A retrospective analysis utilizing the SEER database to develop and validate a prognostic nomogram for middle-aged and older patients diagnosed with squamous cell carcinoma of the mobile tongue

A retrospective analysis utilizing the SEER database to develop and validate a prognostic nomogram for middle-aged and older patients diagnosed with squamous cell carcinoma of the mobile tongue | 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 A retrospective analysis utilizing the SEER database to develop and validate a prognostic nomogram for middle-aged and older patients diagnosed with squamous cell carcinoma of the mobile tongue Junbo Qi, Xiaohan Lun, Xin Li, Weixian Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3853408/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: The overall survival (OS) of squamous cell carcinoma of the mobile tongue remains dismal. This research aimed to develop a predictive nomogram model capable of forecasting the impact of various factors on the OS of mobile tongue squamous cell carcinoma (MTSCC) in middle-aged and older patients. METHODS: The study population consisted of patients diagnosed with MTSCC between 2010 and 2015. These individuals were identified through the utilization of the Surveillance, Epidemiology, and End Results (SEER) database. Following this, at random, these people were separated into two sets: a training set and a validation set. Risk factors for OS in the training set were identified using univariate and multivariate COX regression analyses. Prognostic nomograms for middle-aged and older patients with MTSCC were then created using independent risk factors and validated using the area under the ROC curve, calibration curves, and DCA curves. Finally, a comparison was made between the nomogram and TNM staging in order to assess their respective predictive capabilities. Results: In total, 2354 eligible patients were enrolled; of these, 707 were designated for validation, and 1647 were assigned to the training set. OS-independent prognostic factors included age, race, gender, tumor pathology grading, T-stage, N-stage, whether the primary site was operated on or not, and radiotherapy (all P < 0.05). The AUC values of the OS prognostic nomograms, which were built utilizing independent prognostic parameters, are as follows: The training set yielded an AUC of 0.793, 0.750, and 0.749 for the 1-year, 3-year, and 5-year OS, respectively. The calibration curves of the nomograms exhibited a substantial level of concordance between the projected and observed rates of survival. DCA curve concluded that prognostic net benefit was greater for nomograms featuring broad high-risk thresholds compared to TNM. CONCLUSION: This model has better predictive ability than AJCC staging and it can help oral and maxillofacial surgeons to predict the prognosis of tongue squamous carcinoma. older people cancer squamous cell carcinoma nomogram SEER prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1 Introduction The incidence of MTSCC, a prevalent malignant tumor, is increasing globally. Based on epidemiologic survey data, tongue cancer predominantly manifests in individuals within the middle-aged demographic, specifically between the ages of 40 and 60, with a male predilection(1). This phenomenon might be attributed to the higher prevalence of negative behaviors, such as alcohol consumption and smoking, in the lives of males. (2) Recent research has indicated a progressive increase in the percentage of female patients diagnosed with tongue cancer, as well as a tendency for the disease to affect younger age groups (3). Furthermore, infection with human papillomavirus (HPV) and prolonged ingestion of overheated food are additional risk factors associated with an elevated likelihood of developing tongue cancer(2, 4). The MTSCC exhibits a high rate of malignancy and lymph node metastasis. As a result of the tongue's flexible movement, which is facilitated by an abundance of lymphatic vessels and blood circulation, the 5-year OS for this type of cancer is approximately 56%(5). Survival rate has historically been a crucial metric in tongue cancer treatment. Early diagnosis and treatment are associated with a greater survival rate for patients, as opposed to advanced stages, which are associated with a lesser survival rate. Several studies have shown that early diagnosis of stage I and II lesions greatly improves 5-year survival compared to advanced stage III and IV lesions(4). Therefore, increasing the efficacy of early treatment and early diagnosis is essential for boosting the survival rate of patients with tongue cancer. Current clinical treatments include, among others, surgical resection, radiotherapy, and chemotherapy. In recent years, there has been a continuous evolution of technological interventions for tongue cancer. Recent technological advancements have resulted in revised therapeutic modalities for tongue cancer. The incremental adoption of novel therapeutic approaches, such as molecular targeted therapy and immunotherapy, provides renewed cause for optimism concerning the possibility of augmenting the survival rate of individuals suffering from tongue cancer. The prognostic evaluation of middle-aged and older patients with MTSCC is frequently conducted in clinical practice using the TNM staging system, which was devised by the American Joint Committee on Cancer (AJCC). This system largely utilizes the size of the main tumor and the degree to which it invades surrounding tissues, whether nearby or far away, to categorize it into specific stages. By doing so, it guides subsequent treatment and offers an approximation of the prognosis. Nevertheless, the lack of integration of various treatment options, substantial patient variability, and factors such as age, gender, ethnicity, radiotherapy, chemotherapy, and more into the TNM staging system render it unfeasible to assess the ultimate treatment outcome with precision and efficiency. Consequently, there is an urgent need to develop a highly accurate decision-making tool that can optimize the clinical workflow regarding the selection of treatment options and prognosis for middle-aged and older patients with MTSCC. As a result, an exact decision-making instrument is required immediately to optimize the selection of middle-aged and older MTSCC patients' treatment and prognosis. In recent times, there has been a growing utilization of nomograms in clinical practice for prognosticating malignancy(6, 7). It is widely acknowledged as a valuable statistical predictive instrument that offers advantages to clinicians and patients alike. As of this moment, literature is absent regarding the utilization of nomograms for prognosticating middle-aged and older MTSCC patients. In this study, we developed a nomogram for predicting survival outcomes in middle-aged and older MTSCC patients based on data from 2010 to 2015 in the SEER database and validated its reliability. 2 Materials and methods 2.1Cohort information collection and data processing 2.1.1 Sources of information for the modeling set and validation set The SEER system, created by the National Cancer Institute (NCI), collected data on 60,305 patients diagnosed with tongue cancer from 2010 to 2015. The seventh version of the TNM staging system was applied to the dataset. The national collection of SEER statistics includes information from 18 states, which account for 28% of the US population and are representative of all regions. Social determinants, geographical factors, clinical data, cancer-specific particulars, pathological variables, treatment variables, and outcomes are all encompassed in this data set(8). Specific procedures entailed the following: 1. Acquire a SEER database account via the official website and obtain the SEER*Stat software (version 8.4.2); 2. Construct a novel 'Case Listing Session' within the SEER*Stat software, and in the Date module, choose 'Incidence: SEER Research Data, 17 Registries, Nov 2022 Sub (2000–2020)'; 3. Select 'Site and Morphology' in the 'Selection' module; the tongue was designated as the tumor's location following the ICD-O-3 classification code (site recode ICD-O-3/WHO 2008="tongue"). 4. Select the pertinent factors that require generation for this study within the Table module; 5. Utilize the "Execute" and "Export" buttons to generate data and export the table, respectively. 2.1.2 Inclusion criteria (1) The primary site of the tumor (Primary Site Code) was the mobile tongue (C02.0 ~ C02.3). (2) The patient's pathologic diagnosis was squamous cell carcinoma (ICD-O-3 Hist/behave, malignant: 8070/3 to 8076/3); with or without neck or distant metastases. (3) Patients aged ≥ 45 years. (4) Complete data without vacancies. 2.1.3 Exclusion criteria (1) Age < 45 years old. (2) Patients diagnosed with non-squamous cell carcinoma of the tongue. (3) Incomplete and unclear information (e.g., patients whose primary tumor cannot be evaluated and there was no evidence of a primary tumor, patients who were unable to undergo assessment for the presence or absence of regional lymph node metastasis, patients with an unknown survival time, and patients with unknown information about their treatments and no pathological specimens, etc.) 2.2 Research Methodology 2.2.1 Factors related to inclusion in the study The SEER database and Excel table were used to select and categorize variables. To streamline the subsequent stage of data analysis, the discontinuous variables were initially transformed into categorical variables prior to coding. Some variables include the patient's age at diagnosis (Age), race, sex, tumor primary site (Primary Site), tumor grade (Grade Record), pathology type (ICD-O-3Hist/behave, malignant), AJCC 7th Edition TNM staging (Derived AJCC Stage Group, 7ththed), clinical staging (Derived AJCC Stage Group, 7th ed (2010–2015)), primary tumor surgical approach (RX Summ-Surg Prim Site (1998+)), survival status (Vital status recode (study cut off used)) and survival months, Chemotherapy, Radiation recode Lymphatic management (RX Summ–Scope Reg LN Sur (2003+)). (1) Age distribution: Patients were classified into five age categories at 10-year intervals: those aged 45–54, 55–64, 65–74, 75–84, and 85 years old. (2) Ethnicity was classified as Caucasian, black, and others (yellow and American Indian, etc.). (3) Sex was classified as male and female. (4) The primary site was classified as the dorsal, border, and ventral surfaces of the tongue. (5) The tumor differentiation grades are determined in accordance with the ICD-O-3. Four grades were chosen to categorize tumors: grades I, II, III, and IV. Due to the study's restricted sample size, grades IV and below were excluded. (6) TNM staging: The local staging of cancer is determined according to the AJCC 7th edition, which classifies tumors into four categories: T1, T2, T3, and T4. Based on the same edition, the lymph node staging divides patients with or without lymph node metastases into N0, N1-N3 categories. Lastly, the distant metastasis staging is determined by the same edition and is classified as either M0 or M1. (7) clinical tumor staging as stage I, II, III, and IV. (8) Primary tumor surgery was divided into the non-operated group (Surg Prim Site: 0), the operated group (tumor local resection group (Surg Prim Site: 20 ~ 27), and the extended resection group (Surg Prim Site: 30, 40 ~ 43)). The local excision group included electrodesiccation, cryosurgery, laser cauterization, laser excision, and excisional biopsy, while the extended excision group included partial tongue resection, hemi laryngectomy, radical resection, and radical resection + maxillary/ mandibular resection (segmental/subtotal/total). (9) Chemotherapy records were sorted into the non-chemotherapy group and, the chemotherapy group. (10) The Radiotherapy group was subdivided into two distinct groups: non-radiotherapy and radiotherapy. (11) Neck lymph dissection was divided into the non-lymph dissection group (0, No regional lymph nodes removed), and the lymph dissection group (1–5, Regional lymph node(s) removed, NOS). The selection procedure's flowchart is illustrated in Fig. 1 . 2.2.2 Statistical methods The dataset adopted a non-replacement random sampling method to divide the sample in a 7:3 ratio between a training set and a validation set. Chi-square tests were utilized to evaluate disparities in categorical variables, and categorical data presented as numerical counts (n) and proportions (%). The results derived from the training set, which were applied to construct nomograms and identify parameters for filtering in the nomograms, were validated using the validation set. Using univariate Cox regression, factor associations with OS were identified, whereas multivariate Cox regression was used to identify associated independent risk factors. In univariate Cox regression analysis, hazard ratios (HR) and 95% confidence intervals (CI) were computed for variables that satisfied the condition of having a P value below 0.05. These calculations were performed as a crucial part of the multivariate Cox regression study. Prognostic nomograms were created using independent risk variables and the outcomes of multivariate Cox regression analysis to forecast the likelihood of OS at 1-, 3-, and 5-years. The predictive performance of the TNM stage and the nomogram was additionally assessed through the utilization of ROC curves, DCA curves, and calibration curves. On each variable measure for a given patient, a vertical line was delineated; the point at which this line intersected with the "dot" line indicated the corresponding score for that particular variable. Cumulatively, the ratings for each variable comprise the total score. The nomogram's diagnostic efficacy is frequently quantified using an area under the curve (AUC) value ranging from 0.5 to 1. A bootstrap method was employed to assess the calibration curve, utilizing 1,000 resamples. In R version 4.3.1, the nomograms were constructed and verified through the utilization of various packages, including rmda, hmisc, lattice, survival, formula, pROC, and timeROC. Statistical significance was attributed to a two-sided P < 0.05. Fundamental clinical and demographic attributes Participating in our study were 2,354 eligible patients who were diagnosed with MTSCC from 2010 to 2015. A random division was made of these patients into two sets: the training set (1647, or 70.0%) and the validation set (707, or 30.0%). Table 1 contains detailed information regarding baseline demographics and clinical characteristics. 3 Statistics and results 3.1 Baseline demographic and clinical characteristics Table 1 Baseline demographic and clinical characteristics of the study. Variable,n (%) Total (n = 2354) training set (n = 1647) validation set (n = 707) Statistic P Age, years χ²=3.626 0.459 45–54 490 (20.82) 350 (21.25) 140 (19.80) 55–64 773 (32.84) 534 (32.42) 239 (33.80) 65–74 607 (25.79) 416 (25.26) 191 (27.02) 75–84 362 (15.38) 265 (16.09) 97 (13.72) >=85 122 (5.18) 82 (4.98) 40 (5.66) Sex χ²=1.184 0.277 Female 982 (41.72) 699 (42.44) 283 (40.03) Male 1372 (58.28) 948 (57.56) 424 (59.97) Race χ²=3.290 0.193 White 2035 (86.45) 1410 (85.61) 625 (88.40) Other 206 (8.75) 153 (9.29) 53 (7.50) Black 113 (4.8) 84 (5.10) 29 (4.10) Primary Site χ²=0.127 0.939 Dorsal surface of tongue 323 (13.72) 225 (13.66) 98 (13.86) Border of tongue 1186 (50.38) 827 (50.21) 359 (50.78) Ventral surface of tongue 845 (35.9) 595 (36.13) 250 (35.36) histological Grade χ²=2.240 0.326 I 655 (27.82) 459 (27.87) 196 (27.72) II 1307 (55.52) 902 (54.77) 405 (57.28) III 392 (16.65) 286 (17.36) 106 (14.99) Surgery χ²=0.117 0.732 None 183 (7.77) 126 (7.65) 57 (8.06) Yes 2171 (92.23) 1521 (92.35) 650 (91.94) Stage T χ²=0.691 0.875 T1 1329 (56.46) 927 (56.28) 402 (56.86) T2 657 (27.91) 456 (27.69) 201 (28.43) T3 207 (8.79) 148 (8.99) 59 (8.35) T4 161 (6.84) 116 (7.04) 45 (6.36) Stage N χ²=0.264 0.607 N0 1715 (72.85) 1205 (73.16) 510 (72.14) N1-3 639 (27.15) 442 (26.84) 197 (27.86) Stage M χ²=1.250 0.263 M0 2337 (99.28) 1633 (99.15) 704 (99.58) M1 17 (0.72) 14 (0.85) 3 (0.42) Clinical Grade χ²=0.511 0.916 I 1164 (49.45) 819 (49.73) 345 (48.80) II 412 (17.5) 285 (17.30) 127 (17.96) III 333 (14.15) 229 (13.90) 104 (14.71) IV 445 (18.9) 314 (19.06) 131 (18.53) Radiotherapy χ²=0.133 0.716 None 1561 (66.31) 1096 (66.55) 465 (65.77) Yes 793 (33.69) 551 (33.45) 242 (34.23) Chemotherapy χ²=0.460 0.498 None 1959 (83.22) 1365 (82.88) 594 (84.02) Yes 395 (16.78) 282 (17.12) 113 (15.98) Neck lymph dissection χ²=0.608 0.435 None 1070 (45.45) 740 (44.93) 330 (46.68) Yes 1284 (54.55) 907 (55.07) 377 (53.32) histological Grade Grade I; also called well-differentiated. Grade II; also called moderately differentiated. Grade III; also called poorly differentiated. 3.2 Univariate and multivariate analysis of OS A univariate and multivariate Cox regression analysis was performed on the OS rates in the training set to ascertain independent prognostic variables. The following variables were incorporated into our analysis: age, gender, race, clinical grade, TNM stage, histological grade, surgery, radiotherapy, neck lymph dissection, and chemotherapy. Except for primary site and neck lymph dissection, univariate regression analysis revealed that all the aforementioned variables may have a significant association with OS. Through multivariate analysis, it was also demonstrated that age, sex, race, histological grade, stage TN, radiotherapy, and surgery were all independent predictors of OS. Table 2 HR and p-values for each variable in univariate and multivariate analyses. Table 2 Univariate and multivariate analysis of OS rates in the training cohort. Univariate analysis Multivariate analysis Variable P HR (95%CI) P HR (95%CI) Age 45–54 Ref Ref 55–64 0.997 1.00 (0.81–1.24) 0.297 0.89 (0.72–1.11) 65–74 0.098 1.20 (0.97–1.50) 0.061 1.24 (0.99–1.54) 75–84 < .001 1.90 (1.52–2.38) =85 < .001 3.00 (2.23–4.03) < .001 4.14 (3.04–5.63) Sex Female Ref Ref Male < .001 1.37 (1.18–1.58) < .001 1.32 (1.14–1.53) Race White Ref Ref Other 0.321 0.88 (0.68–1.13) 0.404 0.90 (0.69–1.16) Black < .001 1.97 (1.51–2.58) 0.032 1.37 (1.03–1.82) Primary Site Dorsal surface of tongue Ref Border of tongue 0.169 0.86 (0.70–1.06) Ventral surface of tongue 0.167 0.86 (0.69–1.07) histological Grade I Ref Ref II < .001 1.73 (1.44–2.07) < .001 1.57 (1.31–1.90) III < .001 2.40 (1.93–2.98) < .001 1.91 (1.51–2.40) Surgery None Ref Ref Yes < .001 0.27 (0.22–0.34) < .001 0.46 (0.37–0.58) Stage T T1 Ref Ref T2 < .001 1.86 (1.58–2.19) 0.012 1.52 (1.09–2.12) T3 < .001 3.10 (2.49–3.87) < .001 2.73 (1.92–3.90) T4 < .001 5.05 (4.03–6.33) < .001 3.05 (2.11–4.40) Stage N N0 Ref Ref N1-3 < .001 2.35 (2.03–2.72) 0.001 1.72 (1.24–2.39) Stage M M0 Ref Ref M1 < .001 5.98 (3.45–10.36) 0.114 1.60 (0.89–2.85) Clinical Grade I Ref Ref II < .001 1.74 (1.43–2.13) 0.73 1.07 (0.73–1.58) III < .001 2.18 (1.77–2.69) 0.62 0.90 (0.59–1.37) IV < .001 3.73 (3.13–4.44) 0.365 1.24 (0.78–1.96) Radiotherapy None Ref Ref Yes < .001 1.66 (1.44–1.92) 0.007 0.76 (0.62–0.93) Chemotherapy None Ref Ref Yes < .001 2.09 (1.77–2.46) 0.481 0.92 (0.73–1.16) Neck lymph dissection None Ref Yes 0.803 1.02 (0.88–1.17) 3.3 Development and validation of nomograms The nomograms were established respectively in Fig. 2 according to the independent prognostic variables of OS. The Nomogram uses the principle of cumulative scoring, where each factor is assigned a score at the top "Points", and the scores of each factor are added together to get a total score of "Total Points" which is the same as the "1-year Survival Probability", "3-years Survival Probability" and "5-years Survival Probability". Model discrimination and goodness of fit are frequently employed to evaluate the quality of a predictive model. In this investigation, the discriminability of the model was assessed using the C-index and ROC curve, while the goodness-of-fit was determined using the calibration curve. The results are shown in Figs. 3 and 4 . (1) Discrimination : Within the modeling group, the nomogram graph prediction model yielded a C-index of 0.722 (95% CI: 0.704–0.740) for evaluating prognosis. This value was found to be higher than the C-index of 0.661 (95% CI: 0.641–0.681) for judging prognosis by TNM staging. The C-index for prognosis ascertained by the nomogram graph prediction model was also greater in the validation group compared to that ascertained by TNM staging: 0.727 (95% CI: 0.700-0.754) versus 0.649 (95% CI: 0.6196–0.6784), respectively. As illustrated in Fig. 3 , the nomogram graph prediction model yielded AUC values of 0.793, 0.75, and 0.749 for 1-, 3-, and 5-year overall survival, respectively, in the modeling set. Similarly, the TNM values were 0.763, 0.711, and 0.684, correspondingly. The nomogram graph prediction model yielded AUC values of 0.822, 0.781, and 0.78 for 1-, 3-, and 5-year OS, respectively, in the validation set. In contrast, the TNM staging model predicted AUC values of 0.794, 0.689, and 0.684 for 3- and 5-year OS, respectively. In Fig. 3 , the X-axis denotes 1-specificity, which is the model's false-positive rate; the Y-axis signifies sensitivity, which is the true-positive rate; the blue, green, and red lines in (a, b) represent the 1-,3-, and 5-year OS predicted by the model, respectively; AUC signifies the model's differentiation; the greater the area under the curve, the more effective the differentiation. (2) Degree of Calibration : The correspondence between the predicted and actual survival rates in the calibration curves of the nomograms was exceptionally fit in both the training and validation sets. The 1000-times sampling was performed using the Bootstrap free sampling method; the outcomes are depicted in Fig. 4 . The results indicate that the nomogram graph provides a reasonable fit, as the calibration curve closely approximates the standard curve. Specifically, the predicted 1-, 3-, and 5-year survival rates of the patients closely align with the actual values for these periods. The actual survival rate of the patient at a specific time point is denoted on the Y-axis of Fig. 4 , while the survival rate predicted by the nomogram model is represented on the X-axis. The standardized curve, represented in the figure by the diagonal dashed line at a 45° angle, indicates that the predicted survival rates are in perfect agreement with the true survival rates of the patients. The blue solid line visually represents the correlation between the survival rates predicted by the nomogram model and the actual survival rates of the patients. (3) Applicability of the Model : Clinical decision curves were utilized to assess the nomogram diagram's clinical utility. The DCA curves of the models in both the validation and modeling groups for OS at various time points demonstrated a significant positive net clinical benefit within the specified thresholds, as illustrated in Figs. 5 and 6 . Furthermore, the nomogram diagram model demonstrated superior net clinical benefit in the DCA curves compared to the TNM staging system. 3.4 KM survival curve At the same time points, as shown in Fig. 7 (A), those deemed to be at elevated risk by the model have reduced survival rates. Survival curves for the other variables can also be seen in Fig. 7 . 4 Discussion As previously stated, oral cancer is the sixth most prevalent malignant tumor globally, with MTSCC having the highest incidence rate among oral malignancies. Tongue squamous carcinoma is characterized by invasive growth and rapid progression. As a result of the tongue's abundant blood and lymphatic network and its frequent mobility, regional lymph node metastasis frequently occurs at an early stage; consequently, the prognosis for patients with MTSCC is frequently dismal. As a result, investigating the risk factors that impact the prognosis of MTSCC is vital. Middle-aged and older adults are the preferred age group for MTSCC. While the global consensus regarding tumor prognosis according to TNM staging holds some merit, it fails to account for the unique characteristics of each patient. As a result of variations in gender, age, race, pathologic grading, radiotherapy, chemotherapy, surgery, and other factors, relying solely on the TNM to evaluate the prognosis of middle-aged and older MTSCC patients is insufficiently comprehensive. Based on the total score, the nomogram is a type of clinical predictive modeling that forecasts the probability of survival or the risk of an event (9, 10). Clinicians are progressively becoming intrigued by this innovative and clear-cut predictive model. In recent years, reports of the prognostication of cancer through the use of nomograms have increased. Using a nomogram, Yang et al. (11) demonstrated that YKT6 may serve as a prognostic indicator and immunotherapy response marker for oral squamous cell carcinoma. Wu et al. (12) predicted the overall survival of patients with low-grade endometrial stromal sarcoma using a nomogram. Yu(13)et al. predicted the prognosis of older colorectal cancer patients using a nomogram. In the past, research has utilized column line graphs to forecast OS at the 5-year and 8-year marks in patients with MTSCC(14). Nonetheless, the nomogram diagrams that were generated incorporated a restricted set of demographic and clinicopathological outcomes. Our primary study population consisted of middle-aged and older individuals aged 45 years or older. Radiotherapy was included in the multifactorial analysis, and one-year survival was included in the modeling. Age, race, gender, tumor pathology grading, T-stage, N-stage, and whether the primary site was operated on were identified as independent factors influencing the prognosis of patients with middle-aged and older MTSCC patients (multifactorial analysis; p-value < 0.05). Based on these results, a column-line graph prediction model was constructed. Based on the AUC values of 0.793, 0.75, and 0.749 for the 1-, 3-, and 5-year OS, respectively, the model demonstrates a higher predictive capability and greater reliability compared to the TNM staging AUC values of 0.763, 0.711, and 0.684, respectively. 4.1 Factors associated with the prognosis of patients 4.1.1 Age Recent epidemiologic studies have identified an increased susceptibility to head and neck malignancies, particularly tongue and oropharyngeal cancers, among young adults(15–17). The matter of whether the prognosis for squamous carcinoma of the tongue differs with age is still a subject of contention. Several studies, including those by MukdadLet al. (18) and AnnertzK et al. (19), concluded that young adult patients (18–40 years of age) had higher survival rates than older patients (age > 40 years); While some studies have concluded that the prognosis for young adult patients is inferior to that of older patients (20), others have claimed that there is no discernible difference in prognosis between the two age groups (21). In this research, age was statistically analyzed as an independent risk factor for prognosis. The HR for the patients falling within the '75–84 years old group' was 2.20 (1.75–2.78), while it was 4.14 (3.04–5.63) for those in the '>=85 years old group' as their age increased. In some of the studies that demonstrated a significant prognostic difference between younger and older MTSCC patients, cases were not matched; thus, selection and confounding bias were significant risks. Older and younger patients had comparable prognoses by a wide margin of well-designed case-matched studies; however, each of these matched investigations used a relatively small sample size. We eagerly await matched studies with more substantial sample sizes to attain a more profound comprehension of the prognostic significance of age factors in patients with MTSCC. 4.1.2 Sex Among head and neck tumors, the incidence of oropharyngeal cancer is significantly higher in men (22), ranging from approximately 1.6:1 to 2.6:1(23). Based on their analysis of 132 cases of squamous cell carcinoma of the esophagus, Hopkins et al. (24)concluded that insufficient estrogen and excessive androgen levels in the body might be associated with the development of this condition. Disagreement remains, however, as to whether prognoses differ between the sexes. Despite the lack of statistical significance of the differences, OngTK et al. (25) found at the time of their study that male patients with oral cancer in men had a longer average survival time. Female patients diagnosed with oral cancer exhibit superior disease-free survival (DSS) and surgical survival rates in comparison to their male counterparts, as reported by AmitM et al. (26). The study also identified gender as a major prognostic factor in oral cancer patients. Our study showed that male patients had a worse prognosis than females (HR,1.32; 95% CI: 1.14–1.53, P < 0.05). The reason for this is probably the higher probability of males being exposed to risk factors such as elevated rates of cigarette use and consumption of alcohol. However, it is worth noting that the majority of existing studies on gender and middle-aged and older MTSCC patients are retrospective and lack rigorous experimental controls. We eagerly anticipate the consolidation of studies with bigger sample sizes to provide more clarity on the prognostic importance of gender in patients with MTSCC. 4.1.3 Racist Race was a single factor that had an impact on the prognosis of middle-aged and older MTSCC patients, according to our study; black patients had a reduced OS (HR = 1.37; 95% CI: 1.03–1.82) (P < 0.05). This may be attributable to variables including the socioeconomic status, living environment, dietary practices, and health insurance coverage of the patients. A similar conclusion was reached by Zheng et al. (27) in their investigation of hypopharyngeal squamous cell carcinoma; this variation in mortality risk was observed not only in malignancies of the oral cavity, but also in cancers of the colorectal, pancreatic, lung, and esophagus. With only 4.8% of the participants being black, this study's subgroup cannot be used to represent the circumstances of the entire racial population. The prognostic disparity of MTSCC between races must therefore be substantiated with additional research findings. 4.1.4 TNM staging The TNM staging system is the most predominant method applied to the prediction of tumor prognosis, and several studies have validated its accuracy. The worse the prognosis, the more advanced the staging, as confirmed by a multitude of studies(28, 29). A retrospective study was undertaken by Nobrega et al. on 412 patients who received a diagnosis of MTSCC. The results indicated that T-stage and N-stage had an effect on the OS of these patients. Five-year OS was significantly higher in patients with T1 or T2 stage and no lymphatic metastases (30). In our study, the HR for patients with stage N1-N3 was 1.72 (95% CI: 1.24–2.39). The study found that although the M stage was a risk factor, it was not statistically significant (P > 0.05), likely due to the small proportion of patients (0.72%) with the M1 stage. Nonetheless, it constituted a risk factor, as indicated by the findings of the majority of studies. Since the M stage represents distant tumor metastasis, the M stage also has a very important impact on tumor prognosis, and some studies have shown that metastasis is the most important risk factor for the prognosis of MTSCC patients (31). Furthermore, the M stage has a significant bearing on the prognosis of the tumor; in fact, metastasis is the most significant risk factor for the prognosis of MTSCC patients, according to some studies (31). The depth of tumor infiltration was introduced as a criterion for TNM staging in the 8th edition of the AJCC Cancer St staging Manual. However, the data we gathered and analyzed did not provide a specific description of the depth of tumor infiltration. Therefore, to determine the effect of the depth of tumor infiltration on prognosis, future studies are anticipated. 4.1.5 Surgery, Radiotherapy, Chemotherapy The database is inaccurate about neck surgery, thus this study only classified tumor management into surgical and non-surgical categories. The protective effect of surgery was statistically significant (HR = 0.46; 95% CI: 0.37–0.58) in comparison to the non-operated group. This finding indicates that surgery remains the primary treatment modality for middle-aged and older MTSCC patients. In this investigation, radiotherapy was an additional independent factor that influenced prognosis. Compared to the no-radiotherapy group, it provided a protective effect (P = 0.007), with a hazard ratio of 0.76 (95%CI: 0.62–0.93). Furthermore, the research conducted by Yang et al. (32) implied that the integration of radiotherapy and surgery effectively diminished distant metastasis and recurrence by preventing the spread of micrometastases at the margins and lymph nodes, both of which are potential contributors to disease recurrence. In the treatment of esophageal squamous cell carcinoma, radiotherapy is a crucial component, according to Chen et al. (7). Neoadjuvant radiation improves survival and surgical outcomes over surgery alone. Patients who are incapable or unwilling to undergo medical surgery may be offered radical radiotherapy (RT), which may or may not be combined with chemotherapy. For the management of dysphagia and pain, palliative radiotherapy utilizing intracavitary brachytherapy or external beam radiotherapy is effective. Li et al. found that postoperative radiotherapy improves survival outcomes for patients with AJCC stages III, N1, a large tumor diameter, and lymph node metastases (33), based on a database-based retrospective study of 3,571 patients with vulvar squamous cell carcinoma. In this study, the P > 0.1 for the chemotherapy group may be due to the lack of clarity in the definition of the non-chemotherapy group (none/unknown) in the database. For localized lesion removal, the most controversial issue lies in the extent of the surgical margins. Further immediate tissue resection enhances local control in patients with positive margins of the main specimen of oral tongue cancer, according to the findings of Zhang et al(34). Young et al.(35) study of 2215 patients showed that the risk ratio for margins < 5 mm was significantly higher than that for margins ≥ 5 mm. A further distance from the margin of the tumor also reduces the likelihood of local recurrence. Only local surgery was included in this study (P 0.05). The main reason may be that the positive results of the SEER database for the management of lymph nodes included different modalities such as lymph node biopsy as well as lymph node dissection in different regions. The categorization of the records was not clear, and there was a possibility of selection bias. Looking forward to more in-depth research in the future. 4.1.6 Pathological grading In this study, compared to grade I, those in grades II and III had a poorer prognosis, with HRs of 1.57 (95%CI: 1.31–1.90) and 1.91 (95%CI: 1.51–2.40), respectively, p-value < 0.001. The same conclusion was found in the study, Boxberg et al. (36) believed that the tumor differentiation system was a robust independent predictor of survival in MTSCC, wherein patient survival declined gradually with increasing grade. A retrospective study of 137 patients with papillary renal cell carcinoma by Li et al. (37) showed significantly lower OS and CSS in patients with high-grade renal cell carcinoma. Due to the small number of undifferentiated patients in the final dataset collected by SEER, they were not included in the study, and therefore a larger amount of data is needed for an in-depth study. 5 Conclusion and Shortcomings For middle-aged and older MTSCC patients, age, gender, race, tumor pathology grading, T-stage, N-stage, and whether the primary site is operated are independent factors affecting the prognosis (P < 0.05). The nomogram graph prediction model established in this study has good differentiation, fit, and clinical applicability, and can individually predict the 1-year, 3-year, and 5-year survival of middle-aged and older MTSCC patients, thus better guiding follow-up and subsequent treatment plans. The prognosis of middle-aged and older MTSCC patients in the same stage varies greatly, and the ability of TNM staging to predict prognosis is limited. Shortcomings Postoperative adjuvant treatment of patients was not addressed, and radiotherapy and chemotherapy were not further categorized. Despite the fact that these are the most frequently used treatment modalities for patients with middle-aged and older MTSCC patients, the absence of information regarding postoperative adjuvant treatment compromised the accuracy of the prediction model developed in this investigation. Second, due to the retrospective nature of this research and its reliance on a publicly accessible database, certain details were unverifiable, thereby introducing an unavoidable element of selection bias. Thirdly, this research was conducted internally and lacked external data for validation purposes; thus, prospective studies and large multicenter external samples are still required for additional validation. Abbreviations overall survival (OS);mobile tongue squamous cell carcinoma (MTSCC);the Surveillance, Epidemiology, and End Results (SEER) database;receiver operating characteristic (ROC) curves;the area under the curve (AUC);confidence intervals (CI);hazard ratios (HR);decision curve analysis (DCA) curves;the American Joint Committee on Cancer (AJCC);National Cancer Institute (NCI). Declarations Ethical review of the data : The data used in the modeling cohort were obtained from the SER database, in which the collection of patient information has been declared to be in accordance with the World Medical Association Helsinki Declaration and approved by the relevant Ethics Committees, and the user is required to sign the agreement for the use of the SER database before registering and obtaining an account number. The clinical data of the patients collected in this study are only used for the establishment and validation of the predictive model, do not intervene in the follow-up treatment plan, and do not pose any physiological risk to the patients and can be approved by the Ethics Committee of the hospital without signing the informed consent. Author contributions JBQ, XL, and WXL: study designing. JBQ and XL: data collection. JBQ and XHL: statistical analysis and graphs production. JBQ, and XHL: draft writing. JBQ, XHL, XL, and WXL: final revision. All authors contributed to the article and approved the submitted version. Consent for publication Not applicable. Availability of data and materials The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material. Funding This study is not supported by relevant funding. Competing Interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. References Deneuve S, Pérol O, Dantony E, Guizard AV, Bossard N, Virard F, et al. Diverging incidence trends of oral tongue cancer compared to other head and neck cancers in young adults in France. Int J Cancer. 2022;150(8):1301-9. Miranda-Filho A, Bray F. Global patterns and trends in cancers of the lip, tongue and mouth. Oral Oncol. 2020;102:104551. Paderno A, Morello R, Piazza C. Tongue carcinoma in young adults: a review of the literature. Acta otorhinolaryngologica Italica : organo ufficiale della Societa italiana di otorinolaringologia e chirurgia cervico-facciale. 2018;38(3):175-80. Sciubba JJ. Oral cancer. The importance of early diagnosis and treatment. Am J Clin Dermatol. 2001;2(4):239-51. Farhood Z, Simpson M, Ward GM, Walker RJ, Osazuwa-Peters N. Does anatomic subsite influence oral cavity cancer mortality? A SEER database analysis. Laryngoscope. 2019;129(6):1400-6. Lee CK, Asher R, Friedlander M, Gebski V, Gonzalez-Martin A, Lortholary A, et al. Development and validation of a prognostic nomogram for overall survival in patients with platinum-resistant ovarian cancer treated with chemotherapy. Eur J Cancer. 2019;117:99-106. Chen JY, Chen JJ, Yang BL, Liu ZB, Huang XY, Liu GY, et al. Predicting sentinel lymph node metastasis in a Chinese breast cancer population: assessment of an existing nomogram and a new predictive nomogram. Breast Cancer Res Treat. 2012;135(3):839-48. Moreira DM, Gershman B, Thompson RH, Okuno SH, Robinson SI, Leibovich BC, et al. Clinicopathologic characteristics and survival for adult renal sarcoma: A population-based study. Urol Oncol. 2015;33(12):505.e15-20. Iasonos A, Schrag D, Raj GV, Panageas KS. How to build and interpret a nomogram for cancer prognosis. J Clin Oncol.26(8):1364-70. Mao W, Wang K, Xu B, Zhang H, Sun S, Hu Q, et al. ciRS-7 is a prognostic biomarker and potential gene therapy target for renal cell carcinoma. Mol Cancer.20(1):142. Yang Z, Yan G, Zheng L, Gu W, Liu F, Chen W, et al. YKT6, as a potential predictor of prognosis and immunotherapy response for oral squamous cell carcinoma, is related to cell invasion, metastasis, and CD8+ T cell infiltration. Oncoimmunology. 2021;10(1):1938890. Wu J, Zhang H, Li L, Hu M, Chen L, Xu B, et al. A nomogram for predicting overall survival in patients with low-grade endometrial stromal sarcoma: A population-based analysis. Cancer Commun (Lond).40(7):301-12. Yu C, Zhang Y. Establishment of prognostic nomogram for elderly colorectal cancer patients: a SEER database analysis. BMC Gastroenterol. 2020;20(1):347. Li Y, Zhao Z, Liu X, Ju J, Chai J, Ni Q, et al. Nomograms to estimate long-term overall survival and tongue cancer-specific survival of patients with tongue squamous cell carcinoma. Cancer Med. 2017;6(5):1002-13. Tota JE, Anderson WF, Coffey C, Califano J, Cozen W, Ferris RL, et al. Rising incidence of oral tongue cancer among white men and women in the United States, 1973-2012. (1879-0593 (Electronic)). Valls-Ontañón A, Hernández-Losa J, Somoza Lopez de Haro R, Bellosillo-Paricio B, Ramón Y Cajal S, Bescós-Atín C, et al. Impact of human papilloma virus in patients with oral and oropharyngea l squamous cell carcinomas. Med Clin (Barc).152(5):174-80. Patel SC, Carpenter WR, Tyree S, Couch ME, Weissler M, Hackman T, et al. Increasing incidence of oral tongue squamous cell carcinoma in young white women, age 18 to 44 years. J Clin Oncol. 2011;29(11):1488-94. Mukdad L, Heineman TE, Alonso J, Badran KW, Kuan EC, St John MA. Oral tongue squamous cell carcinoma survival as stratified by age and sex: A surveillance, epidemiology, and end results analysis. The Laryngoscope.129(9):2076-81. Annertz K, Anderson H, Biörklund A, Möller T, Kantola S, Mork J, et al. Incidence and survival of squamous cell carcinoma of the tongue in Scandinavia, with special reference to young adults. Int J Cancer. 2002;101(1):95-9. Adduri R, Sr., Kotapalli V, Gupta NA, Gowrishankar S, Srinivasulu M, Ali MM, et al. P53 nuclear stabilization is associated with FHIT loss and younger age of onset in squamous cell carcinoma of oral tongue. BMC Clin Pathol. 2014;14:37. Miranda Galvis M, Santos-Silva AR, Freitas Jardim J, Paiva Fonseca F, Lopes MA, de Almeida OP, et al. Different patterns of expression of cell cycle control and local invasion-related proteins in oral squamous cell carcinoma affecting young patients. J Oral Pathol Med. 2018;47(1):32-9. Lei L, Zheng R, Peng K, Si L, Peng J, Cai W, et al. Incidence and mortality of oral and oropharyngeal cancer in China, 2015. (1000-9604 (Print)). Dos Santos Costa SF, Brennan PA, Gomez RS, Fregnani ER, Santos-Silva AR, Martins MD, et al. Molecular basis of oral squamous cell carcinoma in young patients: Is it any different from older patients? J Oral Pathol Med. 2018;47(6):541-6. Hopkins MR, Palsgrove DN, Ronnett BM, Vang R, Lin J, Murdock TA. Molecular Analysis of HPV-independent Primary Endometrial Squamous Cell Carcinoma Reveals TP53 and CDKN2A Comutations : A Clinicopathologic Analysis With Re-evaluation of Diagnostic Criteria. Am J Surg Pathol. 2022;46(12):1611-22. Ong TK, Murphy C, Smith AB, Kanatas AN, Mitchell DA. Survival after surgery for oral cancer: a 30-year experience. Br J Oral Maxillofac Surg. 2017;55(9):911-6. Amit M, Yen T-C, Liao C-T, Chaturvedi P, Agarwal JP, Kowalski LP, et al. Improvement in survival of patients with oral cavity squamous cell car cinoma: An international collaborative study. Cancer.119(24):4242-8. Zheng L, Fang S, Ye L, Cai W, Xiang W, Qi Y, et al. Optimal treatment strategy and prognostic analysis for hypopharyngeal squamous-cell carcinoma patients with T3-T4 or node-positive: A popula tion-based study. Eur J Surg Oncol.49(7):1162-70. Zhao R, Dai Y, Li X, Zhu C. Construction and validation of a nomogram for non small cell lung cancer patients with liver metastases based on a population analysis. Sci Rep. 2022;12(1):4011. Yuan C, Yuan J, Xiao H, Li H, Jiang Y, Zhai R, et al. Genomic analysis of matrix metalloproteinases affecting the prognosis and immunogenic profile of gastric cancer. Front Genet. 2023;14:1128088. Nóbrega TD, Queiroz Si Fau - Santos EM, Santos Em Fau - Costa ALL, Costa Al Fau - Pereira-Pinto L, Pereira-Pinto L Fau - de Souza LB, de Souza LB. Clinicopathological evaluation and survival of patients with squamous cell carcinoma of the tongue. (1698-6946 (Electronic)). Sano D, Myers JN. Metastasis of squamous cell carcinoma of the oral tongue. Cancer and Metastasis Reviews. 2007(3/4):26. Yang H, Liu H, Chen Y, Zhu C, Fang W, Yu Z, et al. Long-term Efficacy of Neoadjuvant Chemoradiotherapy Plus Surgery for the Treatment of Locally Advanced Esophageal Squamous Cell Carcinoma: The NEOCRTEC5010 Randomized Clinical Trial. JAMA Surg. 2021;156(8):721-9. Li M, Li J, Wang Z. Prognostic value of postoperative radiotherapy in patients with vulvar squamous carcinoma: findings based on the SEER database. BMC Womens Health. 2023;23(1):361. Zhang L, Judd RT, Zhao S, Rygalski C, Li M, Briody A, et al. Immediate resection of positive margins improves local control in oral tongue cancer. Oral Oncol. 2023;141:106402. Young K, Bulosan H, Kida CC, Bewley AF, Abouyared M, Birkeland AC. Stratification of surgical margin distances by the millimeter on local recurrence in oral cavity cancer: A systematic review and meta-analysis. Head Neck. 2023;45(5):1305-14. Boxberg M, Jesinghaus M, Dorfner C, Mogler C, Drecoll E, Warth A, et al. Tumour budding activity and cell nest size determine patient outcome in oral squamous cell carcinoma: proposal for an adjusted grading system. Histopathology. 2017;70(7):1125-37. Li S, Liu X, Chen X. Prognostic Effect of Subclassification on Oncological Outcomes in Patients with Surgically Treated Localized Papillary Renal Cell Carcinoma: A Retrospective Propensity Score-matched Cohort Study. J Cancer. 2022;13(4):1193-202. Additional Declarations No competing interests reported. Supplementary Files SEERaccount.docx Splitdataset.xlsx rawdata.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-3853408","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":267316334,"identity":"e4a8ee79-33d0-494b-962e-18450020d8f0","order_by":0,"name":"Junbo Qi","email":"","orcid":"","institution":"affiliated with Shengjing Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Junbo","middleName":"","lastName":"Qi","suffix":""},{"id":267316335,"identity":"645c050a-60f7-41fb-97c5-52a489133150","order_by":1,"name":"Xiaohan Lun","email":"","orcid":"","institution":"affiliated with Stomatological Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaohan","middleName":"","lastName":"Lun","suffix":""},{"id":267316336,"identity":"0d9edbe6-df01-4a0e-bc41-9d2dc3b55389","order_by":2,"name":"Xin Li","email":"","orcid":"","institution":"affiliated with Shengjing Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Li","suffix":""},{"id":267316337,"identity":"db550568-841b-46fd-8fe1-d41120a4cae6","order_by":3,"name":"Weixian Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIie3QMQuCQBTA8ScPzuWq1TDqK1wcuFWfJQ6cHNpqCAkC11aj6EsEzYHg1AfwYyQ2FDT0rqI2dQy6//CQg5/eE8Bk+sGaqOcMEBz9IOikirAnOX2IAFZJ9LAiGk8CdYjNPbexC21wl0kxmdx7DDDNL6UX4157fUgQOqnvxkL0I2Bq06kgWX444igLPORC0CW5RKeKjLchrR/IgsioHskXqIlwiYw1sc6lhE1vcUq7OL7eRaoImcISAa1Wspd8HipwFP2xe3ewspeJdS0z75Qer5fTRF6DDL+EqvUVk8lk+psekrw3WL++jx8AAAAASUVORK5CYII=","orcid":"","institution":"affiliated with Shengjing Hospital of China Medical University","correspondingAuthor":true,"prefix":"","firstName":"Weixian","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-01-11 12:29:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3853408/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3853408/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49767535,"identity":"369590c6-fae5-4a75-b268-7f98c6dd8b8b","added_by":"auto","created_at":"2024-01-17 17:16:25","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":251185,"visible":true,"origin":"","legend":"\u003cp\u003eThe study flow chart of the selection process\u003c/p\u003e","description":"","filename":"figure1studyflowchart.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3853408/v1/bc98bfdd9d377b047386d7c2.jpg"},{"id":49767537,"identity":"9d9f1b38-a8c6-4604-9489-5a03a285c3ae","added_by":"auto","created_at":"2024-01-17 17:16:25","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":620092,"visible":true,"origin":"","legend":"\u003cp\u003eThe prognostic nomograms for predicting 1-year 3-year and 5-year OS probabilities of middle-aged and older MTSCC patients in the training cohort.\u003c/p\u003e","description":"","filename":"figure2Nomogram.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3853408/v1/774299b88654be598e74ceed.jpg"},{"id":49767541,"identity":"e0ceb975-7ad0-4342-af42-560ad23e7a6b","added_by":"auto","created_at":"2024-01-17 17:16:25","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":436144,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves of 1-, 3-, and 5-year survival rates (A) predicted by the nomogram in the training set; (B) predicted by the nomogram in the validation set; (C) by the TNM stage in the training set;(D) by the TNM stage in the validation set.\u003c/p\u003e","description":"","filename":"figure3ROC.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3853408/v1/669ccfe02a2b9913195dd7f4.jpg"},{"id":49767540,"identity":"1c2fd14a-57b8-4d1c-9289-f5f1f4947891","added_by":"auto","created_at":"2024-01-17 17:16:25","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":559991,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curves for OS in training set patients （A）1-year OS；（B）3-year OS（C）5-year OS； Calibration curves for OS in validation set patients（D）1-year OS；（E）3-year OS；（F）5-year OS;\u003c/p\u003e","description":"","filename":"figure4Calibrationcurves.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3853408/v1/901cb36043d47af92cca11eb.jpg"},{"id":49767539,"identity":"e619dcdd-e69e-40ac-ad09-e7c1980a3b84","added_by":"auto","created_at":"2024-01-17 17:16:25","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":894722,"visible":true,"origin":"","legend":"\u003cp\u003eDCA curves of the training group model versus conventional TNM staging for predicting 1-, 3- and 5-year OS.\u003c/p\u003e","description":"","filename":"figure5DCATrain.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3853408/v1/17e2fbd123c5a4744fd500e3.jpg"},{"id":49769102,"identity":"df3a7003-938c-423b-b7ba-7df374b2cac8","added_by":"auto","created_at":"2024-01-17 17:24:25","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":899580,"visible":true,"origin":"","legend":"\u003cp\u003eDCA curves of the validation group model versus conventional TNM staging for predicting 1-, 3-, and 5-year OS.\u003c/p\u003e","description":"","filename":"figure6DCAvalidation.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3853408/v1/79fb26cf56b3649cef116293.jpg"},{"id":49767542,"identity":"eb536a0c-860b-4f50-8c9a-51b79703ea70","added_by":"auto","created_at":"2024-01-17 17:16:25","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":471182,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival curves for high versus low risk for different factors. （A）Risk score（B）age（C）stage T（D）stage N（E）histological grade （F）surgery\u003c/p\u003e","description":"","filename":"figure7KMsurvivalcurves.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3853408/v1/fb0929687a1bc628c86aa746.jpg"},{"id":57079063,"identity":"319b27cc-aa71-4cae-a99d-c14b0d227d6d","added_by":"auto","created_at":"2024-05-24 10:05:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5170074,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3853408/v1/5e0d511b-f7ca-4cd7-829b-2790cd0b0d4c.pdf"},{"id":49767534,"identity":"cc991266-3f82-434b-8dd7-696268c69c7f","added_by":"auto","created_at":"2024-01-17 17:16:25","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10535,"visible":true,"origin":"","legend":"","description":"","filename":"SEERaccount.docx","url":"https://assets-eu.researchsquare.com/files/rs-3853408/v1/395d8dd43ef35e6557d41051.docx"},{"id":49769101,"identity":"7d7a32d8-641c-401b-9be6-4eb5f6bbb7f7","added_by":"auto","created_at":"2024-01-17 17:24:25","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":197396,"visible":true,"origin":"","legend":"","description":"","filename":"Splitdataset.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3853408/v1/593f279118a61133d9a2df39.xlsx"},{"id":49767543,"identity":"cd9286fd-347a-4254-87e0-296af147c5ca","added_by":"auto","created_at":"2024-01-17 17:16:25","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":4707033,"visible":true,"origin":"","legend":"","description":"","filename":"rawdata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3853408/v1/8225cc6b2f9e6862dce895ae.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A retrospective analysis utilizing the SEER database to develop and validate a prognostic nomogram for middle-aged and older patients diagnosed with squamous cell carcinoma of the mobile tongue","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe incidence of MTSCC, a prevalent malignant tumor, is increasing globally. Based on epidemiologic survey data, tongue cancer predominantly manifests in individuals within the middle-aged demographic, specifically between the ages of 40 and 60, with a male predilection(1). This phenomenon might be attributed to the higher prevalence of negative behaviors, such as alcohol consumption and smoking, in the lives of males. (2) Recent research has indicated a progressive increase in the percentage of female patients diagnosed with tongue cancer, as well as a tendency for the disease to affect younger age groups (3). Furthermore, infection with human papillomavirus (HPV) and prolonged ingestion of overheated food are additional risk factors associated with an elevated likelihood of developing tongue cancer(2, 4). The MTSCC exhibits a high rate of malignancy and lymph node metastasis. As a result of the tongue's flexible movement, which is facilitated by an abundance of lymphatic vessels and blood circulation, the 5-year OS for this type of cancer is approximately 56%(5). Survival rate has historically been a crucial metric in tongue cancer treatment. Early diagnosis and treatment are associated with a greater survival rate for patients, as opposed to advanced stages, which are associated with a lesser survival rate. Several studies have shown that early diagnosis of stage I and II lesions greatly improves 5-year survival compared to advanced stage III and IV lesions(4). Therefore, increasing the efficacy of early treatment and early diagnosis is essential for boosting the survival rate of patients with tongue cancer. Current clinical treatments include, among others, surgical resection, radiotherapy, and chemotherapy. In recent years, there has been a continuous evolution of technological interventions for tongue cancer. Recent technological advancements have resulted in revised therapeutic modalities for tongue cancer. The incremental adoption of novel therapeutic approaches, such as molecular targeted therapy and immunotherapy, provides renewed cause for optimism concerning the possibility of augmenting the survival rate of individuals suffering from tongue cancer.\u003c/p\u003e \u003cp\u003eThe prognostic evaluation of middle-aged and older patients with MTSCC is frequently conducted in clinical practice using the TNM staging system, which was devised by the American Joint Committee on Cancer (AJCC). This system largely utilizes the size of the main tumor and the degree to which it invades surrounding tissues, whether nearby or far away, to categorize it into specific stages. By doing so, it guides subsequent treatment and offers an approximation of the prognosis. Nevertheless, the lack of integration of various treatment options, substantial patient variability, and factors such as age, gender, ethnicity, radiotherapy, chemotherapy, and more into the TNM staging system render it unfeasible to assess the ultimate treatment outcome with precision and efficiency. Consequently, there is an urgent need to develop a highly accurate decision-making tool that can optimize the clinical workflow regarding the selection of treatment options and prognosis for middle-aged and older patients with MTSCC. As a result, an exact decision-making instrument is required immediately to optimize the selection of middle-aged and older MTSCC patients' treatment and prognosis. In recent times, there has been a growing utilization of nomograms in clinical practice for prognosticating malignancy(6, 7). It is widely acknowledged as a valuable statistical predictive instrument that offers advantages to clinicians and patients alike. As of this moment, literature is absent regarding the utilization of nomograms for prognosticating middle-aged and older MTSCC patients. In this study, we developed a nomogram for predicting survival outcomes in middle-aged and older MTSCC patients based on data from 2010 to 2015 in the SEER database and validated its reliability.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1Cohort information collection and data processing\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Sources of information for the modeling set and validation set\u003c/h2\u003e \u003cp\u003eThe SEER system, created by the National Cancer Institute (NCI), collected data on 60,305 patients diagnosed with tongue cancer from 2010 to 2015. The seventh version of the TNM staging system was applied to the dataset. The national collection of SEER statistics includes information from 18 states, which account for 28% of the US population and are representative of all regions. Social determinants, geographical factors, clinical data, cancer-specific particulars, pathological variables, treatment variables, and outcomes are all encompassed in this data set(8). Specific procedures entailed the following: 1. Acquire a SEER database account via the official website and obtain the SEER*Stat software (version 8.4.2); 2. Construct a novel 'Case Listing Session' within the SEER*Stat software, and in the Date module, choose 'Incidence: SEER Research Data, 17 Registries, Nov 2022 Sub (2000\u0026ndash;2020)'; 3. Select 'Site and Morphology' in the 'Selection' module; the tongue was designated as the tumor's location following the ICD-O-3 classification code (site recode ICD-O-3/WHO 2008=\"tongue\"). 4. Select the pertinent factors that require generation for this study within the Table module; 5. Utilize the \"Execute\" and \"Export\" buttons to generate data and export the table, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 Inclusion criteria\u003c/h2\u003e \u003cp\u003e(1) The primary site of the tumor (Primary Site Code) was the mobile tongue (C02.0\u0026thinsp;~\u0026thinsp;C02.3).\u003c/p\u003e \u003cp\u003e(2) The patient's pathologic diagnosis was squamous cell carcinoma (ICD-O-3 Hist/behave, malignant: 8070/3 to 8076/3); with or without neck or distant metastases.\u003c/p\u003e \u003cp\u003e(3) Patients aged\u0026thinsp;\u0026ge;\u0026thinsp;45 years.\u003c/p\u003e \u003cp\u003e(4) Complete data without vacancies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.1.3 Exclusion criteria\u003c/h2\u003e \u003cp\u003e(1) Age\u0026thinsp;\u0026lt;\u0026thinsp;45 years old.\u003c/p\u003e \u003cp\u003e(2) Patients diagnosed with non-squamous cell carcinoma of the tongue.\u003c/p\u003e \u003cp\u003e(3) Incomplete and unclear information (e.g., patients whose primary tumor cannot be evaluated and there was no evidence of a primary tumor, patients who were unable to undergo assessment for the presence or absence of regional lymph node metastasis, patients with an unknown survival time, and patients with unknown information about their treatments and no pathological specimens, etc.)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Research Methodology\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Factors related to inclusion in the study\u003c/h2\u003e \u003cp\u003eThe SEER database and Excel table were used to select and categorize variables. To streamline the subsequent stage of data analysis, the discontinuous variables were initially transformed into categorical variables prior to coding. Some variables include the patient's age at diagnosis (Age), race, sex, tumor primary site (Primary Site), tumor grade (Grade Record), pathology type (ICD-O-3Hist/behave, malignant), AJCC 7th Edition TNM staging (Derived AJCC Stage Group, 7ththed), clinical staging (Derived AJCC Stage Group, 7th ed (2010\u0026ndash;2015)), primary tumor surgical approach (RX Summ-Surg Prim Site (1998+)), survival status (Vital status recode (study cut off used)) and survival months, Chemotherapy, Radiation recode Lymphatic management (RX Summ\u0026ndash;Scope Reg LN Sur (2003+)).\u003c/p\u003e \u003cp\u003e(1) Age distribution: Patients were classified into five age categories at 10-year intervals: those aged 45\u0026ndash;54, 55\u0026ndash;64, 65\u0026ndash;74, 75\u0026ndash;84, and 85 years old.\u003c/p\u003e \u003cp\u003e(2) Ethnicity was classified as Caucasian, black, and others (yellow and American Indian, etc.).\u003c/p\u003e \u003cp\u003e(3) Sex was classified as male and female.\u003c/p\u003e \u003cp\u003e(4) The primary site was classified as the dorsal, border, and ventral surfaces of the tongue.\u003c/p\u003e \u003cp\u003e(5) The tumor differentiation grades are determined in accordance with the ICD-O-3. Four grades were chosen to categorize tumors: grades I, II, III, and IV. Due to the study's restricted sample size, grades IV and below were excluded.\u003c/p\u003e \u003cp\u003e(6) TNM staging: The local staging of cancer is determined according to the AJCC 7th edition, which classifies tumors into four categories: T1, T2, T3, and T4. Based on the same edition, the lymph node staging divides patients with or without lymph node metastases into N0, N1-N3 categories. Lastly, the distant metastasis staging is determined by the same edition and is classified as either M0 or M1.\u003c/p\u003e \u003cp\u003e(7) clinical tumor staging as stage I, II, III, and IV.\u003c/p\u003e \u003cp\u003e(8) Primary tumor surgery was divided into the non-operated group (Surg Prim Site: 0), the operated group (tumor local resection group (Surg Prim Site: 20\u0026thinsp;~\u0026thinsp;27), and the extended resection group (Surg Prim Site: 30, 40\u0026thinsp;~\u0026thinsp;43)). The local excision group included electrodesiccation, cryosurgery, laser cauterization, laser excision, and excisional biopsy, while the extended excision group included partial tongue resection, hemi laryngectomy, radical resection, and radical resection\u0026thinsp;+\u0026thinsp;maxillary/ mandibular resection (segmental/subtotal/total).\u003c/p\u003e \u003cp\u003e(9) Chemotherapy records were sorted into the non-chemotherapy group and, the chemotherapy group.\u003c/p\u003e \u003cp\u003e(10) The Radiotherapy group was subdivided into two distinct groups: non-radiotherapy and radiotherapy.\u003c/p\u003e \u003cp\u003e(11) Neck lymph dissection was divided into the non-lymph dissection group (0, No regional lymph nodes removed), and the lymph dissection group (1\u0026ndash;5, Regional lymph node(s) removed, NOS).\u003c/p\u003e \u003cp\u003eThe selection procedure's flowchart is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Statistical methods\u003c/h2\u003e \u003cp\u003eThe dataset adopted a non-replacement random sampling method to divide the sample in a 7:3 ratio between a training set and a validation set. Chi-square tests were utilized to evaluate disparities in categorical variables, and categorical data presented as numerical counts (n) and proportions (%). The results derived from the training set, which were applied to construct nomograms and identify parameters for filtering in the nomograms, were validated using the validation set. Using univariate Cox regression, factor associations with OS were identified, whereas multivariate Cox regression was used to identify associated independent risk factors. In univariate Cox regression analysis, hazard ratios (HR) and 95% confidence intervals (CI) were computed for variables that satisfied the condition of having a P value below 0.05. These calculations were performed as a crucial part of the multivariate Cox regression study. Prognostic nomograms were created using independent risk variables and the outcomes of multivariate Cox regression analysis to forecast the likelihood of OS at 1-, 3-, and 5-years. The predictive performance of the TNM stage and the nomogram was additionally assessed through the utilization of ROC curves, DCA curves, and calibration curves. On each variable measure for a given patient, a vertical line was delineated; the point at which this line intersected with the \"dot\" line indicated the corresponding score for that particular variable. Cumulatively, the ratings for each variable comprise the total score. The nomogram's diagnostic efficacy is frequently quantified using an area under the curve (AUC) value ranging from 0.5 to 1. A bootstrap method was employed to assess the calibration curve, utilizing 1,000 resamples. In R version 4.3.1, the nomograms were constructed and verified through the utilization of various packages, including rmda, hmisc, lattice, survival, formula, pROC, and timeROC. Statistical significance was attributed to a two-sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Fundamental clinical and demographic attributes Participating in our study were 2,354 eligible patients who were diagnosed with MTSCC from 2010 to 2015. A random division was made of these patients into two sets: the training set (1647, or 70.0%) and the validation set (707, or 30.0%). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e contains detailed information regarding baseline demographics and clinical characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3 Statistics and results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline demographic and clinical characteristics\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline demographic and clinical characteristics of the study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable,n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;2354)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003etraining set (n\u0026thinsp;=\u0026thinsp;1647)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003evalidation set (n\u0026thinsp;=\u0026thinsp;707)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" colname=\"c7\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, years\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eχ\u0026sup2;=3.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e490 (20.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e350 (21.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e140 (19.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e773 (32.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e534 (32.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e239 (33.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u0026ndash;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e607 (25.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e416 (25.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e191 (27.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75\u0026ndash;84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e362 (15.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e265 (16.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e97 (13.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e122 (5.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82 (4.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e40 (5.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eχ\u0026sup2;=1.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e982 (41.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e699 (42.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e283 (40.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1372 (58.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e948 (57.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e424 (59.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eχ\u0026sup2;=3.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2035 (86.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1410 (85.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e625 (88.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e206 (8.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e153 (9.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e53 (7.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84 (5.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e29 (4.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary Site\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eχ\u0026sup2;=0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.939\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDorsal surface of tongue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e323 (13.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e225 (13.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e98 (13.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBorder of tongue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1186 (50.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e827 (50.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e359 (50.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVentral surface of tongue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e845 (35.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e595 (36.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e250 (35.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ehistological Grade\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eχ\u0026sup2;=2.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e655 (27.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e459 (27.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e196 (27.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1307 (55.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e902 (54.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e405 (57.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e392 (16.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e286 (17.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e106 (14.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgery\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eχ\u0026sup2;=0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.732\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e183 (7.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126 (7.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e57 (8.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2171 (92.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1521 (92.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e650 (91.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage T\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eχ\u0026sup2;=0.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1329 (56.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e927 (56.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e402 (56.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e657 (27.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e456 (27.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e201 (28.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e207 (8.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148 (8.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e59 (8.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e161 (6.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116 (7.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e45 (6.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage N\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eχ\u0026sup2;=0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1715 (72.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1205 (73.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e510 (72.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e639 (27.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e442 (26.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e197 (27.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage M\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eχ\u0026sup2;=1.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2337 (99.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1633 (99.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e704 (99.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e3 (0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical Grade\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eχ\u0026sup2;=0.511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.916\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1164 (49.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e819 (49.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e345 (48.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e412 (17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e285 (17.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e127 (17.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e333 (14.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e229 (13.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e104 (14.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e445 (18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e314 (19.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e131 (18.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRadiotherapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eχ\u0026sup2;=0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.716\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1561 (66.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1096 (66.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e465 (65.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e793 (33.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e551 (33.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e242 (34.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChemotherapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eχ\u0026sup2;=0.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1959 (83.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1365 (82.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e594 (84.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e395 (16.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e282 (17.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e113 (15.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNeck lymph dissection\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eχ\u0026sup2;=0.608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1070 (45.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e740 (44.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e330 (46.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1284 (54.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e907 (55.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e377 (53.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ehistological Grade\u003c/strong\u003e \u003cp\u003eGrade I; also called well-differentiated. Grade II; also called moderately differentiated. Grade III; also called poorly differentiated.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Univariate and multivariate analysis of OS\u003c/h2\u003e \u003cp\u003eA univariate and multivariate Cox regression analysis was performed on the OS rates in the training set to ascertain independent prognostic variables. The following variables were incorporated into our analysis: age, gender, race, clinical grade, TNM stage, histological grade, surgery, radiotherapy, neck lymph dissection, and chemotherapy. Except for primary site and neck lymph dissection, univariate regression analysis revealed that all the aforementioned variables may have a significant association with OS. Through multivariate analysis, it was also demonstrated that age, sex, race, histological grade, stage TN, radiotherapy, and surgery were all independent predictors of OS. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e HR and p-values for each variable in univariate and multivariate analyses.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate analysis of OS rates in the training cohort.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (0.81\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.89 (0.72\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u0026ndash;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.20 (0.97\u0026ndash;1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.24 (0.99\u0026ndash;1.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75\u0026ndash;84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.90 (1.52\u0026ndash;2.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.20 (1.75\u0026ndash;2.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.00 (2.23\u0026ndash;4.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.14 (3.04\u0026ndash;5.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.37 (1.18\u0026ndash;1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.32 (1.14\u0026ndash;1.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88 (0.68\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.90 (0.69\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.97 (1.51\u0026ndash;2.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.37 (1.03\u0026ndash;1.82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary Site\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDorsal surface of tongue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBorder of tongue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.86 (0.70\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVentral surface of tongue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.86 (0.69\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ehistological Grade\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.73 (1.44\u0026ndash;2.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.57 (1.31\u0026ndash;1.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.40 (1.93\u0026ndash;2.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.91 (1.51\u0026ndash;2.40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgery\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.27 (0.22\u0026ndash;0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.46 (0.37\u0026ndash;0.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage T\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.86 (1.58\u0026ndash;2.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.52 (1.09\u0026ndash;2.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.10 (2.49\u0026ndash;3.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.73 (1.92\u0026ndash;3.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.05 (4.03\u0026ndash;6.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.05 (2.11\u0026ndash;4.40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage N\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.35 (2.03\u0026ndash;2.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.72 (1.24\u0026ndash;2.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage M\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.98 (3.45\u0026ndash;10.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.60 (0.89\u0026ndash;2.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical Grade\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.74 (1.43\u0026ndash;2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.07 (0.73\u0026ndash;1.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.18 (1.77\u0026ndash;2.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.90 (0.59\u0026ndash;1.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.73 (3.13\u0026ndash;4.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.24 (0.78\u0026ndash;1.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRadiotherapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.66 (1.44\u0026ndash;1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.76 (0.62\u0026ndash;0.93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChemotherapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.09 (1.77\u0026ndash;2.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.92 (0.73\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNeck lymph dissection\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02 (0.88\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Development and validation of nomograms\u003c/h2\u003e \u003cp\u003eThe nomograms were established respectively in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e according to the independent prognostic variables of OS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Nomogram uses the principle of cumulative scoring, where each factor is assigned a score at the top \"Points\", and the scores of each factor are added together to get a total score of \"Total Points\" which is the same as the \"1-year Survival Probability\", \"3-years Survival Probability\" and \"5-years Survival Probability\".\u003c/p\u003e \u003cp\u003eModel discrimination and goodness of fit are frequently employed to evaluate the quality of a predictive model. In this investigation, the discriminability of the model was assessed using the C-index and ROC curve, while the goodness-of-fit was determined using the calibration curve. The results are shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e(1) \u003cb\u003eDiscrimination\u003c/b\u003e: Within the modeling group, the nomogram graph prediction model yielded a C-index of 0.722 (95% CI: 0.704\u0026ndash;0.740) for evaluating prognosis. This value was found to be higher than the C-index of 0.661 (95% CI: 0.641\u0026ndash;0.681) for judging prognosis by TNM staging. The C-index for prognosis ascertained by the nomogram graph prediction model was also greater in the validation group compared to that ascertained by TNM staging: 0.727 (95% CI: 0.700-0.754) versus 0.649 (95% CI: 0.6196\u0026ndash;0.6784), respectively. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the nomogram graph prediction model yielded AUC values of 0.793, 0.75, and 0.749 for 1-, 3-, and 5-year overall survival, respectively, in the modeling set. Similarly, the TNM values were 0.763, 0.711, and 0.684, correspondingly. The nomogram graph prediction model yielded AUC values of 0.822, 0.781, and 0.78 for 1-, 3-, and 5-year OS, respectively, in the validation set. In contrast, the TNM staging model predicted AUC values of 0.794, 0.689, and 0.684 for 3- and 5-year OS, respectively.\u003c/p\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the X-axis denotes 1-specificity, which is the model's false-positive rate; the Y-axis signifies sensitivity, which is the true-positive rate; the blue, green, and red lines in (a, b) represent the 1-,3-, and 5-year OS predicted by the model, respectively; AUC signifies the model's differentiation; the greater the area under the curve, the more effective the differentiation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(2) \u003cb\u003eDegree of Calibration\u003c/b\u003e: The correspondence between the predicted and actual survival rates in the calibration curves of the nomograms was exceptionally fit in both the training and validation sets. The 1000-times sampling was performed using the Bootstrap free sampling method; the outcomes are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The results indicate that the nomogram graph provides a reasonable fit, as the calibration curve closely approximates the standard curve. Specifically, the predicted 1-, 3-, and 5-year survival rates of the patients closely align with the actual values for these periods.\u003c/p\u003e \u003cp\u003eThe actual survival rate of the patient at a specific time point is denoted on the Y-axis of Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, while the survival rate predicted by the nomogram model is represented on the X-axis. The standardized curve, represented in the figure by the diagonal dashed line at a 45\u0026deg; angle, indicates that the predicted survival rates are in perfect agreement with the true survival rates of the patients. The blue solid line visually represents the correlation between the survival rates predicted by the nomogram model and the actual survival rates of the patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(3) \u003cb\u003eApplicability of the Model\u003c/b\u003e: Clinical decision curves were utilized to assess the nomogram diagram's clinical utility. The DCA curves of the models in both the validation and modeling groups for OS at various time points demonstrated a significant positive net clinical benefit within the specified thresholds, as illustrated in Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Furthermore, the nomogram diagram model demonstrated superior net clinical benefit in the DCA curves compared to the TNM staging system.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 KM survival curve\u003c/h2\u003e \u003cp\u003eAt the same time points, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e(A), those deemed to be at elevated risk by the model have reduced survival rates. Survival curves for the other variables can also be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eAs previously stated, oral cancer is the sixth most prevalent malignant tumor globally, with MTSCC having the highest incidence rate among oral malignancies. Tongue squamous carcinoma is characterized by invasive growth and rapid progression. As a result of the tongue's abundant blood and lymphatic network and its frequent mobility, regional lymph node metastasis frequently occurs at an early stage; consequently, the prognosis for patients with MTSCC is frequently dismal. As a result, investigating the risk factors that impact the prognosis of MTSCC is vital. Middle-aged and older adults are the preferred age group for MTSCC. While the global consensus regarding tumor prognosis according to TNM staging holds some merit, it fails to account for the unique characteristics of each patient. As a result of variations in gender, age, race, pathologic grading, radiotherapy, chemotherapy, surgery, and other factors, relying solely on the TNM to evaluate the prognosis of middle-aged and older MTSCC patients is insufficiently comprehensive. Based on the total score, the nomogram is a type of clinical predictive modeling that forecasts the probability of survival or the risk of an event (9, 10). Clinicians are progressively becoming intrigued by this innovative and clear-cut predictive model. In recent years, reports of the prognostication of cancer through the use of nomograms have increased. Using a nomogram, Yang et al. (11) demonstrated that YKT6 may serve as a prognostic indicator and immunotherapy response marker for oral squamous cell carcinoma. Wu et al. (12) predicted the overall survival of patients with low-grade endometrial stromal sarcoma using a nomogram. Yu(13)et al. predicted the prognosis of older colorectal cancer patients using a nomogram. In the past, research has utilized column line graphs to forecast OS at the 5-year and 8-year marks in patients with MTSCC(14). Nonetheless, the nomogram diagrams that were generated incorporated a restricted set of demographic and clinicopathological outcomes. Our primary study population consisted of middle-aged and older individuals aged 45 years or older. Radiotherapy was included in the multifactorial analysis, and one-year survival was included in the modeling. Age, race, gender, tumor pathology grading, T-stage, N-stage, and whether the primary site was operated on were identified as independent factors influencing the prognosis of patients with middle-aged and older MTSCC patients (multifactorial analysis; p-value \u0026lt; 0.05). Based on these results, a column-line graph prediction model was constructed. Based on the AUC values of 0.793, 0.75, and 0.749 for the 1-, 3-, and 5-year OS, respectively, the model demonstrates a higher predictive capability and greater reliability compared to the TNM staging AUC values of 0.763, 0.711, and 0.684, respectively.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Factors associated with the prognosis of patients\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e4.1.1 Age\u003c/h2\u003e \u003cp\u003eRecent epidemiologic studies have identified an increased susceptibility to head and neck malignancies, particularly tongue and oropharyngeal cancers, among young adults(15–17). The matter of whether the prognosis for squamous carcinoma of the tongue differs with age is still a subject of contention. Several studies, including those by MukdadLet al. (18) and AnnertzK et al. (19), concluded that young adult patients (18–40 years of age) had higher survival rates than older patients (age \u0026gt; 40 years);\u003c/p\u003e \u003cp\u003eWhile some studies have concluded that the prognosis for young adult patients is inferior to that of older patients (20), others have claimed that there is no discernible difference in prognosis between the two age groups (21). In this research, age was statistically analyzed as an independent risk factor for prognosis. The HR for the patients falling within the '75–84 years old group' was 2.20 (1.75–2.78), while it was 4.14 (3.04–5.63) for those in the '\u0026gt;=85 years old group' as their age increased. In some of the studies that demonstrated a significant prognostic difference between younger and older MTSCC patients, cases were not matched; thus, selection and confounding bias were significant risks. Older and younger patients had comparable prognoses by a wide margin of well-designed case-matched studies; however, each of these matched investigations used a relatively small sample size. We eagerly await matched studies with more substantial sample sizes to attain a more profound comprehension of the prognostic significance of age factors in patients with MTSCC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e4.1.2 Sex\u003c/h2\u003e \u003cp\u003eAmong head and neck tumors, the incidence of oropharyngeal cancer is significantly higher in men (22), ranging from approximately 1.6:1 to 2.6:1(23). Based on their analysis of 132 cases of squamous cell carcinoma of the esophagus, Hopkins et al. (24)concluded that insufficient estrogen and excessive androgen levels in the body might be associated with the development of this condition. Disagreement remains, however, as to whether prognoses differ between the sexes. Despite the lack of statistical significance of the differences, OngTK et al. (25) found at the time of their study that male patients with oral cancer in men had a longer average survival time. Female patients diagnosed with oral cancer exhibit superior disease-free survival (DSS) and surgical survival rates in comparison to their male counterparts, as reported by AmitM et al. (26). The study also identified gender as a major prognostic factor in oral cancer patients. Our study showed that male patients had a worse prognosis than females (HR,1.32; 95% CI: 1.14–1.53, P \u0026lt; 0.05). The reason for this is probably the higher probability of males being exposed to risk factors such as elevated rates of cigarette use and consumption of alcohol. However, it is worth noting that the majority of existing studies on gender and middle-aged and older MTSCC patients are retrospective and lack rigorous experimental controls. We eagerly anticipate the consolidation of studies with bigger sample sizes to provide more clarity on the prognostic importance of gender in patients with MTSCC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e4.1.3 Racist\u003c/h2\u003e \u003cp\u003eRace was a single factor that had an impact on the prognosis of middle-aged and older MTSCC patients, according to our study; black patients had a reduced OS (HR = 1.37; 95% CI: 1.03–1.82) (P \u0026lt; 0.05). This may be attributable to variables including the socioeconomic status, living environment, dietary practices, and health insurance coverage of the patients. A similar conclusion was reached by Zheng et al. (27) in their investigation of hypopharyngeal squamous cell carcinoma; this variation in mortality risk was observed not only in malignancies of the oral cavity, but also in cancers of the colorectal, pancreatic, lung, and esophagus. With only 4.8% of the participants being black, this study's subgroup cannot be used to represent the circumstances of the entire racial population. The prognostic disparity of MTSCC between races must therefore be substantiated with additional research findings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e4.1.4 TNM staging\u003c/h2\u003e \u003cp\u003eThe TNM staging system is the most predominant method applied to the prediction of tumor prognosis, and several studies have validated its accuracy. The worse the prognosis, the more advanced the staging, as confirmed by a multitude of studies(28, 29). A retrospective study was undertaken by Nobrega et al. on 412 patients who received a diagnosis of MTSCC. The results indicated that T-stage and N-stage had an effect on the OS of these patients. Five-year OS was significantly higher in patients with T1 or T2 stage and no lymphatic metastases (30). In our study, the HR for patients with stage N1-N3 was 1.72 (95% CI: 1.24–2.39). The study found that although the M stage was a risk factor, it was not statistically significant (P \u0026gt; 0.05), likely due to the small proportion of patients (0.72%) with the M1 stage. Nonetheless, it constituted a risk factor, as indicated by the findings of the majority of studies. Since the M stage represents distant tumor metastasis, the M stage also has a very important impact on tumor prognosis, and some studies have shown that metastasis is the most important risk factor for the prognosis of MTSCC patients (31). Furthermore, the M stage has a significant bearing on the prognosis of the tumor; in fact, metastasis is the most significant risk factor for the prognosis of MTSCC patients, according to some studies (31). The depth of tumor infiltration was introduced as a criterion for TNM staging in the 8th edition of the AJCC Cancer St staging Manual. However, the data we gathered and analyzed did not provide a specific description of the depth of tumor infiltration. Therefore, to determine the effect of the depth of tumor infiltration on prognosis, future studies are anticipated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e4.1.5 Surgery, Radiotherapy, Chemotherapy\u003c/h2\u003e \u003cp\u003eThe database is inaccurate about neck surgery, thus this study only classified tumor management into surgical and non-surgical categories. The protective effect of surgery was statistically significant (HR = 0.46; 95% CI: 0.37–0.58) in comparison to the non-operated group. This finding indicates that surgery remains the primary treatment modality for middle-aged and older MTSCC patients. In this investigation, radiotherapy was an additional independent factor that influenced prognosis. Compared to the no-radiotherapy group, it provided a protective effect (P = 0.007), with a hazard ratio of 0.76 (95%CI: 0.62–0.93). Furthermore, the research conducted by Yang et al. (32) implied that the integration of radiotherapy and surgery effectively diminished distant metastasis and recurrence by preventing the spread of micrometastases at the margins and lymph nodes, both of which are potential contributors to disease recurrence. In the treatment of esophageal squamous cell carcinoma, radiotherapy is a crucial component, according to Chen et al. (7). Neoadjuvant radiation improves survival and surgical outcomes over surgery alone. Patients who are incapable or unwilling to undergo medical surgery may be offered radical radiotherapy (RT), which may or may not be combined with chemotherapy. For the management of dysphagia and pain, palliative radiotherapy utilizing intracavitary brachytherapy or external beam radiotherapy is effective. Li et al. found that postoperative radiotherapy improves survival outcomes for patients with AJCC stages III, N1, a large tumor diameter, and lymph node metastases (33), based on a database-based retrospective study of 3,571 patients with vulvar squamous cell carcinoma. In this study, the P \u0026gt; 0.1 for the chemotherapy group may be due to the lack of clarity in the definition of the non-chemotherapy group (none/unknown) in the database. For localized lesion removal, the most controversial issue lies in the extent of the surgical margins. Further immediate tissue resection enhances local control in patients with positive margins of the main specimen of oral tongue cancer, according to the findings of Zhang et al(34). Young et al.(35) study of 2215 patients showed that the risk ratio for margins \u0026lt; 5 mm was significantly higher than that for margins ≥ 5 mm. A further distance from the margin of the tumor also reduces the likelihood of local recurrence. Only local surgery was included in this study (P \u0026lt; 0.05), while the management of lymph nodes was not included (P \u0026gt; 0.05). The main reason may be that the positive results of the SEER database for the management of lymph nodes included different modalities such as lymph node biopsy as well as lymph node dissection in different regions. The categorization of the records was not clear, and there was a possibility of selection bias. Looking forward to more in-depth research in the future.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e4.1.6 Pathological grading\u003c/h2\u003e \u003cp\u003eIn this study, compared to grade I, those in grades II and III had a poorer prognosis, with HRs of 1.57 (95%CI: 1.31–1.90) and 1.91 (95%CI: 1.51–2.40), respectively, p-value \u0026lt; 0.001. The same conclusion was found in the study, Boxberg et al. (36) believed that the tumor differentiation system was a robust independent predictor of survival in MTSCC, wherein patient survival declined gradually with increasing grade. A retrospective study of 137 patients with papillary renal cell carcinoma by Li et al. (37) showed significantly lower OS and CSS in patients with high-grade renal cell carcinoma. Due to the small number of undifferentiated patients in the final dataset collected by SEER, they were not included in the study, and therefore a larger amount of data is needed for an in-depth study.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5 Conclusion and Shortcomings","content":"\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eFor middle-aged and older MTSCC patients, age, gender, race, tumor pathology grading, T-stage, N-stage, and whether the primary site is operated are independent factors affecting the prognosis (P \u0026lt; 0.05).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe nomogram graph prediction model established in this study has good differentiation, fit, and clinical applicability, and can individually predict the 1-year, 3-year, and 5-year survival of middle-aged and older MTSCC patients, thus better guiding follow-up and subsequent treatment plans.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe prognosis of middle-aged and older MTSCC patients in the same stage varies greatly, and the ability of TNM staging to predict prognosis is limited.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e \u003cb\u003eShortcomings\u003c/b\u003e \u003c/p\u003e\u003cp\u003ePostoperative adjuvant treatment of patients was not addressed, and radiotherapy and chemotherapy were not further categorized. Despite the fact that these are the most frequently used treatment modalities for patients with middle-aged and older MTSCC patients, the absence of information regarding postoperative adjuvant treatment compromised the accuracy of the prediction model developed in this investigation. Second, due to the retrospective nature of this research and its reliance on a publicly accessible database, certain details were unverifiable, thereby introducing an unavoidable element of selection bias. Thirdly, this research was conducted internally and lacked external data for validation purposes; thus, prospective studies and large multicenter external samples are still required for additional validation.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eoverall survival (OS);mobile tongue squamous cell carcinoma (MTSCC);the Surveillance, Epidemiology, and End Results (SEER) database;receiver operating characteristic (ROC) curves;the area under the curve (AUC);confidence intervals (CI);hazard ratios (HR);decision curve analysis (DCA) curves;the American Joint Committee on Cancer (AJCC);National Cancer Institute (NCI).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical review of the data\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eThe data used in the modeling cohort were obtained from the SER database, in which the collection of patient information has been declared to be in accordance with the World Medical Association Helsinki Declaration and approved by the relevant Ethics Committees, and the user is required to sign the agreement for the use of the SER database before registering and obtaining an account number. The clinical data of the patients collected in this study are only used for the establishment and validation of the predictive model, do not intervene in the follow-up treatment plan, and do not pose any physiological risk to the patients and can be approved by the Ethics Committee of the hospital without signing the informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJBQ, XL, and WXL: study designing. JBQ and XL: data collection. JBQ and XHL: statistical analysis and graphs production. JBQ, and XHL: draft writing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJBQ, XHL, XL, and WXL: final revision. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is not supported by relevant funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDeneuve S, P\u0026eacute;rol O, Dantony E, Guizard AV, Bossard N, Virard F, et al. Diverging incidence trends of oral tongue cancer compared to other head and neck cancers in young adults in France. Int J Cancer. 2022;150(8):1301-9.\u003c/li\u003e\n\u003cli\u003eMiranda-Filho A, Bray F. Global patterns and trends in cancers of the lip, tongue and mouth. 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J Cancer. 2022;13(4):1193-202.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"older people, cancer, squamous cell carcinoma, nomogram, SEER, prognosis","lastPublishedDoi":"10.21203/rs.3.rs-3853408/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3853408/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBACKGROUND:\u003c/h2\u003e \u003cp\u003eThe overall survival (OS) of squamous cell carcinoma of the mobile tongue remains dismal. This research aimed to develop a predictive nomogram model capable of forecasting the impact of various factors on the OS of mobile tongue squamous cell carcinoma (MTSCC) in middle-aged and older patients.\u003c/p\u003e\u003ch2\u003eMETHODS:\u003c/h2\u003e \u003cp\u003eThe study population consisted of patients diagnosed with MTSCC between 2010 and 2015. These individuals were identified through the utilization of the Surveillance, Epidemiology, and End Results (SEER) database. Following this, at random, these people were separated into two sets: a training set and a validation set. Risk factors for OS in the training set were identified using univariate and multivariate COX regression analyses. Prognostic nomograms for middle-aged and older patients with MTSCC were then created using independent risk factors and validated using the area under the ROC curve, calibration curves, and DCA curves. Finally, a comparison was made between the nomogram and TNM staging in order to assess their respective predictive capabilities.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eIn total, 2354 eligible patients were enrolled; of these, 707 were designated for validation, and 1647 were assigned to the training set. OS-independent prognostic factors included age, race, gender, tumor pathology grading, T-stage, N-stage, whether the primary site was operated on or not, and radiotherapy (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The AUC values of the OS prognostic nomograms, which were built utilizing independent prognostic parameters, are as follows: The training set yielded an AUC of 0.793, 0.750, and 0.749 for the 1-year, 3-year, and 5-year OS, respectively. The calibration curves of the nomograms exhibited a substantial level of concordance between the projected and observed rates of survival. DCA curve concluded that prognostic net benefit was greater for nomograms featuring broad high-risk thresholds compared to TNM.\u003c/p\u003e\u003ch2\u003eCONCLUSION:\u003c/h2\u003e \u003cp\u003eThis model has better predictive ability than AJCC staging and it can help oral and maxillofacial surgeons to predict the prognosis of tongue squamous carcinoma.\u003c/p\u003e","manuscriptTitle":"A retrospective analysis utilizing the SEER database to develop and validate a prognostic nomogram for middle-aged and older patients diagnosed with squamous cell carcinoma of the mobile tongue","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-17 17:16:20","doi":"10.21203/rs.3.rs-3853408/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":"f0940563-9e19-4210-a882-40145f1fa24a","owner":[],"postedDate":"January 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-24T09:57:35+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-17 17:16:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3853408","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3853408","identity":"rs-3853408","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}