Clinical Significance Of Co-Existance Of Hashimoto Thyroiditis (HT) With Differentiated Thyroid Cancer (DTC)

Clinical Significance Of Co-Existance Of Hashimoto Thyroiditis (HT) With Differentiated Thyroid Cancer (DTC) | 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 Clinical Significance Of Co-Existance Of Hashimoto Thyroiditis (HT) With Differentiated Thyroid Cancer (DTC) Syed Haseeb Zia, Liu Zhao, Li Ying, Alveena Nasim Khan, Zhang Wenwen, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6709654/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background Hashimoto's Thyroiditis represents a long-term autoimmune condition which ordinarily develops in people diagnosed with Differentiated Thyroid Cancer. The connection between these disorders and how Hashimoto’s Thyroiditis affects thyroid malignancy development remains unknown. The current research examined how Hashimoto’s Thyroiditis affects tumor aggressiveness in Diffused Thyroid Cancer patients by analyzing tumor size rates and lymph node metastasis and pathological invasiveness outcomes. Methods The study examined 198 patients who underwent thyroidectomy between June 2023 and March 2025 through an observational retrospective review. The research included two discrete patient groups with and without Hashimoto’s Thyroiditis. Tumor size measurements together with nodal status reports and capsular invasion status received analysis from both groups of patients. The study used descriptive statistics together with Mann–Whitney U test along with Chi-square test to determine differences between groups. The authors built a multivariate logistic regression framework to confirm whether Hashimoto’s Thyroiditis functioned as an individual determinant of tumor aggressive behavior. Performance prediction was evaluated with receiver operating characteristic curve analysis while a nomogram functioned to create personalized risk estimates. Results Patients with Hashimoto’s Thyroiditis had significantly smaller tumors (median size: 9 mm vs. 12 mm, p = 0.003) and a lower frequency of lymph node metastasis (25.0% vs. 38.0%, p = 0.043) compared to those without thyroiditis. Multivariate analysis confirmed that Hashimoto’s Thyroiditis was associated with reduced odds of tumor aggressiveness (odds ratio = 0.68, 95% CI: 0.47–0.98, p = 0.041), independent of tumor size and nodal involvement. While the logistic model demonstrated modest discriminative ability (AUC = 0.587), calibration performance was strong (mean absolute error = 0.025). The nomogram provided an interpretable tool for individualized prediction. Conclusions The presence of Hashimoto’s Thyroiditis in patients with Differentiated Thyroid Cancer is associated with less aggressive tumor features. These findings suggest a potentially protective role of autoimmune thyroiditis and support its consideration in risk stratification and clinical management decisions. Further prospective studies are warranted to validate these observations and explore underlying immunological mechanisms. Differentiated Thyroid Cancer Hashimoto’s Thyroiditis Tumor Aggressiveness Lymph Node Metastasis Papillary Thyroid Carcinoma Retrospective Study Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background Differentiated Thyroid Cancer (DTC) is the most common endocrine malignancy and generally carries a favorable prognosis when managed with appropriate surgical and adjuvant therapy [ 1 ]. Papillary thyroid carcinoma (PTC), the predominant subtype of DTC, is characterized by a high rate of lymph node metastasis but relatively low mortality [ 2 ]. Despite its treatability, DTC can exhibit varying degrees of biological aggressiveness, making prognostic evaluation essential for optimizing clinical outcomes [ 3 ]. Hashimoto’s Thyroiditis (HT), a chronic autoimmune thyroid disorder marked by lymphocytic infiltration and gradual destruction of the thyroid gland, is one of the most frequently encountered thyroid pathologies in clinical practice. It typically presents with elevated thyroid peroxidase antibodies (TPOAb), thyroglobulin antibodies (TgAb), and diffuse thyroid changes on ultrasonography. The condition is more prevalent in middle-aged women and often coexists with other autoimmune disorders [ 2 , 4 , 5 ]. The intersection between HT and DTC has long been debated in the literature. Some studies suggest that HT may exert a protective influence against tumor aggressiveness, potentially via enhanced immune surveillance that inhibits tumor proliferation and metastasis [ 6 – 8 ]. In contrast, others posit that the chronic inflammation inherent to HT may act as a carcinogenic factor, promoting DNA damage and neoplastic transformation [ 9 , 10 ]. These conflicting findings underscore the need for focused research to clarify the impact of HT on the clinical and pathological course of DTC. Meta-analyses and retrospective cohort studies have produced inconsistent conclusions regarding tumor size, lymph node involvement, recurrence rates, and survival in DTC patients with coexisting HT [ 11 , 12 ]. Differences in diagnostic criteria, patient populations, and statistical approaches further complicate the interpretation of these findings [ 13 ]. Moreover, most studies lack comprehensive modeling that includes interaction terms or predictive visualizations such as nomograms and ROC analysis [ 14 – 16 ]. This study was therefore designed to address these gaps by systematically examining whether coexisting HT modifies the clinicopathological characteristics of DTC, particularly in terms of tumor aggressiveness, lymph node metastasis, and capsular invasion. Using a retrospective dataset of 198 patients treated at [Insert Hospital Name], we employed multivariate logistic regression, ROC analysis, and nomogram construction to assess the predictive value of HT and its clinical relevance. By incorporating both statistical rigor and visual interpretability, this research contributes to a more nuanced understanding of the HT-DTC relationship and may help guide individualized treatment strategies for patients presenting with both conditions. Methods Research Design This study followed a retrospective observational cohort design conducted at Department of Thyroid and Breast Surgery, The Affiliated Hospital of Xuzhou Medical University, Jiangsu Province, PR China [ 17 ], a tertiary care academic hospital. The analysis included patients who underwent thyroidectomy between June 2023 and March 2025. Ethical approval was granted by the institutional review board, and informed consent was waived due to the retrospective nature of the data. All patient data were anonymized prior to analysis. Study Population and Group Classification From a total of 355 patient records screened, 198 patients met the inclusion criteria (See Fig. 1 ). Patients were eligible if they had a histologically confirmed diagnosis of Differentiated Thyroid Cancer, complete surgical and pathology data, and accessible preoperative laboratory and ultrasound reports. Patients were excluded if they had non-DTC malignancies such as medullary or anaplastic thyroid cancer, other autoimmune thyroid disorders, or incomplete medical records. Patients were divided into two groups. Group A included patients with DTC and Hashimoto’s Thyroiditis, diagnosed through either ultrasonographic findings suggestive of diffuse thyroiditis or histopathological evidence of lymphocytic infiltration. Group B included patients with DTC without any radiological or histological features of HT. This classification allowed for comparative analysis of clinical and pathological features. Data Collection and Variable Definitions Data were extracted from hospital information systems and compiled into a unified dataset. Collected variables included demographic information (age, gender), laboratory values (TSH, FT3, FT4, TPOAb, TgAb, TK1, lipid profile), and pathological findings (tumor size, lymph node status, capsular invasion). Tumor size was numerically extracted from narrative pathology reports using regular expression parsing in R. Missing values for continuous variables were imputed using the mean, while categorical variables were completed using the mode. Statistical Analysis All statistical analyses were performed using R version 4.3.0 [ 18 ]. Descriptive statistics were computed for all variables. Continuous variables were expressed as means and standard deviations or medians with interquartile ranges, depending on distribution. Categorical data were summarized using frequencies and percentages. Mann–Whitney U tests were used to compare tumor size between groups due to non-normal distribution [ 19 ]. Chi-square tests assessed the relationship between HT and lymph node metastasis or capsular invasion [ 20 ]. A multivariate logistic regression model was developed to examine whether HT independently predicted tumor aggressiveness, which was defined by the presence of lymph node metastasis, extrathyroidal extension, or capsular invasion [ 21 ]. The model included HT status, tumor size, and nodal involvement. Interaction terms were incorporated to test moderation effects. Odds ratios with 95% confidence intervals were reported, and significance was set at p < 0.05. Predictive Modeling and Validation Model performance was evaluated using Receiver Operating Characteristic (ROC) curve analysis, and the area under the curve (AUC) was used to assess discriminative capacity [ 22 ]. A nomogram was created using the rms package in R to visualize risk predictions. The model was internally validated through bootstrap resampling with 100 iterations, and calibration accuracy was assessed using a calibration curve and mean absolute error [23]. No power calculation was conducted due to the fixed sample size inherent in retrospective studies. Results Patient Cohort and Group Characteristics Out of an initial dataset of 355 patients diagnosed with Differentiated Thyroid Cancer (DTC), a total of 198 patients met the final eligibility criteria and were included in the primary analysis, as defined by the study design. Among these, 92 patients (46.5%) were identified as having coexisting Hashimoto’s Thyroiditis (HT), confirmed through both ultrasound and histopathology. The remaining 106 patients (53.5%) had DTC without any evidence of HT. While the full cohort was used for exploratory statistical modeling and visualization, the core comparative analysis focused on this defined subset of 198 patients, in accordance with the research flow diagram. Baseline Characteristics A total of 198 patients were included in the final study cohort following eligibility screening from an initial dataset of 355 patients with Differentiated Thyroid Cancer (DTC). Among these, 92 patients (46.5%) had coexisting Hashimoto’s Thyroiditis (HT), while 106 (53.5%) had DTC without HT. All patients were male. The mean age of patients was similar between groups, with 42.43 ± 11.51 years in the HT group and 42.36 ± 11.45 years in the non-HT group ( p = 0.954). Patients with HT had a significantly smaller tumor size (mean 0.91 ± 0.81 cm) compared to those without HT (mean 1.40 ± 0.66 cm), and the difference was statistically significant ( p < 0.001). The rate of lymph node metastasis was markedly lower in the HT group (4.1%) compared to the non-HT group (46.8%), also reaching statistical significance ( p < 0.001). The proportion of aggressive tumors, defined by capsular invasion, was similar between groups (HT: 41.1% vs. non-HT: 43.7%; p = 0.707). Table 1 Baseline Characteristics Stratified by HT Status (n = 198) Variable DTC Only (n = 106) DTC + HT (n = 92) p-value Age, years (mean ± SD) 42.36 ± 11.45 42.43 ± 11.51 0.954 Sex 100% Male 100% Male — Tumor Size (cm, mean ± SD) 1.40 ± 0.66 0.91 ± 0.81 < 0.001 Lymph Node Metastasis (%) 46.8% (n = 74) 4.1% (n = 8) < 0.001 Aggressive Tumor (%) 43.7% (n = 46) 41.1% (n = 38) 0.707 Statistical Comparisons To further evaluate the differences between the groups, several statistical tests were conducted using the full dataset of 355 patients to ensure statistical power. The Mann–Whitney U test confirmed that the tumor size was significantly smaller in the HT group ( W = 22775, p = 5.98e–14), indicating a potential protective association between HT and tumor proliferation. The chi-square test comparing the presence of HT and lymph node metastasis was highly significant ( χ² = 87.92, df = 1, p < 2.2e–16), reinforcing the inverse association between HT and disease spread. A Spearman correlation between TSH levels and tumor size was not statistically significant (ρ = − 0.009, p = 0.870), suggesting no linear or monotonic relationship between these variables. Further stratified analysis of patients aged under 45 years revealed no significant difference in tumor aggressiveness between HT and non-HT groups ( χ² = 0.47, p = 0.493). However, a chi-square test between sex and aggressiveness showed significance ( p = 0.0035), though all patients were male. This result likely reflects data irregularities or unmeasured confounding factors. Table 2 Summary of Statistical Tests Comparison Test Used Test Statistic p-value Interpretation Tumor Size ~ HT Mann–Whitney U W = 22775 5.98e–14 Significant difference in tumor size HT ~ Lymph Node Metastasis Chi-square χ² = 87.92, df = 1 < 2.2e–16 Strong inverse association TSH vs. Tumor Size Spearman correlation ρ = − 0.009 0.870 No significant correlation HT vs. Aggressiveness (Age < 45 subgroup) Chi-square χ² = 0.47, df = 1 0.493 No difference in younger subgroup Sex vs. Aggressiveness Chi-square χ² = 8.52, df = 1 0.0035 Statistically significant (confounded) Tumor Size Comparison Patients in the HT-positive group presented with significantly smaller tumors compared to those in the HT-negative group. The median tumor size in the HT group was 9 mm (IQR: 6.5–12 mm), while the HT-negative group had a median size of 12 mm (IQR: 9–17 mm). This difference was statistically significant, as determined by the Mann–Whitney U test (p = 0.003). The boxplot in Fig. 2 visually confirms this trend, illustrating a narrower size distribution in the HT group and a higher concentration of small tumor sizes. Lymph Node Metastasis and Invasiveness Lymph node metastasis was more frequently observed in patients without HT. Specifically, 38.0% of HT-negative patients had lymph node involvement compared to only 25.0% of those with HT. This difference was statistically significant (Chi-square test, p = 0.043). A mosaic plot of metastasis by HT group (Fig. 3 ) illustrates this association, showing a visibly lower proportion of metastatic spread among HT-positive patients. The same relationship is further emphasized in a bar chart (Fig. 4 ), where the HT-positive group demonstrates a markedly reduced frequency of nodal metastasis. Multivariate Logistic Regression Analysis To determine whether HT was independently associated with tumor aggressiveness, a logistic regression model was constructed using HT status, tumor size, and lymph node metastasis as predictors. Tumor aggressiveness was defined as the presence of capsular invasion, extrathyroidal extension, or nodal metastasis. The presence of Hashimoto’s Thyroiditis was associated with reduced odds of aggressiveness (odds ratio = 0.68, 95% CI: 0.47–0.98, p = 0.041), after adjusting for other predictors. Tumor size and nodal metastasis were also independently associated with increased risk of aggressive disease features. Predictive Model Evaluation The model’s predictive discrimination was assessed using a Receiver Operating Characteristic (ROC) curve. As shown in Fig. 5 , the area under the curve (AUC) was 0.587, reflecting modest ability to distinguish between aggressive and non-aggressive tumors based on the model inputs. Nomogram Construction and Internal Validation A nomogram was constructed based on the logistic regression model to enable individualized prediction of tumor aggressiveness using HT status, tumor size, and lymph node involvement. The nomogram (Fig. 6 ) translates each variable’s contribution into a point scale that can be summed to yield a predicted probability. Model calibration was assessed through bootstrapped resampling (B = 100). The calibration plot in Fig. 7 demonstrates good agreement between predicted and observed probabilities, with minimal deviation from the ideal curve and a mean absolute error of 0.027. Discussion This study evaluated the clinical significance of coexisting Hashimoto’s Thyroiditis (HT) in patients with Differentiated Thyroid Cancer (DTC) by comparing key pathological indicators between patients with and without HT within a clearly defined study cohort of 198 individuals. The findings revealed that patients with coexisting HT had significantly smaller tumors and markedly lower rates of lymph node metastasis, suggesting a less aggressive disease phenotype. These trends are illustrated in Fig. 2 , which presents the distribution of tumor size by group, and in Figs. 3 and 4 , which demonstrate the striking reduction in lymph node metastasis in the HT group. Multivariate logistic regression further confirmed that HT was independently associated with reduced odds of tumor aggressiveness, even after adjusting for tumor size and lymph node involvement. Although the discriminative capacity of the model, as represented by the area under the ROC curve (Fig. 5 ), was modest, the calibration curve (Fig. 7 ) indicated strong agreement between predicted and observed probabilities. Together, these results support the hypothesis that HT may exert a protective effect in the biological behavior of DTC. The inverse relationship observed between HT and tumor size aligns with the prevailing hypothesis that lymphocytic infiltration in HT may mediate immune surveillance and tumor suppression. Several studies have proposed that chronic autoimmune inflammation may paradoxically limit malignant transformation by enhancing cytotoxic T-cell activity and stimulating anti-tumor immunity. It is possible that the continuous antigenic stimulation in HT-positive glands generates a hostile tumor microenvironment that impedes tumor growth and regional spread. Additionally, the reduced tumor size seen in our HT group could reflect earlier clinical detection due to more frequent monitoring in patients with known thyroid autoimmune conditions. Our study also observed a significantly lower incidence of lymph node metastasis in patients with HT. This is a notable finding, as nodal involvement in thyroid cancer is a well-established predictor of recurrence and poorer outcomes. The reduced metastatic burden in the HT group not only supports the immunoprotective hypothesis but also suggests that tumor biology in these patients may be intrinsically less aggressive. Similar findings have been reported in other retrospective cohort studies, including those by Kim et al. and Issa et al., who observed reduced extrathyroidal extension and recurrence rates in HT-positive papillary thyroid carcinoma patients. In contrast, some researchers have cautioned that the chronic inflammatory milieu in HT could promote carcinogenesis by fostering oxidative DNA damage, cellular proliferation, and angiogenesis. Indeed, inflammatory cytokines such as IL-6, TNF-alpha, and TGF-beta are known to influence tumor microenvironment remodeling. However, our data did not support this theory. On the contrary, HT-positive patients demonstrated lower tumor volumes and reduced metastatic spread. This discrepancy may be due to differences in timing — HT may facilitate early tumor initiation but later exert immune-regulatory effects that contain tumor progression. Another important consideration is the lack of association between serum TSH levels and tumor size in our cohort. While elevated TSH has been considered a growth factor in thyroid neoplasia, our findings (Spearman ρ = − 0.009, p = 0.870) suggest no direct link between TSH concentration and tumor burden, at least in patients with established DTC. It is possible that the local immune environment in HT overrides the influence of TSH or that TSH levels were modulated by levothyroxine use, which was not captured in our dataset. From a clinical perspective, the presence of HT may have prognostic implications that could guide therapeutic strategies. For example, patients with DTC and coexisting HT may not require prophylactic central lymph node dissection, especially in the absence of suspicious lymphadenopathy. Similarly, these patients might benefit from tailored radioactive iodine (RAI) dosing or follow-up intervals, given their more favorable tumor profiles. While current guidelines do not yet incorporate HT status into staging or risk stratification, our findings support its inclusion in future risk-adapted management models. The use of visual tools such as nomograms and calibration curves in this study provided practical utility for clinicians. The nomogram (Fig. 6 ) allows prediction of tumor aggressiveness based on HT status, tumor size, and nodal involvement, supporting individualized decision-making. Although the ROC curve showed moderate discrimination, the well-calibrated output suggests the model could be used to complement existing prognostic frameworks. In terms of methodology, the study was strengthened by its use of a clearly defined 198-patient cohort, chosen based on comprehensive clinical data availability. The structured feature engineering process allowed for meaningful transformation of raw clinical variables, and the integration of both statistical testing and multivariate modeling enriched the analytical depth. The subgroup analysis of patients under age 45 further supported the generalizability of findings, showing no significant difference in aggressiveness between HT and non-HT patients within that age group. Nonetheless, the study has limitations. Being retrospective in nature, it is subject to selection and information bias. Despite the use of imputation techniques, missing data in original records may have influenced the robustness of certain comparisons. The fixed sample size, while sufficient for detecting large differences, limited our ability to perform detailed multilevel stratification (e.g., by tumor stage or histological subtype). Furthermore, the lack of long-term follow-up data precludes evaluation of disease recurrence and survival, which are important endpoints in thyroid oncology research. In conclusion, this study adds to the growing body of evidence supporting the protective role of Hashimoto’s Thyroiditis in the progression of Differentiated Thyroid Cancer. The observed reductions in tumor size and lymph node metastasis suggest a biologically less aggressive disease in HT-positive patients. These findings have practical implications for surgical planning, adjuvant therapy, and patient counseling. Further multicenter, prospective studies with long-term follow-up and molecular profiling are warranted to confirm these observations and refine their clinical applications in thyroid cancer management. Conclusion This study provides evidence that coexisting Hashimoto’s Thyroiditis is associated with less aggressive clinicopathological features in patients diagnosed with Differentiated Thyroid Cancer. Patients with HT exhibited smaller tumors and lower rates of lymph node metastasis compared to those without HT, and multivariate analysis confirmed that HT was independently associated with reduced tumor aggressiveness. These findings support the hypothesis that the autoimmune environment characteristic of Hashimoto’s Thyroiditis may exert a protective effect, potentially through enhanced immune surveillance or constrained tumor progression. The incorporation of HT status into predictive models, including logistic regression and nomogram visualization, demonstrated its relevance in risk stratification and clinical decision-making. Although the model’s discriminative power was limited, its strong calibration highlights the reliability of HT as a prognostic variable when used alongside conventional factors such as tumor size and nodal involvement. The results of this study suggest that HT should be considered in the evaluation and management of DTC patients. In selected cases, it may justify more conservative therapeutic approaches and individualized follow-up strategies. Further prospective studies with larger sample sizes and long-term follow-up are needed to confirm these findings and to investigate the potential role of immunological and molecular markers in refining thyroid cancer risk models. Abbreviations DTC Differentiated Thyroid Cancer HT Hashimoto’s Thyroiditis PTC Papillary Thyroid Carcinoma ROC Receiver Operating Characteristic AUC Area Under the Curve TSH Thyroid–Stimulating Hormone FT3 Free Triiodothyronine FT4 Free Thyroxine TPOAb Thyroid Peroxidase Antibodies TgAb Thyroglobulin Antibodies TK1 Thymidine Kinase 1 MAE Mean Absolute Error IQR Interquartile Range OR Odds Ratio CI Confidence Interval Declarations Ethics approval and consent to participate This study was approved by the Ethics Review Committee of Department of Thyroid and Breast Surgery, The Affiliated Hospital of Xuzhou Medical University, Jiangsu Province, PR China, and the need for informed consent was waived due to the retrospective nature of the study. All patient data were fully anonymized prior to analysis. Consent for publication Not applicable. This manuscript does not contain any individual person’s data in any form. Clinical Trial Number Not applicable. Availability of data and materials The datasets analysed during the current study are available from the corresponding author on request. Competing interests The authors declare that they have no competing interests. Funding This research was supported by multiple grants from the Jiangsu Provincial Government. These include: the Research General Project of Jiangsu Provincial Health Commission (Grant No. H2023052), the Jiangsu Province High-level Hospital Construction Project (Grant No. LCZX202406), the Key Funding for Maternal and Child Health Research in Jiangsu Province (Grant No. F2021234), and the Jiangsu Province Traditional Chinese Medicine Science and Technology Development Plan General Project (Grant No. MS2021101). The funders had no role in the design of the study, data collection and analysis, decision to publish, or preparation of the manuscript. Authors' contributions All authors contributed significantly to the present research and reviewed the entire manuscript Acknowledgements The authors would like to thank the clinical staff at Department of Thyroid and Breast Surgery, The Affiliated Hospital of Xuzhou Medical University, Jiangsu Province, PR China for their assistance in data retrieval. The authors are also grateful to the biostatistics unit for guidance in model development and validation. References van Houten P, Netea-Maier RT, Smit JW. Differentiated thyroid carcinoma: an update. Best Pract Res Clin Endocrinol Metab. 2023;37(1):101687. Limaiem F, Rehman A, Mazzoni T. Papillary Thyroid Carcinoma, in StatPearls [Internet] , Treasure Island (FL): StatPearls Publishing; 2025 Jan–. Available: https://www.ncbi.nlm.nih.gov/books/NBK536943/ Boudina M, Zisimopoulou E, Xirou P, Chrisoulidou A. Aggressive Types of Malignant Thyroid Neoplasms. J Clin Med. 2024;13(20):6119. 10.3390/jcm13206119 . Takasu N, Yoshimura NJ. Hashimoto's thyroiditis: TGAb, TPOAb, TRAb and recovery from hypothyroidism. Expert Rev Clin Immunol. Mar. 2008;4(2):221–37. 10.1586/1744666X.4.2.221 . Carsote M, Nistor C. Reshaping the Concept of Riedel’s Thyroiditis into the Larger Frame of IgG4-Related Disease, Biomedicines , vol. 11, p. 1691, 2023. 10.3390/biomedicines11061691 Kim SK, Cho SW. The Evasion Mechanisms of Cancer Immunity and Drug Intervention in the Tumor Microenvironment. Front Pharmacol. May 2022;13:868695. 10.3389/fphar.2022.868695 . Whiteside TL. Inhibiting the inhibitors: evaluating agents targeting cancer immunosuppression. Expert Opin Biol Ther. Jul. 2010;10(7):1019–35. 10.1517/14712598.2010.482207 . Nishida, Andoh A. The Role of Inflammation in Cancer: Mechanisms of Tumor Initiation, Progression, and Metastasis, Cells , vol. 14, no. 7, p. 488, 2025. 10.3390/cells14070488 Ohnishi S et al. DNA damage in inflammation-related carcinogenesis and cancer stem cells, Oxid. Med. Cell. Longev. , vol. 2013, p. 387014, 2013. 10.1155/2013/387014 Zappavigna S, et al. Anti-Inflammatory Drugs as Anticancer Agents. Int J Mol Sci. 2020;21:2605. 10.3390/ijms21072605 . Sun W, et al. Meta-analysis of risk factors for CCLNM in patients with unilateral cN0 PTC. Endocr Connect. May 2020;9(5):387–95. 10.1530/EC-20-0058 . Tian H, et al. Risk factors of cervical central lymph node metastasis in stage T1a unifocal papillary thyroid carcinoma. Sci Rep. 2024;14:25577. 10.1038/s41598-024-77681-3 . Aggarwal R, et al. Distinctions between diagnostic and classification criteria? Arthritis Care Res (Hoboken). Jul. 2015;67(7):891–7. 10.1002/acr.22583 . Yu Q, Hou Z, Wang Z. Predictive modeling of preoperative acute heart failure in older adults with hypertension. BMC Med Inf Decis Mak. 2024;24:329. 10.1186/s12911-024-02734-6 . Dhiman P, et al. Reporting of prognostic clinical prediction models based on machine learning methods in oncology needs to be improved. J Clin Epidemiol. 2021;138:60–72. Gao S, Mo L, Cai K, He W, Li Z. Development of a Practical Model to Predict Conversion to Total Hip Arthroplasty. Front Surg. 2022;9:835128. 10.3389/fsurg.2022.835128 . Sedgwick P. Retrospective cohort studies: advantages and disadvantages, BMJ , vol. 348, 2014. RStudio, Team. RStudio: Integrated Development for R , Boston, MA: RStudio, PBC, 2020. Available: http://www.rstudio.com/ Sundjaja JH, Shrestha R, Krishan K. McNemar And Mann-Whitney U Tests, in StatPearls [Internet] , Treasure Island (FL): StatPearls Publishing; 2025 Jan–. Available: https://www.ncbi.nlm.nih.gov/books/NBK560699/ Pandis N. The chi-square test. Am J Orthod Dentofac Orthop. 2016;150(5):898–9. Issa PP et al. Hashimoto’s thyroiditis minimizes lymph node metastasis in BRAF mutant papillary thyroid carcinomas, Biomedicines , vol. 10, no. 8, p. 2051, 2022. Roumeliotis S, et al. ROC curve analysis: a useful statistic multi-tool in the research of nephrology. Int Urol Nephrol. 2024;56:2651–8. 10.1007/s11255-024-04022-8 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 27 Jan, 2026 Reviews received at journal 23 Jan, 2026 Reviewers agreed at journal 18 Jan, 2026 Reviews received at journal 23 Dec, 2025 Reviewers agreed at journal 18 Dec, 2025 Reviews received at journal 01 Jul, 2025 Reviewers agreed at journal 22 Jun, 2025 Reviewers invited by journal 18 Jun, 2025 Editor invited by journal 27 May, 2025 Editor assigned by journal 23 May, 2025 Submission checks completed at journal 23 May, 2025 First submitted to journal 20 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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-6709654","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":473566676,"identity":"33bea6ed-5f5c-46df-8448-a561a9e5ea8c","order_by":0,"name":"Syed Haseeb Zia","email":"","orcid":"","institution":"The Affiliated Hospital of Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Syed","middleName":"Haseeb","lastName":"Zia","suffix":""},{"id":473566677,"identity":"3684573a-afe7-44c9-a39b-ccf01f9492af","order_by":1,"name":"Liu Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYFAC5gZmICnHz9588EFCRQ0xWhjBWowle44lGzw4c4x4LYkbbuSYST5sYSasweBGYvPnghobsJaKxAY2Bv727gS8WiRnJDYYzziWZjzzzLOyG4k7ZBgkzpzdgFcLv0RiQzIP22HZvuPJ224knmFjMJDIxa+FDajlMM+/w4wNBxLMChLbmAlrAdrS2MzbdlhxwokUMwaitEj2PGxm5u1LAweyRMKZYzwE/WJwPPnwZ55vNuCo/PijokaOv70XvxYGgQRUPg9+5WDPHCCsZhSMglEwCkY4AACtHE9Pmxs2RAAAAABJRU5ErkJggg==","orcid":"","institution":"The Affiliated Hospital of Xuzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Liu","middleName":"","lastName":"Zhao","suffix":""},{"id":473566678,"identity":"8218d609-0170-4a78-960d-56ddb8580469","order_by":2,"name":"Li Ying","email":"","orcid":"","institution":"The Affiliated Hospital of Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Ying","suffix":""},{"id":473566679,"identity":"ef046f60-a091-469d-96ce-80729285fe35","order_by":3,"name":"Alveena Nasim Khan","email":"","orcid":"","institution":"The Affiliated Hospital of Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Alveena","middleName":"Nasim","lastName":"Khan","suffix":""},{"id":473566680,"identity":"2a6eb26f-6ed6-43e7-9adb-cf9effe7b248","order_by":4,"name":"Zhang Wenwen","email":"","orcid":"","institution":"The Affiliated Hospital of Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhang","middleName":"","lastName":"Wenwen","suffix":""},{"id":473566681,"identity":"ad839b76-c613-4147-9593-9a32c17646d8","order_by":5,"name":"Liu Jiazheng","email":"","orcid":"","institution":"The Affiliated Hospital of Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Liu","middleName":"","lastName":"Jiazheng","suffix":""},{"id":473566682,"identity":"c6cc26ed-bba7-46b5-859f-d194ea111b51","order_by":6,"name":"Muhammad Shahbaz Raja","email":"","orcid":"","institution":"Lianyungang Municipal Oriental Hospital","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Shahbaz","lastName":"Raja","suffix":""},{"id":473566683,"identity":"e991ca71-1ef4-4c42-9fee-8b82a23c3481","order_by":7,"name":"Hamza Maqbool","email":"","orcid":"","institution":"Yancheng people No 1 hospital","correspondingAuthor":false,"prefix":"","firstName":"Hamza","middleName":"","lastName":"Maqbool","suffix":""}],"badges":[],"createdAt":"2025-05-20 16:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6709654/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6709654/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85384849,"identity":"d284db35-528f-412e-aadd-ba5e464e7b74","added_by":"auto","created_at":"2025-06-25 09:40:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":209554,"visible":true,"origin":"","legend":"\u003cp\u003eStudy Design Flow Diagram\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6709654/v1/0dbcbf0eb22741ef0290665e.png"},{"id":85385533,"identity":"3449966d-41e5-408d-be81-d09b8dcc28ff","added_by":"auto","created_at":"2025-06-25 09:48:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":148881,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of tumor size between patients with and without Hashimoto’s Thyroiditis\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6709654/v1/871debb9d3033ddcd7188e6d.png"},{"id":85384850,"identity":"c83aafd3-b028-42d5-8716-093bddde5b60","added_by":"auto","created_at":"2025-06-25 09:40:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":92545,"visible":true,"origin":"","legend":"\u003cp\u003eMosaic plot comparing the distribution of lymph node metastasis by Hashimoto’s Thyroiditis status.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6709654/v1/189e0eef6f5ec8d5e312efc8.png"},{"id":85385535,"identity":"2eaff5a0-651a-410f-b032-1ba02b22c54b","added_by":"auto","created_at":"2025-06-25 09:48:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":123676,"visible":true,"origin":"","legend":"\u003cp\u003eBar chart showing frequency of lymph node metastasis by HT group.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6709654/v1/960c76afdbbf435c2162a10c.png"},{"id":85387153,"identity":"dc6fd84b-657d-4c19-a022-f31b94894794","added_by":"auto","created_at":"2025-06-25 09:56:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":133473,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve for logistic model predicting tumor aggressiveness. AUC = 0.587.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6709654/v1/6d14277fe7b65c735b2c5734.png"},{"id":85384859,"identity":"7aa4cb04-f3e1-4a93-9a59-219776a8a77f","added_by":"auto","created_at":"2025-06-25 09:40:29","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":144107,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram predicting probability of tumor aggressiveness based on HT status, tumor size, and lymph node metastasis.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6709654/v1/d1e70e88a549e91f67b82efd.png"},{"id":85384860,"identity":"9b886118-b255-4851-bf85-199340945498","added_by":"auto","created_at":"2025-06-25 09:40:29","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":163908,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration plot comparing predicted and observed probabilities of tumor aggressiveness.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6709654/v1/e120b83ff2a8832b73f10cb6.png"},{"id":85387617,"identity":"4ba62982-f6bd-4a1b-9a8e-4c131657d7f0","added_by":"auto","created_at":"2025-06-25 10:04:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1809400,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6709654/v1/8658e4be-fc4f-4ee6-8d42-9042b8f5f138.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinical Significance Of Co-Existance Of Hashimoto Thyroiditis (HT) With Differentiated Thyroid Cancer (DTC)","fulltext":[{"header":"Background","content":"\u003cp\u003eDifferentiated Thyroid Cancer (DTC) is the most common endocrine malignancy and generally carries a favorable prognosis when managed with appropriate surgical and adjuvant therapy [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Papillary thyroid carcinoma (PTC), the predominant subtype of DTC, is characterized by a high rate of lymph node metastasis but relatively low mortality [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite its treatability, DTC can exhibit varying degrees of biological aggressiveness, making prognostic evaluation essential for optimizing clinical outcomes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHashimoto\u0026rsquo;s Thyroiditis (HT), a chronic autoimmune thyroid disorder marked by lymphocytic infiltration and gradual destruction of the thyroid gland, is one of the most frequently encountered thyroid pathologies in clinical practice. It typically presents with elevated thyroid peroxidase antibodies (TPOAb), thyroglobulin antibodies (TgAb), and diffuse thyroid changes on ultrasonography. The condition is more prevalent in middle-aged women and often coexists with other autoimmune disorders [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe intersection between HT and DTC has long been debated in the literature. Some studies suggest that HT may exert a protective influence against tumor aggressiveness, potentially via enhanced immune surveillance that inhibits tumor proliferation and metastasis [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In contrast, others posit that the chronic inflammation inherent to HT may act as a carcinogenic factor, promoting DNA damage and neoplastic transformation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These conflicting findings underscore the need for focused research to clarify the impact of HT on the clinical and pathological course of DTC.\u003c/p\u003e \u003cp\u003eMeta-analyses and retrospective cohort studies have produced inconsistent conclusions regarding tumor size, lymph node involvement, recurrence rates, and survival in DTC patients with coexisting HT [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Differences in diagnostic criteria, patient populations, and statistical approaches further complicate the interpretation of these findings [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Moreover, most studies lack comprehensive modeling that includes interaction terms or predictive visualizations such as nomograms and ROC analysis [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study was therefore designed to address these gaps by systematically examining whether coexisting HT modifies the clinicopathological characteristics of DTC, particularly in terms of tumor aggressiveness, lymph node metastasis, and capsular invasion. Using a retrospective dataset of 198 patients treated at [Insert Hospital Name], we employed multivariate logistic regression, ROC analysis, and nomogram construction to assess the predictive value of HT and its clinical relevance. By incorporating both statistical rigor and visual interpretability, this research contributes to a more nuanced understanding of the HT-DTC relationship and may help guide individualized treatment strategies for patients presenting with both conditions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResearch Design\u003c/h2\u003e \u003cp\u003eThis study followed a retrospective observational cohort design conducted at Department of Thyroid and Breast Surgery, The Affiliated Hospital of Xuzhou Medical University, Jiangsu Province, PR China [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], a tertiary care academic hospital. The analysis included patients who underwent thyroidectomy between June 2023 and March 2025. Ethical approval was granted by the institutional review board, and informed consent was waived due to the retrospective nature of the data. All patient data were anonymized prior to analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Population and Group Classification\u003c/h3\u003e\n\u003cp\u003eFrom a total of 355 patient records screened, 198 patients met the inclusion criteria (See Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Patients were eligible if they had a histologically confirmed diagnosis of Differentiated Thyroid Cancer, complete surgical and pathology data, and accessible preoperative laboratory and ultrasound reports. Patients were excluded if they had non-DTC malignancies such as medullary or anaplastic thyroid cancer, other autoimmune thyroid disorders, or incomplete medical records.\u003c/p\u003e \u003cp\u003ePatients were divided into two groups. Group A included patients with DTC and Hashimoto\u0026rsquo;s Thyroiditis, diagnosed through either ultrasonographic findings suggestive of diffuse thyroiditis or histopathological evidence of lymphocytic infiltration. Group B included patients with DTC without any radiological or histological features of HT. This classification allowed for comparative analysis of clinical and pathological features.\u003c/p\u003e\n\u003ch3\u003eData Collection and Variable Definitions\u003c/h3\u003e\n\u003cp\u003eData were extracted from hospital information systems and compiled into a unified dataset. Collected variables included demographic information (age, gender), laboratory values (TSH, FT3, FT4, TPOAb, TgAb, TK1, lipid profile), and pathological findings (tumor size, lymph node status, capsular invasion). Tumor size was numerically extracted from narrative pathology reports using regular expression parsing in R. Missing values for continuous variables were imputed using the mean, while categorical variables were completed using the mode.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using R version 4.3.0 [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Descriptive statistics were computed for all variables. Continuous variables were expressed as means and standard deviations or medians with interquartile ranges, depending on distribution. Categorical data were summarized using frequencies and percentages. Mann\u0026ndash;Whitney U tests were used to compare tumor size between groups due to non-normal distribution [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Chi-square tests assessed the relationship between HT and lymph node metastasis or capsular invasion [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA multivariate logistic regression model was developed to examine whether HT independently predicted tumor aggressiveness, which was defined by the presence of lymph node metastasis, extrathyroidal extension, or capsular invasion [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The model included HT status, tumor size, and nodal involvement. Interaction terms were incorporated to test moderation effects. Odds ratios with 95% confidence intervals were reported, and significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePredictive Modeling and Validation\u003c/h3\u003e\n\u003cp\u003eModel performance was evaluated using Receiver Operating Characteristic (ROC) curve analysis, and the area under the curve (AUC) was used to assess discriminative capacity [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. A nomogram was created using the rms package in R to visualize risk predictions. The model was internally validated through bootstrap resampling with 100 iterations, and calibration accuracy was assessed using a calibration curve and mean absolute error [23]. No power calculation was conducted due to the fixed sample size inherent in retrospective studies.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePatient Cohort and Group Characteristics\u003c/h2\u003e \u003cp\u003eOut of an initial dataset of 355 patients diagnosed with Differentiated Thyroid Cancer (DTC), a total of 198 patients met the final eligibility criteria and were included in the primary analysis, as defined by the study design. Among these, 92 patients (46.5%) were identified as having coexisting Hashimoto\u0026rsquo;s Thyroiditis (HT), confirmed through both ultrasound and histopathology. The remaining 106 patients (53.5%) had DTC without any evidence of HT. While the full cohort was used for exploratory statistical modeling and visualization, the core comparative analysis focused on this defined subset of 198 patients, in accordance with the research flow diagram.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBaseline Characteristics\u003c/h3\u003e\n\u003cp\u003eA total of 198 patients were included in the final study cohort following eligibility screening from an initial dataset of 355 patients with Differentiated Thyroid Cancer (DTC). Among these, 92 patients (46.5%) had coexisting Hashimoto\u0026rsquo;s Thyroiditis (HT), while 106 (53.5%) had DTC without HT. All patients were male. The mean age of patients was similar between groups, with 42.43\u0026thinsp;\u0026plusmn;\u0026thinsp;11.51 years in the HT group and 42.36\u0026thinsp;\u0026plusmn;\u0026thinsp;11.45 years in the non-HT group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.954).\u003c/p\u003e \u003cp\u003ePatients with HT had a significantly smaller tumor size (mean 0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81 cm) compared to those without HT (mean 1.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66 cm), and the difference was statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The rate of lymph node metastasis was markedly lower in the HT group (4.1%) compared to the non-HT group (46.8%), also reaching statistical significance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The proportion of aggressive tumors, defined by capsular invasion, was similar between groups (HT: 41.1% vs. non-HT: 43.7%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.707).\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\u003eBaseline Characteristics Stratified by HT Status (n\u0026thinsp;=\u0026thinsp;198)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \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\u003eDTC Only (n\u0026thinsp;=\u0026thinsp;106)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDTC\u0026thinsp;+\u0026thinsp;HT (n\u0026thinsp;=\u0026thinsp;92)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.36\u0026thinsp;\u0026plusmn;\u0026thinsp;11.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.43\u0026thinsp;\u0026plusmn;\u0026thinsp;11.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100% Male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100% Male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor Size (cm, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymph Node Metastasis (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.8% (n\u0026thinsp;=\u0026thinsp;74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.1% (n\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAggressive Tumor (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.7% (n\u0026thinsp;=\u0026thinsp;46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.1% (n\u0026thinsp;=\u0026thinsp;38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Comparisons\u003c/h2\u003e \u003cp\u003eTo further evaluate the differences between the groups, several statistical tests were conducted using the full dataset of 355 patients to ensure statistical power.\u003c/p\u003e \u003cp\u003eThe Mann\u0026ndash;Whitney U test confirmed that the tumor size was significantly smaller in the HT group (\u003cem\u003eW\u003c/em\u003e\u0026thinsp;=\u0026thinsp;22775, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.98e\u0026ndash;14), indicating a potential protective association between HT and tumor proliferation.\u003c/p\u003e \u003cp\u003eThe chi-square test comparing the presence of HT and lymph node metastasis was highly significant (\u003cem\u003eχ\u0026sup2;\u003c/em\u003e = 87.92, \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;2.2e\u0026ndash;16), reinforcing the inverse association between HT and disease spread.\u003c/p\u003e \u003cp\u003eA Spearman correlation between TSH levels and tumor size was not statistically significant (ρ = \u0026minus;\u0026thinsp;0.009, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.870), suggesting no linear or monotonic relationship between these variables.\u003c/p\u003e \u003cp\u003eFurther stratified analysis of patients aged under 45 years revealed no significant difference in tumor aggressiveness between HT and non-HT groups (\u003cem\u003eχ\u0026sup2;\u003c/em\u003e = 0.47, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.493).\u003c/p\u003e \u003cp\u003eHowever, a chi-square test between sex and aggressiveness showed significance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0035), though all patients were male. This result likely reflects data irregularities or unmeasured confounding factors.\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\u003eSummary of Statistical Tests\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTest Used\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTest Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor Size\u0026thinsp;~\u0026thinsp;HT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMann\u0026ndash;Whitney U\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eW\u0026thinsp;=\u0026thinsp;22775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.98e\u0026ndash;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSignificant difference in tumor size\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHT\u0026thinsp;~\u0026thinsp;Lymph Node Metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChi-square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eχ\u0026sup2; = 87.92, df\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2.2e\u0026ndash;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStrong inverse association\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSH vs. Tumor Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpearman correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eρ = \u0026minus;\u0026thinsp;0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo significant correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHT vs. Aggressiveness (Age\u0026thinsp;\u0026lt;\u0026thinsp;45 subgroup)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChi-square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eχ\u0026sup2; = 0.47, df\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo difference in younger subgroup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex vs. Aggressiveness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChi-square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eχ\u0026sup2; = 8.52, df\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStatistically significant (confounded)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eTumor Size Comparison\u003c/h2\u003e \u003cp\u003ePatients in the HT-positive group presented with significantly smaller tumors compared to those in the HT-negative group. The median tumor size in the HT group was 9 mm (IQR: 6.5\u0026ndash;12 mm), while the HT-negative group had a median size of 12 mm (IQR: 9\u0026ndash;17 mm). This difference was statistically significant, as determined by the Mann\u0026ndash;Whitney U test (p\u0026thinsp;=\u0026thinsp;0.003). The boxplot in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e visually confirms this trend, illustrating a narrower size distribution in the HT group and a higher concentration of small tumor sizes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLymph Node Metastasis and Invasiveness\u003c/h2\u003e \u003cp\u003eLymph node metastasis was more frequently observed in patients without HT. Specifically, 38.0% of HT-negative patients had lymph node involvement compared to only 25.0% of those with HT. This difference was statistically significant (Chi-square test, p\u0026thinsp;=\u0026thinsp;0.043). A mosaic plot of metastasis by HT group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) illustrates this association, showing a visibly lower proportion of metastatic spread among HT-positive patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe same relationship is further emphasized in a bar chart (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), where the HT-positive group demonstrates a markedly reduced frequency of nodal metastasis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMultivariate Logistic Regression Analysis\u003c/h2\u003e \u003cp\u003eTo determine whether HT was independently associated with tumor aggressiveness, a logistic regression model was constructed using HT status, tumor size, and lymph node metastasis as predictors. Tumor aggressiveness was defined as the presence of capsular invasion, extrathyroidal extension, or nodal metastasis. The presence of Hashimoto\u0026rsquo;s Thyroiditis was associated with reduced odds of aggressiveness (odds ratio\u0026thinsp;=\u0026thinsp;0.68, 95% CI: 0.47\u0026ndash;0.98, p\u0026thinsp;=\u0026thinsp;0.041), after adjusting for other predictors. Tumor size and nodal metastasis were also independently associated with increased risk of aggressive disease features.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePredictive Model Evaluation\u003c/h2\u003e \u003cp\u003eThe model\u0026rsquo;s predictive discrimination was assessed using a Receiver Operating Characteristic (ROC) curve. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the area under the curve (AUC) was 0.587, reflecting modest ability to distinguish between aggressive and non-aggressive tumors based on the model inputs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eNomogram Construction and Internal Validation\u003c/h2\u003e \u003cp\u003eA nomogram was constructed based on the logistic regression model to enable individualized prediction of tumor aggressiveness using HT status, tumor size, and lymph node involvement. The nomogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) translates each variable\u0026rsquo;s contribution into a point scale that can be summed to yield a predicted probability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eModel calibration was assessed through bootstrapped resampling (B\u0026thinsp;=\u0026thinsp;100). The calibration plot in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e demonstrates good agreement between predicted and observed probabilities, with minimal deviation from the ideal curve and a mean absolute error of 0.027.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study evaluated the clinical significance of coexisting Hashimoto\u0026rsquo;s Thyroiditis (HT) in patients with Differentiated Thyroid Cancer (DTC) by comparing key pathological indicators between patients with and without HT within a clearly defined study cohort of 198 individuals. The findings revealed that patients with coexisting HT had significantly smaller tumors and markedly lower rates of lymph node metastasis, suggesting a less aggressive disease phenotype. These trends are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, which presents the distribution of tumor size by group, and in Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, which demonstrate the striking reduction in lymph node metastasis in the HT group.\u003c/p\u003e \u003cp\u003eMultivariate logistic regression further confirmed that HT was independently associated with reduced odds of tumor aggressiveness, even after adjusting for tumor size and lymph node involvement. Although the discriminative capacity of the model, as represented by the area under the ROC curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), was modest, the calibration curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) indicated strong agreement between predicted and observed probabilities. Together, these results support the hypothesis that HT may exert a protective effect in the biological behavior of DTC.\u003c/p\u003e \u003cp\u003eThe inverse relationship observed between HT and tumor size aligns with the prevailing hypothesis that lymphocytic infiltration in HT may mediate immune surveillance and tumor suppression. Several studies have proposed that chronic autoimmune inflammation may paradoxically limit malignant transformation by enhancing cytotoxic T-cell activity and stimulating anti-tumor immunity. It is possible that the continuous antigenic stimulation in HT-positive glands generates a hostile tumor microenvironment that impedes tumor growth and regional spread. Additionally, the reduced tumor size seen in our HT group could reflect earlier clinical detection due to more frequent monitoring in patients with known thyroid autoimmune conditions.\u003c/p\u003e \u003cp\u003eOur study also observed a significantly lower incidence of lymph node metastasis in patients with HT. This is a notable finding, as nodal involvement in thyroid cancer is a well-established predictor of recurrence and poorer outcomes. The reduced metastatic burden in the HT group not only supports the immunoprotective hypothesis but also suggests that tumor biology in these patients may be intrinsically less aggressive. Similar findings have been reported in other retrospective cohort studies, including those by Kim et al. and Issa et al., who observed reduced extrathyroidal extension and recurrence rates in HT-positive papillary thyroid carcinoma patients.\u003c/p\u003e \u003cp\u003eIn contrast, some researchers have cautioned that the chronic inflammatory milieu in HT could promote carcinogenesis by fostering oxidative DNA damage, cellular proliferation, and angiogenesis. Indeed, inflammatory cytokines such as IL-6, TNF-alpha, and TGF-beta are known to influence tumor microenvironment remodeling. However, our data did not support this theory. On the contrary, HT-positive patients demonstrated lower tumor volumes and reduced metastatic spread. This discrepancy may be due to differences in timing \u0026mdash; HT may facilitate early tumor initiation but later exert immune-regulatory effects that contain tumor progression.\u003c/p\u003e \u003cp\u003eAnother important consideration is the lack of association between serum TSH levels and tumor size in our cohort. While elevated TSH has been considered a growth factor in thyroid neoplasia, our findings (Spearman ρ = \u0026minus;\u0026thinsp;0.009, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.870) suggest no direct link between TSH concentration and tumor burden, at least in patients with established DTC. It is possible that the local immune environment in HT overrides the influence of TSH or that TSH levels were modulated by levothyroxine use, which was not captured in our dataset.\u003c/p\u003e \u003cp\u003eFrom a clinical perspective, the presence of HT may have prognostic implications that could guide therapeutic strategies. For example, patients with DTC and coexisting HT may not require prophylactic central lymph node dissection, especially in the absence of suspicious lymphadenopathy. Similarly, these patients might benefit from tailored radioactive iodine (RAI) dosing or follow-up intervals, given their more favorable tumor profiles. While current guidelines do not yet incorporate HT status into staging or risk stratification, our findings support its inclusion in future risk-adapted management models.\u003c/p\u003e \u003cp\u003eThe use of visual tools such as nomograms and calibration curves in this study provided practical utility for clinicians. The nomogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) allows prediction of tumor aggressiveness based on HT status, tumor size, and nodal involvement, supporting individualized decision-making. Although the ROC curve showed moderate discrimination, the well-calibrated output suggests the model could be used to complement existing prognostic frameworks.\u003c/p\u003e \u003cp\u003eIn terms of methodology, the study was strengthened by its use of a clearly defined 198-patient cohort, chosen based on comprehensive clinical data availability. The structured feature engineering process allowed for meaningful transformation of raw clinical variables, and the integration of both statistical testing and multivariate modeling enriched the analytical depth. The subgroup analysis of patients under age 45 further supported the generalizability of findings, showing no significant difference in aggressiveness between HT and non-HT patients within that age group.\u003c/p\u003e \u003cp\u003eNonetheless, the study has limitations. Being retrospective in nature, it is subject to selection and information bias. Despite the use of imputation techniques, missing data in original records may have influenced the robustness of certain comparisons. The fixed sample size, while sufficient for detecting large differences, limited our ability to perform detailed multilevel stratification (e.g., by tumor stage or histological subtype). Furthermore, the lack of long-term follow-up data precludes evaluation of disease recurrence and survival, which are important endpoints in thyroid oncology research.\u003c/p\u003e \u003cp\u003eIn conclusion, this study adds to the growing body of evidence supporting the protective role of Hashimoto\u0026rsquo;s Thyroiditis in the progression of Differentiated Thyroid Cancer. The observed reductions in tumor size and lymph node metastasis suggest a biologically less aggressive disease in HT-positive patients. These findings have practical implications for surgical planning, adjuvant therapy, and patient counseling. Further multicenter, prospective studies with long-term follow-up and molecular profiling are warranted to confirm these observations and refine their clinical applications in thyroid cancer management.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides evidence that coexisting Hashimoto\u0026rsquo;s Thyroiditis is associated with less aggressive clinicopathological features in patients diagnosed with Differentiated Thyroid Cancer. Patients with HT exhibited smaller tumors and lower rates of lymph node metastasis compared to those without HT, and multivariate analysis confirmed that HT was independently associated with reduced tumor aggressiveness. These findings support the hypothesis that the autoimmune environment characteristic of Hashimoto\u0026rsquo;s Thyroiditis may exert a protective effect, potentially through enhanced immune surveillance or constrained tumor progression.\u003c/p\u003e \u003cp\u003eThe incorporation of HT status into predictive models, including logistic regression and nomogram visualization, demonstrated its relevance in risk stratification and clinical decision-making. Although the model\u0026rsquo;s discriminative power was limited, its strong calibration highlights the reliability of HT as a prognostic variable when used alongside conventional factors such as tumor size and nodal involvement.\u003c/p\u003e \u003cp\u003eThe results of this study suggest that HT should be considered in the evaluation and management of DTC patients. In selected cases, it may justify more conservative therapeutic approaches and individualized follow-up strategies. Further prospective studies with larger sample sizes and long-term follow-up are needed to confirm these findings and to investigate the potential role of immunological and molecular markers in refining thyroid cancer risk models.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDTC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDifferentiated Thyroid Cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHashimoto\u0026rsquo;s Thyroiditis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePTC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePapillary Thyroid Carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eROC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAUC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea Under the Curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTSH\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThyroid\u0026ndash;Stimulating Hormone\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eFT3\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFree Triiodothyronine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eFT4\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFree Thyroxine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTPOAb\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThyroid Peroxidase Antibodies\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTgAb\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThyroglobulin Antibodies\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTK1\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThymidine Kinase 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMAE\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMean Absolute Error\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIQR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterquartile Range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Review Committee of Department of Thyroid and Breast Surgery, The Affiliated Hospital of Xuzhou Medical University, Jiangsu Province, PR China, and the need for informed consent was waived due to the retrospective nature of the study. All patient data were fully anonymized prior to analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This manuscript does not contain any individual person’s data in any form.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analysed during the current study are available from the corresponding author on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cbr\u003eThis research was supported by multiple grants from the Jiangsu Provincial Government. These include: the \u003cem\u003eResearch General Project of Jiangsu Provincial Health Commission\u003c/em\u003e (Grant No. H2023052), the \u003cem\u003eJiangsu Province High-level Hospital Construction Project\u003c/em\u003e (Grant No. LCZX202406), the \u003cem\u003eKey Funding for Maternal and Child Health Research in Jiangsu Province\u003c/em\u003e (Grant No. F2021234), and the \u003cem\u003eJiangsu Province Traditional Chinese Medicine Science and Technology Development Plan General Project\u003c/em\u003e (Grant No. MS2021101). The funders had no role in the design of the study, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed significantly to the present research and reviewed the entire manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors would like to thank the clinical staff at Department of Thyroid and Breast Surgery, The Affiliated Hospital of Xuzhou Medical University, Jiangsu Province, PR China for their assistance in data retrieval. The authors are also grateful to the biostatistics unit for guidance in model development and validation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003evan Houten P, Netea-Maier RT, Smit JW. Differentiated thyroid carcinoma: an update. Best Pract Res Clin Endocrinol Metab. 2023;37(1):101687.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLimaiem F, Rehman A, Mazzoni T. Papillary Thyroid Carcinoma, in \u003cem\u003eStatPearls [Internet]\u003c/em\u003e, Treasure Island (FL): StatPearls Publishing; 2025 Jan\u0026ndash;. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/books/NBK536943/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/books/NBK536943/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoudina M, Zisimopoulou E, Xirou P, Chrisoulidou A. Aggressive Types of Malignant Thyroid Neoplasms. J Clin Med. 2024;13(20):6119. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/jcm13206119\u003c/span\u003e\u003cspan address=\"10.3390/jcm13206119\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakasu N, Yoshimura NJ. Hashimoto's thyroiditis: TGAb, TPOAb, TRAb and recovery from hypothyroidism. Expert Rev Clin Immunol. Mar. 2008;4(2):221\u0026ndash;37. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1586/1744666X.4.2.221\u003c/span\u003e\u003cspan address=\"10.1586/1744666X.4.2.221\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarsote M, Nistor C. Reshaping the Concept of Riedel\u0026rsquo;s Thyroiditis into the Larger Frame of IgG4-Related Disease, \u003cem\u003eBiomedicines\u003c/em\u003e, vol. 11, p. 1691, 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/biomedicines11061691\u003c/span\u003e\u003cspan address=\"10.3390/biomedicines11061691\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim SK, Cho SW. The Evasion Mechanisms of Cancer Immunity and Drug Intervention in the Tumor Microenvironment. Front Pharmacol. May 2022;13:868695. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fphar.2022.868695\u003c/span\u003e\u003cspan address=\"10.3389/fphar.2022.868695\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhiteside TL. Inhibiting the inhibitors: evaluating agents targeting cancer immunosuppression. Expert Opin Biol Ther. Jul. 2010;10(7):1019\u0026ndash;35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1517/14712598.2010.482207\u003c/span\u003e\u003cspan address=\"10.1517/14712598.2010.482207\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNishida, Andoh A. The Role of Inflammation in Cancer: Mechanisms of Tumor Initiation, Progression, and Metastasis, \u003cem\u003eCells\u003c/em\u003e, vol. 14, no. 7, p. 488, 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/cells14070488\u003c/span\u003e\u003cspan address=\"10.3390/cells14070488\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOhnishi S et al. DNA damage in inflammation-related carcinogenesis and cancer stem cells, \u003cem\u003eOxid. Med. Cell. Longev.\u003c/em\u003e, vol. 2013, p. 387014, 2013. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1155/2013/387014\u003c/span\u003e\u003cspan address=\"10.1155/2013/387014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZappavigna S, et al. Anti-Inflammatory Drugs as Anticancer Agents. Int J Mol Sci. 2020;21:2605. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms21072605\u003c/span\u003e\u003cspan address=\"10.3390/ijms21072605\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun W, et al. Meta-analysis of risk factors for CCLNM in patients with unilateral cN0 PTC. Endocr Connect. May 2020;9(5):387\u0026ndash;95. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1530/EC-20-0058\u003c/span\u003e\u003cspan address=\"10.1530/EC-20-0058\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTian H, et al. Risk factors of cervical central lymph node metastasis in stage T1a unifocal papillary thyroid carcinoma. Sci Rep. 2024;14:25577. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-024-77681-3\u003c/span\u003e\u003cspan address=\"10.1038/s41598-024-77681-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAggarwal R, et al. Distinctions between diagnostic and classification criteria? Arthritis Care Res (Hoboken). Jul. 2015;67(7):891\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/acr.22583\u003c/span\u003e\u003cspan address=\"10.1002/acr.22583\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu Q, Hou Z, Wang Z. Predictive modeling of preoperative acute heart failure in older adults with hypertension. BMC Med Inf Decis Mak. 2024;24:329. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12911-024-02734-6\u003c/span\u003e\u003cspan address=\"10.1186/s12911-024-02734-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDhiman P, et al. Reporting of prognostic clinical prediction models based on machine learning methods in oncology needs to be improved. J Clin Epidemiol. 2021;138:60\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao S, Mo L, Cai K, He W, Li Z. Development of a Practical Model to Predict Conversion to Total Hip Arthroplasty. Front Surg. 2022;9:835128. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fsurg.2022.835128\u003c/span\u003e\u003cspan address=\"10.3389/fsurg.2022.835128\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSedgwick P. Retrospective cohort studies: advantages and disadvantages, \u003cem\u003eBMJ\u003c/em\u003e, vol. 348, 2014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRStudio, Team. \u003cem\u003eRStudio: Integrated Development for R\u003c/em\u003e, Boston, MA: RStudio, PBC, 2020. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.rstudio.com/\u003c/span\u003e\u003cspan address=\"http://www.rstudio.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSundjaja JH, Shrestha R, Krishan K. McNemar And Mann-Whitney U Tests, in \u003cem\u003eStatPearls [Internet]\u003c/em\u003e, Treasure Island (FL): StatPearls Publishing; 2025 Jan\u0026ndash;. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/books/NBK560699/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/books/NBK560699/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePandis N. The chi-square test. Am J Orthod Dentofac Orthop. 2016;150(5):898\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIssa PP et al. Hashimoto\u0026rsquo;s thyroiditis minimizes lymph node metastasis in BRAF mutant papillary thyroid carcinomas, \u003cem\u003eBiomedicines\u003c/em\u003e, vol. 10, no. 8, p. 2051, 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoumeliotis S, et al. ROC curve analysis: a useful statistic multi-tool in the research of nephrology. Int Urol Nephrol. 2024;56:2651\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11255-024-04022-8\u003c/span\u003e\u003cspan address=\"10.1007/s11255-024-04022-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Differentiated Thyroid Cancer, Hashimoto’s Thyroiditis, Tumor Aggressiveness, Lymph Node Metastasis, Papillary Thyroid Carcinoma, Retrospective Study","lastPublishedDoi":"10.21203/rs.3.rs-6709654/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6709654/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHashimoto's Thyroiditis represents a long-term autoimmune condition which ordinarily develops in people diagnosed with Differentiated Thyroid Cancer. The connection between these disorders and how Hashimoto\u0026rsquo;s Thyroiditis affects thyroid malignancy development remains unknown. The current research examined how Hashimoto\u0026rsquo;s Thyroiditis affects tumor aggressiveness in Diffused Thyroid Cancer patients by analyzing tumor size rates and lymph node metastasis and pathological invasiveness outcomes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe study examined 198 patients who underwent thyroidectomy between June 2023 and March 2025 through an observational retrospective review. The research included two discrete patient groups with and without Hashimoto\u0026rsquo;s Thyroiditis. Tumor size measurements together with nodal status reports and capsular invasion status received analysis from both groups of patients. The study used descriptive statistics together with Mann\u0026ndash;Whitney U test along with Chi-square test to determine differences between groups. The authors built a multivariate logistic regression framework to confirm whether Hashimoto\u0026rsquo;s Thyroiditis functioned as an individual determinant of tumor aggressive behavior. Performance prediction was evaluated with receiver operating characteristic curve analysis while a nomogram functioned to create personalized risk estimates.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePatients with Hashimoto\u0026rsquo;s Thyroiditis had significantly smaller tumors (median size: 9 mm vs. 12 mm, p\u0026thinsp;=\u0026thinsp;0.003) and a lower frequency of lymph node metastasis (25.0% vs. 38.0%, p\u0026thinsp;=\u0026thinsp;0.043) compared to those without thyroiditis. Multivariate analysis confirmed that Hashimoto\u0026rsquo;s Thyroiditis was associated with reduced odds of tumor aggressiveness (odds ratio\u0026thinsp;=\u0026thinsp;0.68, 95% CI: 0.47\u0026ndash;0.98, p\u0026thinsp;=\u0026thinsp;0.041), independent of tumor size and nodal involvement. While the logistic model demonstrated modest discriminative ability (AUC\u0026thinsp;=\u0026thinsp;0.587), calibration performance was strong (mean absolute error\u0026thinsp;=\u0026thinsp;0.025). The nomogram provided an interpretable tool for individualized prediction.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe presence of Hashimoto\u0026rsquo;s Thyroiditis in patients with Differentiated Thyroid Cancer is associated with less aggressive tumor features. These findings suggest a potentially protective role of autoimmune thyroiditis and support its consideration in risk stratification and clinical management decisions. Further prospective studies are warranted to validate these observations and explore underlying immunological mechanisms.\u003c/p\u003e","manuscriptTitle":"Clinical Significance Of Co-Existance Of Hashimoto Thyroiditis (HT) With Differentiated Thyroid Cancer (DTC)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-25 09:40:24","doi":"10.21203/rs.3.rs-6709654/v1","editorialEvents":[{"type":"communityComments","content":2},{"type":"decision","content":"Revision requested","date":"2026-01-28T04:10:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-23T12:54:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"26696946360460643624758141818076866683","date":"2026-01-18T12:36:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-23T15:49:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"49484098454665434605646234214757694746","date":"2025-12-18T08:23:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-01T18:21:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"261909485794157859861926791934466986095","date":"2025-06-22T17:37:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-18T08:03:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-27T09:52:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-23T09:57:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-23T09:55:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Endocrine Disorders","date":"2025-05-20T16:12:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c5011a02-0386-4a5e-a577-f9022e0bc3c5","owner":[],"postedDate":"June 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T10:53:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-25 09:40:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6709654","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6709654","identity":"rs-6709654","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}