2.90), aged 18–76 years (mean age 47 years). The lesions were located in the left lobe in 85 cases and the right lobe in 101 cases. Inclusion Criteria: (1)Underwent a preoperative ultrasound examination. (2)Received a preoperative fine-needle aspiration (FNA) biopsy. (3)Underwent thyroidectomy with a definitive postoperative histopathological (paraffin section) diagnosis. (4)Had concordant results confirming that the preoperative ultrasound, FNA, and postoperative pathological assessments all pertained to the same nodule. Exclusion Criteria: (1)A prior history of thyroid surgery or ablation therapy. (2)Nodules located in the thyroid isthmus. Existing data indicate that isthmic nodules differ fundamentally from lobar nodules in their molecular profile and clinical behavior, constituting a high-risk subgroup in terms of malignancy potential, molecular features, and clinical outcomes, which necessitates a specialized diagnostic and therapeutic approach [ 6 , 7 ].Excluding isthmus cases helps minimize confounding factors. (3)A pathological diagnosis of metastatic carcinoma or lymphoma. (4)Failure to complete any one of the following: preoperative ultrasound, FNA, or postoperative histopathological examination. Research Methods Ultrasonography:Examinations were performed using an ultrasound system equipped with a high-frequency linear-array transducer. Patients were placed in the supine position with a pillow under the shoulders to allow moderate neck extension, thereby optimizing exposure of the thyroid region. The transducer was then applied to the anterior neck to systematically acquire two-dimensional grayscale images and color Doppler flow imaging data of the thyroid. The following nodule characteristics were evaluated and recorded: composition, location, aspect ratio, margins, internal echogenicity, and the presence or absence of calcifications. Nodule location was determined in the longitudinal view by dividing the thyroid lobe into thirds, classifying the location as upper, middle, or lower pole[ 8 ]. When a nodule spanned two or more segments, its location was determined by its predominant portion or center. The ultrasound images were independently reviewed by two physicians, each holding an associate chief physician title or higher with at least five years of experience in thyroid ultrasonography. Any discrepant interpretations were resolved through a consensus reading. Fine-Needle Aspiration (FNA) Procedure:The FNA procedure was performed with the patient in a reverse Trendelenburg position, with a pillow placed under the shoulders to achieve neck hyperextension for optimal exposure of the surgical field. Following routine skin disinfection with povidone-iodine and draping with sterile towels, local infiltration anesthesia was administered at the intended puncture site using 2% lidocaine. Sterile ultrasound gel was applied to the skin surface. Under real-time ultrasound guidance, a disposable 10 ml syringe equipped with a puncture needle was percutaneously inserted. The needle advancement was halted immediately upon visualization of the needle tip reaching the anterior margin of the target nodule. Subsequently, rapid back-and-forth movements were performed along the long axis of the nodule for 3–6 seconds under negative pressure suction to obtain cytological specimens. This process was repeated 2–3 times per nodule until satisfactory smears were acquired. All obtained specimens were immediately fixed and sent for pathological examination. Cytological diagnoses were rendered according to The Bethesda System for Reporting Thyroid Cytopathology (TBSRTC). All FNA smears were independently and blindly interpreted by two pathologists, each holding the title of associate chief physician or higher and possessing at least five years of specialized experience in thyroid cytology. Any discrepant diagnoses were resolved through a joint review to establish a final consensus. Statistical Analysis All statistical analyses were performed using SPSS software (version 26.0; IBM Corp., Armonk, NY, USA). Continuous data conforming to a normal distribution are presented as the mean ± standard deviation (x̄ ± s) and were compared between groups using the independent samples t-test. Categorical data are expressed as numbers and percentages and were compared using the Chi-square test or Fisher's exact test, as appropriate. Binary logistic regression analysis was employed to identify independent predictors of malignancy and to construct a combined diagnostic model. The diagnostic performance of individual parameters and the combined model was evaluated by receiver operating Results Paraffin pathology findings Among the 186 nodules, 53 (28.5%) were benign, including 38 cases of nodular goiter, 7 adenoma, and 8 thyroiditis; 133 (71.5%) were malignant, comprising 130 papillary thyroid carcinomas and 3 follicular carcinomas. Univariate Analysis of the Association Between Clinical Indicators and Benign/Malignant Thyroid Nodules Comparative analysis between groups revealed that patient age ≥ 45 years, and the following ultrasound features: hypoechogenicity, calcification, solid composition, aspect ratio ≥ 1, ill-defined margins, and nodule location in the longitudinal view, were significantly associated with malignant thyroid nodules (p 0.05). Notably, univariate analysis demonstrated that the malignancy risk of upper-pole nodules was significantly higher than that of middle- and lower-pole nodules (χ² = 14.005, P = 0.001), suggesting that nodule location may be an important factor influencing the development of thyroid cancer. See Table 1 for details. Table 1 Univariate Analysis Variable n Malignant Benign χ² P Gender 0.800 0.371 Male 47 36(76.6%) 11(23.4%) Female 139 97(69.8%) 42(30.2%) Lobe laterality 1.519 0.218 Left lobe 85 57(67.1%) 28(32.9%) Right lobe 101 76(75.2%) 25(24.8%) Age 6.637 0.010 < 45 years 117 76(65.0%) 41(35.0%) ≥ 45 years 69 57(82.6%) 12(17.4%) Number of nodules 1.151 0.283 Multiple 82 57(69.5%) 25(30.5%) Single 104 76(73.1%) 28(26.9%) Longitudinal location 14.005 0.001 Upper pole 52 46(88.5%) 6(11.5%) Middle pole 63 46(73.0%) 17(27.0%) Lower pole 71 41(57.7%) 30(42.3%) Margin 23.227 0.000 Ill-defined 101 87(86.1%) 14(13.9%) Clear 85 46(54.1%) 39(45.9%) Calcification 14.369 0.000 Present 129 103(79.8%) 26(20.2%) Absent 57 30(52.6%) 27(47.4%) Echogenicity 27.183 0.000 Hypoechoic 159 125(78.6%) 34(21.4%) Non-hypoechoic 27 8(29.6%) 19(70.4%) Composition 33.166 0.000 Solid 170 132(77.6%) 38(22.4%) Cystic 16 1(6.3%) 15(93.8%) Aspect ratio 22.643 0.000 ≥ 1 71 65(91.5%) 6(8.5%) < 1 115 68(59.1%) 47(40.9%) Table 1 . Univariate Analysis Multivariate Analysis of the Relationship Between Clinical Factors and Thyroid Nodule Pathology While conventional ultrasound features such as composition, echogenicity, shape, margins, and echogenic foci have been incorporated into the TI-RADS classification system by various guidelines[ 9 ], the relationship between nodule location and malignancy has been less extensively studied. Since univariate analysis revealed a significantly higher malignancy proportion in upper-pole nodules (P = 0.001; see Table 1 ), we therefore focused our analysis on nodule location in the longitudinal view of the gland. To facilitate the model analysis, location was dichotomized into "upper pole" versus "non-upper pole" (combining the middle and lower poles). This categorization was justified because previous studies suggest a potentially higher malignancy risk for upper-pole nodules, whereas middle and lower poles demonstrate minimal differences in sonographic appearance and risk profile. Combining the latter two categories also ensured balanced group sizes for robust statistical power. Consequently, the ultrasound features that showed statistically significant differences in the univariate analysis—namely, hypoechogenicity, calcification, solid composition, aspect ratio ≥ 1, and ill-defined margins—were included as independent variables. Using postoperative histopathological (paraffin section) results as the dependent variable, a stepwise method was employed to select variables for inclusion in the binary logistic regression model. Ultimately, three independent variables were retained in the final model: solid composition, aspect ratio ≥ 1, and the presence of calcification. These were identified as independent risk factors for malignant thyroid nodules (p < 0.05), as detailed in Table 2 . Table 2 Multivariate Analysis Variable β SE Wald χ² P Exp(B) OR (95% CI) Solid composition 2.643 1.091 5.862 0.015 14.050 (1.654-119.335) Aspect ratio ≥ 1 1.442 0.508 8.065 0.005 4.229 (1.563–11.440) Calcification 0.989 0.482 5.341 0.021 2.689 (1.162–6.220) Table 2 . Multivariate Analysis Cytological Results of Fine-Needle Aspiration in Diagnosing Thyroid Nodules Cytological classification based on the Bethesda system: Category I (n = 28, nondiagnostic), II (n = 29, benign), III (n = 10, atypia/follicular lesion of undetermined significance), IV (n = 13, follicular neoplasm or suspicious for follicular neoplasm), V (n = 66, suspicious for malignancy), VI (n = 40, malignant). When Categories I–III were considered benign and IV–VI malignant, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 71.4%, 54.7%, 79.8%, and 43.3%, respectively. Development of a Diagnostic Prediction Model Integrating Key Ultrasound Features, Fine-Needle Aspiration, and Nodule Location and Its ROC Curve Analysis for Differentiating Benign and Malignant Thyroid Nodules Binary logistic regression incorporating solid composition (X₁), aspect ratio (X₂), calcification (X₃), nodule location (X₄), and FNA category (X₅) produced the following equation: Logit(p) = -5.328 + 3.380X₁ + 1.554X₂ + 1.154X₃ + 1.239X₄ + 0.463X₅ as detailed in Table 3 . Table 3 Logistic regression model for combined diagnosis Variable β SE Waldχ² P值 Exp(B) OR(95%CI) Constant -5.328 1.226 18.887 0.000 0.005 X1 3.380 1.094 9.544 0.002 29.377 (3.441-250.831) X2 1.554 0.512 9.204 0.002 4.731 (1.733–12.914) X3 1.154 0.444 6.769 0.009 3.171 (1.329–7.564) X4 1.239 0.561 4.878 0.027 3.453 (1.150-10.371) X5 0.463 0.122 14.487 0.000 1.588 (1.252–2.015) Note: X1: Nodule composition (cystic = 0, solid = 1); X2: Aspect ratio (< 1 = 0, ≥ 1 = 1); X3: Calcification (absent = 0, present = 1); X4: Nodule location on longitudinal view (non-upper pole = 0, upper pole = 1); X5: Fine-needle aspiration cytology classification (Bethesda category: I-VI = 1–6). Table 3 . Logistic regression model for combined diagnosis Based on the established model, the maximum Youden's index and its corresponding Logit(p) cut-off value were calculated for each diagnostic method. The combined diagnostic model achieved the largest area under the ROC curve (AUC = 0.867). Using this optimal cut-off value to determine the combined diagnosis and comparing it with the postoperative histopathological (paraffin section) results yielded a sensitivity of 87.2% (116/133), specificity of 77.4% (41/53), positive predictive value (PPV) of 90.6% (116/128), and negative predictive value (NPV) of 70.7% (41/58).The AUC for the key ultrasound features alone was 0.803. Diagnosis based on its cut-off value resulted in a sensitivity of 90.2% (120/133), specificity of 56.6% (30/53), PPV of 83.9% (120/143), and NPV of 69.8% (30/43).For fine-needle aspiration cytology (AUC = 0.701), application of its cut-off value produced a sensitivity of 78.2% (104/133), specificity of 52.8% (28/53), PPV of 80.6% (104/129), and NPV of 49.1% (28/57).The detailed results are presented in Tables 4 and 5 . Table 4 ROC analysis of diagnostic methods Method AUC Youden Index Cutoff P Nodule location 0.616 0.233 <0.05 FNA 0.701 0.310 2.500 <0.001 Key US features 0.803 0.468 0.614 <0.001 Combined model 0.867 0.646 0.610 <0.001 Table 5 Comparison of diagnostic performance among methods Pathological results Key US features FNA Combined model Total Benign Malignant Benign Malignant Benign Malignant Benign 30 23 28 25 41 12 53 Malignant 13 120 29 104 17 116 133 Total 43 143 57 129 58 128 186 Sensitivity (% ) 90.2%(120/133) 78.2%(104/133) 87.2%(116/133) Specificity (% ) 56.6%(30/53) 52.8%(28/53) 77.4%(41/53) PPV 83.9%(120/143) 80.6%(104/129) 90.6%(116/128) NPV 69.8%(30/43) 49.1%(28/57) 70.7%(41/58) Table 4 . ROC analysis of diagnostic methods Table 5 . Comparison of diagnostic performance among methods ROC curve analysis revealed that the areas under the curve (AUCs) for distinguishing benign from malignant thyroid nodules were 0.616 for nodule location, 0.701 for fine-needle aspiration (FNA) cytology, 0.803 for key ultrasound features, and 0.867 for the combined diagnostic prediction model. Although the diagnostic performance of nodule location alone was limited, the model's overall efficacy was significantly enhanced when location was integrated with key ultrasound features and FNA (AUC = 0.867, P < 0.001), yielding sensitivity and specificity superior to any single method. This suggests that while nodule location is not the strongest standalone predictor, it serves as a crucial complementary factor that boosts the diagnostic performance of the overall model, indicating high potential for clinical application. The differences in AUC between the combined model and nodule location (Z=-7.449, P < 0.001), FNA cytology (Z=-4.236, P < 0.001), and key ultrasound features (Z=-2.543, P < 0.05) Discussion Over recent years, the global incidence of thyroid cancer has shown a persistent upward trend, and it has become one of the most common malignancies among women in China[10.11]. Given the high detection rate of thyroid nodules, the decision regarding surgical intervention relies on preoperative auxiliary diagnostics to guide clinical decision-making. Within this context, the precise preoperative differentiation between benign and malignant thyroid nodules has emerged as a pressing clinical issue. This study retrospectively analyzed 186 thyroid nodule cases, systematically integrating their clinical parameters for statistical analysis. The results identified typical high-risk sonographic features—hypoechogenicity, calcification, solid composition, aspect ratio ≥ 1, and ill-defined margins—which align closely with recent international Thyroid Imaging Reporting and Data System (TI-RADS) criteria and numerous related studies [ 12 – 14 ], indicating their strong clinical generalizability and predictive value. Furthermore, this study revealed that nodule location in the upper pole of the thyroid lobe significantly increased the risk of malignancy. Our results demonstrated that among the 186 patients, those with upper-pole nodules had a substantially higher malignancy rate of 88.5%, which was significantly greater than that of middle-pole (73.0%) and lower-pole nodules (57.7%), with a statistically significant difference (P = 0.001). After incorporating this factor into the logistic regression analysis, nodule location (upper pole vs. non-upper pole) remained an independent risk factor for malignant thyroid nodules (OR = 3.453, 95% CI: 1.150–10.371, P = 0.027), indicating a stable correlation between upper-pole location and malignant risk.This finding complements the traditional TI-RADS system, which is primarily based on ultrasound characteristics. It suggests that, in addition to conventional features such as composition, echogenicity, calcification, aspect ratio, and margins, the specific location of a nodule can also serve as an important reference for preoperative risk assessment.However, the relationship between nodule location and malignant risk remains somewhat controversial in the existing literature. Some scholars have reported a higher malignancy risk for upper-pole nodules [ 15 ], while other studies found no significant correlation between location and malignancy [ 16 ]. Some reports even suggested that lower-pole or isthmus nodules might be more prone to malignancy in certain populations [ 17 ].In this study, we employed a "upper pole vs. non-upper pole" dichotomization for analysis. The results confirmed a significantly higher malignancy rate in upper-pole nodules compared to non-upper-pole nodules. This finding aligns with studies supporting high risk in the upper pole but contradicts those observing no such difference. The discrepancies might be attributed to several factors: variations in sample size and population characteristics across studies affecting statistical power, and the lack of uniformity in the classification of nodule location. Our use of a concise "upper pole vs. non-upper pole" classification helps highlight the clinical utility of this factor.From a mechanistic perspective, the upper pole is adjacent to the entry point of the recurrent laryngeal nerve and may have differences in embryological origin [ 18 ]. Additionally, upper-pole nodules are often less detectable in routine physical examinations, potentially leading to delayed diagnosis [ 3 ]. Moreover, the blood supply and microenvironmental characteristics of the upper pole region might be more conducive to tumorigenesis and progression [ 19 ]. Finally, the accessibility of upper-pole nodules during ultrasonography and FNA procedures might also influence detection and diagnostic sensitivity, indirectly contributing to a higher observed malignancy rate. In summary, our findings indicate that nodule location, particularly within the upper pole, should be considered an important factor influencing the malignant risk of thyroid nodules. In clinical practice, incorporating nodule location into risk assessment models can provide valuable reference for cases with equivocal imaging boundaries or indeterminate fine-needle aspiration results. Nevertheless, the current body of literature on the relationship between nodule location and malignancy remains limited, and the above conclusions warrant further validation through multi-center, large-sample prospective studies [ 3 , 20 ]. Fine-needle aspiration (FNA) cytology, serving as a key reference in the preoperative evaluation of thyroid nodules, had its diagnostic performance re-validated in this study. When analyzed using the conventional clinical grouping method (categories I–III classified as benign, IV–VI as malignant), FNA demonstrated a sensitivity of 71.4% and a specificity of 54.7%. This indicates a certain advantage in detecting malignant nodules but insufficient accuracy in identifying benign ones, with a negative predictive value of only 43.3%. These findings are largely consistent with previous reports stating that approximately 30% of FNA cases in clinical practice yield indeterminate diagnoses [ 3 ].Further ROC analysis revealed that the optimal diagnostic cut-off value for FNA in this study was 2.5, meaning that considering Bethesda category III nodules as "suspicious/malignant" could yield improved diagnostic performance. Under this criterion, the sensitivity of FNA increased to 78.2%, with an AUC of 0.701, suggesting that the malignancy risk of Category III nodules in our study population was significantly higher than expected. If these nodules were still categorized as "benign," some malignant cases might be missed. This finding highlights the need for more cautious clinical management of Bethesda III nodules, recommending comprehensive assessment incorporating high-risk ultrasound features and other clinical factors rather than relying solely on cytological results. It is noteworthy that when FNA was integrated with key ultrasound features and nodule location into a multivariate logistic regression model, the resulting combined diagnostic model demonstrated a significant improvement in diagnostic performance. The model achieved an area under the ROC curve of 0.867, with a sensitivity of 87.2% and a specificity of 77.4%, outperforming both FNA alone (AUC = 0.701) and ultrasound features alone (AUC = 0.803). The inclusion of nodule location as an additional variable contributed notably to enhancing the model's specificity, allowing the combined model to maintain high malignant detection rates while reducing the over-diagnosis of benign nodules.From a clinical perspective, the findings of this study can inform decision-making in differentiating benign and malignant thyroid nodules. First, for nodules exhibiting multiple high-risk ultrasound features, located in the upper pole, and with FNA results indicating suspicion or malignancy, the model's high probability output can further substantiate the rationale for surgical intervention. Conversely, for nodules with atypical ultrasound features and FNA results leaning toward low risk but situated in the upper pole, the model can highlight their potential risk, thereby avoiding under-diagnosis. Second, the combined model can provide supplementary evidence for cases classified as Bethesda III or those with indeterminate results. Sole reliance on cytology might lead to a recommendation for active surveillance, whereas a high-risk prediction from the combined model could prompt more proactive management strategies, such as repeat FNA, molecular testing, or early surgery. Finally, this model can serve as a quantitative tool in multidisciplinary team (MDT) discussions, providing a consistent risk assessment framework among sonographers, pathologists, and surgeons, thereby minimizing subjective discrepancies and enhancing decision-making consistency. Certainly, this study has several limitations. First, as a single-center retrospective analysis with a limited sample size and a low number of rare subtypes such as follicular carcinoma, the statistical power and generalizability of the findings may be constrained. Second, although a strong association was identified between nodule location and malignancy risk, its relationship with tumor molecular characteristics and patient prognosis remains unclear. Finally, the combined diagnostic model was developed primarily based on imaging and cytological parameters and did not incorporate emerging approaches such as serological biomarkers, molecular pathology, or artificial intelligence-based image analysis. Thus, its clinical utility requires further validation. Future research should adopt a multi-center, large-sample prospective design and integrate molecular diagnostics with artificial intelligence technologies to develop more accurate and comprehensive predictive models, thereby providing a more robust evidence-based foundation for risk stratification and personalized management of thyroid nodules. In summary, the preoperative diagnosis of thyroid nodules has evolved from reliance on a single modality such as ultrasonography or fine-needle aspiration toward a comprehensive assessment system integrating multimodal and multiparameter data. Conventional ultrasonography and its extended techniques (such as contrast-enhanced US and elastography) play a central role in nodule risk stratification, while FNA remains a crucial method for confirming benign or malignant nature, despite its limitations in indeterminate nodules. This study proposes a novel strategy of a combined model, offering a fresh perspective for the preoperative differential diagnosis of thyroid nodules. Its clinical significance lies in optimizing risk stratification, reducing missed diagnoses and misdiagnoses, and providing a more reliable evidence-based foundation for formulating individualized treatment plans. Declarations Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of .Tongling People's Hospital Informed consent was obtained from all participants. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Authors' information Not applicable. Funding This work was supported by the Natural Science Foundation of Scientific Research Project of Universities in Anhui Province(No:2024AH051910). Author Contribution Y.T. and H.W. (Haiyi Wang) designed the study. Y.T., K.Z. and Z.W. collected and analyzed the data. H.W. (Honghong Wu) and H.W. (Haiyi Wang) interpreted the results. Y.T. drafted the manuscript. H.W. (Haiyi Wang) critically revised the manuscript. All authors read and approved the final manuscript. Acknowledgements Not applicable. Data Availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. References Xi NM, Wang L, Yang C. Improving the diagnosis of thyroid cancer by machine learning and clinical data. Sci Rep. 2022;12(1):11143. Chinese Practical Surgery Journal Editorial Board. Thyroid cancer diagnosis and treatment guidelines (2022 edition). Chin J Pract Surg. 2022;42(12):1343–1357 + 1363. [In Chinese] Baradaranfar M, Zand V, Meybodian M, Baradaranfar A, Edraki MR, Mohamadzadeh Z. Investigating the possible association between thyroid nodule location and the malignancy risk of the nodules in FNA samples. Am J Otolaryngol. 2022;43(5):103589. Asakly M, Farhat R, El Khatib N, Alrajraisi M, Alhaj M. Ultrasound-guided fine needle aspiration of deep thyroid nodule: is there a correlation between the nodule's depth and nondiagnostic results? J Thyroid Res. 2022;2022:8212636. Li Z, Wang K, Qu J, Li Q, Li F. Identification of novel biomarkers for malignant thyroid nodules: a preliminary study based on ultrasound omics. Ann Biomed Eng. 2025;53(5):1192–1198. Campennì A, Ruggeri RM, Siracusa M, Baldari S, Tuccari G, Ieni A, et al. Isthmus topography is a risk factor for persistent disease in patients with differentiated thyroid cancer. Eur J Endocrinol. 2021;185(3):397–404. Jasim S, Golding A, Bimston D, Al-Hilli Z, Gargya A, Rist P, et al. Cytologic and molecular assessment of isthmus thyroid nodules and carcinomas. Thyroid. 2025;35(3):255–264. Wang GJ, Nie F, Wang YF, Lu XH, Zhang YJ. Auxiliary value of thyroid micronodule location in differentiating benign and malignant nodules. Chin J Med Ultrasound (Electron Ed). 2021;18(1):25–29. [In Chinese] Chinese Journal of Medical Ultrasound Editorial Board. Expert consensus on several common clinical issues in thyroid and related cervical lymph node ultrasound (2018 edition). Chin J Med Ultrasound (Electron Ed). 2019;35(3):193–204. [In Chinese] Zhu Q, Liu J, Hu J, Wang Y, Li X, Zhang L, et al. The epidemiological landscape of thyroid cancer and estimates of overdiagnosis in China: a population-based study. Thyroid. 2025;35(3):307–320. Li M, Dal Maso L, Pizzato M, Francischi S, Guzzinati S, Rugge M, et al. Evolving epidemiological patterns of thyroid cancer and estimates of overdiagnosis in 2013-17 in 63 countries worldwide: a population-based study. Lancet Diabetes Endocrinol. 2024;12(11):824–836. Al-Ghanimi IA, Al-Sharydah AM, Al-Mulhim S, Al-Suliman A, Al-Abdulwahhab A. Diagnostic accuracy of ultrasonography in classifying thyroid nodules compared with fine-needle aspiration. Saudi J Med Med Sci. 2020;8(1):25–31. Huang L, Xu Y, Wang S, Li W, Chen Z. SRT: Swin-residual transformer for benign and malignant nodules classification in thyroid ultrasound images. Med Eng Phys. 2024;124:104101. Li YH, Zhang K. Comparative analysis of clinical characteristics between benign and malignant thyroid nodules. China Mod Dr. 2023;61(36):68–71 + 77. [In Chinese] Zhang F, Oluwo O, Castillo FB, Farrell C, Ojo S, Sheth P, et al. Thyroid nodule location on ultrasonography as a predictor of malignancy. Endocr Pract. 2019;25(2):131–137. Ramundo V, Lamartina L, Falcone R, Ciotti L, Lomonaco C, Biffoni M, et al. Is thyroid nodule location associated with malignancy risk? Ultrasonography. 2019;38(3):231–235. Raposo L, Freitas C, Martins R, Xavier C, Soares P, Sobrinho-Simões M, et al. Malignancy risk of thyroid nodules: quality assessment of the thyroid ultrasound report. BMC Med Imaging. 2022;22(1):61. Yu K, Wu X, Dai L, Wang Y, Li H. Risk factors associated with lymph node metastasis in papillary thyroid cancer: a retrospective analysis based on 2,428 cases. Front Oncol. 2024;14:1473858 Xia Y, Fu Y, Qian M, Chen S, Wang L. Risk factors of recurrent thyroid nodules after radiofrequency ablation. Afr Health Sci. 2023;23(3):584–592. Ageeli RS, Mossery RA, Othathi RJ, Hakami AM, Moafa MN, Alhazmi AH. The importance of the thyroid nodule location in determining the risk of malignancy: a retrospective study. Cureus. 2022;14(9):e29421. 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7975029","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":557775205,"identity":"08e74d24-89c2-4145-99fb-dc3eee3753a8","order_by":0,"name":"Yuanguang Tian","email":"","orcid":"","institution":"Bengbu Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuanguang","middleName":"","lastName":"Tian","suffix":""},{"id":557775206,"identity":"3c752ade-f95d-4e18-9dfa-88f895b067c6","order_by":1,"name":"Kaikai Zhai","email":"","orcid":"","institution":"Bengbu Medical University","correspondingAuthor":false,"prefix":"","firstName":"Kaikai","middleName":"","lastName":"Zhai","suffix":""},{"id":557775207,"identity":"e2f61655-ca05-495c-ad27-875df481f5f0","order_by":2,"name":"Honghong Wu","email":"","orcid":"","institution":"Wannan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Honghong","middleName":"","lastName":"Wu","suffix":""},{"id":557775210,"identity":"adc02743-b9f0-4dae-b6a3-06f4d4f0d611","order_by":3,"name":"Zheng Wang","email":"","orcid":"","institution":"Bengbu Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zheng","middleName":"","lastName":"Wang","suffix":""},{"id":557775211,"identity":"c10a2c59-fb6b-4e7b-ae7d-bb204af5ab63","order_by":4,"name":"Haiyi Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYBACNvnDB4z//vvPzE+0Fj4JtoQCHjZmdskGYrXISfAYfABq4Tc4QLTDpNsSN0jwsEkbH0/ewPCjYhsRWmQOHzYwkOAxNjvzrICx58xtIrQwpKUZJBhIJJvdyDFgZmwjSkuO+Y8DCQb1m2cQrUUix8Cw4UACs4EE0Vp4jiUYMzYcYJYA+uUgUX6Rb28+ANbC35688cGPCiK0IIEE4qMGoYVUHaNgFIyCUTBCAADM/TjLS77SNAAAAABJRU5ErkJggg==","orcid":"","institution":"Bengbu Medical University","correspondingAuthor":true,"prefix":"","firstName":"Haiyi","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-10-29 03:45:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7975029/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7975029/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98430456,"identity":"830435be-303b-45ac-b6b2-e870dd9ebd9e","added_by":"auto","created_at":"2025-12-17 16:45:29","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":44519,"visible":true,"origin":"","legend":"","description":"","filename":"Article.docx","url":"https://assets-eu.researchsquare.com/files/rs-7975029/v1/8a96ab1b3c571513217262a2.docx"},{"id":98098596,"identity":"8d4d029a-5321-4adc-8e0a-4071df339031","added_by":"auto","created_at":"2025-12-12 19:44:00","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6496,"visible":true,"origin":"","legend":"","description":"","filename":"f06d5ad29d714d848228c59b2436f290.json","url":"https://assets-eu.researchsquare.com/files/rs-7975029/v1/a8a10257f64345db0dcb4b95.json"},{"id":98429489,"identity":"af5940e6-b5de-4af2-a98e-7a52aef9273f","added_by":"auto","created_at":"2025-12-17 16:43:33","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":90661,"visible":true,"origin":"","legend":"","description":"","filename":"f06d5ad29d714d848228c59b2436f2901enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7975029/v1/2c5e70b112073b69d64a8fbe.xml"},{"id":98429765,"identity":"a1b2e0e6-fd83-4985-a01f-5d2ebad62d32","added_by":"auto","created_at":"2025-12-17 16:44:07","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":90758,"visible":true,"origin":"","legend":"","description":"","filename":"f06d5ad29d714d848228c59b2436f2901structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7975029/v1/8634723b4fab1186f9c2eb59.xml"},{"id":98429701,"identity":"d3fc8e0d-26d3-4e8e-bca5-32a247a017b1","added_by":"auto","created_at":"2025-12-17 16:44:02","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":96666,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7975029/v1/fdc26cf437077703de0618ad.html"},{"id":105563716,"identity":"6595e400-e54e-4ba2-a79a-26543310c482","added_by":"auto","created_at":"2026-03-27 12:47:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1090860,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7975029/v1/cd4e605b-60da-44ad-91d6-3c6563430262.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Key Ultrasonographic Features, Fine-Needle Aspiration, and Nodule Location in the Preoperative Differentiation of Benign and Malignant Thyroid Nodules: A Retrospective Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThyroid cancer, the most common endocrine malignancy, has a preoperative diagnostic accuracy that directly influences both surgical planning and patient acceptance of the proposed procedure[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Thyroid nodules are a common clinical finding, with an ultrasound detection rate as high as 70%, yet only 5\u0026ndash;10% are malignant[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Ultrasound-guided fine-needle aspiration (US-FNA) is currently regarded as the reference standard for preoperative assessment of thyroid nodules; however, approximately 30% of cases still fail to yield a definitive diagnosis[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Studies have indicated that clinical factors such as nodule location, ultrasound features, and patient age are all associated with the risk of malignancy.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This study conducted a retrospective analysis of clinical data from 186 patients with thyroid nodules to systematically evaluate the independent predictive value of nodule location, ultrasonographic features, and clinical indicators, and to develop a combined diagnostic model based on key ultrasound features, fine-needle aspiration cytology, and nodule location.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eBaseline Characteristics\u003c/h2\u003e\u003cp\u003e With the approval of our institutional ethics committee, clinical data were collected from 186 eligible patients with thyroid nodules who presented to Tongling People's Hospital between January 2019 and May 2025. The cohort comprised 47 males and 139 females (male-to-female ratio 1:2.90), aged 18\u0026ndash;76 years (mean age 47 years). The lesions were located in the left lobe in 85 cases and the right lobe in 101 cases.\u003c/p\u003e\u003cp\u003eInclusion Criteria:\u003c/p\u003e\u003cp\u003e(1)Underwent a preoperative ultrasound examination.\u003c/p\u003e\u003cp\u003e(2)Received a preoperative fine-needle aspiration (FNA) biopsy.\u003c/p\u003e\u003cp\u003e(3)Underwent thyroidectomy with a definitive postoperative histopathological (paraffin section) diagnosis.\u003c/p\u003e\u003cp\u003e(4)Had concordant results confirming that the preoperative ultrasound, FNA, and postoperative pathological assessments all pertained to the same nodule.\u003c/p\u003e\u003cp\u003eExclusion Criteria:\u003c/p\u003e\u003cp\u003e(1)A prior history of thyroid surgery or ablation therapy.\u003c/p\u003e\u003cp\u003e(2)Nodules located in the thyroid isthmus. Existing data indicate that isthmic nodules differ fundamentally from lobar nodules in their molecular profile and clinical behavior, constituting a high-risk subgroup in terms of malignancy potential, molecular features, and clinical outcomes, which necessitates a specialized diagnostic and therapeutic approach [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].Excluding isthmus cases helps minimize confounding factors.\u003c/p\u003e\u003cp\u003e(3)A pathological diagnosis of metastatic carcinoma or lymphoma.\u003c/p\u003e\u003cp\u003e(4)Failure to complete any one of the following: preoperative ultrasound, FNA, or postoperative histopathological examination.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eResearch Methods\u003c/h3\u003e\n\u003cp\u003eUltrasonography:Examinations were performed using an ultrasound system equipped with a high-frequency linear-array transducer. Patients were placed in the supine position with a pillow under the shoulders to allow moderate neck extension, thereby optimizing exposure of the thyroid region. The transducer was then applied to the anterior neck to systematically acquire two-dimensional grayscale images and color Doppler flow imaging data of the thyroid. The following nodule characteristics were evaluated and recorded: composition, location, aspect ratio, margins, internal echogenicity, and the presence or absence of calcifications. Nodule location was determined in the longitudinal view by dividing the thyroid lobe into thirds, classifying the location as upper, middle, or lower pole[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. When a nodule spanned two or more segments, its location was determined by its predominant portion or center. The ultrasound images were independently reviewed by two physicians, each holding an associate chief physician title or higher with at least five years of experience in thyroid ultrasonography. Any discrepant interpretations were resolved through a consensus reading.\u003c/p\u003e\u003cp\u003eFine-Needle Aspiration (FNA) Procedure:The FNA procedure was performed with the patient in a reverse Trendelenburg position, with a pillow placed under the shoulders to achieve neck hyperextension for optimal exposure of the surgical field. Following routine skin disinfection with povidone-iodine and draping with sterile towels, local infiltration anesthesia was administered at the intended puncture site using 2% lidocaine. Sterile ultrasound gel was applied to the skin surface. Under real-time ultrasound guidance, a disposable 10 ml syringe equipped with a puncture needle was percutaneously inserted. The needle advancement was halted immediately upon visualization of the needle tip reaching the anterior margin of the target nodule. Subsequently, rapid back-and-forth movements were performed along the long axis of the nodule for 3\u0026ndash;6 seconds under negative pressure suction to obtain cytological specimens. This process was repeated 2\u0026ndash;3 times per nodule until satisfactory smears were acquired. All obtained specimens were immediately fixed and sent for pathological examination.\u003c/p\u003e\u003cp\u003eCytological diagnoses were rendered according to The Bethesda System for Reporting Thyroid Cytopathology (TBSRTC). All FNA smears were independently and blindly interpreted by two pathologists, each holding the title of associate chief physician or higher and possessing at least five years of specialized experience in thyroid cytology. Any discrepant diagnoses were resolved through a joint review to establish a final consensus.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed using SPSS software (version 26.0; IBM Corp., Armonk, NY, USA). Continuous data conforming to a normal distribution are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (x̄ \u0026plusmn; s) and were compared between groups using the independent samples t-test. Categorical data are expressed as numbers and percentages and were compared using the Chi-square test or Fisher's exact test, as appropriate. Binary logistic regression analysis was employed to identify independent predictors of malignancy and to construct a combined diagnostic model. The diagnostic performance of individual parameters and the combined model was evaluated by receiver operating\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eParaffin pathology findings\u003c/h2\u003e\u003cp\u003eAmong the 186 nodules, 53 (28.5%) were benign, including 38 cases of nodular goiter, 7 adenoma, and 8 thyroiditis; 133 (71.5%) were malignant, comprising 130 papillary thyroid carcinomas and 3 follicular carcinomas.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eUnivariate Analysis of the Association Between Clinical Indicators and Benign/Malignant Thyroid Nodules\u003c/h2\u003e\u003cp\u003eComparative analysis between groups revealed that patient age\u0026thinsp;\u0026ge;\u0026thinsp;45 years, and the following ultrasound features: hypoechogenicity, calcification, solid composition, aspect ratio\u0026thinsp;\u0026ge;\u0026thinsp;1, ill-defined margins, and nodule location in the longitudinal view, were significantly associated with malignant thyroid nodules (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In contrast, no statistically significant differences were found between benign and malignant nodules in terms of patient sex, involved thyroid lobe, or nodule number (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Notably, univariate analysis demonstrated that the malignancy risk of upper-pole nodules was significantly higher than that of middle- and lower-pole nodules (χ\u0026sup2; = 14.005, P\u0026thinsp;=\u0026thinsp;0.001), suggesting that nodule location may be an important factor influencing the development of thyroid cancer. See Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for details.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate Analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMalignant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBenign\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.371\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36(76.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11(23.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e97(69.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42(30.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLobe laterality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.519\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.218\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeft lobe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e57(67.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28(32.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRight lobe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e76(75.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25(24.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.637\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;45 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e76(65.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e41(35.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;45 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e57(82.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12(17.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of nodules\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.283\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultiple\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e57(69.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25(30.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e76(73.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28(26.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLongitudinal location\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUpper pole\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e46(88.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6(11.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle pole\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e46(73.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17(27.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLower pole\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41(57.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30(42.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMargin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23.227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIll-defined\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e87(86.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14(13.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClear\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e46(54.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e39(45.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14.369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e103(79.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26(20.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbsent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30(52.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27(47.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEchogenicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e27.183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypoechoic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e125(78.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e34(21.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-hypoechoic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8(29.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19(70.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComposition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e33.166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSolid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e132(77.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38(22.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCystic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1(6.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15(93.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAspect ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e22.643\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e65(91.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6(8.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e68(59.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e47(40.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. \u003cb\u003eUnivariate Analysis\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMultivariate Analysis of the Relationship Between Clinical Factors and Thyroid Nodule Pathology\u003c/h3\u003e\n\u003cp\u003eWhile conventional ultrasound features such as composition, echogenicity, shape, margins, and echogenic foci have been incorporated into the TI-RADS classification system by various guidelines[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], the relationship between nodule location and malignancy has been less extensively studied. Since univariate analysis revealed a significantly higher malignancy proportion in upper-pole nodules (P\u0026thinsp;=\u0026thinsp;0.001; see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), we therefore focused our analysis on nodule location in the longitudinal view of the gland. To facilitate the model analysis, location was dichotomized into \"upper pole\" versus \"non-upper pole\" (combining the middle and lower poles). This categorization was justified because previous studies suggest a potentially higher malignancy risk for upper-pole nodules, whereas middle and lower poles demonstrate minimal differences in sonographic appearance and risk profile. Combining the latter two categories also ensured balanced group sizes for robust statistical power.\u003c/p\u003e\u003cp\u003eConsequently, the ultrasound features that showed statistically significant differences in the univariate analysis\u0026mdash;namely, hypoechogenicity, calcification, solid composition, aspect ratio\u0026thinsp;\u0026ge;\u0026thinsp;1, and ill-defined margins\u0026mdash;were included as independent variables. Using postoperative histopathological (paraffin section) results as the dependent variable, a stepwise method was employed to select variables for inclusion in the binary logistic regression model. Ultimately, three independent variables were retained in the final model: solid composition, aspect ratio\u0026thinsp;\u0026ge;\u0026thinsp;1, and the presence of calcification. These were identified as independent risk factors for malignant thyroid nodules (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariate Analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWald χ\u0026sup2;\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eExp(B)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSolid composition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.643\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.862\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14.050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(1.654-119.335)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAspect ratio\u0026thinsp;\u0026ge;\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.442\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.508\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.229\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(1.563\u0026ndash;11.440)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.989\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.482\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.689\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(1.162\u0026ndash;6.220)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. \u003cb\u003eMultivariate Analysis\u003c/b\u003e\u003c/p\u003e\n\u003ch3\u003eCytological Results of Fine-Needle Aspiration in Diagnosing Thyroid Nodules\u003c/h3\u003e\n\u003cp\u003eCytological classification based on the Bethesda system:\u003c/p\u003e\u003cp\u003eCategory I (n\u0026thinsp;=\u0026thinsp;28, nondiagnostic), II (n\u0026thinsp;=\u0026thinsp;29, benign), III (n\u0026thinsp;=\u0026thinsp;10, atypia/follicular lesion of undetermined significance), IV (n\u0026thinsp;=\u0026thinsp;13, follicular neoplasm or suspicious for follicular neoplasm), V (n\u0026thinsp;=\u0026thinsp;66, suspicious for malignancy), VI (n\u0026thinsp;=\u0026thinsp;40, malignant).\u003c/p\u003e\u003cp\u003eWhen Categories I\u0026ndash;III were considered benign and IV\u0026ndash;VI malignant, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 71.4%, 54.7%, 79.8%, and 43.3%, respectively.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDevelopment of a Diagnostic Prediction Model Integrating Key Ultrasound Features, Fine-Needle Aspiration, and Nodule Location and Its ROC Curve Analysis for Differentiating Benign and Malignant Thyroid Nodules\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBinary logistic regression incorporating solid composition (X₁), aspect ratio (X₂), calcification (X₃), nodule location (X₄), and FNA category (X₅) produced the following equation:\u003c/p\u003e\u003cp\u003e\u003cb\u003eLogit(p) = -5.328\u0026thinsp;+\u0026thinsp;3.380X₁ + 1.554X₂ + 1.154X₃ + 1.239X₄ + 0.463X₅\u003c/b\u003e as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLogistic regression model for combined diagnosis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWaldχ\u0026sup2;\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP值\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eExp(B)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eOR(95%CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-5.328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eX1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.380\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.094\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.544\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e29.377\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(3.441-250.831)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eX2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.554\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.512\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.731\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(1.733\u0026ndash;12.914)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eX3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.444\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(1.329\u0026ndash;7.564)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eX4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.561\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.453\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(1.150-10.371)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eX5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.463\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.487\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.588\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(1.252\u0026ndash;2.015)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: X1: Nodule composition (cystic\u0026thinsp;=\u0026thinsp;0, solid\u0026thinsp;=\u0026thinsp;1); X2: Aspect ratio (\u0026lt;\u0026thinsp;1\u0026thinsp;=\u0026thinsp;0, \u0026ge;\u0026thinsp;1\u0026thinsp;=\u0026thinsp;1); X3: Calcification (absent\u0026thinsp;=\u0026thinsp;0, present\u0026thinsp;=\u0026thinsp;1); X4: Nodule location on longitudinal view (non-upper pole\u0026thinsp;=\u0026thinsp;0, upper pole\u0026thinsp;=\u0026thinsp;1); X5: Fine-needle aspiration cytology classification (Bethesda category: I-VI\u0026thinsp;=\u0026thinsp;1\u0026ndash;6).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. \u003cb\u003eLogistic regression model for combined diagnosis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBased on the established model, the maximum Youden's index and its corresponding Logit(p) cut-off value were calculated for each diagnostic method. The combined diagnostic model achieved the largest area under the ROC curve (AUC\u0026thinsp;=\u0026thinsp;0.867). Using this optimal cut-off value to determine the combined diagnosis and comparing it with the postoperative histopathological (paraffin section) results yielded a sensitivity of 87.2% (116/133), specificity of 77.4% (41/53), positive predictive value (PPV) of 90.6% (116/128), and negative predictive value (NPV) of 70.7% (41/58).The AUC for the key ultrasound features alone was 0.803. Diagnosis based on its cut-off value resulted in a sensitivity of 90.2% (120/133), specificity of 56.6% (30/53), PPV of 83.9% (120/143), and NPV of 69.8% (30/43).For fine-needle aspiration cytology (AUC\u0026thinsp;=\u0026thinsp;0.701), application of its cut-off value produced a sensitivity of 78.2% (104/133), specificity of 52.8% (28/53), PPV of 80.6% (104/129), and NPV of 49.1% (28/57).The detailed results are presented in Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eROC analysis of diagnostic methods\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMethod\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAUC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYouden Index\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCutoff\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNodule location\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.616\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.701\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKey US features\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.803\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.468\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.614\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCombined model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.646\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.610\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of diagnostic performance among methods\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePathological results\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKey US features\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFNA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCombined model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBenign Malignant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBenign Malignant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBenign Malignant\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBenign\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30 23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28 25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41 12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMalignant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13 120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29 104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17 116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e133\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43 143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57 129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58 128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e186\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSensitivity\u003c/b\u003e (% )\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90.2%(120/133)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78.2%(104/133)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87.2%(116/133)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSpecificity\u003c/b\u003e (% )\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56.6%(30/53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52.8%(28/53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e77.4%(41/53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePPV\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83.9%(120/143)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80.6%(104/129)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e90.6%(116/128)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNPV\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69.8%(30/43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49.1%(28/57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70.7%(41/58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. \u003cb\u003eROC analysis of diagnostic methods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. \u003cb\u003eComparison of diagnostic performance among methods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eROC curve analysis revealed that the areas under the curve (AUCs) for distinguishing benign from malignant thyroid nodules were 0.616 for nodule location, 0.701 for fine-needle aspiration (FNA) cytology, 0.803 for key ultrasound features, and 0.867 for the combined diagnostic prediction model. Although the diagnostic performance of nodule location alone was limited, the model's overall efficacy was significantly enhanced when location was integrated with key ultrasound features and FNA (AUC\u0026thinsp;=\u0026thinsp;0.867, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), yielding sensitivity and specificity superior to any single method. This suggests that while nodule location is not the strongest standalone predictor, it serves as a crucial complementary factor that boosts the diagnostic performance of the overall model, indicating high potential for clinical application. The differences in AUC between the combined model and nodule location (Z=-7.449, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), FNA cytology (Z=-4.236, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and key ultrasound features (Z=-2.543, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOver recent years, the global incidence of thyroid cancer has shown a persistent upward trend, and it has become one of the most common malignancies among women in China[10.11]. Given the high detection rate of thyroid nodules, the decision regarding surgical intervention relies on preoperative auxiliary diagnostics to guide clinical decision-making. Within this context, the precise preoperative differentiation between benign and malignant thyroid nodules has emerged as a pressing clinical issue. This study retrospectively analyzed 186 thyroid nodule cases, systematically integrating their clinical parameters for statistical analysis. The results identified typical high-risk sonographic features\u0026mdash;hypoechogenicity, calcification, solid composition, aspect ratio\u0026thinsp;\u0026ge;\u0026thinsp;1, and ill-defined margins\u0026mdash;which align closely with recent international Thyroid Imaging Reporting and Data System (TI-RADS) criteria and numerous related studies [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], indicating their strong clinical generalizability and predictive value.\u003c/p\u003e\u003cp\u003eFurthermore, this study revealed that nodule location in the upper pole of the thyroid lobe significantly increased the risk of malignancy. Our results demonstrated that among the 186 patients, those with upper-pole nodules had a substantially higher malignancy rate of 88.5%, which was significantly greater than that of middle-pole (73.0%) and lower-pole nodules (57.7%), with a statistically significant difference (P\u0026thinsp;=\u0026thinsp;0.001). After incorporating this factor into the logistic regression analysis, nodule location (upper pole vs. non-upper pole) remained an independent risk factor for malignant thyroid nodules (OR\u0026thinsp;=\u0026thinsp;3.453, 95% CI: 1.150\u0026ndash;10.371, P\u0026thinsp;=\u0026thinsp;0.027), indicating a stable correlation between upper-pole location and malignant risk.This finding complements the traditional TI-RADS system, which is primarily based on ultrasound characteristics. It suggests that, in addition to conventional features such as composition, echogenicity, calcification, aspect ratio, and margins, the specific location of a nodule can also serve as an important reference for preoperative risk assessment.However, the relationship between nodule location and malignant risk remains somewhat controversial in the existing literature. Some scholars have reported a higher malignancy risk for upper-pole nodules [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], while other studies found no significant correlation between location and malignancy [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Some reports even suggested that lower-pole or isthmus nodules might be more prone to malignancy in certain populations [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].In this study, we employed a \"upper pole vs. non-upper pole\" dichotomization for analysis. The results confirmed a significantly higher malignancy rate in upper-pole nodules compared to non-upper-pole nodules. This finding aligns with studies supporting high risk in the upper pole but contradicts those observing no such difference. The discrepancies might be attributed to several factors: variations in sample size and population characteristics across studies affecting statistical power, and the lack of uniformity in the classification of nodule location. Our use of a concise \"upper pole vs. non-upper pole\" classification helps highlight the clinical utility of this factor.From a mechanistic perspective, the upper pole is adjacent to the entry point of the recurrent laryngeal nerve and may have differences in embryological origin [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Additionally, upper-pole nodules are often less detectable in routine physical examinations, potentially leading to delayed diagnosis [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Moreover, the blood supply and microenvironmental characteristics of the upper pole region might be more conducive to tumorigenesis and progression [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Finally, the accessibility of upper-pole nodules during ultrasonography and FNA procedures might also influence detection and diagnostic sensitivity, indirectly contributing to a higher observed malignancy rate.\u003c/p\u003e\u003cp\u003eIn summary, our findings indicate that nodule location, particularly within the upper pole, should be considered an important factor influencing the malignant risk of thyroid nodules. In clinical practice, incorporating nodule location into risk assessment models can provide valuable reference for cases with equivocal imaging boundaries or indeterminate fine-needle aspiration results. Nevertheless, the current body of literature on the relationship between nodule location and malignancy remains limited, and the above conclusions warrant further validation through multi-center, large-sample prospective studies [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFine-needle aspiration (FNA) cytology, serving as a key reference in the preoperative evaluation of thyroid nodules, had its diagnostic performance re-validated in this study. When analyzed using the conventional clinical grouping method (categories I\u0026ndash;III classified as benign, IV\u0026ndash;VI as malignant), FNA demonstrated a sensitivity of 71.4% and a specificity of 54.7%. This indicates a certain advantage in detecting malignant nodules but insufficient accuracy in identifying benign ones, with a negative predictive value of only 43.3%. These findings are largely consistent with previous reports stating that approximately 30% of FNA cases in clinical practice yield indeterminate diagnoses [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].Further ROC analysis revealed that the optimal diagnostic cut-off value for FNA in this study was 2.5, meaning that considering Bethesda category III nodules as \"suspicious/malignant\" could yield improved diagnostic performance. Under this criterion, the sensitivity of FNA increased to 78.2%, with an AUC of 0.701, suggesting that the malignancy risk of Category III nodules in our study population was significantly higher than expected. If these nodules were still categorized as \"benign,\" some malignant cases might be missed. This finding highlights the need for more cautious clinical management of Bethesda III nodules, recommending comprehensive assessment incorporating high-risk ultrasound features and other clinical factors rather than relying solely on cytological results.\u003c/p\u003e\u003cp\u003eIt is noteworthy that when FNA was integrated with key ultrasound features and nodule location into a multivariate logistic regression model, the resulting combined diagnostic model demonstrated a significant improvement in diagnostic performance. The model achieved an area under the ROC curve of 0.867, with a sensitivity of 87.2% and a specificity of 77.4%, outperforming both FNA alone (AUC\u0026thinsp;=\u0026thinsp;0.701) and ultrasound features alone (AUC\u0026thinsp;=\u0026thinsp;0.803). The inclusion of nodule location as an additional variable contributed notably to enhancing the model's specificity, allowing the combined model to maintain high malignant detection rates while reducing the over-diagnosis of benign nodules.From a clinical perspective, the findings of this study can inform decision-making in differentiating benign and malignant thyroid nodules. First, for nodules exhibiting multiple high-risk ultrasound features, located in the upper pole, and with FNA results indicating suspicion or malignancy, the model's high probability output can further substantiate the rationale for surgical intervention. Conversely, for nodules with atypical ultrasound features and FNA results leaning toward low risk but situated in the upper pole, the model can highlight their potential risk, thereby avoiding under-diagnosis. Second, the combined model can provide supplementary evidence for cases classified as Bethesda III or those with indeterminate results. Sole reliance on cytology might lead to a recommendation for active surveillance, whereas a high-risk prediction from the combined model could prompt more proactive management strategies, such as repeat FNA, molecular testing, or early surgery. Finally, this model can serve as a quantitative tool in multidisciplinary team (MDT) discussions, providing a consistent risk assessment framework among sonographers, pathologists, and surgeons, thereby minimizing subjective discrepancies and enhancing decision-making consistency.\u003c/p\u003e\u003cp\u003eCertainly, this study has several limitations. First, as a single-center retrospective analysis with a limited sample size and a low number of rare subtypes such as follicular carcinoma, the statistical power and generalizability of the findings may be constrained. Second, although a strong association was identified between nodule location and malignancy risk, its relationship with tumor molecular characteristics and patient prognosis remains unclear. Finally, the combined diagnostic model was developed primarily based on imaging and cytological parameters and did not incorporate emerging approaches such as serological biomarkers, molecular pathology, or artificial intelligence-based image analysis. Thus, its clinical utility requires further validation. Future research should adopt a multi-center, large-sample prospective design and integrate molecular diagnostics with artificial intelligence technologies to develop more accurate and comprehensive predictive models, thereby providing a more robust evidence-based foundation for risk stratification and personalized management of thyroid nodules.\u003c/p\u003e\u003cp\u003eIn summary, the preoperative diagnosis of thyroid nodules has evolved from reliance on a single modality such as ultrasonography or fine-needle aspiration toward a comprehensive assessment system integrating multimodal and multiparameter data. Conventional ultrasonography and its extended techniques (such as contrast-enhanced US and elastography) play a central role in nodule risk stratification, while FNA remains a crucial method for confirming benign or malignant nature, despite its limitations in indeterminate nodules. This study proposes a novel strategy of a combined model, offering a fresh perspective for the preoperative differential diagnosis of thyroid nodules. Its clinical significance lies in optimizing risk stratification, reducing missed diagnoses and misdiagnoses, and providing a more reliable evidence-based foundation for formulating individualized treatment plans.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003e This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of .Tongling People's Hospital Informed consent was obtained from all participants.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eAuthors' information\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was supported by the Natural Science Foundation of Scientific Research Project of Universities in Anhui Province(No:2024AH051910).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eY.T. and H.W. (Haiyi Wang) designed the study. Y.T., K.Z. and Z.W. collected and analyzed the data. H.W. (Honghong Wu) and H.W. (Haiyi Wang) interpreted the results. Y.T. drafted the manuscript. H.W. (Haiyi Wang) critically revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eXi NM, Wang L, Yang C. Improving the diagnosis of thyroid cancer by machine learning and clinical data. Sci Rep. 2022;12(1):11143.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChinese Practical Surgery Journal Editorial Board. Thyroid cancer diagnosis and treatment guidelines (2022 edition). Chin J Pract Surg. 2022;42(12):1343\u0026ndash;1357\u0026thinsp;+\u0026thinsp;1363. [In Chinese]\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaradaranfar M, Zand V, Meybodian M, Baradaranfar A, Edraki MR, Mohamadzadeh Z. Investigating the possible association between thyroid nodule location and the malignancy risk of the nodules in FNA samples. Am J Otolaryngol. 2022;43(5):103589.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAsakly M, Farhat R, El Khatib N, Alrajraisi M, Alhaj M. Ultrasound-guided fine needle aspiration of deep thyroid nodule: is there a correlation between the nodule's depth and nondiagnostic results? J Thyroid Res. 2022;2022:8212636.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi Z, Wang K, Qu J, Li Q, Li F. Identification of novel biomarkers for malignant thyroid nodules: a preliminary study based on ultrasound omics. Ann Biomed Eng. 2025;53(5):1192\u0026ndash;1198.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCampenn\u0026igrave; A, Ruggeri RM, Siracusa M, Baldari S, Tuccari G, Ieni A, et al. Isthmus topography is a risk factor for persistent disease in patients with differentiated thyroid cancer. Eur J Endocrinol. 2021;185(3):397\u0026ndash;404.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJasim S, Golding A, Bimston D, Al-Hilli Z, Gargya A, Rist P, et al. Cytologic and molecular assessment of isthmus thyroid nodules and carcinomas. Thyroid. 2025;35(3):255\u0026ndash;264.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang GJ, Nie F, Wang YF, Lu XH, Zhang YJ. Auxiliary value of thyroid micronodule location in differentiating benign and malignant nodules. Chin J Med Ultrasound (Electron Ed). 2021;18(1):25\u0026ndash;29. [In Chinese]\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChinese Journal of Medical Ultrasound Editorial Board. Expert consensus on several common clinical issues in thyroid and related cervical lymph node ultrasound (2018 edition). Chin J Med Ultrasound (Electron Ed). 2019;35(3):193\u0026ndash;204. [In Chinese]\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu Q, Liu J, Hu J, Wang Y, Li X, Zhang L, et al. The epidemiological landscape of thyroid cancer and estimates of overdiagnosis in China: a population-based study. Thyroid. 2025;35(3):307\u0026ndash;320.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi M, Dal Maso L, Pizzato M, Francischi S, Guzzinati S, Rugge M, et al. Evolving epidemiological patterns of thyroid cancer and estimates of overdiagnosis in 2013-17 in 63 countries worldwide: a population-based study. Lancet Diabetes Endocrinol. 2024;12(11):824\u0026ndash;836.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAl-Ghanimi IA, Al-Sharydah AM, Al-Mulhim S, Al-Suliman A, Al-Abdulwahhab A. Diagnostic accuracy of ultrasonography in classifying thyroid nodules compared with fine-needle aspiration. Saudi J Med Med Sci. 2020;8(1):25\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang L, Xu Y, Wang S, Li W, Chen Z. SRT: Swin-residual transformer for benign and malignant nodules classification in thyroid ultrasound images. Med Eng Phys. 2024;124:104101.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi YH, Zhang K. Comparative analysis of clinical characteristics between benign and malignant thyroid nodules. China Mod Dr. 2023;61(36):68\u0026ndash;71\u0026thinsp;+\u0026thinsp;77. [In Chinese]\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang F, Oluwo O, Castillo FB, Farrell C, Ojo S, Sheth P, et al. Thyroid nodule location on ultrasonography as a predictor of malignancy. Endocr Pract. 2019;25(2):131\u0026ndash;137.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRamundo V, Lamartina L, Falcone R, Ciotti L, Lomonaco C, Biffoni M, et al. Is thyroid nodule location associated with malignancy risk? Ultrasonography. 2019;38(3):231\u0026ndash;235.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRaposo L, Freitas C, Martins R, Xavier C, Soares P, Sobrinho-Sim\u0026otilde;es M, et al. Malignancy risk of thyroid nodules: quality assessment of the thyroid ultrasound report. BMC Med Imaging. 2022;22(1):61.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu K, Wu X, Dai L, Wang Y, Li H. Risk factors associated with lymph node metastasis in papillary thyroid cancer: a retrospective analysis based on 2,428 cases. Front Oncol. 2024;14:1473858\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXia Y, Fu Y, Qian M, Chen S, Wang L. Risk factors of recurrent thyroid nodules after radiofrequency ablation. Afr Health Sci. 2023;23(3):584\u0026ndash;592.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAgeeli RS, Mossery RA, Othathi RJ, Hakami AM, Moafa MN, Alhazmi AH. The importance of the thyroid nodule location in determining the risk of malignancy: a retrospective study. Cureus. 2022;14(9):e29421.\u003c/span\u003e\u003c/li\u003e\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":"Thyroid Nodule, Thyroid Cancer, Anatomic Location, Ultrasonography, Fine-Needle Aspiration","lastPublishedDoi":"10.21203/rs.3.rs-7975029/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7975029/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThis study aimed to explore clinical and ultrasonographic factors associated with malignant thyroid nodules and to develop a diagnostic prediction model integrating key ultrasound features, fine-needle aspiration (FNA), and nodule location for preoperative risk stratification.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe retrospectively analyzed 186 patients with thyroid nodules confirmed by surgery and pathology. Clinical data, ultrasound features, and FNA results were compared between benign and malignant groups. Variables with statistical significance in univariate analysis were entered into binary logistic regression to identify independent predictors. A combined diagnostic model was constructed and validated using receiver operating characteristic (ROC) curves.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong the 186 patients, 53 (28.5%) had benign and 133 (71.5%) malignant nodules. Solid composition, aspect ratio\u0026thinsp;\u0026ge;\u0026thinsp;1, calcification, and upper-pole location were independent risk factors for malignancy (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The combined model integrating key ultrasound features, FNA, and nodule location achieved an AUC of 0.867 with 87.2% sensitivity and 77.4% specificity, significantly outperforming any single method.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThe combined model markedly improves preoperative differentiation between benign and malignant thyroid nodules and may serve as a valuable tool for personalized management.\u003c/p\u003e","manuscriptTitle":"Key Ultrasonographic Features, Fine-Needle Aspiration, and Nodule Location in the Preoperative Differentiation of Benign and Malignant Thyroid Nodules: A Retrospective Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-12 19:43:56","doi":"10.21203/rs.3.rs-7975029/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":"8eebccfe-a808-4c0d-927a-fc6515b0b4fa","owner":[],"postedDate":"December 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-21T17:53:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-12 19:43:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7975029","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7975029","identity":"rs-7975029","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}