Development and validation of a nomogram for predicting distant metastasis in oral squamous cell carcinoma

Development and validation of a nomogram for predicting distant metastasis in oral squamous cell carcinoma | 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 Development and validation of a nomogram for predicting distant metastasis in oral squamous cell carcinoma Mirai Higaki, Fumitaka Obayashi, Yukina Kobayashi, Kota Morishita, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8766333/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective This study aimed to identify and quantify risk factors for distant metastasis (DM) in patients with oral squamous cell carcinoma (OSCC). Methods A retrospective cohort study was conducted in patients with OSCC who underwent curative surgery and had histopathologically confirmed cervical lymph node metastasis. After excluding 16 patients with uncontrolled primary tumors, the remaining patients were assigned to a training cohort (n = 85) and a validation cohort (n = 41). Multivariable logistic regression was used to identify independent predictors of DM. These predictors were incorporated into a nomogram. The nomogram was internally validated using the training cohort and externally validated using the validation cohort to assess its predictive performance. Results Histological grade (adjusted odds ratio [aOR]: 3.75, 95% confidence interval [CI]: 1.19–13.51, p = 0.02), number of positive nodes (aOR: 6.57, 95% CI: 1.55–35.16, p = 0.01), and extent of extranodal extension (aOR: 1.50, 95% CI: 1.01–2.26, p = 0.04) were identified as independent predictors of DM and incorporated into a nomogram. The internal and external validation cohorts demonstrated that the nomogram had good discrimination (area under the curve: 0.819 and 0.776, respectively), calibration, and clinical utility. Conclusion We developed a nomogram that accurately predicted the risk of DM in patients with OSCC. Clinical relevance: This tool may facilitate the development of individualized postoperative treatment strategies for patients undergoing curative surgery for OSCC. Oral squamous cell carcinoma Nomogram Distant metastasis Prediction Personalized medicine Extranodal extension Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Oral cancer is estimated to account for approximately 2% of all malignancies, with approximately 378,000 new cases and 178,000 deaths reported worldwide in 2020 [ 1 ]. The most common pathological subtype is oral squamous cell carcinoma (OSCC), which comprises more than 90% of all oral cancer cases. Although advancements in surgical techniques, radiation therapy, and systemic treatments have improved local control in OSCC, the 5-year overall survival (OS) rate remains approximately 60% [ 2 ]. Distant metastasis (DM) is the primary factor reducing OSCC survival rates. Despite improvements in local control rates, the incidence of DM has not improved, presenting a major challenge. Current treatments for DM in OSCC consist primarily of systemic therapy or palliative care. In recent years, systemic therapeutic options for DM have advanced, with immune-modulating antibodies such as pembrolizumab becoming widely adopted [ 3 ]. However, the improvement in OS has been modest. Surgical resection of metastatic lesions has also been attempted in selected patients. A meta-analysis reported a 5-year survival rate of 29.1% following pulmonary metastasectomy for head and neck squamous cell carcinoma; however, the 5-year survival rate for oral cancer was substantially poorer, ranging from 9.2% to 15.4% [ 4 ]. Thus, current treatments for DM in OSCC have not achieved satisfactory outcomes. Moreover, all available therapies target the disease only after DM has developed, and no established strategy exists for preventing its occurrence. This is attributable in part to the absence of an established framework for stratifying the risk of DM. Multiple studies have investigated risk factors for DM in OSCC, with cervical lymph node metastasis consistently identified as the most significant predictor. Multiple additional risk factors have been reported and the development of DM in OSCC appears to be a multifactorial process. However, quantitative prediction of the likelihood of DM in individual patients based on a comprehensive assessment of these factors remains challenging. Currently, no practical model specifically designed to predict DM in OSCC is available. Developing predictive models that incorporate the relevant risk factors is therefore of clinical importance to facilitate the development of personalized treatment strategies. A nomogram is a statistical tool that estimates the individualized probability of a clinical event by integrating multiple variables [ 5 ]. This approach provides an intuitive and readily interpretable scoring system and has been widely applied to predict survival and other clinical outcomes for a range of cancer types [ 6 – 8 ]. The objective of this study was to identify risk factors for DM in patients with OSCC who had achieved local control and to construct a nomogram based on these factors to use as a clinical tool to facilitate the development of treatment strategies tailored to the individual risk of DM. Patients and Methods Study Design and Ethics This study had a retrospective cohort design. This study was conducted in accordance with the ethical principles of the Declaration of Helsinki and was approved by the Ethics Review Committee of Hiroshima University (No. E2023-0025). Informed consent was not required given the retrospective study design. However, patients had the option to opt-out of having their clinical data used for research purposes. Patients Patients who underwent neck dissection in at our institution between January 2013 and December 2024 were included. Eligible patients were aged 20 years or older and had undergone surgery with curative intent. Patients without histopathologically confirmed lymph node metastasis or with uncontrolled local disease were excluded. At our institution, oral cancer treatment is performed independently across multiple departments. Eligible patients who underwent neck dissection in the Department of Oral and Maxillofacial Surgery were included in the training cohort. This cohort served as the training cohort for nomogram development. Eligible patients who underwent neck dissection in the Department of Oral and Maxillofacial Reconstructive Surgery were included in the validation cohort. This cohort was used for external validation. (The Department of Oral and Maxillofacial Surgery and the Department of Oral and Maxillofacial Reconstructive Surgery are separate and independent departments.) Data were extracted from patient records on age at diagnosis, sex, primary tumor site, TNM stage, histopathological parameters, and metastatic status. Tumor staging was determined according to the 8th edition of the TNM classification [ 9 ], and histological grade was assessed based on World Health Organization criteria [ 10 ]. Treatment All patients underwent either primary surgical resection with simultaneous neck dissection or primary surgical resection followed by delayed neck dissection. Patients with extranodal extension (ENE) were treated with radiotherapy combined with cisplatin-based chemotherapy according to established guidelines. Patients with positive resection margins generally underwent additional surgical resection, or chemoradiotherapy if further resection was not feasible. At our institution, the primary indications for neck dissection include a strong suspicion of cervical lymph node metastasis based on preoperative imaging, or the need for free flap reconstruction. Patients without preoperatively detected cervical lymph node metastasis do not routinely undergo neck dissection and are managed with careful surveillance. Follow-up The initial follow-up was conducted 14 days after discharge. Thereafter, patients were followed monthly for the first 2 years postoperatively, every 2 months during the third year, and every 3 months during the fourth and fifth years. Computed tomography (CT) imaging was performed postoperatively every 3 months for the first 2 years, and then every 6 months for the next 3 years. Neck ultrasonography or positron emission tomography-CT were performed as clinically indicated. DM was diagnosed based primarily on imaging findings, with histopathological confirmation performed only when differentiation from other primary tumors was required. Histopathological Analysis Histopathological analyses were conducted in accordance with the institutional protocol. Two or more experienced pathologists evaluated the specimens independently. Assessment of ENE was performed according to a previously described method [ 11 ]. Other histopathological parameters were extracted from the pathology reports. Statistical Analysis The statistical analysis was performed using SPSS version 30.0 (IBM Corp., Armonk, NY, USA) and R version 4.5.0 (The R Foundation for Statistical Computing, Vienna, Austria). Clinical variables (including age, sex, primary tumor site, clinical T stage, clinical N stage, and mandibular bone invasion), histopathological parameters (including histological differentiation grade, perineural invasion, vascular invasion, Yamamoto-Kohama [YK] classification, number of positive nodes, and extent of ENE), and treatment-related factors, such as timing of neck dissection, were evaluated as potential predictors of DM. The primary endpoint was the occurrence of DM, and the analysis identified factors associated with DM occurrence. Continuous variables were reported as the median and interquartile range (IQR), and categorical variables were reported as absolute and relative frequencies. Intergroup comparisons were performed using the Mann–Whitney U test or Fisher’s exact test for continuous and categorical variables, respectively. Unadjusted and multivariable logistic regression analyses were conducted to identify factors associated with DM, and the results were expressed as odds ratios (OR) and 95% confidence intervals (CI). Variables included in the multivariable analysis were selected based on P < 0.05 in the unadjusted analysis and consideration of previously reported predictors. A predictive nomogram was constructed based on the significant independent predictors identified in the multivariable logistic regression analysis. Each predictor was assigned a score value in the nomogram, with higher total scores corresponding to a higher risk of DM. The performance and clinical utility of the nomogram were assessed in the training and validation cohorts using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). A larger area under the curve (AUC) in the ROC analysis indicated greater discriminatory ability. Calibration plots were generated by comparing predicted probabilities with observed outcomes using 1,000 bootstrap resamples. DCA evaluated the clinical usefulness of the model across a range of threshold probabilities. The risk score for DM was calculated for all patients using the constructed nomogram. The cutoff value for the risk score was determined using the Youden index derived from the ROC curve in the training cohort. Based on this cutoff value, patients were stratified into low- and high-risk groups. Kaplan–Meier analysis was used to estimate survival rates for each group, and the log-rank test was used to assess the statistical significance of differences in survival between groups. Two-tailed P values less than 0.05 were considered statistically significant.. Results Patient Characteristics A total of 126 patients were included in the study, with 85 in the training cohort and 41 in the validation cohort. Patient characteristics are summarized in Table 1 . The median age was 69 years (IQR, 55.8–76 years), and the cohort comprised 82 males (65.1%) and 44 females (34.9%). The most common primary tumor sites were the tongue (n = 61, 48.4%), gingiva (n = 40, 31.8%) and floor of the mouth (n = 13, 10.3%). Table 1 Characteristics of patients in the training and validation cohorts Variable Overall N = 126 Training cohort N = 85 Validation cohort N = 41 p Age (years), median (IQR) 69 (55.8–76) 69 (58.5–76) 68 (53–74) 0.502 Sex, n (%) Male 82 (65.1) 57 (67) 25 (61) 0.447 Female 44 (34.9) 28 (33) 16 (39) BMI (kg/m 2 ), median (IQR) 22.1 (19.7–24.1) 21.8 (19.3–24.2) 22.3 (20.0–23.8) 0.585 Primary site, n (%) Tongue 61 (48.4) 35 (41) 26 (64) 0.012 Gingiva 40 (31.8) 28 (33) 12 (29) Buccal 10 (7.9) 10 (12) 0 (0) Primary intraosseous 2 (1.6) 2 (2) 0 (0) Floor of mouth 13 (10.3) 10 (12) 3 (7) Clinical T stage, n (%) T1 13 (10.3) 9 (11) 4 (10) 0.165 T2 41 (32.5) 27 (32) 14 (34) T3 18 (14.3) 15 (18) 3 (7) T4 54 (42.9) 34 (40) 20 (49) Clinical N stage, n (%) N0 65 (51.6) 43 (51) 22 (54) 0.025 N1 18 (14.3) 13 (15) 5 (12) N2 32 (25.4) 18 (21) 14 (34) N3 11 (8.7) 11 (13) 0 (0) Bone invasion, n (%) Present 36 (28.6) 27 (32) 9 (22) 0.246 Absent 90 (71.4) 58 (68) 32 (78) Histological grade, n (%) Well-differentiated 63 (50.0) 40 (47) 23 (56) 0.587 Moderately differentiated 51 (40.5) 37 (44) 14 (34) Poorly differentiated 12 (9.5) 8 (9) 4 (10) PNI, n (%) Present 53 (42.1) 35 (41) 18 (44) 0.084 Absent 73 (57.9) 50 (59) 23 (56) LVI, n (%) Present 54 (42.9) 37 (44) 17 (41) 0.826 Absent 72 (57.1) 48 (56) 24 (59) YK classification, n (%) 1 1 (0.8) 1 (1) 0 (0) 0.024 2 0 (0) 0 (0) 0 (0) 3 57 (45.2) 31 (37) 26 (64) 4C 59 (46.8) 45 (53) 14 (34) 4D 9 (7.2) 8 (9) 1 (2) Number of positive nodes, median (IQR) 2 (1–4) 2 (1–4) 2 (1–3) 0.524 ENE, n (%) Present 67 (53.2) 47 (55) 20 (49) 0.493 Absent 59 (46.8) 38 (45) 21 (51) Timing of neck dissection, n (%) Initial treatment 71 (56.4) 50 (59) 21 (51) 0.411 After treatment of primary site 55 (43.6) 35 (41) 20 (49) Distant metastasis, n (%) Present 38 (30.2) 25 (29) 13 (32) 0.793 Absent 88 (69.8) 60 (71) 28 (68) ENE, extranodal extension; IQR, interquartile range; LVI, lymphovascular invasion; PNI, perineural invasion; YK classification, Yamamoto-Kohama classification. Histologically, 63 of 126 tumors (50.0%) were classified as well-differentiated, 51 (40.5%) as moderately differentiated, and 12 (9.5%) as poorly differentiated. Perineural invasion and lymphovascular invasion were identified in 53 patients (42.1%) and 54 patients (42.9%), respectively. The median number of metastatic lymph nodes was 2 (IQR, 1–4), and 67 of the 126 patients (53.2%) developed ENE. Overall, 38 of the 126 patients (30.2%) developed DM, including 25 of 85 patients (29%) in the training cohort and 13 of 41 patients (32%) in the validation cohort. Characteristics of DM The distribution of distant metastatic sites is presented in Table 2 . In the 38 patients with DM, the lung was the most frequent site (28 patients, 74%), followed by non-cervical lymph nodes (4 patients, 10%), bone (3 patients, 8%), and the liver (3 patients, 8%). Table 2 Specific metastatic sites in the training and validation cohorts Specific metastatic site Overall, n (%) N = 38 Training cohort, n (%) N = 25 Validation cohort, n (%) N = 13 p Lung 28 (74) 19 (76) 9 (70) 0.237 Non-cervical lymph node 4 (10) 2 (8) 2 (15) Bone 3 (8) 3 (12) 0 (0) Liver 3 (8) 1 (4) 2 (15) Training cohort analysis Identification of predictors for DM The results of the unadjusted and multivariable logistic regression analyses to identify predictors of DM are shown in Table 3 . In unadjusted analysis, histological grade, YK classification, perineural invasion, number of metastatic lymph nodes, and extent of ENE were identified as candidate predictors. In the multivariable analysis, histological grade (OR, 3.75; 95% CI, 1.19–13.51; p = 0.02), number of positive nodes ≥ 5 (OR, 6.57; 95% CI, 1.55–35.16; p = 0.01), and extent of ENE (OR, 1.50; 95% CI, 1.01–2.26; p = 0.04) were identified as independent predictors of DM. Table 3 Unadjusted and multivariable logistic regression analyses of factors predicting distant metastasis in the training cohort Variable Unadjusted analysis Multivariable analysis OR 95% CI p OR 95% CI p Age (years) 0.123 — — — < 75 1.00 reference ≥ 75 2.16 0.80–5.77 Sex 0.376 — — — Female 1.00 reference Male 0.64 0.24–1.72 BMI (kg/m 2 ) 0.123 — — — ≥ 20 1.00 reference < 20 2.16 0.81–5.77 Primary site 0.725 — — — Other than tongue 1.00 reference Tongue 0.84 0.31–2.20 Clinical T stage 0.208 — — — 1–2 1.00 reference 3–4 1.86 0.72–5.16 Clinical N stage 0.463 — — — 0–1 1.00 reference 2–3 1.44 0.53–3.77 Bone invasion 0.628 — — — Absent 1.00 reference Present 0.778 0.26–2.11 Histological grade 0.015 0.023 Well-defined 1.00 reference 1.00 reference Moderately/poorly-defined 3.36 1.26–9.77 3.75 1.19–13.51 YK classification 0.025 — — — 1–3 1.00 reference 4C/D 3.27 1.15–10.9 PNI 0.023 — — — Absent 1.00 reference Present 3 1.15–8.07 LVI 0.135 — — — Absent 1.00 reference Present 2.04 0.79–5.37 Number of positive nodes < 0.001 0.01 < 5 1.00 reference 1.00 reference ≥ 5 12.7 3.40–62.0 6.57 1.55–35.2 Extent of ENE (mm) 1.66 1.20–2.35 0.002 1.50 1.01–2.26 0.044 Timing of neck dissection 0.411 — — — Initial treatment 1.00 reference After treatment of primary site 1.48 0.57–3.83 BMI, body mass index; ENE, extranodal extension; LVI, lymphovascular invasion; PNI, perineural invasion; YK classification, Yamamoto-Kohama classification. Establishment of the nomogram All independent predictors identified through the multivariable logistic regression analysis were incorporated into the nomogram for predicting DM. The resulting nomogram is presented in Fig. 1 . Each variable was assigned a specific point value, and the cumulative score provided an estimated probability of DM, with higher scores indicating a higher risk. Nomogram validation The predictive performance of the nomogram was evaluated in both the training and validation cohorts. ROC curve analysis demonstrated strong discriminatory ability, with an AUC of 0.819 in the training cohort and 0.828 in the validation cohort (Fig. 2 ). Calibration curves for both cohorts showed good agreement between the predicted and observed probabilities of DM (Fig. 3 ). Additionally, DCA showed that the nomogram provided greater net clinical benefit than both the treat-all and the treat-none strategies across a broad range of threshold probabilities in both cohorts (Fig. 4 ). Survival analysis based on risk stratification using the DM risk score A risk score for DM was calculated for each patient using the nomogram. ROC analysis was conducted in the training cohort to identify the optimal cutoff value for predicting DM. The optimal threshold was determined to be 0.288, and patients were subsequently categorized into low-risk and high-risk groups based on this cutoff. In the training cohort, the 5-year OS rates were 82% in the low-risk group and 33% in the high-risk group (Fig. 5 a). Similarly, in the validation cohort, the 5-year OS rates were 81% in the low-risk group and 24% in the high-risk group (Fig. 5 b). The difference in survival between the low-risk and high-risk groups was significant in both cohorts. Discussion In this study, histological grade, number of positive nodes, and extent of ENE were identified as independent risk factors for DM in patients with OSCC. Based on these parameters, we developed and validated a nomogram capable of predicting DM with high accuracy. This model provides a simple method for risk stratification and has the potential to facilitate individualized management strategies in patients with OSCC. DM is a major determinant of poor prognosis in OSCC [ 12 ]. Consequently, effective prediction and control of distant metastatic spread remain critical challenges in improving clinical outcomes for affected patients. Consistent with the results of previous studies [ 13 , 14 ], the lung was the most common site of DM in our cohort, and non-cervical lymph nodes, bone, and liver were less commonly involved sites. Among the known risk factors for DM, cervical lymph node metastasis are the most frequently implicated risk factor [ 15 – 18 ]. Tomioka et al. [ 13 ] reported that DM occurred in 31 of 245 patients (12.7%) with cervical nodal involvement, compared with only 5 of 642 patients (0.8%) without nodal metastasis. Because cervical lymph node metastasis was already a known predictor of DM, we restricted our study population to patients with confirmed cervical lymph node metastasis to characterize high-risk individuals more accurately and refine the identification of predictors of DM. Other established risk factors for DM include histological grade [ 16 , 18 ], ENE [ 16 – 18 ], number of positive nodes [ 13 ], and primary intraosseous carcinoma of the mandible [ 13 ]. In this study, histological grade, extent of ENE, and number of positive nodes were identified as independent predictors, consistent with previous findings. Previous studies have shown that a greater extent of ENE is associated with a more aggressive pattern of invasion [ 19 ] and the expression of epithelial cell adhesion molecules [ 20 ]. The American Joint Committee on Cancer (AJCC) guidelines recommend reporting the extent of extranodal invasion using a 2-mm cutoff, and several studies have shown that a greater invasion distance is associated with poorer prognosis [ 11 , 21 , 22 ]. The poorer prognosis is thought to be partially attributable to the increased likelihood of distant metastatic dissemination [ 11 , 22 , 23 ]. In this analysis, rather than applying a predefined cutoff value for ENE, we treated it as a continuous variable, and demonstrated that it remained a significant predictor of DM. This finding is clinically meaningful, as it suggests a distance-dependent increase in metastatic risk associated with ENE. In OSCC with DM survival remains poor, despite attempts to manage it using various therapeutic strategies. Given this poor prognosis, shifting the clinical focus from post-metastatic treatment to prevention or early detection at the micrometastatic stage may be key to improving patient survival. Within this context, accurately predicting the risk of DM is essential for optimizing disease control in OSCC. Although multiple studies have identified risk factors for DM in OSCC, few have provided a quantitative estimate of the likelihood of metastasis. Yu et al. [ 24 ] proposed a nomogram for predicting lung metastasis in OSCC; however, their model incorporated bone and liver metastases as predictive variables, thereby limiting its utility as a tool for DM control in OSCC. In contrast, this study enables quantitative, individualized risk assessment using easily obtainable predictors derived primarily from postoperative pathological findings, an approach that, to our knowledge, has not been reported previously. In this study, OS differed significantly between the low- and high-risk groups not only in the training cohort but also in the validation cohort, confirming the robustness of this stratification. Tailoring postoperative therapeutic strategies according to the individual risk of DM, combined with meticulous, risk-based surveillance, is likely to improve the overall prognosis of patients with OSCC. The nomogram developed in this study has the potential to serve as a valuable tool to support clinicians in making such personalized management decisions. The National Comprehensive Cancer Network (NCCN) guidelines recommend cisplatin-based chemoradiotherapy as standard postoperative therapy for patients at high risk of locoregional recurrence. This recommendation is supported by clinical trials such as RTOG 9501 [ 25 ] and EORTC 22931 [ 26 ], which demonstrated improved locoregional control and disease-free survival (DFS) with the addition of cisplatin to postoperative radiotherapy in head and neck squamous cell carcinoma (HNSCC). However, these studies also indicated that the addition of cisplatin did not reduce the incidence of DM, highlighting the ongoing need for more effective systemic approaches targeting distant dissemination. A Phase II clinical trial, RTOG 0234, investigated postoperative adjuvant therapy for patients with high-risk HNSCC [ 27 ]. This study evaluated the addition of docetaxel plus cetuximab, or cisplatin plus cetuximab, to radiotherapy and demonstrated a significant reduction in the incidence of DM and improvements in both OS and DFS compared with the OS and DFS for cisplatin monotherapy. Notably, the docetaxel-containing regimen showed particularly favorable control of distant metastatic spread. Building on these results, the randomized Phase II/III trial RTOG 1216 [ 28 ] is currently in progress, and the results may provide further evidence to refine postoperative therapeutic strategies for preventing DM in patients with high-risk HNSCC. More recently, the GORTEC 2018-01 NIVOPOSTOP [ 29 ] and the KEYNOTE-689 [ 30 ] trials have reported encouraging outcomes with the addition of immunotherapy to standard postoperative treatment. Both studies demonstrated improvements in DFS and suggested a potential role for immunotherapy in suppressing micrometastatic disease. This study has several limitations. First, as all data were retrospectively collected from a single institution, this may have introduced selection bias. In addition, the relatively small sample size may have contributed to inherent statistical variability, potentially limiting the generalizability of the findings. Second, the histopathological assessment of ENE may be affected by the orientation and angle of tissue sectioning, which could lead to measurement variability. To reduce measurement variability, two pathologists performed independent assessments. Third, the analysis focused primarily on clinical and histopathological features, whereas other potentially relevant predictors, such as imaging findings, molecular biomarkers, and inflammation- or nutrition-related biomarkers, were not included. These factors may influence the prediction of DM. Future research should aim to address these limitations through conducting large-scale, prospective, multicenter studies to further validate and enhance the generalizability of the proposed nomogram. Conclusion In this study, we developed and validated a nomogram incorporating three histopathological parameters to predict the risk of DM following surgical treatment of OSCC. The proposed nomogram demonstrated high predictive accuracy and is suitable for use by clinicians to stratify patients according to their risk of DM. This tool may facilitate individualized clinical decision-making and optimize postoperative management strategies in patients with OSCC. Declarations Conflict of interest: The authors have no conflicts of interest to declare that are relevant to the content of this article. Ethical approval: This study was conducted in accordance with the ethical principles of the Declaration of Helsinki and was approved by the Ethics Review Committee of Hiroshima University (No. E2023-0025). Informed consent: Informed consent was not required given the retrospective study design. However, patients had the option to opt-out of having their clinical data used for research purposes. Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contribution MH: Conceptualization, writing – original draft, methodology, investigation, data curation. FO: Writing – review & editing, data curation. YK: Writing – review & editing, data curation. KM: Methodology, writing – review & editing. AH: Methodology, writing – review & editing. SY: Methodology, writing – review & editing. KK: Methodology, writing – review & editing. TAn: Writing – review & editing, data curation. TAi: Writing – review & editing. SY: Writing – review & editing, supervision, project administration. Acknowledgement We would like to thank Editage (www.editage.jp) for English language editing. Data Availability The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. References Goldoni R, Scolaro A, Boccalari E, Dolci C, Scarano A, Inchingolo F, Ravazzani P, Muti P, Tartaglia G (2021) Malignancies and biosensors: A focus on oral cancer detection through salivary biomarkers. Biosens (Basel) 11:396. https://doi.org/10.3390/bios11100396 Chi AC, Day TA, Neville BW (2015) Oral cavity and oropharyngeal squamous cell carcinoma—an update. 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Anticancer Res 43:5155–5166. https://doi.org/10.21873/anticanres.16716 Mamic M, Lucijanic M, Manojlovic L, Muller D, Suton P, Luksic I (2021) Prognostic significance of extranodal extension in oral cavity squamous cell carcinoma with occult neck metastases. Int J Oral Maxillofac Surg 50:309–315. https://doi.org/10.1016/j.ijom.2020.07.006 Joshi K, Agarwal M, Pasricha S, Singh A, Garg S, Rai S, Tandon S (2023) Macroscopic extranodal extension in oral squamous cell carcinoma-A subgroup with poor survival. Laryngoscope 133:588–593. https://doi.org/10.1002/lary.30158 de Almeida JR, Truong T, Khan NM et al (2020) Treatment implications of postoperative chemoradiotherapy for squamous cell carcinoma of the oral cavity with minor and major extranodal extension. Oral Oncol 110:104845. https://doi.org/10.1016/j.oraloncology.2020.104845 Yu D, Guo R, Zhu L (2024) The risk and prognostic factors for lung metastases in oral squamous cell carcinoma: a population-based analysis of the SEER database. J Stomatol Oral Maxillofac Surg 125:101713. https://doi.org/10.1016/j.jormas.2023.101713 Cooper JS, Pajak TF, Forastiere AA et al (2004) Postoperative concurrent radiotherapy and chemotherapy for high-risk squamous-cell carcinoma of the head and neck. N Engl J Med 350:1937–1944. https://doi.org/10.1056/NEJMoa032646 Bernier J, Domenge C, Ozsahin M et al (2004) Postoperative irradiation with or without concomitant chemotherapy for locally advanced head and neck cancer. N Engl J Med 350:1945–1952. https://doi.org/10.1056/NEJMoa032641 Harari PM, Harris J, Kies MS et al (2014) Postoperative chemoradiotherapy and cetuximab for high-risk squamous cell carcinoma of the head and neck: Radiation Therapy Oncology Group RTOG-0234. J Clin Oncol 32:2486–2495. https://doi.org/10.1200/jco.2013.53.9163 Zhang QE, Wu Q, Harari PM, Rosenthal DI (2019) Randomized phase II/III confirmatory treatment selection design with a change of survival end points: statistical design of Radiation Therapy Oncology Group 1216. Head Neck 41:37–45. https://doi.org/10.1002/hed.25359 Bourhis J, Auperin A, Borel C et al (2025) NIVOPOSTOP (GORTEC 2018–01): A phase III randomized trial of adjuvant nivolumab added to radio-chemotherapy in patients with resected head and neck squamous cell carcinoma at high risk of relapse. J Clin Oncol 43:LBA2–LBA2. https://doi.org/10.1200/JCO.2025.43.17_suppl.LBA2 Uppaluri R, Haddad RI, Tao Y et al (2025) Neoadjuvant and adjuvant pembrolizumab in locally advanced head and neck cancer. N Engl J Med 393:37–50. https://doi.org/10.1056/NEJMoa2415434 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8766333","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":590538597,"identity":"1381911b-a18b-4a82-8da5-ea06a2c80b3e","order_by":0,"name":"Mirai Higaki","email":"data:image/png;base64,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","orcid":"","institution":"Hiroshima University","correspondingAuthor":true,"prefix":"","firstName":"Mirai","middleName":"","lastName":"Higaki","suffix":""},{"id":590538598,"identity":"c609e4b2-61f8-467c-aa9f-2fd5bc0ebb28","order_by":1,"name":"Fumitaka Obayashi","email":"","orcid":"","institution":"Hiroshima University","correspondingAuthor":false,"prefix":"","firstName":"Fumitaka","middleName":"","lastName":"Obayashi","suffix":""},{"id":590538599,"identity":"73619354-58d6-496c-8b87-b108e36c29d4","order_by":2,"name":"Yukina Kobayashi","email":"","orcid":"","institution":"Hiroshima University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yukina","middleName":"","lastName":"Kobayashi","suffix":""},{"id":590538600,"identity":"bd1f8db2-b147-4fa8-a156-46bd66204180","order_by":3,"name":"Kota Morishita","email":"","orcid":"","institution":"Hiroshima University","correspondingAuthor":false,"prefix":"","firstName":"Kota","middleName":"","lastName":"Morishita","suffix":""},{"id":590538601,"identity":"6907563a-1c23-4339-94b6-7cdc08785165","order_by":4,"name":"Nanako Ito","email":"","orcid":"","institution":"Hiroshima University","correspondingAuthor":false,"prefix":"","firstName":"Nanako","middleName":"","lastName":"Ito","suffix":""},{"id":590538602,"identity":"8f74b1eb-9bef-4016-a21d-12a10cd26247","order_by":5,"name":"Atsuko Hamada","email":"","orcid":"","institution":"Hiroshima University","correspondingAuthor":false,"prefix":"","firstName":"Atsuko","middleName":"","lastName":"Hamada","suffix":""},{"id":590538603,"identity":"910b9f24-bf2a-4aef-a710-7ac8941590b3","order_by":6,"name":"Sachiko Yamasaki","email":"","orcid":"","institution":"Hiroshima University","correspondingAuthor":false,"prefix":"","firstName":"Sachiko","middleName":"","lastName":"Yamasaki","suffix":""},{"id":590538604,"identity":"ccbd2745-ff5c-4e45-af61-15ec86c852b2","order_by":7,"name":"Koichi Koizumi","email":"","orcid":"","institution":"Hiroshima University","correspondingAuthor":false,"prefix":"","firstName":"Koichi","middleName":"","lastName":"Koizumi","suffix":""},{"id":590538605,"identity":"01c58ef4-e1b0-4603-ad89-7c3a371a6dca","order_by":8,"name":"Toshinori Ando","email":"","orcid":"","institution":"Hiroshima University","correspondingAuthor":false,"prefix":"","firstName":"Toshinori","middleName":"","lastName":"Ando","suffix":""},{"id":590538606,"identity":"e1950da5-b3fa-4c4c-bad1-129a868c1d71","order_by":9,"name":"Tomonao Aikawa","email":"","orcid":"","institution":"Hiroshima University","correspondingAuthor":false,"prefix":"","firstName":"Tomonao","middleName":"","lastName":"Aikawa","suffix":""},{"id":590538607,"identity":"548e3ae6-9de1-4dcd-aa34-273f29983a24","order_by":10,"name":"Souichi Yanamoto","email":"","orcid":"","institution":"Hiroshima University","correspondingAuthor":false,"prefix":"","firstName":"Souichi","middleName":"","lastName":"Yanamoto","suffix":""}],"badges":[],"createdAt":"2026-02-02 14:54:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8766333/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8766333/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102862462,"identity":"3b8e6ec1-a820-402c-97c7-19c983edab40","added_by":"auto","created_at":"2026-02-17 16:17:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":36106,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting distant metastasis (DM) in patients with oral squamous cell carcinoma (OSCC) and cervical lymph node metastasis following surgery. Each variable is assigned a point value, and the total score corresponds to the estimated probability of DM. ENE, extranodal extension\u003c/p\u003e","description":"","filename":"OnlineFig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-8766333/v1/8a93dbb2b60f90abfb84fd1c.png"},{"id":102862459,"identity":"f6395d4c-5783-4d94-8013-5d81fbfebfdf","added_by":"auto","created_at":"2026-02-17 16:17:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":38863,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curves of the nomogram for predicting distant metastasis in the training cohort (a) and validation cohort (b). AUC, area under the ROC curve\u003c/p\u003e","description":"","filename":"OnlineFig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-8766333/v1/86595b0ef8a3f4eb02c153ac.png"},{"id":102862463,"identity":"9317273b-02b7-41e3-8526-a77275b977e6","added_by":"auto","created_at":"2026-02-17 16:17:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":94083,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration plots of the nomogram in the training cohort (a) and validation cohort (b), showing agreement between predicted and observed probabilities of distant metastasis\u003c/p\u003e","description":"","filename":"OnlineFig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-8766333/v1/924e757635171aed33a92e56.png"},{"id":102862461,"identity":"37db13fa-8a38-49a8-a055-147638cebacf","added_by":"auto","created_at":"2026-02-17 16:17:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":59889,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis of the nomogram for predicting distant metastasis in the training cohort (a) and validation cohort (b), demonstrating a net clinical benefit across a range of threshold probabilities\u003c/p\u003e","description":"","filename":"OnlineFig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-8766333/v1/35d8e560b8049f10ce260d99.png"},{"id":102962992,"identity":"a21afba0-4679-425a-9914-8d6c8bff2843","added_by":"auto","created_at":"2026-02-19 04:12:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":67295,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier survival curves for cumulative overall survival (OS) in the training cohort (a) and validation cohort (b). Patients were stratified into high-risk and low-risk groups based on their nomogram scores. The high-risk group had significantly shorter OS than that of the low-risk group in both cohorts\u003c/p\u003e","description":"","filename":"OnlineFig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-8766333/v1/376e95683005ad4f65fee1d6.png"},{"id":103835191,"identity":"ffd269ad-2ff8-4f49-868d-6fd7da02cc87","added_by":"auto","created_at":"2026-03-03 13:43:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1575950,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8766333/v1/3e259508-6ec2-41e4-8a62-8d53cc6a88f5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and validation of a nomogram for predicting distant metastasis in oral squamous cell carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOral cancer is estimated to account for approximately 2% of all malignancies, with approximately 378,000 new cases and 178,000 deaths reported worldwide in 2020 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The most common pathological subtype is oral squamous cell carcinoma (OSCC), which comprises more than 90% of all oral cancer cases. Although advancements in surgical techniques, radiation therapy, and systemic treatments have improved local control in OSCC, the 5-year overall survival (OS) rate remains approximately 60% [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Distant metastasis (DM) is the primary factor reducing OSCC survival rates. Despite improvements in local control rates, the incidence of DM has not improved, presenting a major challenge. Current treatments for DM in OSCC consist primarily of systemic therapy or palliative care. In recent years, systemic therapeutic options for DM have advanced, with immune-modulating antibodies such as pembrolizumab becoming widely adopted [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, the improvement in OS has been modest. Surgical resection of metastatic lesions has also been attempted in selected patients. A meta-analysis reported a 5-year survival rate of 29.1% following pulmonary metastasectomy for head and neck squamous cell carcinoma; however, the 5-year survival rate for oral cancer was substantially poorer, ranging from 9.2% to 15.4% [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Thus, current treatments for DM in OSCC have not achieved satisfactory outcomes. Moreover, all available therapies target the disease only after DM has developed, and no established strategy exists for preventing its occurrence. This is attributable in part to the absence of an established framework for stratifying the risk of DM.\u003c/p\u003e \u003cp\u003eMultiple studies have investigated risk factors for DM in OSCC, with cervical lymph node metastasis consistently identified as the most significant predictor. Multiple additional risk factors have been reported and the development of DM in OSCC appears to be a multifactorial process. However, quantitative prediction of the likelihood of DM in individual patients based on a comprehensive assessment of these factors remains challenging. Currently, no practical model specifically designed to predict DM in OSCC is available. Developing predictive models that incorporate the relevant risk factors is therefore of clinical importance to facilitate the development of personalized treatment strategies.\u003c/p\u003e \u003cp\u003eA nomogram is a statistical tool that estimates the individualized probability of a clinical event by integrating multiple variables [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This approach provides an intuitive and readily interpretable scoring system and has been widely applied to predict survival and other clinical outcomes for a range of cancer types [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe objective of this study was to identify risk factors for DM in patients with OSCC who had achieved local control and to construct a nomogram based on these factors to use as a clinical tool to facilitate the development of treatment strategies tailored to the individual risk of DM.\u003c/p\u003e"},{"header":"Patients and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Ethics\u003c/h2\u003e \u003cp\u003eThis study had a retrospective cohort design. This study was conducted in accordance with the ethical principles of the Declaration of Helsinki and was approved by the Ethics Review Committee of Hiroshima University (No. E2023-0025). Informed consent was not required given the retrospective study design. However, patients had the option to opt-out of having their clinical data used for research purposes.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePatients\u003c/h3\u003e\n\u003cp\u003ePatients who underwent neck dissection in at our institution between January 2013 and December 2024 were included. Eligible patients were aged 20 years or older and had undergone surgery with curative intent. Patients without histopathologically confirmed lymph node metastasis or with uncontrolled local disease were excluded.\u003c/p\u003e \u003cp\u003eAt our institution, oral cancer treatment is performed independently across multiple departments. Eligible patients who underwent neck dissection in the Department of Oral and Maxillofacial Surgery were included in the training cohort. This cohort served as the training cohort for nomogram development. Eligible patients who underwent neck dissection in the Department of Oral and Maxillofacial Reconstructive Surgery were included in the validation cohort. This cohort was used for external validation. (The Department of Oral and Maxillofacial Surgery and the Department of Oral and Maxillofacial Reconstructive Surgery are separate and independent departments.)\u003c/p\u003e \u003cp\u003eData were extracted from patient records on age at diagnosis, sex, primary tumor site, TNM stage, histopathological parameters, and metastatic status. Tumor staging was determined according to the 8th edition of the TNM classification [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and histological grade was assessed based on World Health Organization criteria [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eTreatment\u003c/h3\u003e\n\u003cp\u003eAll patients underwent either primary surgical resection with simultaneous neck dissection or primary surgical resection followed by delayed neck dissection. Patients with extranodal extension (ENE) were treated with radiotherapy combined with cisplatin-based chemotherapy according to established guidelines. Patients with positive resection margins generally underwent additional surgical resection, or chemoradiotherapy if further resection was not feasible. At our institution, the primary indications for neck dissection include a strong suspicion of cervical lymph node metastasis based on preoperative imaging, or the need for free flap reconstruction. Patients without preoperatively detected cervical lymph node metastasis do not routinely undergo neck dissection and are managed with careful surveillance.\u003c/p\u003e\n\u003ch3\u003eFollow-up\u003c/h3\u003e\n\u003cp\u003eThe initial follow-up was conducted 14 days after discharge. Thereafter, patients were followed monthly for the first 2 years postoperatively, every 2 months during the third year, and every 3 months during the fourth and fifth years. Computed tomography (CT) imaging was performed postoperatively every 3 months for the first 2 years, and then every 6 months for the next 3 years. Neck ultrasonography or positron emission tomography-CT were performed as clinically indicated. DM was diagnosed based primarily on imaging findings, with histopathological confirmation performed only when differentiation from other primary tumors was required.\u003c/p\u003e\n\u003ch3\u003eHistopathological Analysis\u003c/h3\u003e\n\u003cp\u003eHistopathological analyses were conducted in accordance with the institutional protocol. Two or more experienced pathologists evaluated the specimens independently. Assessment of ENE was performed according to a previously described method [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Other histopathological parameters were extracted from the pathology reports.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe statistical analysis was performed using SPSS version 30.0 (IBM Corp., Armonk, NY, USA) and R version 4.5.0 (The R Foundation for Statistical Computing, Vienna, Austria).\u003c/p\u003e \u003cp\u003eClinical variables (including age, sex, primary tumor site, clinical T stage, clinical N stage, and mandibular bone invasion), histopathological parameters (including histological differentiation grade, perineural invasion, vascular invasion, Yamamoto-Kohama [YK] classification, number of positive nodes, and extent of ENE), and treatment-related factors, such as timing of neck dissection, were evaluated as potential predictors of DM. The primary endpoint was the occurrence of DM, and the analysis identified factors associated with DM occurrence. Continuous variables were reported as the median and interquartile range (IQR), and categorical variables were reported as absolute and relative frequencies.\u003c/p\u003e \u003cp\u003eIntergroup comparisons were performed using the Mann\u0026ndash;Whitney U test or Fisher\u0026rsquo;s exact test for continuous and categorical variables, respectively. Unadjusted and multivariable logistic regression analyses were conducted to identify factors associated with DM, and the results were expressed as odds ratios (OR) and 95% confidence intervals (CI). Variables included in the multivariable analysis were selected based on P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the unadjusted analysis and consideration of previously reported predictors.\u003c/p\u003e \u003cp\u003eA predictive nomogram was constructed based on the significant independent predictors identified in the multivariable logistic regression analysis. Each predictor was assigned a score value in the nomogram, with higher total scores corresponding to a higher risk of DM. The performance and clinical utility of the nomogram were assessed in the training and validation cohorts using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). A larger area under the curve (AUC) in the ROC analysis indicated greater discriminatory ability. Calibration plots were generated by comparing predicted probabilities with observed outcomes using 1,000 bootstrap resamples. DCA evaluated the clinical usefulness of the model across a range of threshold probabilities. The risk score for DM was calculated for all patients using the constructed nomogram. The cutoff value for the risk score was determined using the Youden index derived from the ROC curve in the training cohort.\u003c/p\u003e \u003cp\u003eBased on this cutoff value, patients were stratified into low- and high-risk groups. Kaplan\u0026ndash;Meier analysis was used to estimate survival rates for each group, and the log-rank test was used to assess the statistical significance of differences in survival between groups. Two-tailed P values less than 0.05 were considered statistically significant..\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePatient Characteristics\u003c/h2\u003e \u003cp\u003eA total of 126 patients were included in the study, with 85 in the training cohort and 41 in the validation cohort. Patient characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The median age was 69 years (IQR, 55.8\u0026ndash;76 years), and the cohort comprised 82 males (65.1%) and 44 females (34.9%). The most common primary tumor sites were the tongue (n\u0026thinsp;=\u0026thinsp;61, 48.4%), gingiva (n\u0026thinsp;=\u0026thinsp;40, 31.8%) and floor of the mouth (n\u0026thinsp;=\u0026thinsp;13, 10.3%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of patients in the training and validation cohorts\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;126\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining cohort\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;85\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eValidation cohort\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;41\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69 (55.8\u0026ndash;76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (58.5\u0026ndash;76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68 (53\u0026ndash;74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n (%)\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 \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\u003e82 (65.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.447\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\u003e44 (34.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.1 (19.7\u0026ndash;24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.8 (19.3\u0026ndash;24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.3 (20.0\u0026ndash;23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary site, n (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTongue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61 (48.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGingiva\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (31.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuccal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary intraosseous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFloor of mouth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical T stage, n (%)\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 \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\u003e13 (10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.165\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\u003e41 (32.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (42.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical N stage, n (%)\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 \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\u003e65 (51.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone invasion, n (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (28.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90 (71.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 (68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistological grade, n (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWell-differentiated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.587\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerately differentiated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (40.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorly differentiated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePNI, n (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (42.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (57.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVI, n (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (42.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 (57.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYK classification, n (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57 (45.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59 (46.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of positive nodes, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (1\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENE, n (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67 (53.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59 (46.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTiming of neck dissection, n (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInitial treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71 (56.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.411\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAfter treatment of primary site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 (43.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistant metastasis, n (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (30.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88 (69.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eENE, extranodal extension; IQR, interquartile range; LVI, lymphovascular invasion; PNI, perineural invasion; YK classification, Yamamoto-Kohama classification.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHistologically, 63 of 126 tumors (50.0%) were classified as well-differentiated, 51 (40.5%) as moderately differentiated, and 12 (9.5%) as poorly differentiated. Perineural invasion and lymphovascular invasion were identified in 53 patients (42.1%) and 54 patients (42.9%), respectively. The median number of metastatic lymph nodes was 2 (IQR, 1\u0026ndash;4), and 67 of the 126 patients (53.2%) developed ENE. Overall, 38 of the 126 patients (30.2%) developed DM, including 25 of 85 patients (29%) in the training cohort and 13 of 41 patients (32%) in the validation cohort.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of DM\u003c/h2\u003e \u003cp\u003eThe distribution of distant metastatic sites is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In the 38 patients with DM, the lung was the most frequent site (28 patients, 74%), followed by non-cervical lymph nodes (4 patients, 10%), bone (3 patients, 8%), and the liver (3 patients, 8%).\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\u003eSpecific metastatic sites in the training and validation cohorts\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecific metastatic site\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall, n (%)\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;38\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining cohort, n (%)\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;25\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eValidation cohort, n (%)\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;13\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-cervical lymph node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eTraining cohort analysis\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003eIdentification of predictors for DM\u003c/h2\u003e \u003cp\u003eThe results of the unadjusted and multivariable logistic regression analyses to identify predictors of DM are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In unadjusted analysis, histological grade, YK classification, perineural invasion, number of metastatic lymph nodes, and extent of ENE were identified as candidate predictors. In the multivariable analysis, histological grade (OR, 3.75; 95% CI, 1.19\u0026ndash;13.51; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02), number of positive nodes\u0026thinsp;\u0026ge;\u0026thinsp;5 (OR, 6.57; 95% CI, 1.55\u0026ndash;35.16; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01), and extent of ENE (OR, 1.50; 95% CI, 1.01\u0026ndash;2.26; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04) were identified as independent predictors of DM.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnadjusted and multivariable logistic regression analyses of factors predicting distant metastasis in the training cohort\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=\"char\" char=\".\" 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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnadjusted analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eMultivariable analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ereference\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 \u003ctd align=\"left\" colname=\"c7\"\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\u0026ge;\u0026thinsp;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80\u0026ndash;5.77\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 \u003ctd align=\"left\" colname=\"c7\"\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\u003eSex\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\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\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ereference\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 \u003ctd align=\"left\" colname=\"c7\"\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\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.24\u0026ndash;1.72\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 \u003ctd align=\"left\" colname=\"c7\"\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\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ereference\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 \u003ctd align=\"left\" colname=\"c7\"\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\u0026lt;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.81\u0026ndash;5.77\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 \u003ctd align=\"left\" colname=\"c7\"\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\u003ePrimary site\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther than tongue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ereference\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 \u003ctd align=\"left\" colname=\"c7\"\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\u003eTongue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.31\u0026ndash;2.20\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 \u003ctd align=\"left\" colname=\"c7\"\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\u003eClinical T stage\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ereference\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 \u003ctd align=\"left\" colname=\"c7\"\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\u003e3\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.72\u0026ndash;5.16\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 \u003ctd align=\"left\" colname=\"c7\"\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\u003eClinical N stage\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ereference\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 \u003ctd align=\"left\" colname=\"c7\"\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\u003e2\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.53\u0026ndash;3.77\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 \u003ctd align=\"left\" colname=\"c7\"\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\u003eBone invasion\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ereference\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 \u003ctd align=\"left\" colname=\"c7\"\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\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.26\u0026ndash;2.11\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 \u003ctd align=\"left\" colname=\"c7\"\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\u003eHistological grade\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWell-defined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ereference\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\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ereference\u003c/p\u003e \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\u003eModerately/poorly-defined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.26\u0026ndash;9.77\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\u003e3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.19\u0026ndash;13.51\u003c/p\u003e \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\u003eYK classification\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ereference\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 \u003ctd align=\"left\" colname=\"c7\"\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\u003e4C/D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.15\u0026ndash;10.9\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 \u003ctd align=\"left\" colname=\"c7\"\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\u003ePNI\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ereference\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 \u003ctd align=\"left\" colname=\"c7\"\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\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.15\u0026ndash;8.07\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 \u003ctd align=\"left\" colname=\"c7\"\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\u003eLVI\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ereference\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 \u003ctd align=\"left\" colname=\"c7\"\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\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.79\u0026ndash;5.37\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 \u003ctd align=\"left\" colname=\"c7\"\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\u003eNumber of positive nodes\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ereference\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\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ereference\u003c/p\u003e \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\u0026ge;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.40\u0026ndash;62.0\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\u003e6.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.55\u0026ndash;35.2\u003c/p\u003e \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\u003eExtent of ENE (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.20\u0026ndash;2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.01\u0026ndash;2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.044\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTiming of neck dissection\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInitial treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ereference\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 \u003ctd align=\"left\" colname=\"c7\"\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\u003eAfter treatment of primary site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.57\u0026ndash;3.83\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eBMI, body mass index; ENE, extranodal extension; LVI, lymphovascular invasion; PNI, perineural invasion; YK classification, Yamamoto-Kohama classification.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eEstablishment of the nomogram\u003c/h2\u003e \u003cp\u003eAll independent predictors identified through the multivariable logistic regression analysis were incorporated into the nomogram for predicting DM. The resulting nomogram is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Each variable was assigned a specific point value, and the cumulative score provided an estimated probability of DM, with higher scores indicating a higher risk.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eNomogram validation\u003c/h2\u003e \u003cp\u003eThe predictive performance of the nomogram was evaluated in both the training and validation cohorts. ROC curve analysis demonstrated strong discriminatory ability, with an AUC of 0.819 in the training cohort and 0.828 in the validation cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Calibration curves for both cohorts showed good agreement between the predicted and observed probabilities of DM (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Additionally, DCA showed that the nomogram provided greater net clinical benefit than both the treat-all and the treat-none strategies across a broad range of threshold probabilities in both cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSurvival analysis based on risk stratification using the DM risk score\u003c/h2\u003e \u003cp\u003eA risk score for DM was calculated for each patient using the nomogram. ROC analysis was conducted in the training cohort to identify the optimal cutoff value for predicting DM. The optimal threshold was determined to be 0.288, and patients were subsequently categorized into low-risk and high-risk groups based on this cutoff.\u003c/p\u003e \u003cp\u003eIn the training cohort, the 5-year OS rates were 82% in the low-risk group and 33% in the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Similarly, in the validation cohort, the 5-year OS rates were 81% in the low-risk group and 24% in the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). The difference in survival between the low-risk and high-risk groups was significant in both cohorts.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, histological grade, number of positive nodes, and extent of ENE were identified as independent risk factors for DM in patients with OSCC. Based on these parameters, we developed and validated a nomogram capable of predicting DM with high accuracy. This model provides a simple method for risk stratification and has the potential to facilitate individualized management strategies in patients with OSCC.\u003c/p\u003e \u003cp\u003eDM is a major determinant of poor prognosis in OSCC [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Consequently, effective prediction and control of distant metastatic spread remain critical challenges in improving clinical outcomes for affected patients.\u003c/p\u003e \u003cp\u003eConsistent with the results of previous studies [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], the lung was the most common site of DM in our cohort, and non-cervical lymph nodes, bone, and liver were less commonly involved sites.\u003c/p\u003e \u003cp\u003eAmong the known risk factors for DM, cervical lymph node metastasis are the most frequently implicated risk factor [\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Tomioka et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] reported that DM occurred in 31 of 245 patients (12.7%) with cervical nodal involvement, compared with only 5 of 642 patients (0.8%) without nodal metastasis. Because cervical lymph node metastasis was already a known predictor of DM, we restricted our study population to patients with confirmed cervical lymph node metastasis to characterize high-risk individuals more accurately and refine the identification of predictors of DM.\u003c/p\u003e \u003cp\u003eOther established risk factors for DM include histological grade [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], ENE [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], number of positive nodes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and primary intraosseous carcinoma of the mandible [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In this study, histological grade, extent of ENE, and number of positive nodes were identified as independent predictors, consistent with previous findings.\u003c/p\u003e \u003cp\u003ePrevious studies have shown that a greater extent of ENE is associated with a more aggressive pattern of invasion [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and the expression of epithelial cell adhesion molecules [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The American Joint Committee on Cancer (AJCC) guidelines recommend reporting the extent of extranodal invasion using a 2-mm cutoff, and several studies have shown that a greater invasion distance is associated with poorer prognosis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The poorer prognosis is thought to be partially attributable to the increased likelihood of distant metastatic dissemination [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In this analysis, rather than applying a predefined cutoff value for ENE, we treated it as a continuous variable, and demonstrated that it remained a significant predictor of DM. This finding is clinically meaningful, as it suggests a distance-dependent increase in metastatic risk associated with ENE.\u003c/p\u003e \u003cp\u003eIn OSCC with DM survival remains poor, despite attempts to manage it using various therapeutic strategies. Given this poor prognosis, shifting the clinical focus from post-metastatic treatment to prevention or early detection at the micrometastatic stage may be key to improving patient survival. Within this context, accurately predicting the risk of DM is essential for optimizing disease control in OSCC. Although multiple studies have identified risk factors for DM in OSCC, few have provided a quantitative estimate of the likelihood of metastasis. Yu et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] proposed a nomogram for predicting lung metastasis in OSCC; however, their model incorporated bone and liver metastases as predictive variables, thereby limiting its utility as a tool for DM control in OSCC. In contrast, this study enables quantitative, individualized risk assessment using easily obtainable predictors derived primarily from postoperative pathological findings, an approach that, to our knowledge, has not been reported previously. In this study, OS differed significantly between the low- and high-risk groups not only in the training cohort but also in the validation cohort, confirming the robustness of this stratification.\u003c/p\u003e \u003cp\u003eTailoring postoperative therapeutic strategies according to the individual risk of DM, combined with meticulous, risk-based surveillance, is likely to improve the overall prognosis of patients with OSCC. The nomogram developed in this study has the potential to serve as a valuable tool to support clinicians in making such personalized management decisions.\u003c/p\u003e \u003cp\u003e The National Comprehensive Cancer Network (NCCN) guidelines recommend cisplatin-based chemoradiotherapy as standard postoperative therapy for patients at high risk of locoregional recurrence. This recommendation is supported by clinical trials such as RTOG 9501 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and EORTC 22931 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], which demonstrated improved locoregional control and disease-free survival (DFS) with the addition of cisplatin to postoperative radiotherapy in head and neck squamous cell carcinoma (HNSCC). However, these studies also indicated that the addition of cisplatin did not reduce the incidence of DM, highlighting the ongoing need for more effective systemic approaches targeting distant dissemination.\u003c/p\u003e \u003cp\u003eA Phase II clinical trial, RTOG 0234, investigated postoperative adjuvant therapy for patients with high-risk HNSCC [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This study evaluated the addition of docetaxel plus cetuximab, or cisplatin plus cetuximab, to radiotherapy and demonstrated a significant reduction in the incidence of DM and improvements in both OS and DFS compared with the OS and DFS for cisplatin monotherapy. Notably, the docetaxel-containing regimen showed particularly favorable control of distant metastatic spread. Building on these results, the randomized Phase II/III trial RTOG 1216 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] is currently in progress, and the results may provide further evidence to refine postoperative therapeutic strategies for preventing DM in patients with high-risk HNSCC.\u003c/p\u003e \u003cp\u003eMore recently, the GORTEC 2018-01 NIVOPOSTOP [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and the KEYNOTE-689 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] trials have reported encouraging outcomes with the addition of immunotherapy to standard postoperative treatment. Both studies demonstrated improvements in DFS and suggested a potential role for immunotherapy in suppressing micrometastatic disease.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, as all data were retrospectively collected from a single institution, this may have introduced selection bias. In addition, the relatively small sample size may have contributed to inherent statistical variability, potentially limiting the generalizability of the findings. Second, the histopathological assessment of ENE may be affected by the orientation and angle of tissue sectioning, which could lead to measurement variability. To reduce measurement variability, two pathologists performed independent assessments. Third, the analysis focused primarily on clinical and histopathological features, whereas other potentially relevant predictors, such as imaging findings, molecular biomarkers, and inflammation- or nutrition-related biomarkers, were not included. These factors may influence the prediction of DM.\u003c/p\u003e \u003cp\u003eFuture research should aim to address these limitations through conducting large-scale, prospective, multicenter studies to further validate and enhance the generalizability of the proposed nomogram.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we developed and validated a nomogram incorporating three histopathological parameters to predict the risk of DM following surgical treatment of OSCC. The proposed nomogram demonstrated high predictive accuracy and is suitable for use by clinicians to stratify patients according to their risk of DM. This tool may facilitate individualized clinical decision-making and optimize postoperative management strategies in patients with OSCC.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eConflict of interest:\u003c/strong\u003e \u003cp\u003eThe authors have no conflicts of interest to declare that are relevant to the content of this article.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical approval:\u003c/strong\u003e \u003cp\u003eThis study was conducted in accordance with the ethical principles of the Declaration of Helsinki and was approved by the Ethics Review Committee of Hiroshima University (No. E2023-0025).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInformed consent:\u003c/strong\u003e \u003cp\u003eInformed consent was not required given the retrospective study design. However, patients had the option to opt-out of having their clinical data used for research purposes.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMH: Conceptualization, writing \u0026ndash; original draft, methodology, investigation, data curation. FO: Writing \u0026ndash; review \u0026amp; editing, data curation. YK: Writing \u0026ndash; review \u0026amp; editing, data curation. KM: Methodology, writing \u0026ndash; review \u0026amp; editing. AH: Methodology, writing \u0026ndash; review \u0026amp; editing. SY: Methodology, writing \u0026ndash; review \u0026amp; editing. KK: Methodology, writing \u0026ndash; review \u0026amp; editing. TAn: Writing \u0026ndash; review \u0026amp; editing, data curation. TAi: Writing \u0026ndash; review \u0026amp; editing. SY: Writing \u0026ndash; review \u0026amp; editing, supervision, project administration.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to thank Editage (www.editage.jp) for English language editing.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGoldoni R, Scolaro A, Boccalari E, Dolci C, Scarano A, Inchingolo F, Ravazzani P, Muti P, Tartaglia G (2021) Malignancies and biosensors: A focus on oral cancer detection through salivary biomarkers. 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N Engl J Med 393:37\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1056/NEJMoa2415434\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa2415434\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":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":"Oral squamous cell carcinoma, Nomogram, Distant metastasis, Prediction, Personalized medicine, Extranodal extension","lastPublishedDoi":"10.21203/rs.3.rs-8766333/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8766333/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aimed to identify and quantify risk factors for distant metastasis (DM) in patients with oral squamous cell carcinoma (OSCC).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective cohort study was conducted in patients with OSCC who underwent curative surgery and had histopathologically confirmed cervical lymph node metastasis. After excluding 16 patients with uncontrolled primary tumors, the remaining patients were assigned to a training cohort (n\u0026thinsp;=\u0026thinsp;85) and a validation cohort (n\u0026thinsp;=\u0026thinsp;41). Multivariable logistic regression was used to identify independent predictors of DM. These predictors were incorporated into a nomogram. The nomogram was internally validated using the training cohort and externally validated using the validation cohort to assess its predictive performance.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eHistological grade (adjusted odds ratio [aOR]: 3.75, 95% confidence interval [CI]: 1.19\u0026ndash;13.51, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02), number of positive nodes (aOR: 6.57, 95% CI: 1.55\u0026ndash;35.16, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01), and extent of extranodal extension (aOR: 1.50, 95% CI: 1.01\u0026ndash;2.26, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04) were identified as independent predictors of DM and incorporated into a nomogram. The internal and external validation cohorts demonstrated that the nomogram had good discrimination (area under the curve: 0.819 and 0.776, respectively), calibration, and clinical utility.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eWe developed a nomogram that accurately predicted the risk of DM in patients with OSCC.\u003c/p\u003e\u003ch2\u003eClinical relevance:\u003c/h2\u003e \u003cp\u003eThis tool may facilitate the development of individualized postoperative treatment strategies for patients undergoing curative surgery for OSCC.\u003c/p\u003e","manuscriptTitle":"Development and validation of a nomogram for predicting distant metastasis in oral squamous cell carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-17 16:17:25","doi":"10.21203/rs.3.rs-8766333/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":"9a3a6cd5-10fb-4fca-8b3d-56dfc1d22df8","owner":[],"postedDate":"February 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-03T13:42:45+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-17 16:17:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8766333","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8766333","identity":"rs-8766333","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}