Prognostic Characterization of Papillary Thyroid Carcinoma: Insights into the Role of PD-L1 and Mutational Landscape in Disease Aggressiveness and Outcome

Prognostic Characterization of Papillary Thyroid Carcinoma: Insights into the Role of PD-L1 and Mutational Landscape in Disease Aggressiveness and Outcome | 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 Prognostic Characterization of Papillary Thyroid Carcinoma: Insights into the Role of PD-L1 and Mutational Landscape in Disease Aggressiveness and Outcome Chanchal Rana, Prabhakar Mishra, Isha Makkar, Kulranjan Singh, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6175152/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Papillary thyroid carcinoma (PTC) is the most common thyroid malignancy, with generally favorable outcomes. However, a subset of patients experiences aggressive disease progression, recurrence, and metastasis, necessitating refined prognostic tools. This study investigates the prognostic significance of PD-L1 expression, measured by the Tumor Proportion Score (TPS), in conjunction with genetic mutations (BRAF, TERT, RAS) and clinicopathological features in PTC. Methods: A retrospective analysis was conducted on 84 PTC patients diagnosed between July 2016 and June 2024 at King George’s Medical University, India. Formalin-fixed paraffin-embedded (FFPE) tissues were evaluated for PD-L1 expression using immunohistochemistry (IHC), with TPS categorized as low or high based on median values. Molecular analysis identified BRAF, TERT, and RAS mutations. Clinicopathological data, including tumor size, lymph node involvement, extrathyroidal extension, and recurrence, were collected. Statistical analyses assessed associations between TPS, molecular markers, and clinical outcomes. Results: PD-L1 expression was observed in 60.7% of cases, with 38.1% classified as TPS high. PD-L1 high was significantly associated with older age, advanced tumour stage, lymph node involvement, and aggressive histological variants (p < 0.05). BRAF and TERT mutations were more prevalent in PD-L1 high cases (p = 0.032 and p = 0.038, respectively). PD-L1 high correlated with increased recurrence (p = 0.043) and distant metastasis (p = 0.002). Multivariable analysis identified multiple mutations as independent predictors of poor survival (AOR = 15.8, p = 0.021) and recurrence (AOR = 11.22, p < 0.001). Conclusion: PD-L1 expression, particularly when combined with BRAF and TERT mutations, serves as a valuable prognostic marker in PTC. PD-L1 high is associated with aggressive tumour behaviour, recurrence, and metastasis, highlighting its potential for risk stratification and personalized treatment strategies. Further prospective studies are needed to validate these findings and explore the therapeutic implications of PD-L1 in PTC. Thyroid PD-L1 expression Prognosis Outcome Papillary thyroid carcinoma Figures Figure 1 INTRODUCTION Papillary thyroid carcinoma (PTC) accounts for over 80% of differentiated thyroid cancers (DTC) and is generally associated with a favourable prognosis. However, 15% to 35% of patients experience recurrence, and 10% to 15% of these cases progress to distant metastasis after primary surgery (1–5). In some instances, PTC may undergo dedifferentiation into high-grade tumours, complicating treatment and worsening patient outcomes (6). Moreover, therapeutic options remain limited for inoperable and radioiodine (RAI)-refractory DTC (1,2,4). While clinicopathological factors have been studied for prognostic significance, tumour stage alone does not reliably predict disease-free survival. Dynamic risk stratification models have faced challenges, underscoring the need for additional biomarkers (1,7–9). Although genetic mutations, such as BRAF V600E and TERT promoter mutations, have been linked to aggressive disease, their clinical utility remains questionable, and they are not currently recommended for postoperative prognostication and management of PTC (1,10–12). PD-L1 has been associated with aggressive disease in certain cancers, but is primarily a predictive marker of immunotherapeutic significance (13,14). However, its role as a prognostic marker in thyroid cancers, particularly in PTC, remains largely unexplored (15). Identifying reliable biomarkers is essential for predicting disease progression and tailoring treatment strategies to individual patients, ultimately helping to reduce unnecessary testing, overtreatment, and psychological stress in low-risk cases. This study aims to evaluate the prognostic role of PD-L1 expression as a risk stratification marker, specifically in relation to genetic mutations and other clinicopathological parameters. We aim to assess its association with tumour aggressiveness, recurrence, metastasis, and survival outcomes, providing potential insights into its clinical relevance in PTC. MATERIAL AND METHODS Case Selection This retrospective study was conducted at King George’s Medical University (KGMU) in Lucknow, India, in collaboration with the Department of Endocrine Surgery. We retrieved formalin-fixed paraffin-embedded (FFPE) tissue samples from patients diagnosed with PTC between July 2016 and June 2024. Clinical data, including patient demographics (age, gender), tumour characteristics (size, histopathological subtype, lymph node involvement, extrathyroidal extension, and recurrence), and survival outcomes, were collected from medical records. To ensure the quality of the study, cases with insufficient tissue for molecular or immunohistochemical analysis or incomplete clinical data were excluded. The study was approved by the institutional ethical committee and has been conducted in accordance with relevant institutional guidelines. Histopathological and Cytological Evaluation Histopathological evaluation was performed in line with the College of American Pathologists (CAP) protocol ( 16 ) Tumour specimens obtained through various surgical methods (lobectomy, hemithyroidectomy, and core needle biopsy) were analysed for tumour size, histological subtype, lymph node involvement, extrathyroidal extension, and lymphovascular invasion. All the cases were reviewed and classified following the 2022 WHO classification system for Thyroid tumours ( 17 ). Cytological cases were also reviewed and reported as per The 2023 Bethesda system of Reporting Thyroid Cytopathology ( 18 ). Immunohistochemistry and interpretation : Sections of 3–5 µm thickness from formalin-fixed paraffin-embedded (FFPE) tissues were prepared on 3-aminopropyl triethoxysilane-coated glass slides. After deparaffinization and rehydration, endogenous peroxidase activity was blocked using 3% hydrogen peroxide. Antigen retrieval was performed at 98°C for 15 minutes using a microwave oven in target retrieval buffer. Tissue sections were incubated with primary antibodies against PD-L1 (mouse monoclonal antibody clone SP142; Abcam; 1:100 dilution). Signal amplification was achieved using the HiDef 2-Step Polymer Detection Kit (Cell Marque, USA) and DAB (3,3'-diaminobenzidine tetrahydrochloride) as a chromogen. Nuclei were counterstained with Mayer’s haematoxylin, followed by dehydration and mounting for light microscopy. Tonsil/placenta was used as a positive control. Omission of the primary antibody served as the negative control. Both positive and negative controls were included in each batch of immunohistochemical staining. PD-L1 expression was evaluated using the Tumour Proportion Score (TPS ), measuring the percentage of tumour cells showing partial or complete membranous positivity, excluding cytoplasmic staining. A minimum of 500 viable tumour cells were assessed, with a TPS of ≥ 1% considered positive ( 19 ). Based on the median TPS value, cases were categorized as PD-L1 low or high . Molecular Analysis Molecular analysis focused on identifying mutations in BRAF, TERT promoter, and RAS genes. DNA was extracted from FFPE tumour tissue using a commercial DNA extraction kit (QIAamp DNA FFPE Tissue Kit, Qiagen) following the manufacturer’s protocol. DNA concentration and purity were assessed using a NanoDrop spectrophotometer. PCR amplification was used for exon 15 of the BRAF gene, the TERT promoter region and and exons 2 and 3 of NRAS followed by sanger sequencing for detection of BRAFV6001, C228T and C250T for TERT and Q61 for RAS. Positive controls (known mutated samples) and negative controls (water or non-template control) were run in each PCR to ensure the reliability of the assay. Statistical analysis Categorical variables, such as PD-L1 expression levels (TPS low vs. TPS high), BRAF, TERT, and RAS mutation status, and clinicopathological parameters were summarized as frequencies and percentages. Continuous variables (e.g., age, tumour size, TPS) were presented as means with standard deviations or medians with interquartile ranges, depending on their distribution. Due to the presence of several outliers, the median was selected instead of the mean as the cutoff for TPS. This approach more accurately categorizes cases into TPS high and TPS low groups while minimizing the impact of extreme values that could distort the results To identify potential prognostic factors, univariate analysis was performed for each clinicopathological variable, PD-L1 expression, and molecular markers. Chi-square or Fisher’s exact test was used to compare categorical variables, while continuous variables were analyzed using independent t-tests or Mann-Whitney U tests, as appropriate. Variables with a p-value < 0.05 in the univariate analysis were considered for inclusion in further multivariate analysis. Multivariable binary logistic regression analysis was used to assess the factors predicting the likelihood of each of the non-survive and recurrence of the disease. The outcome variables were categorized as present or absent. Independent variables were identified from univariate analysis. The logistic regression model provided adjusted odds ratios (ORs) with 95% confidence intervals (CIs) for each predictor, identifying independent prognostic factors. A p-value < 0.05 was considered statistically significant. All statistical analyses were performed using SPSS software for windows. version 23.0 (SPSS-23, IBM, Chicago, USA) and STATA-16. P value < 0.05 was considered as statistically significant. RESULTS Demographics, Clinical, and Radiological Features : A total of 84 patients diagnosed with papillary thyroid carcinoma (PTC) were included in this study, with ages ranging from 14 to 70 years (mean age: 38.2 years). The cohort exhibited a strong female predominance (79.8%), consistent with the well-documented gender predilection for PTC. Tumour sizes varied from 0.8 cm to 8.5 cm, with a mean diameter of 3.5 cm. Staging based on the AJCC 8th edition classification ( 20 ) revealed that 48.6% of cases were categorized as T3, followed by 28.6% as T2, 19% as T1, and 3.6% as T4. Lymph node metastasis was identified in 37.6% of patients, with 14.3% presenting with N1B involvement. The majority (76.2%) were classified as stage 1 disease. Surgical resection was the cornerstone of treatment, with total thyroidectomy performed in 77.4% of cases, while 22.6% underwent lobectomy. Postoperative radioiodine therapy was administered to 23.8% of patients. Disease recurrence was observed in 20.2% of cases, and distant metastasis was documented in 8.3% of patients. Pathological and Molecular Characteristics Histopathological evaluation revealed that 79.8% of tumours were unifocal, with the classical variant of PTC being the most prevalent, comprising 63.1% of cases. Other histological subtypes included the invasive follicular variant (21.4%), solid variant (4.8%), tall cell variant (3.6%), and the aggressive hobnail variant (1.2%). Extrathyroidal extension was observed in 31% of cases, while lymphovascular invasion was identified in 26.2%. Lymph node involvement was detected in 39.3% of patients (33/84), with a significant proportion (72.7%) exhibiting metastasis in more than five lymph nodes. Additionally, associated lymphocytic thyroiditis in the surrounding thyroid parenchyma was documented in 35.7% of cases. Preoperative cytological assessment was performed in 56 patients, with the majority (66.7%) classified as Bethesda VI, confirming malignancy in most cases. Additionally, 13.1% of cases fell into indeterminate Bethesda categories (III, IV, and V), reflecting diagnostic uncertainty. Notably, 11 cases were cytologically reported as benign (Bethesda II), highlighting the limitations of preoperative fine-needle aspiration in certain instances Mutational analysis revealed the presence of the BRAF mutation in 35.7% of patients, reaffirming its role as a critical molecular marker in PTC. TERT promoter mutations were identified in 16.7% of cases, while RAS mutations were also detected in 16.7% of patients, predominantly in the invasive follicular variant of PTC. A subset of patients (n = 13; 15.4%) exhibited coexisting mutations, although these were less common than single mutations. The most frequent co-occurring alterations were BRAF with TERT (8 cases), followed by BRAF with RAS (4 cases), with only one case demonstrating TERT and RAS co-mutation. Notably, over half of the patients (53.6%) did not harbour any detectable mutations, underscoring the genetic heterogeneity of PTC. Association of PD-L1 Expression with Clinicopathological and Molecular Features in PTC PD-L1 expression was detected in 60.7% of cases (51 patients), while 39.3% (33 patients) showed no PD-L1 expression. The Tumour Proportion Score (TPS) ranged from 0–98%, with a median score of 20. Based on this cutoff, cases were categorized into PD-L1 high (38.1%; n = 32) and PD-L1 low (61.9%; n = 52). A significant age-related difference was observed, with the median age of the PD-L1 high group being significantly higher than that of the PD-L1 low group (45 years vs. 30.5 years; p = 0.001). Moreover, the majority of patients in the PD-L1 high group were older than 45 years (p = 0.002). The paediatric population predominantly fell into the PD-L1 low category, contrasting with adults, though this association did not reach statistical significance. Gender distribution showed no significant difference between the PD-L1 high and low groups, with a consistent female predominance across both categories. Radiological analysis revealed variations in tumour size, with the PD-L1 high group tending to have larger tumours (> 3.5 cm); however, this difference did not reach statistical significance (p = 0.092). Tumour staging analysis demonstrated a significant correlation between PD-L1 expression and advanced disease, with a higher proportion of T3 and T4 tumours in the PD-L1 high group compared to the PD-L1 low group (p = 0.045). Lymph node involvement was also significantly more frequent in the PD-L1 high group, suggesting a tendency toward locally advanced disease (p = 0.038). Furthermore, prognostic staging revealed a clear association between PD-L1 expression and disease severity, as stage 1 disease was more prevalent in the PD-L1 low group (73.4%), whereas stages 3 and 4 were predominantly observed in the PD-L1 high group, a statistically significant finding (p = 0.002). These findings underscore the potential role of PD-L1 expression as a biomarker of aggressive tumour behaviour and poor prognosis in papillary thyroid carcinoma. Pathological evaluation revealed a higher prevalence of multifocality in the PD-L1 high group, suggesting a possible link between increased proliferative activity and tumour multiplicity. Additionally, aggressive pathological features, including extrathyroidal extension and lymphovascular invasion, were more frequently observed in the PD-L1 high group, whereas these characteristics were significantly less common in the PD-L1 low group. While these findings indicate a potential association between PD-L1 expression and aggressive tumour behaviour, none of these factors reached statistical significance, emphasizing the need for further investigation. Notably, poor histological variants, such as tall cell, hobnail, diffuse sclerosing, and columnar subtypes, were significantly more prevalent in the PD-L1 high group (p = 0.038), reinforcing the association of PD-L1 expression with aggressive tumour phenotypes. Conversely, lymphocytic thyroiditis, present in 57.1% of patients, showed a strong inverse correlation with PD-L1 expression (p = 0.001), suggesting a potential protective role of the immune microenvironment in limiting tumour progression. Molecular analysis revealed a high prevalence of BRAF mutations (35.7%), which were significantly associated with the PD-L1 high group (p = 0.032). Similarly, TERT promoter mutations were detected in 11.9% of cases and showed a strong correlation with PD-L1 high expression (p = 0.038), suggesting a potential link between aggressive tumour behaviour and immune checkpoint expression. In contrast, RAS mutations (16.7%) did not demonstrate a significant correlation with TPS. However, coexisting mutations, particularly BRAF and TERT, were observed in 16.7% of cases and were strongly linked to poor prognostic outcomes, including PD-L1 high expression and tumour recurrence (p < 0.05). These findings highlight the interplay between molecular alterations and immune evasion mechanisms, warranting further research into targeted therapeutic strategies. Please refer to Table 1 . Table 1 Demographic, clinical and radiological details of patients with Papillary thyroid carcinoma (N = 84) Characteristics N=84 TPS High (n=32) TPS Low (n=52) P value Age distribution Age range - Paediatric - Adults Mean age Median age Age distribution - 60 yrs Age group I - Age ≤ 45 years - Age > 45 years Gender distribution - Male - Females 14-70 years 08 76 38.2 years 35 years 09 (10.7%) 40 (47.6%) 29 (34.5%) 06 (7.1%) 61 (72.6 %) 15 (27.4 %) 17 (20.2%) 67 (79.8%) 17-35 years 01 (12.5%) 31 (40.8%) 40 years 45 years 01 (11.1%) 10 (25.0%) 16 (55.2%) 05 (83.3%) 17 (27.9%) 15 (65.2%) 04 (23.5%) 28 (41.8%) 14-70 years 07 (87.5%) 45 (59.2%) 38 years 30.5 years 8 (88.9%) 30 (75%) 13 (44.8%) 01 (16.7%) 44 (72.1%) 08 (34.8%) 3 (76.5%) 39 (58.2%) 0.147 0.001 0.002 0.002 0.116 Radiological details Size Range Mean size - >3.5 cm - <3.5 cm Prognostic Clinical stage (TNM AJCC 8 th edition) T staging - T1 - T2 - T3 - T4 N staging - N0 - N1 - N1B - N2 Prognostic staging Stage 1 Stage 2 Stage 3 Stage 4 0.8 – 8.5 cm 3.5 cm 32 (4.38%) 41 (56.2%) 16 (19%) 24 (28.6%) 41 (48.6%) 03 (3.6%) 17 (20.2%) 19 (22.6%) 12 (14.3%) 02 (2.4%) 64 (76.2%) 8 (9.5%) 9 (10.7%) 3 (3.6%) 14 (43.8%) 14 (34.1%) 03 (18.8%) 11 (45.8%) 15 (36.6%) 00 (00%) 8 (47.1%) 9 (47.4%) 5 (51.7%) 2 (100%) 17 (26.6%) 6 (62.5%) 7 (77.8%) 3 (100) 18 (56.3%) 27 (65.9%) 13 (81.3%) 13 (54.2%) 26 (63.4%) 03 (100%) 9 (52.9%) 10 (52.6%) 7 (58.3%) 0 (0%) 47 (73.4%) 3 (37.5%) 2 (22.2%) 0 (0%) 0.402 0.045 0.098 < 0.001 Treatment and follow up details Surgical procedure - Lobectomy - Total thyroidectomy 19 (22.6%) 65 (77.4%) 4 (21.1%) 28 (43.1%) 15 (78.9%) 37 (56.9%) 0.080 Radio-iodine therapy - Present - Absent 20 923.8%) 64 (76.2%) 8 (40.0%) 24 (37.2%) 12 (60.0%) 40 (62.5%) 0.841 Recurrence - Present - Absent 17 (20.2%) 67 (79.8%) 22 (32.4%) 10 (62.4%) 46 (67.6%) 06 (37.5%) 0.043 Distant metastasis - Present - Absent 07 (8.3%) 77 (91.7%) 5 (71.4%) 27 (35.1%) 2(28.6%) 50 (64.9%) 0.100 Pathological details Focality - Unifocal - Multifocal Morphological variants - Classical - Invasive Follicular - Solid - Tall cell - Hobnail - Diffuse sclerosing - Columnar Poor variants - Present - Absent Lymph node dissection - >5 lymph nodes involved - < 5 lymph nodes involved Associated lymphocytic thyroiditis - Present - Absent Extra-thyroidal Extension - Present - Absent Lymphovascular invasion - Present - Absent Molecular details BRAF - Present - Absent TERT - Present - Absent RAS - Present - Absent Multiple mutation - BRAF + TERT - BRAF + RAS - TERT + RAS Single mutation - No mutation 67 (79.8%) 17 (20.2%) 53 (63.1%) 18 (21.4%) 4 (4.8%) 3 (3.6%) 1 (1.2%) 1 (1.2%) 1 (1.2%) 10 (11.9%) 74 (88.1%) 24/33 (72.7%) 09/33 (27.3%) 48 (57.1%) 36 (42.9%) 26 (31.0%) 58 (69.0%) 22 (26.2%) 73.8%) 30 (35.7%) 54 (64.3%) 10 (11.9%) 70 (83.3%) 14 (16.7%) 71 (84.5%) 13 (15.4%) 8 4 1 26 (31.0 %) 45 (53.6%) 25 (37.3%) 07 (41.2%) 7 (70.0%) 25 (33.8%) 14 (58.3%) 2 (22.2%) 11 (22.9%) 21 (58.3%) 13 (50%) 21 (33.9%) 11 (50%) 21 (33.9%) 16 (53.3%) 16 (29.6%) 7 (21.9%) 25 (33.8%) 4 (28.6%) 28 (40.0%) 9 (69.2%) 6 3 0 7 (26.9%) 16 (35.6%) 42 (62.3%) 10 (58.8%) 3 (30.0%) 49 (66.2%) 10 (41.7%) 7 (77.8%) 37 (77.1%) 15(41.7%) 13 (50%) 39 (67.2%) 11 (50%) 41 (66.1%) 14 (46.7%) 38 (70.4%) 3 (30.0%) 49 (66.2%) 10 (71.4%) 42 (60.0%) 4 (30.8%) 2 1 1 19 (73.1%) 29 (64.4%) 0.770 0.038 0.118 0.001 0.133 0.181 0.032 0.038 0.421 0.046 Data are presented in Median (Inter quartile range) [Mean] and compared using Mann Whitney U test. Number (%) and compared using Chi square test / Fisher exact test. P value < 0.05 significant. Clinical Outcomes and Predictors of Prognosis Recurrence was observed in 19% of patients, with a significantly higher risk in the PD-L1 high group (p = 0.043). The median TPS in recurrent cases was 63.5, compared to 11 in non-recurrent cases (p = 0.044), reinforcing the association between higher PD-L1 expression and disease recurrence. Additionally, histopathological features such as poor-variant histology and extrathyroidal extension were significantly linked to recurrence. Distant metastasis occurred in 8.3% of cases and was strongly associated with PD-L1 high expression and multiple mutations (p = 0.002). At the last follow-up, 92.9% of patients were alive, while 7.1% had succumbed to the disease. Non-survivors exhibited significantly higher TPS, frequent distant metastases, and a higher prevalence of multiple mutations (p < 0.001). Please refer to Table 2 . Table 2 Association of Survival of patients with variables (N = 84) Variable’s Non-Survivors (n = 6) Survivors (n = 78) P value Age in Years 45(29,45) 35(27,53) 0.855 Maximum size 5.2(2.2,7) 3.5(2,4.7) 0.295 TPS 23.5(0,90) 20(0,65) 0.727 PD1 13(1,50) 2( 1 , 10 ) 0.241 Pediatric/adults Paediatric 1(12.5) 7(87.5) 0.462 Adults 5(6.6) 71(93.4) Age groups 60 0(0) 6(100) Age cut off 45 years 45 1(4.3) 22(95.7) Sex Female 5(7.5) 62(92.5) 0.99 Male 1(5.9) 16(94.1) Specimen type Hemi Thyroidectomy 3(15.8) 16(84.2) 0.126 Total Thyroidectomy 3(4.6) 62(95.4) Mean Size > 3.5 cm 4(12.5) 28(87.5) 0.394 <= 3.5 cm 2(4.9) 39(95.1) T Staging T1 1(6.3) 15(93.8) 0.022 T2 1(4.2) 23(95.8) T3 2(4.9) 39(95.1) T4 2(66.7) 1(33.3) N staging N staging 3(8.8) 31(91.2) 0.702 N0 0(0) 17(100) N1 2(10.5) 17(89.5) N1B 1(8.3) 11(91.7) N2 0(0) 2(100) Lymph node Metastasis Present 3(9.1) 30(90.9) 0.542 Absent 0(0) 17(100) No. of LN involved > 5 LN involved 3(12.5) 21(87.5) 0.545 <=5 LN involved 0(0) 9(100) Focality Unifocal 4( 6 ) 63(94) 0.596 Multifocal 2(11.8) 15(88.2) Histological Variant Classical 3(5.7) 50(94.3) 0.724 Columnar 0(0) 1(100) DS 0(0) 1(100) Hobnail 0(0) 1(100) IFV 2(11.1) 16(88.9) Micropapillary 1(33.3) 2(66.7) Solid 0(0) 4(100) Tall cell 0(0) 3(100) Poor Variant morphology Present 0(0) 10(100) 0.99 Absent 6(8.1) 68(91.9) Extrathyroidal Extension Present 3(11.5) 23(88.5) 0.368 Absent 3(5.2) 55(94.8) Lymph vascular Extension Present 3(13.6) 19(86.4) 0.182 Absent 3(4.8) 59(95.2) Perineural Extension Present 0(0) 4(100) 0.99 Absent 6(7.5) 74(92.5) Perinodal extension Present 3(16.7) 15(83.3) 0.109 Absent 3(4.5) 63(95.5) Prognostic staging 1 3(4.7) 61(95.3) 0.030 2 0(0) 8(100) 3 1(11.1) 8(88.9) 4 2(66.7) 1(33.3) PDL1_expression Present 4(7.8) 47(92.2) 0.99 Absent 2(6.1) 31(93.9) TPS(High/Low) mean 2(6.1) 31(93.9) 0.874 PD-L1high 3(9.4) 29(90.6) PD-L1 Low 1(5.3) 18(94.7) Lymphocytic thyroiditis Present 2(4.2) 46(95.8) 0.395 Absent 4(11.1) 32(88.9) BRAF mutation Present 4(13.3) 26(86.7) 0.180 Absent 2(3.7) 52(96.3) TERT promoter mutation Present 2( 20 ) 8(80) 0.148 Absent 4(5.4) 70(94.6) RAS Present 2(14.3) 12(85.7) 0.261 Absent 4(5.7) 66(94.3) Multiple mutation Present 3(21.4) 11(78.6) 0.055 Absent 3(4.3) 67(95.7) Combinations of mutations BRAF + TERT 1(12.5) 7(87.5) 0.583 BRAF + RAS 1( 25 ) 3(75) TERT + RAS 0(0) 1(100) Single mutation 2(7.7) 24(92.3) No mutation 2(4.4) 43(95.6) Radioidone_therapy Present 3( 15 ) 17(85) 0.143 Absent 3(4.7) 61(95.3) Recurrence Absent 3(4.4) 65(95.6) 0.080 Present 3(18.8) 13(81.3) Distant metastasis Present 4(57.1) 3(42.9) < 0.001 Absent 2(2.6) 75(97.4) Data are presented in Median (Inter quartile range) [Mean] and compared using Mann Whitney U test. Number (%) and compared using Chi square test / Fisher exact test. P value < 0.05 significant. Multivariable logistic regression analysis identified multiple mutations as independent predictors of poor survival (AOR = 15.8, p = 0.021) and recurrence (AOR = 11.22, p < 0.001). Notably, total thyroidectomy was associated with improved survival (AOR = 10.77, p = 0.046). Although poor-variant histology showed a trend toward predicting recurrence, it did not reach statistical significance (AOR = 4.16, p = 0.079). Please refer to Table 3 and Fig. 1. Table 3 Independent Predictors of Patient Outcomes (N = 84) Variable's AOR 95% CI P value Patients Survival Multiple mutations 15.80 1.51–165.60 0.021 Specimen type 10.77 1.04-111.61 0.046 Patients Recurrence Poor variant 4.16 0.85–20.38 0.079 Multiple mutations 11.22 2.94–42.90 < 0.001 AOR = Adjusted Odds ratio. Multivariable binary logistic regression analysis used. P < 0.05 significant DISCUSSION Papillary thyroid carcinoma (PTC), the most common form of differentiated thyroid cancer (DTC), generally has a favourable prognosis. However, 15–35% of patients experience recurrence, and 10–15% develop distant metastases following primary surgery ( 1 – 5 ). In some cases, PTC undergoes dedifferentiation into high-grade tumours, further complicating treatment and worsening patient outcomes ( 6 ). Additionally, therapeutic options remain limited for inoperable or radioiodine (RAI)-refractory DTC ( 1 , 2 , 4 ). Although clinicopathological factors have been extensively studied, tumour stage alone is not a reliable predictor of disease-free survival. Dynamic risk stratification models have been proposed but face challenges in accurately predicting outcomes, emphasizing the need for additional biomarkers ( 1 , 7 – 9 ). Genetic alterations, including BRAF V600E and TERT promoter mutations, have been associated with more aggressive disease, but their role in routine postoperative prognostication and management of PTC also remains controversial. ( 21 ). This underscores the necessity of identifying robust prognostic biomarkers that can better predict disease progression, guide treatment decisions, and minimize unnecessary interventions. The PD-1/PD-L1 pathway is increasingly recognized as a critical target in immunotherapy for aggressive cancers. However, the role of PD-L1 expression in differentiated thyroid carcinoma (DTC), particularly papillary thyroid carcinoma (PTC), remains inadequately understood ( 15 ). India faces a significant thyroid cancer burden, ranking fourth in incidence and second in mortality, with a large proportion of cases diagnosed at advanced stages ( 22 ). Despite this high burden, much of the existing research has been centred on Western populations, highlighting the need for more localized data ( 15 ). Our study is one of the few to investigate PD-L1 expression in a large cohort of PTC patients from India, shedding new light on the molecular and clinical features of these tumours. This study uniquely addresses not only PD-L1 expression but also its prognostic and survival implications, particularly when combined with key molecular alterations like BRAF V600E and TERT. Our findings offer critical insights into how demographic, genetic, and racial factors may shape tumour characteristics, paving the way for more precise risk stratification and personalized therapeutic strategies in PTC. While the FDA has approved several PD-L1 antibody clones for immunohistochemical (IHC) testing to guide immune checkpoint inhibitor treatment in cancers such as lung, urothelial, breast, melanoma, and renal cell carcinoma, there are currently no established PD-L1 clones or approved immunotherapies for aggressive thyroid cancers ( 23 ). This remains an under-explored area in thyroid cancer research. Studies on PD-L1 expression in thyroid carcinoma using cytometry and cytology samples are limited, with most relying on traditional IHC methods ( 19 , 24 – 33 ). Variability in antibody clones (e.g., E1L3N, 22C3, SP142, SP263, 28 − 8), cut-off thresholds, and subcellular localization further complicates assessment, leading to inconsistent findings. To mitigate this, our study focused on membranous (complete/partial) expression and used the tumour proportion score (TPS) median value as the cut-off, offering a data-driven, unbiased approach that reflects the natural distribution of PD-L1 expression in the cohort. Unlike arbitrary thresholds, this median-based cut-off improves statistical robustness, reduces misclassification due to assay variability, and provides a more clinically relevant patient stratification. Several studies have explored PD-L1 expression in thyroid carcinoma, but data specifically addressing papillary thyroid carcinoma (PTC) remain limited and inconsistent In our cohort, PD-L1 expression was observed in 60.7% of cases, aligning with prior reports that have documented expression levels as high as 60% in PTC ( 24 , 27 – 30 , 34 ). However, a large-scale study by Ahn S et al. ( 19 ), analysing 407 thyroid carcinoma cases, found PD-L1 expression in only 6.1% of PTC cases using a 1% cutoff, which further declined to 0.9% with a 5% threshold. Notably, the intensity of PD-L1 expression in these cases was weak. One potential explanation for this discrepancy is the use of tissue microarrays (TMAs), which may not fully capture PD-L1 expression across the entire tumour. Contrary to this, Shi et al., who also analysed 260 PTC cases using TMAs, reported PD-L1 expression in 52.3% of cases ( 34 ). These findings emphasis that the different PD-L1 clone and the evaluation criteria could lead to the variations in results. We used a full faced tissue sections to eliminate this issue. In general, studies that employed full tissue sections consistently reported higher levels of PD-L1 expression, emphasizing how sampling methods can influence outcomes. These variations highlight the need for standardized approaches in PD-L1 assessment to ensure accurate, reproducible results in PTC. The correlation between PD-L1 expression and clinicopathological factors in papillary thyroid carcinoma (PTC) remains controversial, with limited and inconsistent findings across studies ( 15 , 19 , 27 , 30 ). Ahn S et al., in their study of 326 PTC cases, did not find any significant correlation between PD-L1 expression and clinicopathological factors, recurrence, or BRAF/TERT mutation status ( 19 ). In contrast, Aghajani et al. reported that PD-L1 expression in PTC was associated with aggressive features such as lymphovascular invasion and extrathyroidal extension ( 27 ). Similarly, An HJ et al. found a significant association between PD-L1 expression and lymph node metastasis (p = 0.036) ( 30 ). Literature on PD-L1 as a prognostic marker in PTC is even scare, some studies have attempted to explore this relationship. Chowdhury et al. conducted one of the earliest studies on the prognostic role of PD-L1 in PTC, revealing that cytoplasmic PD-L1 positivity correlated with recurrence, while membrane positivity was linked to metastasis or death in Stage IV patients. PD-L1-positive tumours had significantly shorter disease-free survival (DFS) (36–49 months) compared to PD-L1-negative tumours (186 months) ( 32 ). Similarly, Shi et al. highlighted the prognostic impact of PD-L1 expression, showing its negative influence on recurrence-free survival (RFS). PD-L1 overexpression was found to be an independent marker of poor prognosis, particularly in patients with larger tumours, multifocal disease, extrathyroidal extension, lymph node metastasis, older age, and male sex ( 34 ). This finding was recently reinforced by a meta-analysis by Girolami et al., which reviewed 18 studies and confirmed a significant association between PD-L1 expression and reduced disease-free survival in differentiated thyroid carcinoma, especially in PTC. However, no such association was observed in dedifferentiated thyroid cancers (anaplastic and poorly differentiated thyroid carcinoma), and PD-L1 expression showed no correlation with overall survival in either group ( 15 ). In the present study, using the tumour proportion score (TPS) median as the cutoff and focusing solely on membranous positivity, we found that PD-L1 high was significantly associated with larger tumour sizes, advanced stages, and poorer histological variants. Following Bai et al., ours is one of the few studies to analyse different PTC variants as subgroups, including columnar, diffuse sclerosing, hobnail, and tall cell variants, which are considered poor prognostic types ( 24 ). Unlike Bai et al., we observed a significant correlation between PD-L1 high group and these aggressive variants. Another noteworthy finding from our study was the significant association between PD-L1 expression and background lymphocytic thyroiditis. Only a few other studies have reported such a correlation ( 24 , 27 , 33 , 34 ). Lubin et al. observed increased PD-L1 expression in PTC arising in a background of Hashimoto's thyroiditis (HT) compared to normal thyroid tissue. They also found a strong correlation between PD-L1 expression in primary PTC and local nodal metastases. Their study suggested that PTC in HT may be biologically distinct, with the underlying thyroid pathology influencing PD-L1 expression and potentially affecting treatment response ( 33 ). While surgery and postoperative radioiodine therapy remain the cornerstone of treatment for differentiated thyroid carcinoma, a subset of papillary thyroid carcinoma (PTC) cases, particularly those harbouring the BRAFV600E mutation, exhibit resistance to radioiodine therapy ( 21 ). Understanding the interplay between PD-L1/PD-1 expression and BRAF status is crucial, as it could help identify patients who might benefit from immunotherapy. Emerging evidence suggests that combining BRAF inhibitors with anti-PD-L1 therapy enhances tumor regression and boosts antitumor immunity in preclinical models, indicating the potential of combination approaches to improve outcomes in refractory PTC cases ( 11 , 28 , 29 , 35 ). Angell et al. were the first to report a significant positive association between BRAFV600E and PD-L1 expression in PTC, hypothesizing that BRAFV600E tumours may exploit PD-L1 expression to evade immune destruction, thus highlighting their immunosuppressive profile and impaired tumour immune surveillance ( 28 ). In contrast, Bai et al. found no such correlation, likely due to differences in sample selection, with the former study comprising more early-stage cases and the latter including more advanced cases ( 24 ). However, in a 2018 follow-up study, Bai et al. did observe a correlation between BRAFV600E expression and PD-L1 expression in PTC, suggesting that the BRAF mutation might influence the immune microenvironment. Despite this, no significant associations were found between BRAFV600E/ PD-L1 expression, and other clinicopathological factors ( 29 ). In our study BRAF mutations (35.7%) and TERT mutations (16.7%) were more prevalent in PD-L1 high cases. We also found that PD-L1 high correlated with a higher incidence of recurrence (p = 0.043) and distant metastasis (p = 0.002). Multivariable analysis identified multiple mutations as independent predictors of poor survival and recurrence. These findings underlines the potential role of BRAF/TERT mutations in modulating PD-L1 expression, with possible implications for immunotherapy in PTC. Our findings align with a recent meta-analysis by Girolmai et al that highlights PD-L1 as a potential biomarker in PTC management ( 15 ). This similarity suggests, that using the median TPS as a cutoff may yield more reliable results, and integrating PD-L1 assessment with mutation analysis could enhance prognostic classification, aiding in risk stratification and more precise patient management. This study's strength lies in its comprehensive and multifaceted analysis of the clinicopathological, molecular, and immunological characteristics of papillary thyroid carcinoma (PTC) in an Indian cohort. By integrating tumour proportion score (TPS) median cut-off for categorizing patients into PD-L1 low and high groups, alongside key genetic mutations such as BRAF and TERT, the study provides novel insights into the prognostic significance of PD-L1 expression. A key aspect is the incorporation of the 2022 WHO classification, ensuring reclassification of pre-NIFTP cases, which addresses a limitation in earlier studies. These findings contribute to better risk stratification and may aid in personalized treatment strategies, while also accounting for different PTC variants. However, the study has its fair share of some limitations. Its retrospective, single-centre design may restrict generalizability, and the use of immunohistochemistry alone may not fully capture the complexity of the immune microenvironment. Moreover, small subgroup sample sizes limit statistical power, and other molecular alterations potentially influencing PTC progression were not comprehensively assessed. Future prospective, multicentre studies are essential to validate these findings across diverse populations and refine their clinical application. In conclusion, this study highlights the prognostic value of PD-L1 expression in papillary thyroid carcinoma (PTC), particularly when combined with genetic mutations like BRAF and TERT. These findings offer insights into improved risk stratification and personalized treatment strategies. Declarations Financial disclosure: This project has been funded by Indian Council of Medical Research (ICMR) Conflict of interest: No, I declare that the authors have no competing interests as defined by Nature Research, or other interests that might be perceived to influence the results and/or discussion reported in this paper. Data Availability statement: The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. Clinical trial number : not applicable. References Haugen BR, Alexander EK, Bible KC, Doherty GM, Mandel SJ, Nikiforov YE, et al. 2015 American Thyroid Association Management Guidelines for Adult Patients with Thyroid Nodules and Differentiated Thyroid Cancer: The American Thyroid Association Guidelines Task Force on Thyroid Nodules and Differentiated Thyroid Cancer. Thyroid Off J Am Thyroid Assoc. 2016;26(1):1–133. Sun W, Di L, Chen L, Li D, Wu Y, Xiang J, et al. The outcomes and prognostic factors of patients who underwent reoperation for persistent/recurrent papillary thyroid carcinoma. BMC Surg. 2022;22(1):374. Bai Y, Kakudo K, Li Y, Liu Z, Ozaki T, Ito Y, et al. Subclassification of non-solid-type papillary thyroid carcinoma identification of high-risk group in common type. Cancer Sci. 2008;99(10):1908–15. Ulisse S, Baldini E, Lauro A, Pironi D, Tripodi D, Lori E, et al. Papillary Thyroid Cancer Prognosis: Evol Field Cancers. 2021;13(21):5567. Lam AK. Papillary Thyroid Carcinoma: Current Position in Epidemiology, Genomics, and Classification. Methods Mol Biol Clifton NJ. 2022;2534:1–15. Papp S, Asa SL. When thyroid carcinoma goes bad: a morphological and molecular analysis. Head Neck Pathol. 2015;9(1):16–23. Kim Y, Roh JL, Song D, Cho KJ, Choi SH, Nam SY, et al. Predictors of recurrence after total thyroidectomy plus neck dissection and radioactive iodine ablation for high-risk papillary thyroid carcinoma. J Surg Oncol. 2020;122(5):906–13. Ito Y, Onoda N, Okamoto T. The revised clinical practice guidelines on the management of thyroid tumors by the Japan Associations of Endocrine Surgeons: Core questions and recommendations for treatments of thyroid cancer. Endocr J. 2020;67(7):669–717. Filetti S, Durante C, Hartl D, Leboulleux S, Locati LD, Newbold K, et al. Thyroid cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up†. Ann Oncol Off J Eur Soc Med Oncol. 2019;30(12):1856–83. Abdullah MI, Junit SM, Ng KL, Jayapalan JJ, Karikalan B, Hashim OH. Papillary Thyroid Cancer: Genetic Alterations and Molecular Biomarker Investigations. Int J Med Sci. 2019;16(3):450–60. Cappola AR, Mandel SJ. Molecular testing in thyroid cancer: BRAF mutation status and mortality. JAMA. 2013;309(14):1529–30. Poongkodi K, Periyasamy S, Gurunathan RA, Krishnasamy V, Jayakumar D, Subburaman R, et al. The genomic landscape of papillary thyroid carcinoma on next-generation sequencing in patients undergoing total thyroidectomy. World J Surg. 2024;48(12):2880–91. Li H, Van Der Merwe PA, Sivakumar S. Biomarkers of response to PD-1 pathway blockade. Br J Cancer. 2022;126(12):1663–75. Paver EC, Cooper WA, Colebatch AJ, Ferguson PM, Hill SK, Lum T, et al. Programmed death ligand-1 (PD-L1) as a predictive marker for immunotherapy in solid tumours: a guide to immunohistochemistry implementation and interpretation. Pathol (Phila). 2021;53(2):141–56. Girolami I, Pantanowitz L, Mete O, Brunelli M, Marletta S, Colato C, et al. Programmed Death-Ligand 1 (PD-L1) Is a Potential Biomarker of Disease-Free Survival in Papillary Thyroid Carcinoma: a Systematic Review and Meta-Analysis of PD-L1 Immunoexpression in Follicular Epithelial Derived Thyroid Carcinoma. Endocr Pathol. 2020;31(3):291–300. Rana C, Babu S, Agarwal H, Singhai A, Kumar M, Singh V, et al. Diagnostic Relevance of GATA 3 Expression in Urinary Bladder Carcinoma of Divergent Differentiation and Other Histological Variants. Indian J Surg Oncol. 2021;12(4):678–85. Bychkov A, Jung CK. What’s new in thyroid pathology 2024: updates from the new WHO classification and bethesda system. J Pathol Transl Med. 2024;58(2):98–101. Ali SZ, Baloch ZW, Cochand-Priollet B, Schmitt FC, Vielh P, VanderLaan PA. The 2023 Bethesda System for Reporting Thyroid Cytopathology. Thyroid Off J Am Thyroid Assoc. 2023;33(9):1039–44. Ahn S, Kim TH, Kim SW, Ki CS, Jang HW, Kim JS, et al. Comprehensive screening for PD-L1 expression in thyroid cancer. Endocr Relat Cancer. 2017;24(2):97–106. Tuttle RM, Haugen B, Perrier ND. Updated American Joint Committee on Cancer/Tumor-Node-Metastasis Staging System for Differentiated and Anaplastic Thyroid Cancer (Eighth Edition): What Changed and Why? Thyroid. 2017;27(6):751–6. Haugen BR, Alexander EK, Bible KC, Doherty GM, Mandel SJ, Nikiforov YE, et al. 2015 American Thyroid Association Management Guidelines for Adult Patients with Thyroid Nodules and Differentiated Thyroid Cancer: The American Thyroid Association Guidelines Task Force on Thyroid Nodules and Differentiated Thyroid Cancer. Thyroid Off J Am Thyroid Assoc. 2016;26(1):1–133. Kitahara CM, Sosa JA. Understanding the ever-changing incidence of thyroid cancer. Nat Rev Endocrinol. 2020;16(11):617–8. Prince EA, Sanzari JK, Pandya D, Huron D, Edwards R. Analytical Concordance of PD-L1 Assays Utilizing Antibodies From FDA-Approved Diagnostics in Advanced Cancers: A Systematic Literature Review. JCO Precis Oncol. 2021;(5):953–73. Bai Y, Niu D, Huang X, Jia L, Kang Q, Dou F, et al. PD-L1 and PD-1 expression are correlated with distinctive clinicopathological features in papillary thyroid carcinoma. Diagn Pathol. 2017;12(1):72. Dell’Aquila M, Granitto A, Martini M, Capodimonti S, Cocomazzi A, Musarra T, et al. PD-L1 and thyroid cytology: A possible diagnostic and prognostic marker. Cancer Cytopathol. 2020;128(3):177–89. Bastman JJ, Serracino HS, Zhu Y, Koenig MR, Mateescu V, Sams SB, et al. Tumor-Infiltrating T Cells and the PD-1 Checkpoint Pathway in Advanced Differentiated and Anaplastic Thyroid Cancer. J Clin Endocrinol Metab. 2016;101(7):2863–73. Aghajani MJ, Cooper A, McGuire H, Jeffries T, Saab J, Ismail K, et al. Pembrolizumab for anaplastic thyroid cancer: a case study. Cancer Immunol Immunother CII. 2019;68(12):1921–34. Angell TE, Lechner MG, Jang JK, Correa AJ, LoPresti JS, Epstein AL. BRAF V600E in Papillary Thyroid Carcinoma Is Associated with Increased Programmed Death Ligand 1 Expression and Suppressive Immune Cell Infiltration. Thyroid. 2014;24(9):1385–93. Bai Y, Guo T, Huang X, Wu Q, Niu D, Ji X, et al. In papillary thyroid carcinoma, expression by immunohistochemistry of BRAF V600E, PD-L1, and PD-1 is closely related. Virchows Arch. 2018;472(5):779–87. An HJ, Ko GH, Lee JH, Lee JS, Kim DC, Yang JW, et al. Programmed Death-Ligand 1 Expression and Its Correlation with Lymph Node Metastasis in Papillary Thyroid Carcinoma. J Pathol Transl Med. 2018;52(1):9–13. Chintakuntlawar AV, Rumilla KM, Smith CY, Jenkins SM, Foote RL, Kasperbauer JL, et al. Expression of PD-1 and PD-L1 in Anaplastic Thyroid Cancer Patients Treated With Multimodal Therapy: Results From a Retrospective Study. J Clin Endocrinol Metab. 2017;102(6):1943–50. Chowdhury S, Veyhl J, Jessa F, Polyakova O, Alenzi A, MacMillan C, et al. Programmed death-ligand 1 overexpression is a prognostic marker for aggressive papillary thyroid cancer and its variants. Oncotarget. 2016;7(22):32318–28. Lubin D, Baraban E, Lisby A, Jalali-Farahani S, Zhang P, Livolsi V. Papillary Thyroid Carcinoma Emerging from Hashimoto Thyroiditis Demonstrates Increased PD-L1 Expression, Which Persists with Metastasis. Endocr Pathol. 2018;29(4):317–23. Shi Rliang, Qu N, Luo T, xian, Xiang J, Liao T, Sun G et al. hua,. Programmed Death-Ligand 1 Expression in Papillary Thyroid Cancer and Its Correlation with Clinicopathologic Factors and Recurrence. Thyroid. 2017;27(4):537–45. Brauner E, Gunda V, Vanden Borre P, Zurakowski D, Kim YS, Dennett KV, et al. Combining BRAF inhibitor and anti PD-L1 antibody dramatically improves tumor regression and anti tumor immunity in an immunocompetent murine model of anaplastic thyroid cancer. Oncotarget. 2016;7(13):17194–211. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6175152","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":428206836,"identity":"e9186e0f-6d4d-43b4-a424-97cb384d1608","order_by":0,"name":"Chanchal Rana","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYDACCSBmbGCQYWMG8SoYGAyI1cLDxw7inSFFixw/iNFGhBb+2c3PHvzcYcfDxsz8TPLrvMPy5uzNBxh+VGzDbcmdY+aGvWeSgVrYzKRltx023NlzLIGx58xtnFoMJBLMpBnbmIFaGMykJbcdZtxwI8eAmbENn5b0b0At9UAt7N+kJecctidCSw7IlsNALTxmkh8bDicS1CJxI6dMsrftOEhLsTXDsfTkDWeOJRzE5xf+GenbJH62VcvJ9x/fePNHjbXthuPNBx/8qMCtBQUw8zA0gxkHiFMPBIw/GOqIVjwKRsEoGAUjBwAA0JdRyOdvDhYAAAAASUVORK5CYII=","orcid":"","institution":"King George’s Medical University","correspondingAuthor":true,"prefix":"","firstName":"Chanchal","middleName":"","lastName":"Rana","suffix":""},{"id":428206837,"identity":"afd816c8-a651-45ee-bff0-9b3ce3dc14cd","order_by":1,"name":"Prabhakar Mishra","email":"","orcid":"","institution":"Sanjay Gandhi Postgraduate Institute of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Prabhakar","middleName":"","lastName":"Mishra","suffix":""},{"id":428206838,"identity":"2d92116e-d812-4a24-9e6c-8633b75639b6","order_by":2,"name":"Isha Makkar","email":"","orcid":"","institution":"King George’s Medical University","correspondingAuthor":false,"prefix":"","firstName":"Isha","middleName":"","lastName":"Makkar","suffix":""},{"id":428206839,"identity":"3c42938b-de2f-4857-bae0-2fc57ce8013a","order_by":3,"name":"Kulranjan Singh","email":"","orcid":"","institution":"King George’s Medical University","correspondingAuthor":false,"prefix":"","firstName":"Kulranjan","middleName":"","lastName":"Singh","suffix":""},{"id":428206840,"identity":"364e166f-7109-4e5a-ae1f-c8d54e498a54","order_by":4,"name":"Pooja Ramakant","email":"","orcid":"","institution":"King George’s Medical University","correspondingAuthor":false,"prefix":"","firstName":"Pooja","middleName":"","lastName":"Ramakant","suffix":""},{"id":428206841,"identity":"a41d727f-8997-4f3e-8df0-a9c5a8d67d8f","order_by":5,"name":"Anand Mishra","email":"","orcid":"","institution":"King George’s Medical University","correspondingAuthor":false,"prefix":"","firstName":"Anand","middleName":"","lastName":"Mishra","suffix":""},{"id":428206842,"identity":"62f11b7d-5961-4e37-a593-78f3fef0a788","order_by":6,"name":"Gagan Chhabra","email":"","orcid":"","institution":"King George’s Medical University","correspondingAuthor":false,"prefix":"","firstName":"Gagan","middleName":"","lastName":"Chhabra","suffix":""},{"id":428206843,"identity":"d8b3ce8c-21d8-4bec-b04f-9f97e11ee185","order_by":7,"name":"Komal Verma","email":"","orcid":"","institution":"King George’s Medical University","correspondingAuthor":false,"prefix":"","firstName":"Komal","middleName":"","lastName":"Verma","suffix":""}],"badges":[],"createdAt":"2025-03-07 05:38:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6175152/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6175152/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78685861,"identity":"79832cb6-4adc-4a9a-957b-ca45fd708325","added_by":"auto","created_at":"2025-03-17 15:22:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":151570,"visible":true,"origin":"","legend":"\u003cp\u003eThe figure provide details of key independent predictors of patient outcomes based on a study of 84 patients of papillary thyroid carcinoma a) age; b) size; c) surgical procedure; d) PD-L1 expression and e) BRAF mutation.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6175152/v1/150f3ba687a67ffa2578a473.png"},{"id":90314589,"identity":"eed6d33f-9384-4373-a02a-ca0636ee4c81","added_by":"auto","created_at":"2025-09-01 10:09:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1626538,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6175152/v1/4095302a-d578-41a4-9970-529522f32122.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003ePrognostic Characterization of Papillary Thyroid Carcinoma: Insights into the Role of PD-L1 and Mutational Landscape in Disease Aggressiveness and Outcome\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003ePapillary thyroid carcinoma (PTC) accounts for over 80% of differentiated thyroid cancers (DTC) and is generally associated with a favourable prognosis. However, 15% to 35% of patients experience recurrence, and 10% to 15% of these cases progress to distant metastasis after primary surgery (1–5). In some instances, PTC may undergo dedifferentiation into high-grade tumours, complicating treatment and worsening patient outcomes (6). Moreover, therapeutic options remain limited for inoperable and radioiodine (RAI)-refractory DTC (1,2,4).\u003c/p\u003e\n\u003cp\u003eWhile clinicopathological factors have been studied for prognostic significance, tumour stage alone does not reliably predict disease-free survival. Dynamic risk stratification models have faced challenges, underscoring the need for additional biomarkers (1,7–9). Although genetic mutations, such as BRAF V600E and TERT promoter mutations, have been linked to aggressive disease, their clinical utility remains questionable, and they are not currently recommended for postoperative prognostication and management of PTC (1,10–12).\u003c/p\u003e\n\u003cp\u003ePD-L1 has been associated with aggressive disease in certain cancers, but is primarily a predictive marker of immunotherapeutic significance (13,14). However, its role as a prognostic marker in thyroid cancers, particularly in PTC, remains largely unexplored (15). Identifying reliable biomarkers is essential for predicting disease progression and tailoring treatment strategies to individual patients, ultimately helping to reduce unnecessary testing, overtreatment, and psychological stress in low-risk cases.\u003c/p\u003e\n\u003cp\u003eThis study aims to evaluate the prognostic role of PD-L1 expression as a risk stratification marker, specifically in relation to genetic mutations and other clinicopathological parameters. We aim to assess its association with tumour aggressiveness, recurrence, metastasis, and survival outcomes, providing potential insights into its clinical relevance in PTC.\u0026nbsp;\u003c/p\u003e"},{"header":"MATERIAL AND METHODS","content":"\u003cp\u003e \u003cstrong\u003eCase Selection\u003c/strong\u003e \u003cp\u003eThis retrospective study was conducted at King George\u0026rsquo;s Medical University (KGMU) in Lucknow, India, in collaboration with the Department of Endocrine Surgery. We retrieved formalin-fixed paraffin-embedded (FFPE) tissue samples from patients diagnosed with PTC between July 2016 and June 2024. Clinical data, including patient demographics (age, gender), tumour characteristics (size, histopathological subtype, lymph node involvement, extrathyroidal extension, and recurrence), and survival outcomes, were collected from medical records. To ensure the quality of the study, cases with insufficient tissue for molecular or immunohistochemical analysis or incomplete clinical data were excluded. The study was approved by the institutional ethical committee and has been conducted in accordance with relevant institutional guidelines.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHistopathological and Cytological Evaluation\u003c/strong\u003e \u003cp\u003eHistopathological evaluation was performed in line with the College of American Pathologists (CAP) protocol (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) Tumour specimens obtained through various surgical methods (lobectomy, hemithyroidectomy, and core needle biopsy) were analysed for tumour size, histological subtype, lymph node involvement, extrathyroidal extension, and lymphovascular invasion. All the cases were reviewed and classified following the 2022 WHO classification system for Thyroid tumours (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Cytological cases were also reviewed and reported as per The 2023 Bethesda system of Reporting Thyroid Cytopathology (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eImmunohistochemistry and interpretation\u003c/b\u003e: Sections of 3\u0026ndash;5 \u0026micro;m thickness from formalin-fixed paraffin-embedded (FFPE) tissues were prepared on 3-aminopropyl triethoxysilane-coated glass slides. After deparaffinization and rehydration, endogenous peroxidase activity was blocked using 3% hydrogen peroxide. Antigen retrieval was performed at 98\u0026deg;C for 15 minutes using a microwave oven in target retrieval buffer. Tissue sections were incubated with primary antibodies against PD-L1 (mouse monoclonal antibody clone SP142; Abcam; 1:100 dilution). Signal amplification was achieved using the HiDef 2-Step Polymer Detection Kit (Cell Marque, USA) and DAB (3,3'-diaminobenzidine tetrahydrochloride) as a chromogen. Nuclei were counterstained with Mayer\u0026rsquo;s haematoxylin, followed by dehydration and mounting for light microscopy. Tonsil/placenta was used as a positive control. Omission of the primary antibody served as the negative control. Both positive and negative controls were included in each batch of immunohistochemical staining.\u003c/p\u003e \u003cp\u003ePD-L1 expression was evaluated using the \u003cem\u003eTumour Proportion Score (TPS\u003c/em\u003e), measuring the percentage of tumour cells showing partial or complete membranous positivity, excluding cytoplasmic staining. A minimum of 500 viable tumour cells were assessed, with a TPS of \u0026ge;\u0026thinsp;1% considered positive (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Based on the median TPS value, cases were categorized as \u003cem\u003ePD-L1 low or high\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eMolecular Analysis\u003c/strong\u003e \u003cp\u003eMolecular analysis focused on identifying mutations in BRAF, TERT promoter, and RAS genes. DNA was extracted from FFPE tumour tissue using a commercial DNA extraction kit (QIAamp DNA FFPE Tissue Kit, Qiagen) following the manufacturer\u0026rsquo;s protocol. DNA concentration and purity were assessed using a NanoDrop spectrophotometer. PCR amplification was used for exon 15 of the BRAF gene, the TERT promoter region and and exons 2 and 3 of NRAS followed by sanger sequencing for detection of BRAFV6001, C228T and C250T for TERT and Q61 for RAS. Positive controls (known mutated samples) and negative controls (water or non-template control) were run in each PCR to ensure the reliability of the assay.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStatistical analysis\u003c/strong\u003e \u003cp\u003eCategorical variables, such as PD-L1 expression levels (TPS low vs. TPS high), BRAF, TERT, and RAS mutation status, and clinicopathological parameters were summarized as frequencies and percentages. Continuous variables (e.g., age, tumour size, TPS) were presented as means with standard deviations or medians with interquartile ranges, depending on their distribution. Due to the presence of several outliers, the median was selected instead of the mean as the cutoff for TPS. This approach more accurately categorizes cases into TPS high and TPS low groups while minimizing the impact of extreme values that could distort the results\u003c/p\u003e \u003c/p\u003e \u003cp\u003eTo identify potential prognostic factors, univariate analysis was performed for each clinicopathological variable, PD-L1 expression, and molecular markers. Chi-square or Fisher\u0026rsquo;s exact test was used to compare categorical variables, while continuous variables were analyzed using independent t-tests or Mann-Whitney U tests, as appropriate. Variables with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the univariate analysis were considered for inclusion in further multivariate analysis.\u003c/p\u003e \u003cp\u003eMultivariable binary logistic regression analysis was used to assess the factors predicting the likelihood of each of the non-survive and recurrence of the disease. The outcome variables were categorized as present or absent. Independent variables were identified from univariate analysis. The logistic regression model provided adjusted odds ratios (ORs) with 95% confidence intervals (CIs) for each predictor, identifying independent prognostic factors. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. All statistical analyses were performed using SPSS software for windows. version 23.0 (SPSS-23, IBM, Chicago, USA) and STATA-16. P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered as statistically significant.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003eDemographics, Clinical, and Radiological Features\u003c/strong\u003e: A total of 84 patients diagnosed with papillary thyroid carcinoma (PTC) were included in this study, with ages ranging from 14 to 70 years (mean age: 38.2 years). The cohort exhibited a strong female predominance (79.8%), consistent with the well-documented gender predilection for PTC. Tumour sizes varied from 0.8 cm to 8.5 cm, with a mean diameter of 3.5 cm. Staging based on the AJCC 8th edition classification (\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e) revealed that 48.6% of cases were categorized as T3, followed by 28.6% as T2, 19% as T1, and 3.6% as T4. Lymph node metastasis was identified in 37.6% of patients, with 14.3% presenting with N1B involvement. The majority (76.2%) were classified as stage 1 disease.\u003c/p\u003e\n\u003cp\u003eSurgical resection was the cornerstone of treatment, with total thyroidectomy performed in 77.4% of cases, while 22.6% underwent lobectomy. Postoperative radioiodine therapy was administered to 23.8% of patients. Disease recurrence was observed in 20.2% of cases, and distant metastasis was documented in 8.3% of patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePathological and Molecular Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHistopathological evaluation revealed that 79.8% of tumours were unifocal, with the classical variant of PTC being the most prevalent, comprising 63.1% of cases. Other histological subtypes included the invasive follicular variant (21.4%), solid variant (4.8%), tall cell variant (3.6%), and the aggressive hobnail variant (1.2%). Extrathyroidal extension was observed in 31% of cases, while lymphovascular invasion was identified in 26.2%.\u003c/p\u003e\n\u003cp\u003eLymph node involvement was detected in 39.3% of patients (33/84), with a significant proportion (72.7%) exhibiting metastasis in more than five lymph nodes. Additionally, associated lymphocytic thyroiditis in the surrounding thyroid parenchyma was documented in 35.7% of cases.\u003c/p\u003e\n\u003cp\u003ePreoperative cytological assessment was performed in 56 patients, with the majority (66.7%) classified as Bethesda VI, confirming malignancy in most cases. Additionally, 13.1% of cases fell into indeterminate Bethesda categories (III, IV, and V), reflecting diagnostic uncertainty. Notably, 11 cases were cytologically reported as benign (Bethesda II), highlighting the limitations of preoperative fine-needle aspiration in certain instances\u003c/p\u003e\n\u003cp\u003eMutational analysis revealed the presence of the BRAF mutation in 35.7% of patients, reaffirming its role as a critical molecular marker in PTC. TERT promoter mutations were identified in 16.7% of cases, while RAS mutations were also detected in 16.7% of patients, predominantly in the invasive follicular variant of PTC.\u003c/p\u003e\n\u003cp\u003eA subset of patients (n\u0026thinsp;=\u0026thinsp;13; 15.4%) exhibited coexisting mutations, although these were less common than single mutations. The most frequent co-occurring alterations were BRAF with TERT (8 cases), followed by BRAF with RAS (4 cases), with only one case demonstrating TERT and RAS co-mutation. Notably, over half of the patients (53.6%) did not harbour any detectable mutations, underscoring the genetic heterogeneity of PTC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation of PD-L1 Expression with Clinicopathological and Molecular Features in PTC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePD-L1 expression was detected in 60.7% of cases (51 patients), while 39.3% (33 patients) showed no PD-L1 expression. The Tumour Proportion Score (TPS) ranged from 0\u0026ndash;98%, with a median score of 20. Based on this cutoff, cases were categorized into PD-L1 high (38.1%; n\u0026thinsp;=\u0026thinsp;32) and PD-L1 low (61.9%; n\u0026thinsp;=\u0026thinsp;52).\u003c/p\u003e\n\u003cp\u003eA significant age-related difference was observed, with the median age of the PD-L1 high group being significantly higher than that of the PD-L1 low group (45 years vs. 30.5 years; p\u0026thinsp;=\u0026thinsp;0.001). Moreover, the majority of patients in the PD-L1 high group were older than 45 years (p\u0026thinsp;=\u0026thinsp;0.002). The paediatric population predominantly fell into the PD-L1 low category, contrasting with adults, though this association did not reach statistical significance.\u003c/p\u003e\n\u003cp\u003eGender distribution showed no significant difference between the PD-L1 high and low groups, with a consistent female predominance across both categories.\u003c/p\u003e\n\u003cp\u003eRadiological analysis revealed variations in tumour size, with the PD-L1 high group tending to have larger tumours (\u0026gt;\u0026thinsp;3.5 cm); however, this difference did not reach statistical significance (p\u0026thinsp;=\u0026thinsp;0.092). Tumour staging analysis demonstrated a significant correlation between PD-L1 expression and advanced disease, with a higher proportion of T3 and T4 tumours in the PD-L1 high group compared to the PD-L1 low group (p\u0026thinsp;=\u0026thinsp;0.045).\u003c/p\u003e\n\u003cp\u003eLymph node involvement was also significantly more frequent in the PD-L1 high group, suggesting a tendency toward locally advanced disease (p\u0026thinsp;=\u0026thinsp;0.038). Furthermore, prognostic staging revealed a clear association between PD-L1 expression and disease severity, as stage 1 disease was more prevalent in the PD-L1 low group (73.4%), whereas stages 3 and 4 were predominantly observed in the PD-L1 high group, a statistically significant finding (p\u0026thinsp;=\u0026thinsp;0.002). These findings underscore the potential role of PD-L1 expression as a biomarker of aggressive tumour behaviour and poor prognosis in papillary thyroid carcinoma.\u003c/p\u003e\n\u003cp\u003ePathological evaluation revealed a higher prevalence of multifocality in the PD-L1 high group, suggesting a possible link between increased proliferative activity and tumour multiplicity. Additionally, aggressive pathological features, including extrathyroidal extension and lymphovascular invasion, were more frequently observed in the PD-L1 high group, whereas these characteristics were significantly less common in the PD-L1 low group. While these findings indicate a potential association between PD-L1 expression and aggressive tumour behaviour, none of these factors reached statistical significance, emphasizing the need for further investigation.\u003c/p\u003e\n\u003cp\u003eNotably, poor histological variants, such as tall cell, hobnail, diffuse sclerosing, and columnar subtypes, were significantly more prevalent in the PD-L1 high group (p\u0026thinsp;=\u0026thinsp;0.038), reinforcing the association of PD-L1 expression with aggressive tumour phenotypes. Conversely, lymphocytic thyroiditis, present in 57.1% of patients, showed a strong inverse correlation with PD-L1 expression (p\u0026thinsp;=\u0026thinsp;0.001), suggesting a potential protective role of the immune microenvironment in limiting tumour progression.\u003c/p\u003e\n\u003cp\u003eMolecular analysis revealed a high prevalence of BRAF mutations (35.7%), which were significantly associated with the PD-L1 high group (p\u0026thinsp;=\u0026thinsp;0.032). Similarly, TERT promoter mutations were detected in 11.9% of cases and showed a strong correlation with PD-L1 high expression (p\u0026thinsp;=\u0026thinsp;0.038), suggesting a potential link between aggressive tumour behaviour and immune checkpoint expression.\u003c/p\u003e\n\u003cp\u003eIn contrast, RAS mutations (16.7%) did not demonstrate a significant correlation with TPS. However, coexisting mutations, particularly BRAF and TERT, were observed in 16.7% of cases and were strongly linked to poor prognostic outcomes, including PD-L1 high expression and tumour recurrence (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These findings highlight the interplay between molecular alterations and immune evasion mechanisms, warranting further research into targeted therapeutic strategies. Please refer to Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\" style=\"margin-right: calc(0%); width: 100%;\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDemographic, clinical and radiological details of patients with Papillary thyroid carcinoma (N\u0026thinsp;=\u0026thinsp;84)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003eN=84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003eTPS High\u003c/p\u003e\n \u003cp\u003e(n=32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.4706%;\"\u003e\n \u003cp\u003eTPS Low\u003c/p\u003e\n \u003cp\u003e(n=52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.1625%;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge distribution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eAge range\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Paediatric\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Adults\u003c/p\u003e\n \u003cp\u003eMean age\u003c/p\u003e\n \u003cp\u003eMedian age\u003c/p\u003e\n \u003cp\u003eAge distribution\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026lt;=20 yrs\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; 21-40 ys\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; 41-60 yrs\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026gt;60 yrs\u003c/p\u003e\n \u003cp\u003eAge group I\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Age \u0026le; 45 years\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Age \u0026gt; 45 years\u003c/p\u003e\n \u003cp\u003eGender distribution\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Male\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Females\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e14-70 years\u003c/p\u003e\n \u003cp\u003e08\u003c/p\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003cp\u003e38.2 years\u003c/p\u003e\n \u003cp\u003e35 years\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e09 (10.7%)\u003c/p\u003e\n \u003cp\u003e40 (47.6%)\u003c/p\u003e\n \u003cp\u003e29 (34.5%)\u003c/p\u003e\n \u003cp\u003e06 (7.1%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e61 (72.6 %)\u003c/p\u003e\n \u003cp\u003e15 (27.4 %)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e17 (20.2%)\u003c/p\u003e\n \u003cp\u003e67 (79.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e17-35 years\u003c/p\u003e\n \u003cp\u003e01 (12.5%)\u003c/p\u003e\n \u003cp\u003e31 (40.8%)\u003c/p\u003e\n \u003cp\u003e40 years\u003c/p\u003e\n \u003cp\u003e45 years\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e01 (11.1%)\u003c/p\u003e\n \u003cp\u003e10 (25.0%)\u003c/p\u003e\n \u003cp\u003e16 (55.2%)\u003c/p\u003e\n \u003cp\u003e05 (83.3%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e17 (27.9%)\u003c/p\u003e\n \u003cp\u003e15 (65.2%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e04 (23.5%)\u003c/p\u003e\n \u003cp\u003e28 (41.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.4706%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e14-70 years\u003c/p\u003e\n \u003cp\u003e07 (87.5%)\u003c/p\u003e\n \u003cp\u003e45 (59.2%)\u003c/p\u003e\n \u003cp\u003e38 years\u003c/p\u003e\n \u003cp\u003e30.5 years\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8 (88.9%)\u003c/p\u003e\n \u003cp\u003e30 (75%)\u003c/p\u003e\n \u003cp\u003e13 (44.8%)\u003c/p\u003e\n \u003cp\u003e01 (16.7%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e44 (72.1%)\u003c/p\u003e\n \u003cp\u003e08 (34.8%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;3 (76.5%)\u003c/p\u003e\n \u003cp\u003e39 (58.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.1625%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRadiological details\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eSize Range\u003c/p\u003e\n \u003cp\u003eMean size\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026gt;3.5 cm\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026lt;3.5 cm\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePrognostic Clinical stage (TNM AJCC 8\u003csup\u003eth\u003c/sup\u003e edition)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eT staging\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; T1\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; T2\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; T3\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; T4\u003c/p\u003e\n \u003cp\u003eN staging\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; N0\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; N1\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; N1B\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; N2\u003c/p\u003e\n \u003cp\u003ePrognostic staging\u003c/p\u003e\n \u003cp\u003eStage 1\u003c/p\u003e\n \u003cp\u003eStage 2\u003c/p\u003e\n \u003cp\u003eStage 3\u003c/p\u003e\n \u003cp\u003eStage 4\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.8 \u0026ndash; 8.5 cm\u003c/p\u003e\n \u003cp\u003e3.5 cm\u003c/p\u003e\n \u003cp\u003e32 (4.38%)\u003c/p\u003e\n \u003cp\u003e41 (56.2%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e16 (19%)\u003c/p\u003e\n \u003cp\u003e24 (28.6%)\u003c/p\u003e\n \u003cp\u003e41 (48.6%)\u003c/p\u003e\n \u003cp\u003e03 (3.6%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e17 (20.2%)\u003c/p\u003e\n \u003cp\u003e19 (22.6%)\u003c/p\u003e\n \u003cp\u003e12 (14.3%)\u003c/p\u003e\n \u003cp\u003e02 (2.4%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e64 (76.2%)\u003c/p\u003e\n \u003cp\u003e8 (9.5%)\u003c/p\u003e\n \u003cp\u003e9 (10.7%)\u003c/p\u003e\n \u003cp\u003e3 (3.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e14 (43.8%)\u003c/p\u003e\n \u003cp\u003e14 (34.1%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e03 (18.8%)\u003c/p\u003e\n \u003cp\u003e11 (45.8%)\u003c/p\u003e\n \u003cp\u003e15 (36.6%)\u003c/p\u003e\n \u003cp\u003e00 (00%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8 (47.1%)\u003c/p\u003e\n \u003cp\u003e9 (47.4%)\u003c/p\u003e\n \u003cp\u003e5 (51.7%)\u003c/p\u003e\n \u003cp\u003e2 (100%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e17 (26.6%)\u003c/p\u003e\n \u003cp\u003e6 (62.5%)\u003c/p\u003e\n \u003cp\u003e7 (77.8%)\u003c/p\u003e\n \u003cp\u003e3 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.4706%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e18 (56.3%)\u003c/p\u003e\n \u003cp\u003e27 (65.9%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e13 (81.3%)\u003c/p\u003e\n \u003cp\u003e13 (54.2%)\u003c/p\u003e\n \u003cp\u003e26 (63.4%)\u003c/p\u003e\n \u003cp\u003e03 (100%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9 (52.9%)\u003c/p\u003e\n \u003cp\u003e10 (52.6%)\u003c/p\u003e\n \u003cp\u003e7 (58.3%)\u003c/p\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e47 (73.4%)\u003c/p\u003e\n \u003cp\u003e3 (37.5%)\u003c/p\u003e\n \u003cp\u003e2 (22.2%)\u003c/p\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.1625%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.402\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.045\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatment and follow up details\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eSurgical procedure\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Lobectomy\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Total thyroidectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e19 (22.6%)\u003c/p\u003e\n \u003cp\u003e65 (77.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4 (21.1%)\u003c/p\u003e\n \u003cp\u003e28 (43.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.4706%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e15 (78.9%)\u003c/p\u003e\n \u003cp\u003e37 (56.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.1625%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eRadio-iodine therapy\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Present\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Absent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e20 923.8%)\u003c/p\u003e\n \u003cp\u003e64 (76.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8 (40.0%)\u003c/p\u003e\n \u003cp\u003e24 (37.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.4706%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e12 (60.0%)\u003c/p\u003e\n \u003cp\u003e40 (62.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.1625%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.841\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eRecurrence\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Present\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Absent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e17 (20.2%)\u003c/p\u003e\n \u003cp\u003e67 (79.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e22 (32.4%)\u003c/p\u003e\n \u003cp\u003e10 (62.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.4706%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e46 (67.6%)\u003c/p\u003e\n \u003cp\u003e06 (37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.1625%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.043\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003eDistant metastasis\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Present\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Absent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e07 (8.3%)\u003c/p\u003e\n \u003cp\u003e77 (91.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5 (71.4%)\u003c/p\u003e\n \u003cp\u003e27 (35.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.4706%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2(28.6%)\u003c/p\u003e\n \u003cp\u003e50 (64.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.1625%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePathological details\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eFocality\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Unifocal\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Multifocal\u003c/p\u003e\n \u003cp\u003eMorphological variants\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Classical\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Invasive Follicular\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Solid\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Tall cell\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Hobnail\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Diffuse sclerosing\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Columnar\u003c/p\u003e\n \u003cp\u003ePoor variants\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Present\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Absent\u003c/p\u003e\n \u003cp\u003eLymph node dissection\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026gt;5 lymph nodes involved\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026lt; 5 lymph nodes involved\u003c/p\u003e\n \u003cp\u003eAssociated lymphocytic thyroiditis\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Present\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Absent\u003c/p\u003e\n \u003cp\u003eExtra-thyroidal Extension\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Present\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Absent\u003c/p\u003e\n \u003cp\u003eLymphovascular invasion\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Present\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Absent\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMolecular details\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eBRAF\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Present\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Absent\u003c/p\u003e\n \u003cp\u003eTERT\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Present\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Absent\u003c/p\u003e\n \u003cp\u003eRAS\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Present\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Absent\u003c/p\u003e\n \u003cp\u003eMultiple mutation\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; BRAF + TERT\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; BRAF + RAS\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; TERT + RAS\u003c/p\u003e\n \u003cp\u003eSingle mutation\u003c/p\u003e\n \u003cp\u003e-\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; No mutation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e67 (79.8%)\u003c/p\u003e\n \u003cp\u003e17 (20.2%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e53 (63.1%)\u003c/p\u003e\n \u003cp\u003e18 (21.4%)\u003c/p\u003e\n \u003cp\u003e4 (4.8%)\u003c/p\u003e\n \u003cp\u003e3 (3.6%)\u003c/p\u003e\n \u003cp\u003e1 (1.2%)\u003c/p\u003e\n \u003cp\u003e1 (1.2%)\u003c/p\u003e\n \u003cp\u003e1 (1.2%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10 (11.9%)\u003c/p\u003e\n \u003cp\u003e74 (88.1%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e24/33 (72.7%)\u003c/p\u003e\n \u003cp\u003e09/33 (27.3%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e48 (57.1%)\u003c/p\u003e\n \u003cp\u003e36 (42.9%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e26 (31.0%)\u003c/p\u003e\n \u003cp\u003e58 (69.0%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e22 (26.2%)\u003c/p\u003e\n \u003cp\u003e73.8%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e30 (35.7%)\u003c/p\u003e\n \u003cp\u003e54 (64.3%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10 (11.9%)\u003c/p\u003e\n \u003cp\u003e70 (83.3%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e14 (16.7%)\u003c/p\u003e\n \u003cp\u003e71 (84.5%)\u003c/p\u003e\n \u003cp\u003e13 (15.4%)\u003c/p\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e26 (31.0 %)\u003c/p\u003e\n \u003cp\u003e45 (53.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e25 (37.3%)\u003c/p\u003e\n \u003cp\u003e07 (41.2%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7 (70.0%)\u003c/p\u003e\n \u003cp\u003e25 (33.8%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e14 (58.3%)\u003c/p\u003e\n \u003cp\u003e2 (22.2%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e11 (22.9%)\u003c/p\u003e\n \u003cp\u003e21 (58.3%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e13 (50%)\u003c/p\u003e\n \u003cp\u003e21 (33.9%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e11 (50%)\u003c/p\u003e\n \u003cp\u003e21 (33.9%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e16 (53.3%)\u003c/p\u003e\n \u003cp\u003e16 (29.6%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7 (21.9%)\u003c/p\u003e\n \u003cp\u003e25 (33.8%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4 (28.6%)\u003c/p\u003e\n \u003cp\u003e28 (40.0%)\u003c/p\u003e\n \u003cp\u003e9 (69.2%)\u003c/p\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e7 (26.9%)\u003c/p\u003e\n \u003cp\u003e16 (35.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.4706%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e42 (62.3%)\u003c/p\u003e\n \u003cp\u003e10 (58.8%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3 (30.0%)\u003c/p\u003e\n \u003cp\u003e49 (66.2%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10 (41.7%)\u003c/p\u003e\n \u003cp\u003e7 (77.8%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e37 (77.1%)\u003c/p\u003e\n \u003cp\u003e15(41.7%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e13 (50%)\u003c/p\u003e\n \u003cp\u003e39 (67.2%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e11 (50%)\u003c/p\u003e\n \u003cp\u003e41 (66.1%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e14 (46.7%)\u003c/p\u003e\n \u003cp\u003e38 (70.4%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3 (30.0%)\u003c/p\u003e\n \u003cp\u003e49 (66.2%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10 (71.4%)\u003c/p\u003e\n \u003cp\u003e42 (60.0%)\u003c/p\u003e\n \u003cp\u003e4 (30.8%)\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e19 (73.1%)\u003c/p\u003e\n \u003cp\u003e29 (64.4%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.1625%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.770\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.038\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.032\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.038\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.421\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.046\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 601px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eData are presented in Median (Inter quartile range) [Mean] and compared using Mann Whitney U test. Number (%) and compared using Chi square test / Fisher exact test. P value \u0026lt; 0.05 significant.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eClinical Outcomes and Predictors of Prognosis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eRecurrence was observed in 19% of patients, with a significantly higher risk in the PD-L1 high group (p\u0026thinsp;=\u0026thinsp;0.043). The median TPS in recurrent cases was 63.5, compared to 11 in non-recurrent cases (p\u0026thinsp;=\u0026thinsp;0.044), reinforcing the association between higher PD-L1 expression and disease recurrence. Additionally, histopathological features such as poor-variant histology and extrathyroidal extension were significantly linked to recurrence. Distant metastasis occurred in 8.3% of cases and was strongly associated with PD-L1 high expression and multiple mutations (p\u0026thinsp;=\u0026thinsp;0.002).\u003c/p\u003e\n \u003cp\u003eAt the last follow-up, 92.9% of patients were alive, while 7.1% had succumbed to the disease. Non-survivors exhibited significantly higher TPS, frequent distant metastases, and a higher prevalence of multiple mutations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Please refer to Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAssociation of Survival of patients with variables (N\u0026thinsp;=\u0026thinsp;84)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u0026rsquo;s\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-Survivors (n\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSurvivors\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;78)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge in Years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45(29,45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35(27,53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.855\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaximum size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.2(2.2,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.5(2,4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTPS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.5(0,90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20(0,65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.727\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePD1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(1,50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.241\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePediatric/adults\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePaediatric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(87.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.462\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdults\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71(93.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge groups\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;=20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(88.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e0.243\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u0026ndash;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39(97.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u0026ndash;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25(86.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge cut off 45 years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;=45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56(91.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22(95.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62(92.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16(94.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecimen type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHemi Thyroidectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16(84.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Thyroidectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62(95.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;3.5 cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28(87.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.394\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;= 3.5 cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39(95.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eT Staging\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15(93.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23(95.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39(95.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eN staging\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN staging\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31(91.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" align=\"left\"\u003e\n \u003cp\u003e0.702\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17(89.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN1B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(91.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLymph node Metastasis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30(90.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.542\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. of LN involved\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;5 LN involved\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21(87.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.545\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;=5 LN involved\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFocality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnifocal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63(94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.596\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultifocal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15(88.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistological Variant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClassical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50(94.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"8\" align=\"left\"\u003e\n \u003cp\u003e0.724\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eColumnar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHobnail\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIFV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16(88.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMicropapillary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSolid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTall cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePoor Variant morphology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68(91.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eExtrathyroidal Extension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23(88.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.368\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55(94.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLymph vascular Extension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19(86.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59(95.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePerineural Extension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74(92.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePerinodal extension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15(83.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63(95.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrognostic staging\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61(95.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.030\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(88.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePDL1_expression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47(92.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31(93.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTPS(High/Low) mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31(93.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e0.874\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD-L1high\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29(90.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePD-L1 Low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18(94.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLymphocytic thyroiditis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46(95.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.395\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32(88.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBRAF mutation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26(86.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52(96.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTERT promoter mutation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70(94.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eRAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(85.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.261\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66(94.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultiple mutation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(78.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67(95.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCombinations of mutations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBRAF\u0026thinsp;+\u0026thinsp;TERT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(87.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" align=\"left\"\u003e\n \u003cp\u003e0.583\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBRAF\u0026thinsp;+\u0026thinsp;RAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTERT\u0026thinsp;+\u0026thinsp;RAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingle mutation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24(92.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo mutation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43(95.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRadioidone_therapy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17(85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61(95.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecurrence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65(95.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(81.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistant metastasis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(57.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(42.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75(97.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eData are presented in Median (Inter quartile range) [Mean] and compared using Mann Whitney U test. Number (%) and compared using Chi square test / Fisher exact test. \u003cstrong\u003eP value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 significant.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eMultivariable logistic regression analysis identified multiple mutations as independent predictors of poor survival (AOR\u0026thinsp;=\u0026thinsp;15.8, p\u0026thinsp;=\u0026thinsp;0.021) and recurrence (AOR\u0026thinsp;=\u0026thinsp;11.22, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, total thyroidectomy was associated with improved survival (AOR\u0026thinsp;=\u0026thinsp;10.77, p\u0026thinsp;=\u0026thinsp;0.046). Although poor-variant histology showed a trend toward predicting recurrence, it did not reach statistical significance (AOR\u0026thinsp;=\u0026thinsp;4.16, p\u0026thinsp;=\u0026thinsp;0.079). Please refer to Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;1.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eIndependent Predictors of Patient Outcomes (N\u0026thinsp;=\u0026thinsp;84)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u0026apos;s\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePatients Survival\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultiple mutations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.51\u0026ndash;165.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpecimen type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04-111.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatients Recurrence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoor variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u0026ndash;20.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultiple mutations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.94\u0026ndash;42.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eAOR\u0026thinsp;=\u0026thinsp;Adjusted Odds ratio. Multivariable binary logistic regression analysis used. \u003cstrong\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05 significant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003ePapillary thyroid carcinoma (PTC), the most common form of differentiated thyroid cancer (DTC), generally has a favourable prognosis. However, 15\u0026ndash;35% of patients experience recurrence, and 10\u0026ndash;15% develop distant metastases following primary surgery (\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In some cases, PTC undergoes dedifferentiation into high-grade tumours, further complicating treatment and worsening patient outcomes (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Additionally, therapeutic options remain limited for inoperable or radioiodine (RAI)-refractory DTC (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough clinicopathological factors have been extensively studied, tumour stage alone is not a reliable predictor of disease-free survival. Dynamic risk stratification models have been proposed but face challenges in accurately predicting outcomes, emphasizing the need for additional biomarkers (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Genetic alterations, including BRAF V600E and TERT promoter mutations, have been associated with more aggressive disease, but their role in routine postoperative prognostication and management of PTC also remains controversial. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). This underscores the necessity of identifying robust prognostic biomarkers that can better predict disease progression, guide treatment decisions, and minimize unnecessary interventions.\u003c/p\u003e \u003cp\u003eThe PD-1/PD-L1 pathway is increasingly recognized as a critical target in immunotherapy for aggressive cancers. However, the role of PD-L1 expression in differentiated thyroid carcinoma (DTC), particularly papillary thyroid carcinoma (PTC), remains inadequately understood (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). India faces a significant thyroid cancer burden, ranking fourth in incidence and second in mortality, with a large proportion of cases diagnosed at advanced stages (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Despite this high burden, much of the existing research has been centred on Western populations, highlighting the need for more localized data (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Our study is one of the few to investigate PD-L1 expression in a large cohort of PTC patients from India, shedding new light on the molecular and clinical features of these tumours. This study uniquely addresses not only PD-L1 expression but also its prognostic and survival implications, particularly when combined with key molecular alterations like BRAF V600E and TERT. Our findings offer critical insights into how demographic, genetic, and racial factors may shape tumour characteristics, paving the way for more precise risk stratification and personalized therapeutic strategies in PTC.\u003c/p\u003e \u003cp\u003eWhile the FDA has approved several PD-L1 antibody clones for immunohistochemical (IHC) testing to guide immune checkpoint inhibitor treatment in cancers such as lung, urothelial, breast, melanoma, and renal cell carcinoma, there are currently no established PD-L1 clones or approved immunotherapies for aggressive thyroid cancers (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). This remains an under-explored area in thyroid cancer research. Studies on PD-L1 expression in thyroid carcinoma using cytometry and cytology samples are limited, with most relying on traditional IHC methods (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan additionalcitationids=\"CR25 CR26 CR27 CR28 CR29 CR30 CR31 CR32\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Variability in antibody clones (e.g., E1L3N, 22C3, SP142, SP263, 28\u0026thinsp;\u0026minus;\u0026thinsp;8), cut-off thresholds, and subcellular localization further complicates assessment, leading to inconsistent findings. To mitigate this, our study focused on membranous (complete/partial) expression and used the tumour proportion score (TPS) median value as the cut-off, offering a data-driven, unbiased approach that reflects the natural distribution of PD-L1 expression in the cohort. Unlike arbitrary thresholds, this median-based cut-off improves statistical robustness, reduces misclassification due to assay variability, and provides a more clinically relevant patient stratification.\u003c/p\u003e \u003cp\u003eSeveral studies have explored PD-L1 expression in thyroid carcinoma, but data specifically addressing papillary thyroid carcinoma (PTC) remain limited and inconsistent In our cohort, PD-L1 expression was observed in 60.7% of cases, aligning with prior reports that have documented expression levels as high as 60% in PTC (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). However, a large-scale study by Ahn S et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), analysing 407 thyroid carcinoma cases, found PD-L1 expression in only 6.1% of PTC cases using a 1% cutoff, which further declined to 0.9% with a 5% threshold. Notably, the intensity of PD-L1 expression in these cases was weak. One potential explanation for this discrepancy is the use of tissue microarrays (TMAs), which may not fully capture PD-L1 expression across the entire tumour. Contrary to this, Shi et al., who also analysed 260 PTC cases using TMAs, reported PD-L1 expression in 52.3% of cases (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). These findings emphasis that the different PD-L1 clone and the evaluation criteria could lead to the variations in results. We used a full faced tissue sections to eliminate this issue. In general, studies that employed full tissue sections consistently reported higher levels of PD-L1 expression, emphasizing how sampling methods can influence outcomes. These variations highlight the need for standardized approaches in PD-L1 assessment to ensure accurate, reproducible results in PTC.\u003c/p\u003e \u003cp\u003eThe correlation between PD-L1 expression and clinicopathological factors in papillary thyroid carcinoma (PTC) remains controversial, with limited and inconsistent findings across studies (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Ahn S et al., in their study of 326 PTC cases, did not find any significant correlation between PD-L1 expression and clinicopathological factors, recurrence, or BRAF/TERT mutation status (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). In contrast, Aghajani et al. reported that PD-L1 expression in PTC was associated with aggressive features such as lymphovascular invasion and extrathyroidal extension (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Similarly, An HJ et al. found a significant association between PD-L1 expression and lymph node metastasis (p\u0026thinsp;=\u0026thinsp;0.036) (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLiterature on PD-L1 as a prognostic marker in PTC is even scare, some studies have attempted to explore this relationship. Chowdhury et al. conducted one of the earliest studies on the prognostic role of PD-L1 in PTC, revealing that cytoplasmic PD-L1 positivity correlated with recurrence, while membrane positivity was linked to metastasis or death in Stage IV patients. PD-L1-positive tumours had significantly shorter disease-free survival (DFS) (36\u0026ndash;49 months) compared to PD-L1-negative tumours (186 months) (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Similarly, Shi et al. highlighted the prognostic impact of PD-L1 expression, showing its negative influence on recurrence-free survival (RFS). PD-L1 overexpression was found to be an independent marker of poor prognosis, particularly in patients with larger tumours, multifocal disease, extrathyroidal extension, lymph node metastasis, older age, and male sex (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). This finding was recently reinforced by a meta-analysis by Girolami et al., which reviewed 18 studies and confirmed a significant association between PD-L1 expression and reduced disease-free survival in differentiated thyroid carcinoma, especially in PTC. However, no such association was observed in dedifferentiated thyroid cancers (anaplastic and poorly differentiated thyroid carcinoma), and PD-L1 expression showed no correlation with overall survival in either group (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the present study, using the tumour proportion score (TPS) median as the cutoff and focusing solely on membranous positivity, we found that PD-L1 high was significantly associated with larger tumour sizes, advanced stages, and poorer histological variants. Following Bai et al., ours is one of the few studies to analyse different PTC variants as subgroups, including columnar, diffuse sclerosing, hobnail, and tall cell variants, which are considered poor prognostic types (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Unlike Bai et al., we observed a significant correlation between PD-L1 high group and these aggressive variants. Another noteworthy finding from our study was the significant association between PD-L1 expression and background lymphocytic thyroiditis. Only a few other studies have reported such a correlation (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Lubin et al. observed increased PD-L1 expression in PTC arising in a background of Hashimoto's thyroiditis (HT) compared to normal thyroid tissue. They also found a strong correlation between PD-L1 expression in primary PTC and local nodal metastases. Their study suggested that PTC in HT may be biologically distinct, with the underlying thyroid pathology influencing PD-L1 expression and potentially affecting treatment response (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile surgery and postoperative radioiodine therapy remain the cornerstone of treatment for differentiated thyroid carcinoma, a subset of papillary thyroid carcinoma (PTC) cases, particularly those harbouring the BRAFV600E mutation, exhibit resistance to radioiodine therapy (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Understanding the interplay between PD-L1/PD-1 expression and BRAF status is crucial, as it could help identify patients who might benefit from immunotherapy. Emerging evidence suggests that combining BRAF inhibitors with anti-PD-L1 therapy enhances tumor regression and boosts antitumor immunity in preclinical models, indicating the potential of combination approaches to improve outcomes in refractory PTC cases (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAngell et al. were the first to report a significant positive association between BRAFV600E and PD-L1 expression in PTC, hypothesizing that BRAFV600E tumours may exploit PD-L1 expression to evade immune destruction, thus highlighting their immunosuppressive profile and impaired tumour immune surveillance (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). In contrast, Bai et al. found no such correlation, likely due to differences in sample selection, with the former study comprising more early-stage cases and the latter including more advanced cases (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). However, in a 2018 follow-up study, Bai et al. did observe a correlation between BRAFV600E expression and PD-L1 expression in PTC, suggesting that the BRAF mutation might influence the immune microenvironment. Despite this, no significant associations were found between BRAFV600E/ PD-L1 expression, and other clinicopathological factors (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). In our study BRAF mutations (35.7%) and TERT mutations (16.7%) were more prevalent in PD-L1 high cases. We also found that PD-L1 high correlated with a higher incidence of recurrence (p\u0026thinsp;=\u0026thinsp;0.043) and distant metastasis (p\u0026thinsp;=\u0026thinsp;0.002). Multivariable analysis identified multiple mutations as independent predictors of poor survival and recurrence. These findings underlines the potential role of BRAF/TERT mutations in modulating PD-L1 expression, with possible implications for immunotherapy in PTC. Our findings align with a recent meta-analysis by Girolmai et al that highlights PD-L1 as a potential biomarker in PTC management (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). This similarity suggests, that using the median TPS as a cutoff may yield more reliable results, and integrating PD-L1 assessment with mutation analysis could enhance prognostic classification, aiding in risk stratification and more precise patient management.\u003c/p\u003e \u003cp\u003eThis study's strength lies in its comprehensive and multifaceted analysis of the clinicopathological, molecular, and immunological characteristics of papillary thyroid carcinoma (PTC) in an Indian cohort. By integrating tumour proportion score (TPS) median cut-off for categorizing patients into PD-L1 low and high groups, alongside key genetic mutations such as BRAF and TERT, the study provides novel insights into the prognostic significance of PD-L1 expression. A key aspect is the incorporation of the 2022 WHO classification, ensuring reclassification of pre-NIFTP cases, which addresses a limitation in earlier studies. These findings contribute to better risk stratification and may aid in personalized treatment strategies, while also accounting for different PTC variants.\u003c/p\u003e \u003cp\u003eHowever, the study has its fair share of some limitations. Its retrospective, single-centre design may restrict generalizability, and the use of immunohistochemistry alone may not fully capture the complexity of the immune microenvironment. Moreover, small subgroup sample sizes limit statistical power, and other molecular alterations potentially influencing PTC progression were not comprehensively assessed. Future prospective, multicentre studies are essential to validate these findings across diverse populations and refine their clinical application.\u003c/p\u003e \u003cp\u003eIn conclusion, this study highlights the prognostic value of PD-L1 expression in papillary thyroid carcinoma (PTC), particularly when combined with genetic mutations like BRAF and TERT. These findings offer insights into improved risk stratification and personalized treatment strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFinancial disclosure:\u003c/strong\u003e This project has been funded by Indian Council of Medical Research (ICMR)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest: \u003c/strong\u003eNo, I declare that the authors have no competing interests as defined by Nature Research, or other interests that might be perceived to influence the results and/or discussion reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability statement: \u003c/strong\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e: not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHaugen BR, Alexander EK, Bible KC, Doherty GM, Mandel SJ, Nikiforov YE, et al. 2015 American Thyroid Association Management Guidelines for Adult Patients with Thyroid Nodules and Differentiated Thyroid Cancer: The American Thyroid Association Guidelines Task Force on Thyroid Nodules and Differentiated Thyroid Cancer. Thyroid Off J Am Thyroid Assoc. 2016;26(1):1\u0026ndash;133.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun W, Di L, Chen L, Li D, Wu Y, Xiang J, et al. The outcomes and prognostic factors of patients who underwent reoperation for persistent/recurrent papillary thyroid carcinoma. BMC Surg. 2022;22(1):374.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBai Y, Kakudo K, Li Y, Liu Z, Ozaki T, Ito Y, et al. Subclassification of non-solid-type papillary thyroid carcinoma identification of high-risk group in common type. Cancer Sci. 2008;99(10):1908\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUlisse S, Baldini E, Lauro A, Pironi D, Tripodi D, Lori E, et al. Papillary Thyroid Cancer Prognosis: Evol Field Cancers. 2021;13(21):5567.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLam AK. Papillary Thyroid Carcinoma: Current Position in Epidemiology, Genomics, and Classification. Methods Mol Biol Clifton NJ. 2022;2534:1\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePapp S, Asa SL. When thyroid carcinoma goes bad: a morphological and molecular analysis. Head Neck Pathol. 2015;9(1):16\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim Y, Roh JL, Song D, Cho KJ, Choi SH, Nam SY, et al. Predictors of recurrence after total thyroidectomy plus neck dissection and radioactive iodine ablation for high-risk papillary thyroid carcinoma. J Surg Oncol. 2020;122(5):906\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIto Y, Onoda N, Okamoto T. The revised clinical practice guidelines on the management of thyroid tumors by the Japan Associations of Endocrine Surgeons: Core questions and recommendations for treatments of thyroid cancer. Endocr J. 2020;67(7):669\u0026ndash;717.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFiletti S, Durante C, Hartl D, Leboulleux S, Locati LD, Newbold K, et al. Thyroid cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up\u0026dagger;. Ann Oncol Off J Eur Soc Med Oncol. 2019;30(12):1856\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbdullah MI, Junit SM, Ng KL, Jayapalan JJ, Karikalan B, Hashim OH. Papillary Thyroid Cancer: Genetic Alterations and Molecular Biomarker Investigations. Int J Med Sci. 2019;16(3):450\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCappola AR, Mandel SJ. Molecular testing in thyroid cancer: BRAF mutation status and mortality. JAMA. 2013;309(14):1529\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePoongkodi K, Periyasamy S, Gurunathan RA, Krishnasamy V, Jayakumar D, Subburaman R, et al. The genomic landscape of papillary thyroid carcinoma on next-generation sequencing in patients undergoing total thyroidectomy. World J Surg. 2024;48(12):2880\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi H, Van Der Merwe PA, Sivakumar S. Biomarkers of response to PD-1 pathway blockade. Br J Cancer. 2022;126(12):1663\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaver EC, Cooper WA, Colebatch AJ, Ferguson PM, Hill SK, Lum T, et al. Programmed death ligand-1 (PD-L1) as a predictive marker for immunotherapy in solid tumours: a guide to immunohistochemistry implementation and interpretation. Pathol (Phila). 2021;53(2):141\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGirolami I, Pantanowitz L, Mete O, Brunelli M, Marletta S, Colato C, et al. Programmed Death-Ligand 1 (PD-L1) Is a Potential Biomarker of Disease-Free Survival in Papillary Thyroid Carcinoma: a Systematic Review and Meta-Analysis of PD-L1 Immunoexpression in Follicular Epithelial Derived Thyroid Carcinoma. Endocr Pathol. 2020;31(3):291\u0026ndash;300.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRana C, Babu S, Agarwal H, Singhai A, Kumar M, Singh V, et al. Diagnostic Relevance of GATA 3 Expression in Urinary Bladder Carcinoma of Divergent Differentiation and Other Histological Variants. Indian J Surg Oncol. 2021;12(4):678\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBychkov A, Jung CK. What\u0026rsquo;s new in thyroid pathology 2024: updates from the new WHO classification and bethesda system. J Pathol Transl Med. 2024;58(2):98\u0026ndash;101.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAli SZ, Baloch ZW, Cochand-Priollet B, Schmitt FC, Vielh P, VanderLaan PA. The 2023 Bethesda System for Reporting Thyroid Cytopathology. Thyroid Off J Am Thyroid Assoc. 2023;33(9):1039\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhn S, Kim TH, Kim SW, Ki CS, Jang HW, Kim JS, et al. Comprehensive screening for PD-L1 expression in thyroid cancer. Endocr Relat Cancer. 2017;24(2):97\u0026ndash;106.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTuttle RM, Haugen B, Perrier ND. Updated American Joint Committee on Cancer/Tumor-Node-Metastasis Staging System for Differentiated and Anaplastic Thyroid Cancer (Eighth Edition): What Changed and Why? Thyroid. 2017;27(6):751\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaugen BR, Alexander EK, Bible KC, Doherty GM, Mandel SJ, Nikiforov YE, et al. 2015 American Thyroid Association Management Guidelines for Adult Patients with Thyroid Nodules and Differentiated Thyroid Cancer: The American Thyroid Association Guidelines Task Force on Thyroid Nodules and Differentiated Thyroid Cancer. Thyroid Off J Am Thyroid Assoc. 2016;26(1):1\u0026ndash;133.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKitahara CM, Sosa JA. Understanding the ever-changing incidence of thyroid cancer. Nat Rev Endocrinol. 2020;16(11):617\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrince EA, Sanzari JK, Pandya D, Huron D, Edwards R. Analytical Concordance of PD-L1 Assays Utilizing Antibodies From FDA-Approved Diagnostics in Advanced Cancers: A Systematic Literature Review. JCO Precis Oncol. 2021;(5):953\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBai Y, Niu D, Huang X, Jia L, Kang Q, Dou F, et al. PD-L1 and PD-1 expression are correlated with distinctive clinicopathological features in papillary thyroid carcinoma. Diagn Pathol. 2017;12(1):72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDell\u0026rsquo;Aquila M, Granitto A, Martini M, Capodimonti S, Cocomazzi A, Musarra T, et al. PD-L1 and thyroid cytology: A possible diagnostic and prognostic marker. Cancer Cytopathol. 2020;128(3):177\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBastman JJ, Serracino HS, Zhu Y, Koenig MR, Mateescu V, Sams SB, et al. Tumor-Infiltrating T Cells and the PD-1 Checkpoint Pathway in Advanced Differentiated and Anaplastic Thyroid Cancer. J Clin Endocrinol Metab. 2016;101(7):2863\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAghajani MJ, Cooper A, McGuire H, Jeffries T, Saab J, Ismail K, et al. Pembrolizumab for anaplastic thyroid cancer: a case study. Cancer Immunol Immunother CII. 2019;68(12):1921\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAngell TE, Lechner MG, Jang JK, Correa AJ, LoPresti JS, Epstein AL. BRAF \u003csup\u003eV600E\u003c/sup\u003e in Papillary Thyroid Carcinoma Is Associated with Increased Programmed Death Ligand 1 Expression and Suppressive Immune Cell Infiltration. Thyroid. 2014;24(9):1385\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBai Y, Guo T, Huang X, Wu Q, Niu D, Ji X, et al. In papillary thyroid carcinoma, expression by immunohistochemistry of BRAF V600E, PD-L1, and PD-1 is closely related. Virchows Arch. 2018;472(5):779\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAn HJ, Ko GH, Lee JH, Lee JS, Kim DC, Yang JW, et al. Programmed Death-Ligand 1 Expression and Its Correlation with Lymph Node Metastasis in Papillary Thyroid Carcinoma. J Pathol Transl Med. 2018;52(1):9\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChintakuntlawar AV, Rumilla KM, Smith CY, Jenkins SM, Foote RL, Kasperbauer JL, et al. Expression of PD-1 and PD-L1 in Anaplastic Thyroid Cancer Patients Treated With Multimodal Therapy: Results From a Retrospective Study. J Clin Endocrinol Metab. 2017;102(6):1943\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChowdhury S, Veyhl J, Jessa F, Polyakova O, Alenzi A, MacMillan C, et al. Programmed death-ligand 1 overexpression is a prognostic marker for aggressive papillary thyroid cancer and its variants. Oncotarget. 2016;7(22):32318\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLubin D, Baraban E, Lisby A, Jalali-Farahani S, Zhang P, Livolsi V. Papillary Thyroid Carcinoma Emerging from Hashimoto Thyroiditis Demonstrates Increased PD-L1 Expression, Which Persists with Metastasis. Endocr Pathol. 2018;29(4):317\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi Rliang, Qu N, Luo T, xian, Xiang J, Liao T, Sun G et al. hua,. Programmed Death-Ligand 1 Expression in Papillary Thyroid Cancer and Its Correlation with Clinicopathologic Factors and Recurrence. Thyroid. 2017;27(4):537\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrauner E, Gunda V, Vanden Borre P, Zurakowski D, Kim YS, Dennett KV, et al. Combining BRAF inhibitor and anti PD-L1 antibody dramatically improves tumor regression and anti tumor immunity in an immunocompetent murine model of anaplastic thyroid cancer. Oncotarget. 2016;7(13):17194\u0026ndash;211.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Thyroid, PD-L1 expression, Prognosis, Outcome, Papillary thyroid carcinoma","lastPublishedDoi":"10.21203/rs.3.rs-6175152/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6175152/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Papillary thyroid carcinoma (PTC) is the most common thyroid malignancy, with generally favorable outcomes. However, a subset of patients experiences aggressive disease progression, recurrence, and metastasis, necessitating refined prognostic tools. This study investigates the prognostic significance of PD-L1 expression, measured by the Tumor Proportion Score (TPS), in conjunction with genetic mutations (BRAF, TERT, RAS) and clinicopathological features in PTC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A retrospective analysis was conducted on 84 PTC patients diagnosed between July 2016 and June 2024 at King George’s Medical University, India. Formalin-fixed paraffin-embedded (FFPE) tissues were evaluated for PD-L1 expression using immunohistochemistry (IHC), with TPS categorized as low or high based on median values. Molecular analysis identified BRAF, TERT, and RAS mutations. Clinicopathological data, including tumor size, lymph node involvement, extrathyroidal extension, and recurrence, were collected. Statistical analyses assessed associations between TPS, molecular markers, and clinical outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e PD-L1 expression was observed in 60.7% of cases, with 38.1% classified as TPS high. PD-L1 high was significantly associated with older age, advanced tumour stage, lymph node involvement, and aggressive histological variants (p \u0026lt; 0.05). BRAF and TERT mutations were more prevalent in PD-L1 high cases (p = 0.032 and p = 0.038, respectively). PD-L1 high correlated with increased recurrence (p = 0.043) and distant metastasis (p = 0.002). Multivariable analysis identified multiple mutations as independent predictors of poor survival (AOR = 15.8, p = 0.021) and recurrence (AOR = 11.22, p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e PD-L1 expression, particularly when combined with BRAF and TERT mutations, serves as a valuable prognostic marker in PTC. PD-L1 high is associated with aggressive tumour behaviour, recurrence, and metastasis, highlighting its potential for risk stratification and personalized treatment strategies. Further prospective studies are needed to validate these findings and explore the therapeutic implications of PD-L1 in PTC.\u003c/p\u003e","manuscriptTitle":"Prognostic Characterization of Papillary Thyroid Carcinoma: Insights into the Role of PD-L1 and Mutational Landscape in Disease Aggressiveness and Outcome","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-17 15:14:39","doi":"10.21203/rs.3.rs-6175152/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dedee4cd-4fe2-440b-94ee-ef51d30774a3","owner":[],"postedDate":"March 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-01T10:08:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-17 15:14:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6175152","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6175152","identity":"rs-6175152","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}