A New Nomogram for Predicting Extrahepatic Metastases in Patients With Hepatocellular Carcinoma： A population-based study of the SEER database and a Chinese single-institutional cohort

A New Nomogram for Predicting Extrahepatic Metastases in Patients With Hepatocellular Carcinoma： A population-based study of the SEER database and a Chinese single-institutional cohort | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A New Nomogram for Predicting Extrahepatic Metastases in Patients With Hepatocellular Carcinoma： A population-based study of the SEER database and a Chinese single-institutional cohort Li Xu, Zhi-Lei Li, Na Zhang, Quan-Quan Sun, Peng Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3823499/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 Purpose This study aimed to identify risk factors associated with the occurrence of extrahepatic metastases (EHM) in patients with hepatocellular carcinoma (HCC) and to establish an effective predictive nomogram. Methods We extracted eligible data of HCC patients from the Surveillance, Epidemiology, and End Results (SEER) database. This study also included 196 HCC patients from the Zhejiang Cancer Hospital in China. A nomogram for predicting extrahepatic metastases in patients with hepatocellular carcinoma was developed according to the independent variables that were found by univariate and multivariate logistic analysis analyses. The effective performance of the nomogram was evaluated using the areas under the curves (AUC), receiver operating characteristic curve (ROC), and calibration curves. The clinical practicability was evaluated using decision curve analysis (DCA). Results Sex, N stage, histological grade, tumor size, AFP, vascular Invasion (VI), and surgery were all included as independent predictors in a nomogram to predict HCC patients for extrahepatic metastases. In the training cohort, internal validation cohort, and external validation cohort, the AUC of the prediction model were 0.830, 0.834, and 0.831, respectively, while the AUC of the AJCC Stage were 0.692, 0.693, and 0.650. Among patients with extrahepatic metastases, the most common metastasis site was lung (37.38%), followed by bone (36.0%), and lymph nodes (30.6%). Conclusion Based on the SEER database and the Chinese single-institutional cohort, we have developed and validated a nomogram to forecast EHM in HCC patients. The AUC indicated that the nomogram showed adequate accuracy in discriminating EHM. Additionally, the nomogram fared well in the validation cohort and could support clinical decision-making. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Globally, primary liver malignancy ranks as the sixth most common cancer and the third leading cause of cancer-related death, ranking second in male tumor mortality[ 1 , 2 ]. Hepatocellular carcinoma (HCC) is the most common pathogenic form of liver cancer, making up around 90% of initial liver malignancies. When distant metastases occur, even with comprehensive diagnosis and treatment, the median survival period is just 1-1.5 years for patients with early-stage HCC, despite a 5-year survival rate of more than 70% following hepatectomy[ 3 , 4 ]. In addition, with the progress of surgical techniques and the diversity of treatment methods, the local recurrence rate of HCC patients after operation is gradually reduced, and extrahepatic metastasis has become the main cause of death. According to the existing studies, the incidence of extrahepatic metastases in HCC is 10%-36.7%[ 5 – 7 ], and the common sites for extrahepatic metastasis are Lung, lymph node, bone, adrenal gland, brain, and other organs[ 8 ]. Extrahepatic metastasis (EHM) of HCC is more concealed than intrahepatic metastasis, which is difficult to detect and seriously affects the early and effective treatment of patients. The National Comprehensive Cancer Network (NCCN) and BCLC guidelines only address systematic treatment, such as targeted therapy, immunotherapy, systemic chemotherapy, radiotherapy, and other symptomatic supportive treatments. Currently, there is no single accepted standard for the management of HCC combined with extrahepatic metastasis. Therefore, in a bid to ensure patients receive the best treatment, fully prolong survival, and enhance their standard of living, in clinical settings, the patient's condition should be accurately evaluated by integrating the preoperative general data, laboratory data, and imaging data to optimize the individualized treatment plan. Nomogram has been a popular prediction tool in cancer in recent years, helping doctors forecast desired results and contributing significantly to the advancement of personalized medicine[ 9 ]. Even though several prediction models already exist to assess the EHM risk factors in HCC[ 10 , 11 ], they did not discuss whether vascular invasion (VI) could impact the EHM of HCC or not. Hepatic vascular invasion may be classified into two categories: macrovascular invasion and microvascular invasion(mVI). In our investigation, using the SEER database and a Chinese cohort as our bases, we constructed and validated such a nomogram to predict EHM in patients with HCC. Patients and Methods Patients We conducted a retrospective cohort analysis by querying the Surveillance, Epidemiology, and End Results (SEER) database for the information on patients diagnosed with liver cancer from 2000 to 2018 (primary site labeled: C22.0 Liver). The demographic features that were chosen were marital status, sex, age at diagnosis, and race. The following characteristics of the tumor were also present: vascular invasion, AFP, AJCC stage, histological grade, tumor number, tumor size, and fibrosis score (Ishak 0–4: No to moderate fibrosis, Ishak 5–6: Advanced/severe fibrosis). Treatment and follow-up information including surgery, radiotherapy, chemotherapy, survival time, and survival status were also collected. Inclusion criteria were as follows: (I) Age ≥ 18 years old; (II) Diagnosed with pathological evidence (ICD.O.3.Hist.behav:8170/3- 8175/3); (III) HCC is the first primary malignant tumor; (IV) Complete AJCC stage(TNM), Tumor size, Race, VI, Surgery, Survival months, Vital status and known cause of death. The stepwise extraction process from the SEER database is shown in Fig. 1 . The same criteria were used to gather data from Zhejiang Cancer Hospital in China on HCC patients between September 2020 and September 2021. 196 Patients were ultimately enrolled in a cohort for external validation and underwent further analysis. Statistical analysis Overall survival was measured from the date of surgery to the date of death, or the study closure date of December 31, 2022. In the current study, the statistical analysis was conducted using R software (version 4.2.2) and SPSS 26.0. Age and tumor size, two continuous variables, were transformed into categorical variables based on the ideal cut-off values determined using the R software's "pROC" package. As a result, patients were divided into subgroups based on tumor diameters of 3.7 cm and larger as well as age groups of 58 years and older. The demographics were summarized using descriptive statistics, and the characteristics between the Training and Testing cohorts were compared using a chi-square test. Continuous variables were summarized as “mean ± SD” and the categorical variables were shown as number (percent). The chi-squared test or Fisher's exact test was used to investigate associations between categorical data, and the Student's t-test or Mann-Whitney test, when applicable, was used to assess continuous values. In the present study, a p-value < 0.05 (two sides) was considered as statistical significance. To find the variables connected to EHM, univariate logistic analysis was used. The multivariate binary logistic regression analysis included the variables with p value < 0.05 in the univariate logistic analysis to identify independent risk factors of EHM in patients with newly diagnosed HCC. Prognostic factors were identified using the univariate Cox regression analysis, and then the multivariate Cox regression analysis was performed to evaluate the independent prognostic factors for HCC with EHM. Significant variables (P < 0.05) were included in this study. Nomograms for diagnosis and prognosis were created using the considerably independent risk factors. Prognostic factors were identified using the univariate Cox regression analysis, and then the multivariate Cox regression analysis was performed to evaluate the independent prognostic factors for HCC with EHM. Significant variables (P < 0.05) were included in this study. Nomograms for diagnosis and prognosis were created using the considerably independent risk factors. The predictive power and accuracy were validated using the ROC curve, calibration curve, and decision curve analysis (DCA). Then, the reliability and accuracy of the created nomograms were assessed once more using the validation cohort. Additionally, the AJCC Stage's ROC curve was created, and its AUC was contrasted with the nomogram's AUC. Results The characteristics of the study population Eventually, we obtained the information on 12375 eligible HCC patients from the SEER database. These patients were randomized 7:3 into a training cohort (n=8662) and an internal validation cohort (n=3713). As an external validation cohort, 196 HCC patients from Zhejiang Cancer Hospital were totaled. The clinicopathological features of patients with hepatocellular carcinoma (HCC) are presented in Table 1 and summarized below. In the SEER database, 1110 cases (9.0%) with EHM and 11265 cases (91.0%) without it. In the external validation cohort, 25 cases (12.8%) with EHM and 171 cases (87.2%) without it. Most of the patients were male (76.5%) and older than 58 years old (70.9%). Notably, 83.1% of the tumors were T1-2 and 53.1% of the patients had positive AFP status before treatment. Different metastasis patterns The incidence of EHM in patients with HCC was about 16.19% (3759/23222). Among all patients with EHM, 36.0% had bone metastasis, 37.8% of patients had lung metastasis, 30.6% had lymph node metastasis, and 24.3% had brain and other sites metastasis (Table 2). Two-site metastasis accounted for 20.6%, specifically the most common of these are extrahepatic metastases from a combination of lungs and lymph nodes. Three-site metastasis accounted for 3.7% and four-site metastasis accounted for 0.2% (Table 2). Risk factors of extrahepatic metastases in HCC patients In the training cohort, univariate and multivariate logistic regression analyses were performed to assess every EHM-related characteristic in patients with HCC (Table 3). Sex, Race, Marital, T stage, N stage, Histological Grade, Tumor size, Tumor number, AFP, VI, Surgery, and Chemotherapy were significantly(P<0.05) associated with EHM in univariate logistic regression analysis. The further multivariate regression logistic analysis showed that Sex (P<0.001), N stage (P<0.01), Histological Grade (P<0.001), Tumor size (P<0.001), AFP (P<0.01), VI (P<0.05), and Surgery (P<0.001) were independent predictors for EHM (Table 3). Based on the seven independent EHM-related variables, we constructed a diagnostic nomogram for the risk assessment of EHM in HCC patients (Fig. 2). Meanwhile, the ROC curves of both the training set and testing set were established, and the AUC of the nomogram was 0.830 in the training set, 0.834 in internal validation cohort and 0.831 in the external validation cohort (Figs3a,3b,3c). More importantly, the ROC curves of the AJCC Stage were also generated. The results showed that the AUC of the AJCC Stage was lower than that of the nomogram in both the training and validation cohorts. Furthermore, both in the training set and the validation cohort, the calibration curves showed a robust calibration of the nomogram (Figs3d,3e,3f), and the DCA curve indicated that the nomogram was a better diagnostic tool for EHM in HCC patients than the AJCC Stage (Figs3g,3h,3i). Prognostic factors in HCC patients with Extrahepatic Metastases. Univariate and multivariate Cox regression analyses were carried out on the training cohort to evaluate the prognostic factors in HCC patients with EHM (Table 4). AFP, Grade, Surgery, Chemotherapy, and Lung metastases were independent prognostic factors for HCC patients with EHM (Table 4). Development and validation of a prognostic nomogram for HCC patients with EHM. Based on the five independent prognostic factors, we constructed a nomogram to predict the prognosis of HCC patients with EHM (Fig. 4). The area under the curve (AUC) for our nomogram in predicting the 6-, 9-, and 12-month overall survival (OS) of HCC patients with EHM was 0.707, 0.705, and 0.705 in the training group, and in the validation group, the AUC values were 0.693, 0.730, and 0.705 respectively (Fig.5). Furthermore, both in the training and validation cohorts, the calibration curves displayed a robust calibration of the nomogram (Figs6), with the DCA curve demonstrating that the nomogram was a better diagnostic tool for EHM in HCC patients compared to the AJCC Stage (Figs7). Discussion The incidence of HCC is steadily rising due to the high prevalence of viral hepatitis, non-alcoholic steatohepatitis, and metabolic syndrome[ 12 ]. Treatment for HCC with extrahepatic metastases lacks a standardized protocol. The National Comprehensive Cancer Network (NCCN) guidelines as well as the BCLC guidelines in the United States[ 13 , 14 ] only mention systemic targeted, immunotherapy, systemic chemotherapy, radiotherapy, and other symptomatic supportive treatments. To prolong survival in selected patients, some research has been conducted to show that combinations of radiation therapy for bone metastasis[ 15 ], liver resection-based pneumonectomy for lung metastasis[ 16 – 18 ], gamma knife, surgical resection, surgical resection followed by whole-brain radiation therapy, or antiangiogenic targeted therapy for brain metastasis can be effective[ 19 ]. However, the improvements are insufficient. It is crucial to detect extrahepatic metastases at the time of the first HCC diagnosis in order to increase survival benefits, as prompt and suitable medication may influence survival. The majority of researchers studying extrahepatic metastasis in HCC concentrate on treating and predicting acute outcomes of this type of metastasis, with very few studies examining clinical risk factors. To make matters worse, most of these studies use small sample sizes and are based on a single institution, which severely restricts the predictive power of the model. Our study broadened the cases of HCC patients with EHM based on the SEER database from 2010 to 2018. In our present study, we constructed a nomogram to predict EHM among HCC patients. Our results showed that Sex, N stage, Histological Grade, Tumor size, AFP, VI, and Surgery are independent predictors of EHM in HCC patients. Our retrospective study found that the most common metastasis of hepatocellular liver cancer was bone, followed by lungs and lymph nodes, which is different from previous studies[ 8 ] and may be due to the development of imaging techniques and increased diagnostic capabilities of physicians nowadays, especially the increased use of bone imaging. Also, this study proves that two metastases account for 20% of the cases, showing that we need to be more thorough. Vascular invasion is one of the nomogram model's predictors and has long been linked to an increased risk of EHM in HCC[ 20 ]. In our study, HCC patients with vascular invasion faced a significantly heightened risk of presenting with extrahepatic metastasis, especially patients with macrovascular invasion, and VI was an independent risk for EHM in HCC patients. As is known to all, the liver blood supply is rich, besides the arteriovenous system still exists a portal system, which provides a natural and convenient channel for cancer metastasis, portal vein tumor thrombosis (PVTT) is the most common type of macrovascular invasion. HCC with PVTT was regarded as an advanced stage and had an inferior prognosis, with an overall survival (OS) as low as 2.7–4 months[ 21 ] if receiving supportive care treatment only. The multi-tyrosine kinase inhibitors (TKIs) are recommended as first-line treatments for advanced HCC with PVTT[ 22 , 23 ]. As far as anyone knows, HCC patients with advanced stages have a poor prognosis. Kiminori et al.[ 6 ] reported that HCC patients with advanced intrahepatic tumor stage (T3, T4) were at a poor prognosis. Our study also demonstrated that HCC with advanced tumor stage (N1 stage) were more likely to have extrahepatic metastases. It is widely accepted that one of the most important indicators of prognosis is the histological grade of HCC, which represents the biological behavior of the tumor[ 24 ]. Our study also showed that compared with Well-Moderately histological grade, patients with Poorly-Undifferentiated histological grade were more likely to appear extrahepatic metastases and that histological grade was an independent predictor for EHM in HCC patients. Furthermore, the female sex was an independent protective factor of EHM for HCC, in contrast to the male sex. The result of this study suggested that patients with large tumor diameter and positive serum AFP were more likely to raise the risk of extrahepatic metastasis than patients with small tumor diameter and negative serum AFP. In patients with HCC, serum AFP level and tumor size were independent risk factors for extrahepatic metastasis. Hsu et al. observed 357 patients with extrahepatic metastasis of primary liver cancer and found that higher serum AFP levels and larger tumor diameters indicated a more widespread tumor burden in HCC patients with EHM[ 5 ]. AFP is a biomarker for HCC that has been connected to angiogenesis, cell proliferation, and enhanced cell resistance to tumor necrosis factor-related apoptosis[ 25 , 26 ]. HCC patients are still best treated with surgical resection[ 27 ], nonetheless, the majority of HCC patients do not exhibit any symptoms during the initial stages and are detected in the advanced stages, making surgery unfeasible. For advanced HCC, our current recommendations do not suggest surgery[ 28 ]. Nonetheless, some research revealed that individuals who had surgery when the disease enabled it had better results than those who did not, even in cases with distant metastases[ 29 , 30 ]. Our study showed that HCC patients who did not undergo surgery had a high risk of appearing in EHM, whether surgery was an independent risk factor for extrahepatic metastases in HCC patients. However, there were certain limitations to our study as well. First, selection bias and information bias were unavoidably included in the study due to its retrospective design and use of data from the SEER database, for instance, excluding "unknown" information from the SEER database is improper. Secondly, we didn't assess several objective variables, including the region's degree of medical development, insurance policies for medical care, and economic situations. Finally, specific information on immunotherapy and targeted therapy regimens, which potentially lower the risk of EHM, are not available in the SEER database. Declarations Data Availability Statement The authors promise to provide the pertinent patient-level anonymized data upon reasonable request. Funding Statement This work was supported by The Medical and Health Research Project of Zhejiang province (LTGY23H160016, JZ). Conflict of Interest Statement There is no conflict of interest reported by any author. Ethics Approval Statement This study scheme was conducted by the Declaration of Helsinki and approved by the institutional review Committee and Ethics Committee of our institution (Approval No. IRB-2023-807). Due to the retrospective nature of the study without patient information being identifiable, the requirement for consent for participation was judged superfluous. The requirement for informed consent was waived. Authors’ Contributions QQS and PL conceived and designed the study. LX and ZLL analyzed and interpreted the data, LX wrote the first draft, NZ reviewed and revised the paper, and LP finalized it. All authors contributed to the article and approved the submitted version. References Sung, H., et al., Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin, 2021. 71(3): p. 209-249. Ferlay, J., et al., Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods. Int J Cancer, 2019. 144(8): p. 1941-1953. Villanueva, A., Hepatocellular Carcinoma. Reply. N Engl J Med, 2019. 381(1): p. e2. Llovet, J.M., et al., Hepatocellular carcinoma. Nat Rev Dis Primers, 2021. 7(1): p. 6. Hsu, C.Y., et al., Metastasis in patients with hepatocellular carcinoma: Prevalence, determinants, prognostic impact and ability to improve the Barcelona Clinic Liver Cancer system. 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Mao, K., et al., The impact of liver resection on survival outcomes of hepatocellular carcinoma patients with extrahepatic metastases: A propensity score matching study. Cancer Med, 2018. 7(9): p. 4475-4484. Tables Table 1 The baseline clinical characteristics of the HCC in training cohort, internal validation cohort, and external validation cohort. Training cohort (N=8662) Internal Validation cohort (N=3713) External validation cohort (N=196) Age (years) ≤58 2486 (28.7%) 1079 (29.1%) 87 (44.4%) >58 6176 (71.3%) 2634 (70.9%) 109 (55.6%) Sex Female 2036 (23.5%) 881 (23.7%) 33 (16.8%) Male 6626 (76.5%) 2832 (76.3%) 163 (83.2%) Race White 5864 (67.7%) 2521 (67.9%) NA Asian or Pacific 1497 (17.3%) 651 (17.5%) 196 (100%) Others a 1301 (15.0%) 541 (14.6%) NA Marital Married 4638 (53.5%) 2010 (54.1%) 191 (97.4%) Unmarried 1697 (19.6%) 760 (20.5%) 5 (2.6%) Others b 2327 (26.9%) 943 (25.4%) NA T T1-2 7209 (83.2%) 3077 (82.9%) 159 (81.1%) T3-4 1453 (16.8%) 636 (17.1%) 37 (18.9%) N N0 8176 (94.4%) 3520 (94.8%) 188 (95.9%) N1 486 (5.6%) 193 (5.2%) 8 (4.1%) Histological grade H-M 4437 (51.2%) 1916 (51.6%) 110 (56.1%) L-U 1108 (12.8%) 466 (12.6%) 55 (28.1%) Unknow 3117 (36.0%) 1331 (35.8%) 31 (15.8%) Tumor size (cm) ≤3.7 3712 (42.9%) 1616 (43.5%) 67 (34.2%) >3.7 4950 (57.1%) 2097 (56.5%) 129 (65.8%) Tumor number Single 2702 (31.2%) 1144 (30.8%) 154 (78.6%) Multiple 1286 (14.8%) 543 (14.6%) 42 (21.4%) Unknow 4674 (54.0%) 2026 (54.6%) NA AFP Negative 2484 (28.7%) 1038 (28.0%) 63 (32.1%) Positive 4611 (53.2%) 1940 (52.2%) 129 (65.8%) Unknow 1567 (18.1%) 735 (19.8%) 4 (2.0%) Ishak Ishak 0-4 773 (8.9%) 307 (8.3%) 80 (40.8%) Ishak 5-6 2005 (23.1%) 846 (22.8%) 100 (51.0%) Unknow 5884 (67.9%) 2560 (68.9%) 16 (8.2%) VI No 6221 (71.8%) 2671 (71.9%) 128 (65.3%) mVI 1503 (17.4%) 610 (16.4%) 55 (28.1%) PVTT 938 (10.8%) 432 (11.6%) 13 (6.6%) Surgery No 4529 (52.3%) 1918 (51.7%) 4 (2.0%) Local destruction 1294 (14.9%) 567 (15.3%) 1 (0.5%) Resection 2839 (32.8%) 1228 (33.1%) 191 (97.4%) Chemotherapy No 5158 (59.5%) 2261 (60.9%) 97 (49.5%) Yes 3504 (40.5%) 1452 (39.1%) 99 (50.5%) Radiotherapy No 7610 (87.9%) 3271 (88.1%) 186 (94.9%) Yes 1052 (12.1%) 442 (11.9%) 10 (5.1%) Extrahepatic Metastases No 7878 (90.9%) 3387 (91.2%) 171(87.2%) Yes 784 (9.1%) 326 (8.8%) 25 (12.8%) Abbreviation: AFP, alpha fetoprotein. Others a : include Black, American Indian/Alaskan Native. Others b : include divorced, separated, widowed, and unknown. H-M: Well or Moderately differentiated. L-U: Poorly differentiated or Undifferentiated. *p < 0.05; **p < 0.01; ***p < 0.001. Table 2 Sites of extrahepatic metastasis of hepatocellular carcinoma. Metastasis Number % Total patients 3759 100% Lung 1422 37.8% Bone 1354 36.0% Brain and other sites 912 24.3% Lymph nodes 1151 30.6% Two sites 775 20.6% Three sites 138 3.7% four sites 8 0.2% Table 3 Univariate and multivariate logistic analyses of the clinicopathological parameters using the SEER training cohort. Variables Univariate Multivariate HR (95%CI) P value HR(95%CI) P value Age ≤58 1.00 >58 1.11 (0.94-1.31) 0.21 Sex Female 1.00 1.00 Male 1.32 (1.10-1.59) 0.003** 1.23 (1.01-1.51) 0.046* Race White 1.00 1.00 Asian or Pacific 0.80 (0.64- 0.99) 0.03* 1.06 (0.84-1.34) 0.6 Others 1.04 (0.84-1.27) 0.74 0.90 (0.71-1.12) 0.35 Marital Married 1.00 1.00 Unmarried 1.30 (1.07-1.56) 0.007** 1.22 (0.99-1.51) 0.064 Others 1.27 (1.07-1.50) 0.007** 1.17 (0.96-1.41) 0.11 T T1-2 1.00 1.00 T3-4 3.80 (3.25-4.45) < 0.001*** 1.12 (0.75-1.70) 0.56 N N0 1.00 1.00 N1 10.08 (8.26-12.29) < 0.001*** 4.74 (3.82-5.88) < 0.001*** Grade H-M 1.00 1.00 L-U 2.38 (1.89-2.98) < 0.001*** 1.79 (1.39-2.31) < 0.001*** Unknow 2.92 (2.48-3.46) < 0.001*** 1.94 (1.62-2.32) 3.7 3.46 (2.89-4.17) < 0.001*** 1.80 (1.46-2.21) < 0.001*** Tumor number Single 1.00 1.00 Multiple 1.31 (1.04-1.63) 0.02* 1.10 (0.77-1.54) 0.59 Unknow 1.08 (0.92-1.29) 0.35 0.77 (0.62-0.95) 0.016 AFP Negative 1.00 1.00 Positive 2.18 (1.80-2.67) < 0.001*** 1.46 (1.18-1.82) < 0.001*** Unknow 1.94 (1.53-2.48) < 0.001*** 1.69 (1.30-2.20) < 0.001*** Ishak Ishak 0-4 1.00 Ishak 5-6 0.88 (0.62-1.25) 0.46 Unknow 1.80 (1.34-2.47) < 0.001*** VI No 1.00 1.00 mVI 0.99 (0.80-1.230 0.95 1.26 (0.98-1.61) 0.064 PVTT 3.92 (3.27-4.68) < 0.001*** 1.84 (1.15-2.93) 0.01* Surgery No 1.00 1.00 Local destruction 0.10 (0.06-0.14) < 0.001*** 0.19 (0.12-0.28) < 0.001*** Resection 0.05 (0.04-0.08) < 0.001*** 0.09 (0.06-0.14) < 0.001*** Chemotherapy No 1.00 1.00 Yes 1.69 (1.45-1.95) < 0.001*** 1.02 (0.87-1.21) 0.78 Table 4 Univariate and multivariate cox logistic analyses of the clinicopathological parameters using the SEER training cohort. Variables Univariate Multivariate HR (95%CI) P value HR(95%CI) P value Age ≤58 1.000 >58 0.896 (0.759-1.057) 0.192 Sex Female 1.000 Male 1.033 (0.856-1.248) 0.735 Race White 1.000 Asian or Pacific 1.211 (0.980-1.497) 0.076 Others 0.969 (0.790-1.188) 0.762 Marital Married 1.000 Unmarried 0.948 (0.785-1.145) 0.578 Others 0.936 (0.789-1.111) 0.451 T T1-2 1.000 1.000 T3-4 1.393 (1.199-1.618) < 0.001*** 1.12 (0.75-1.70) 0.892 N N0 1.000 1.00 N1 1.195 (1.012-1.412) 0.036* 4.74 (3.82-5.88) 0.077 Grade H-M 1.000 1.00 L-U 1.277 (1.020-1.598) 0.033* 1.79 (1.39-2.31) 0.033 Unknow 1.057 (0.894-1.251) 0.514 1.94 (1.62-2.32) 0.758 Tumor size ≤3.7 1.000 1.00 >3.7 1.279 (1.054-1.552) 0.013* 1.80 (1.46-2.21) 0.102 Tumor number Single 1.000 1.00 Multiple 1.213 (0.972-1.514) 0.087 1.10 (0.77-1.54) 0.258 Unknow 1.247 (1.049-1.482) 0.012* 0.77 (0.62-0.95) 0.608 AFP Negative 1.000 1.00 Positive 1.349 (1.097-1.658) 0.004** 1.46 (1.18-1.82) 0.002** Unknow 1.422 (1.110-1.821) 0.005** 1.69 (1.30-2.20) 0.011* Ishak Ishak 0-4 1.000 Ishak 5-6 1.286 (0.892-1.856) 0.178 Unknow 1.341 (0.973-1.848) 0.074 VI No 1.000 1.00 mVI 0.928 (0.739-1.166 0.522 1.26 (0.98-1.61) 0.809 PVTT 1.401 (1.185-1.656) < 0.001*** 1.84 (1.15-2.93) 0.105 Surgery No 1.000 1.00 Local destruction 0.452 (0.282-0.723) < 0.001*** 0.19 (0.12-0.28) < 0.001*** Resection 0.359 (0.233-0.551) < 0.001*** 0.09 (0.06-0.14) < 0.001*** Chemotherapy No 1.000 1.00 Yes 0.635 (0.548-0.736) < 0.001*** 1.02 (0.87-1.21) < 0.001*** Radiotherapy No 1.000 1.00 Yes 0.770 (0.655-0.906) 0.002** 1.02 (0.87-1.21) 0.118 Lung Metastases No 1.000 1.00 Yes 1.382 (1.183-1.614) < 0.001*** 1.02 (0.87-1.21) 0.002** Bone Metastases No 1.000 Yes 0.979 (0.840-1.141) 0.786 LN metastases No 1.000 Yes 1.130 (0.967-1.320) 0.125 Other Metastases No 1.000 Yes 0.877 (0.731-1.052) 0.156 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3823499","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":264977622,"identity":"dac81faf-f7fc-4e3d-95c2-7f8f3805abdd","order_by":0,"name":"Li Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYFAC5oYDQFIOwmEjSgsjWIsxaVpAZGID0VoMbiQ2HmD4dTh9w7UzBgwfyg4z8M9uwK9FckZiwwHGvsO5G27nGDDOOHeYQeLOAfxa+CVAWnoO524DamHmbTvMYCCRgF8LG1RLuhlIy19itIBtYfhxOAGshZEYLZI9D4G2NKQb7r+dVnCw51w6j8QNAloMjicf/sDwx1pecnbyxgc/yqzl+GcQ0AICQC9AGAeAmIewejD4Q6S6UTAKRsEoGJkAAELVR6O7BFcmAAAAAElFTkSuQmCC","orcid":"","institution":"Postgraduate training base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital)","correspondingAuthor":true,"prefix":"","firstName":"Li","middleName":"","lastName":"Xu","suffix":""},{"id":264977623,"identity":"e5c39b04-ce45-4a93-b5b6-1833333734fd","order_by":1,"name":"Zhi-Lei Li","email":"","orcid":"","institution":"Postgraduate training base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital)","correspondingAuthor":false,"prefix":"","firstName":"Zhi-Lei","middleName":"","lastName":"Li","suffix":""},{"id":264977624,"identity":"0fe25afc-5961-4980-971b-53c46ba8cf94","order_by":2,"name":"Na Zhang","email":"","orcid":"","institution":"Zhejiang Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Na","middleName":"","lastName":"Zhang","suffix":""},{"id":264977625,"identity":"3d7db44d-e46c-41ee-b3a4-a67daa54ac81","order_by":3,"name":"Quan-Quan Sun","email":"","orcid":"","institution":"Zhejiang Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Quan-Quan","middleName":"","lastName":"Sun","suffix":""},{"id":264977626,"identity":"d744b681-aa3c-4d33-b83e-f14691dd0f1f","order_by":4,"name":"Peng Liu","email":"","orcid":"","institution":"Zhejiang Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2023-12-30 08:14:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3823499/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3823499/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49236040,"identity":"9ddfd997-2307-4021-a04d-384e97313e0d","added_by":"auto","created_at":"2024-01-05 17:52:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":743410,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of patient selection from the SEER database.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-3823499/v1/8a5c3f2951ac7ba423f58a51.png"},{"id":49236037,"identity":"e73e70bc-6838-42f9-b3a5-911f15839c31","added_by":"auto","created_at":"2024-01-05 17:52:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":477685,"visible":true,"origin":"","legend":"\u003cp\u003eA nomogram for predicting EHM in HCC patients.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-3823499/v1/1a551e78e042a158b6f4402c.png"},{"id":49236035,"identity":"ff8e1785-60ea-47b5-81c0-746399ed8a6d","added_by":"auto","created_at":"2024-01-05 17:52:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1328645,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves, Calibration curves, and Decision curve analysis in training cohort(a, d, g), internal validation cohort(b, e, h), and external validation cohort(c, f, i).\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-3823499/v1/ccc8aba7a6144cf9e8496bd1.png"},{"id":49236036,"identity":"991118e1-3fbc-4641-917f-f7b7f815b196","added_by":"auto","created_at":"2024-01-05 17:52:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":98005,"visible":true,"origin":"","legend":"\u003cp\u003eDeveloped prognosis nomogram model for OS. Nomogram for predicting 6-month, 9-month, and 1-year OS of HCC patients with EHM.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-3823499/v1/1f5fc7b0dbaae9842784f3db.png"},{"id":49236041,"identity":"6732c8e0-9092-458c-b94b-ab036f292c8e","added_by":"auto","created_at":"2024-01-05 17:52:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":948623,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves: 6-month, 9-month, and 1-year AUC of OS in the training cohort (a,b,c) and the validation cohort (d,e,f).\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-3823499/v1/19720f9b409d8b6999709dab.png"},{"id":49236039,"identity":"2000b5f8-f65b-424a-b4a7-56997c5fcf0b","added_by":"auto","created_at":"2024-01-05 17:52:19","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":540522,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curve of the nomogram at 6-month, 9-month, and 1-year in the training cohort (a,b,c) and the validation cohort (d,e,f).\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-3823499/v1/5fb688cf2c08037daa47eb9c.png"},{"id":49237236,"identity":"206116b4-e89c-4230-9123-ca582d9b0e7f","added_by":"auto","created_at":"2024-01-05 18:00:19","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":591586,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis (DCA) of 6-month, 9-month, and 1-year in the training cohort (a,b,c) and the validation cohort (d,e,f).\u003c/p\u003e","description":"","filename":"Fig.7.png","url":"https://assets-eu.researchsquare.com/files/rs-3823499/v1/8043aae7edbae1a8ec92fb85.png"},{"id":49706890,"identity":"ac4ec9d7-1c04-46d2-87c5-c0b548e9d83a","added_by":"auto","created_at":"2024-01-16 18:38:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1382821,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3823499/v1/795d8bcf-49e0-417c-ac2c-9e29073164e5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A New Nomogram for Predicting Extrahepatic Metastases in Patients With Hepatocellular Carcinoma： A population-based study of the SEER database and a Chinese single-institutional cohort","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobally, primary liver malignancy ranks as the sixth most common cancer and the third leading cause of cancer-related death, ranking second in male tumor mortality[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Hepatocellular carcinoma (HCC) is the most common pathogenic form of liver cancer, making up around 90% of initial liver malignancies. When distant metastases occur, even with comprehensive diagnosis and treatment, the median survival period is just 1-1.5 years for patients with early-stage HCC, despite a 5-year survival rate of more than 70% following hepatectomy[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition, with the progress of surgical techniques and the diversity of treatment methods, the local recurrence rate of HCC patients after operation is gradually reduced, and extrahepatic metastasis has become the main cause of death. According to the existing studies, the incidence of extrahepatic metastases in HCC is 10%-36.7%[\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and the common sites for extrahepatic metastasis are Lung, lymph node, bone, adrenal gland, brain, and other organs[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eExtrahepatic metastasis (EHM) of HCC is more concealed than intrahepatic metastasis, which is difficult to detect and seriously affects the early and effective treatment of patients. The National Comprehensive Cancer Network (NCCN) and BCLC guidelines only address systematic treatment, such as targeted therapy, immunotherapy, systemic chemotherapy, radiotherapy, and other symptomatic supportive treatments. Currently, there is no single accepted standard for the management of HCC combined with extrahepatic metastasis. Therefore, in a bid to ensure patients receive the best treatment, fully prolong survival, and enhance their standard of living, in clinical settings, the patient's condition should be accurately evaluated by integrating the preoperative general data, laboratory data, and imaging data to optimize the individualized treatment plan.\u003c/p\u003e \u003cp\u003eNomogram has been a popular prediction tool in cancer in recent years, helping doctors forecast desired results and contributing significantly to the advancement of personalized medicine[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEven though several prediction models already exist to assess the EHM risk factors in HCC[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], they did not discuss whether vascular invasion (VI) could impact the EHM of HCC or not. Hepatic vascular invasion may be classified into two categories: macrovascular invasion and microvascular invasion(mVI). In our investigation, using the SEER database and a Chinese cohort as our bases, we constructed and validated such a nomogram to predict EHM in patients with HCC.\u003c/p\u003e"},{"header":"Patients and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective cohort analysis by querying the Surveillance, Epidemiology, and End Results (SEER) database for the information on patients diagnosed with liver cancer from 2000 to 2018 (primary site labeled: C22.0 Liver). The demographic features that were chosen were marital status, sex, age at diagnosis, and race. The following characteristics of the tumor were also present: vascular invasion, AFP, AJCC stage, histological grade, tumor number, tumor size, and fibrosis score (Ishak 0\u0026ndash;4: No to moderate fibrosis, Ishak 5\u0026ndash;6: Advanced/severe fibrosis). Treatment and follow-up information including surgery, radiotherapy, chemotherapy, survival time, and survival status were also collected. Inclusion criteria were as follows: (I) Age\u0026thinsp;\u0026ge;\u0026thinsp;18 years old; (II) Diagnosed with pathological evidence (ICD.O.3.Hist.behav:8170/3- 8175/3); (III) HCC is the first primary malignant tumor; (IV) Complete AJCC stage(TNM), Tumor size, Race, VI, Surgery, Survival months, Vital status and known cause of death. The stepwise extraction process from the SEER database is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The same criteria were used to gather data from Zhejiang Cancer Hospital in China on HCC patients between September 2020 and September 2021. 196 Patients were ultimately enrolled in a cohort for external validation and underwent further analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eOverall survival was measured from the date of surgery to the date of death, or the study closure date of December 31, 2022. In the current study, the statistical analysis was conducted using R software (version 4.2.2) and SPSS 26.0. Age and tumor size, two continuous variables, were transformed into categorical variables based on the ideal cut-off values determined using the R software's \"pROC\" package. As a result, patients were divided into subgroups based on tumor diameters of 3.7 cm and larger as well as age groups of 58 years and older. The demographics were summarized using descriptive statistics, and the characteristics between the Training and Testing cohorts were compared using a chi-square test. Continuous variables were summarized as \u0026ldquo;mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u0026rdquo; and the categorical variables were shown as number (percent). The chi-squared test or Fisher's exact test was used to investigate associations between categorical data, and the Student's t-test or Mann-Whitney test, when applicable, was used to assess continuous values. In the present study, a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two sides) was considered as statistical significance.\u003c/p\u003e \u003cp\u003eTo find the variables connected to EHM, univariate logistic analysis was used. The multivariate binary logistic regression analysis included the variables with p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the univariate logistic analysis to identify independent risk factors of EHM in patients with newly diagnosed HCC. Prognostic factors were identified using the univariate Cox regression analysis, and then the multivariate Cox regression analysis was performed to evaluate the independent prognostic factors for HCC with EHM. Significant variables (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were included in this study. Nomograms for diagnosis and prognosis were created using the considerably independent risk factors. Prognostic factors were identified using the univariate Cox regression analysis, and then the multivariate Cox regression analysis was performed to evaluate the independent prognostic factors for HCC with EHM. Significant variables (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were included in this study. Nomograms for diagnosis and prognosis were created using the considerably independent risk factors.\u003c/p\u003e \u003cp\u003eThe predictive power and accuracy were validated using the ROC curve, calibration curve, and decision curve analysis (DCA). Then, the reliability and accuracy of the created nomograms were assessed once more using the validation cohort. Additionally, the AJCC Stage's ROC curve was created, and its AUC was contrasted with the nomogram's AUC.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eThe characteristics of the study population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEventually, we obtained the information on 12375 eligible HCC patients from the SEER database. These patients were randomized 7:3 into a training cohort (n=8662) and an internal validation cohort (n=3713).\u0026nbsp;As an external validation cohort, 196 HCC patients from Zhejiang Cancer Hospital were totaled.\u0026nbsp;The clinicopathological features of patients with hepatocellular carcinoma (HCC) are presented in Table 1\u0026nbsp;and summarized below. In the SEER database, 1110 cases (9.0%) with EHM and 11265\u0026nbsp;cases (91.0%) without it. In the external validation cohort, 25 cases (12.8%) with EHM and 171 cases (87.2%) without it. Most of the patients were male (76.5%) and older than 58 years old (70.9%).\u0026nbsp;Notably, 83.1% of the tumors were T1-2 and 53.1% of the patients had positive AFP status before treatment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferent metastasis patterns\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe incidence of EHM in patients with HCC was about 16.19% (3759/23222). Among all patients with EHM, 36.0% had bone metastasis, 37.8% of patients had lung metastasis, 30.6% had lymph node metastasis, and 24.3% had brain and other sites metastasis (Table 2).\u0026nbsp;Two-site metastasis accounted for 20.6%, specifically the most common of these are extrahepatic metastases from a combination of lungs and lymph nodes. Three-site metastasis accounted for 3.7% and four-site metastasis accounted for 0.2% (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRisk factors of extrahepatic metastases in HCC patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the training cohort, univariate and multivariate logistic regression analyses were performed to assess every EHM-related characteristic in patients with HCC (Table 3). Sex, Race, Marital, T stage, N stage, Histological Grade, Tumor size, Tumor number, AFP, VI, Surgery, and Chemotherapy were significantly(P\u0026lt;0.05) associated with EHM in univariate logistic regression analysis. The further multivariate regression logistic analysis showed that Sex (P\u0026lt;0.001), N stage (P\u0026lt;0.01), Histological Grade (P\u0026lt;0.001), Tumor size (P\u0026lt;0.001), AFP (P\u0026lt;0.01), VI (P\u0026lt;0.05), and Surgery (P\u0026lt;0.001) were independent predictors for EHM (Table 3).\u003c/p\u003e\n\u003cp\u003eBased on the seven independent EHM-related variables, we constructed a diagnostic nomogram for the risk assessment of EHM in HCC patients (Fig. 2). Meanwhile, the ROC curves of both the training set and testing set were established, and the AUC of the nomogram was 0.830 in the training set, 0.834 in internal validation cohort and 0.831 in the external validation cohort (Figs3a,3b,3c). More importantly, the ROC curves of the AJCC Stage were also generated. The results showed that the AUC of the AJCC Stage was lower than that of the nomogram in both the training and validation cohorts. Furthermore, both in the training set and the validation cohort, the calibration curves showed a robust calibration of the nomogram (Figs3d,3e,3f), and the DCA curve indicated that the nomogram was a better diagnostic tool for EHM in HCC patients than the AJCC Stage (Figs3g,3h,3i).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrognostic factors in HCC patients with Extrahepatic Metastases.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariate and multivariate Cox regression analyses were carried out on the training cohort to evaluate the prognostic factors in HCC patients with EHM (Table 4). AFP, Grade, Surgery, Chemotherapy, and Lung metastases were independent prognostic factors for HCC patients with EHM (Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDevelopment and validation of a prognostic nomogram for HCC patients with EHM.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the five independent prognostic factors, we constructed a nomogram to predict the prognosis of HCC patients with EHM (Fig. 4). The area under the curve (AUC) for our nomogram in predicting the 6-, 9-, and 12-month overall survival (OS) of HCC patients with EHM was 0.707, 0.705, and 0.705 in the training group, and in the validation group, the AUC values were 0.693, 0.730, and 0.705 respectively (Fig.5). Furthermore, both in the training and validation cohorts, the calibration curves displayed a robust calibration of the nomogram (Figs6), with the DCA curve demonstrating that the nomogram was a better diagnostic tool for EHM in HCC patients compared to the AJCC Stage (Figs7).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe incidence of HCC is steadily rising due to the high prevalence of viral hepatitis, non-alcoholic steatohepatitis, and metabolic syndrome[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Treatment for HCC with extrahepatic metastases lacks a standardized protocol. The National Comprehensive Cancer Network (NCCN) guidelines as well as the BCLC guidelines in the United States[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] only mention systemic targeted, immunotherapy, systemic chemotherapy, radiotherapy, and other symptomatic supportive treatments. To prolong survival in selected patients, some research has been conducted to show that combinations of radiation therapy for bone metastasis[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], liver resection-based pneumonectomy for lung metastasis[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], gamma knife, surgical resection, surgical resection followed by whole-brain radiation therapy, or antiangiogenic targeted therapy for brain metastasis can be effective[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, the improvements are insufficient. It is crucial to detect extrahepatic metastases at the time of the first HCC diagnosis in order to increase survival benefits, as prompt and suitable medication may influence survival.\u003c/p\u003e \u003cp\u003eThe majority of researchers studying extrahepatic metastasis in HCC concentrate on treating and predicting acute outcomes of this type of metastasis, with very few studies examining clinical risk factors. To make matters worse, most of these studies use small sample sizes and are based on a single institution, which severely restricts the predictive power of the model. Our study broadened the cases of HCC patients with EHM based on the SEER database from 2010 to 2018. In our present study, we constructed a nomogram to predict EHM among HCC patients. Our results showed that Sex, N stage, Histological Grade, Tumor size, AFP, VI, and Surgery are independent predictors of EHM in HCC patients.\u003c/p\u003e \u003cp\u003eOur retrospective study found that the most common metastasis of hepatocellular liver cancer was bone, followed by lungs and lymph nodes, which is different from previous studies[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and may be due to the development of imaging techniques and increased diagnostic capabilities of physicians nowadays, especially the increased use of bone imaging. Also, this study proves that two metastases account for 20% of the cases, showing that we need to be more thorough.\u003c/p\u003e \u003cp\u003eVascular invasion is one of the nomogram model's predictors and has long been linked to an increased risk of EHM in HCC[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In our study, HCC patients with vascular invasion faced a significantly heightened risk of presenting with extrahepatic metastasis, especially patients with macrovascular invasion, and VI was an independent risk for EHM in HCC patients. As is known to all, the liver blood supply is rich, besides the arteriovenous system still exists a portal system, which provides a natural and convenient channel for cancer metastasis, portal vein tumor thrombosis (PVTT) is the most common type of macrovascular invasion. HCC with PVTT was regarded as an advanced stage and had an inferior prognosis, with an overall survival (OS) as low as 2.7\u0026ndash;4 months[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] if receiving supportive care treatment only. The multi-tyrosine kinase inhibitors (TKIs) are recommended as first-line treatments for advanced HCC with PVTT[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs far as anyone knows, HCC patients with advanced stages have a poor prognosis. Kiminori et al.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] reported that HCC patients with advanced intrahepatic tumor stage (T3, T4) were at a poor prognosis. Our study also demonstrated that HCC with advanced tumor stage (N1 stage) were more likely to have extrahepatic metastases. It is widely accepted that one of the most important indicators of prognosis is the histological grade of HCC, which represents the biological behavior of the tumor[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Our study also showed that compared with Well-Moderately histological grade, patients with Poorly-Undifferentiated histological grade were more likely to appear extrahepatic metastases and that histological grade was an independent predictor for EHM in HCC patients. Furthermore, the female sex was an independent protective factor of EHM for HCC, in contrast to the male sex.\u003c/p\u003e \u003cp\u003eThe result of this study suggested that patients with large tumor diameter and positive serum AFP were more likely to raise the risk of extrahepatic metastasis than patients with small tumor diameter and negative serum AFP. In patients with HCC, serum AFP level and tumor size were independent risk factors for extrahepatic metastasis. Hsu et al. observed 357 patients with extrahepatic metastasis of primary liver cancer and found that higher serum AFP levels and larger tumor diameters indicated a more widespread tumor burden in HCC patients with EHM[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. AFP is a biomarker for HCC that has been connected to angiogenesis, cell proliferation, and enhanced cell resistance to tumor necrosis factor-related apoptosis[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHCC patients are still best treated with surgical resection[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], nonetheless, the majority of HCC patients do not exhibit any symptoms during the initial stages and are detected in the advanced stages, making surgery unfeasible. For advanced HCC, our current recommendations do not suggest surgery[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Nonetheless, some research revealed that individuals who had surgery when the disease enabled it had better results than those who did not, even in cases with distant metastases[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Our study showed that HCC patients who did not undergo surgery had a high risk of appearing in EHM, whether surgery was an independent risk factor for extrahepatic metastases in HCC patients.\u003c/p\u003e \u003cp\u003eHowever, there were certain limitations to our study as well. First, selection bias and information bias were unavoidably included in the study due to its retrospective design and use of data from the SEER database, for instance, excluding \"unknown\" information from the SEER database is improper. Secondly, we didn't assess several objective variables, including the region's degree of medical development, insurance policies for medical care, and economic situations. Finally, specific information on immunotherapy and targeted therapy regimens, which potentially lower the risk of EHM, are not available in the SEER database.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors promise to provide the pertinent patient-level anonymized data upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by The Medical and Health Research Project of Zhejiang province (LTGY23H160016, JZ).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no conflict of interest reported by any author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study scheme was conducted by the Declaration of Helsinki and approved by the institutional review Committee and Ethics Committee of our institution (Approval No. IRB-2023-807). Due to the retrospective nature of the study without patient information being identifiable, the requirement for consent for participation was judged superfluous. The requirement for informed consent was waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQQS and PL conceived and designed the study. LX and ZLL analyzed and interpreted the data, LX wrote the first draft, NZ reviewed and revised the paper, and LP finalized it. All authors contributed to the article and approved the submitted version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung, H., et al., Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin, 2021. 71(3): p. 209-249.\u003c/li\u003e\n\u003cli\u003eFerlay, J., et al., Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods. Int J Cancer, 2019. 144(8): p. 1941-1953.\u003c/li\u003e\n\u003cli\u003eVillanueva, A., Hepatocellular Carcinoma. Reply. N Engl J Med, 2019. 381(1): p. e2.\u003c/li\u003e\n\u003cli\u003eLlovet, J.M., et al., Hepatocellular carcinoma. Nat Rev Dis Primers, 2021. 7(1): p. 6.\u003c/li\u003e\n\u003cli\u003eHsu, C.Y., et al., Metastasis in patients with hepatocellular carcinoma: Prevalence, determinants, prognostic impact and ability to improve the Barcelona Clinic Liver Cancer system. Liver Int, 2018. 38(10): p. 1803-1811.\u003c/li\u003e\n\u003cli\u003eUka, K., et al., Clinical features and prognosis of patients with extrahepatic metastases from hepatocellular carcinoma. World J Gastroenterol, 2007. 13(3): p. 414-20.\u003c/li\u003e\n\u003cli\u003eKatyal, S., et al., Extrahepatic metastases of hepatocellular carcinoma. Radiology, 2000. 216(3): p. 698-703.\u003c/li\u003e\n\u003cli\u003eYan, B., et al., Characteristics and risk differences of different tumor sizes on distant metastases of hepatocellular carcinoma: A retrospective cohort study in the SEER database. Int J Surg, 2020. 80: p. 94-100.\u003c/li\u003e\n\u003cli\u003eBalachandran, V.P., et al., Nomograms in oncology: more than meets the eye. Lancet Oncol, 2015. 16(4): p. e173-80.\u003c/li\u003e\n\u003cli\u003eHu, C., et al., Diagnostic and prognostic nomograms for bone metastasis in hepatocellular carcinoma. BMC Cancer, 2020. 20(1): p. 494.\u003c/li\u003e\n\u003cli\u003eWang, L., et al., Risk Factors for Brain Metastasis of Hepatocellular Carcinoma. J Healthc Eng, 2022. 2022: p. 7848143.\u003c/li\u003e\n\u003cli\u003eMcGlynn, K.A., J.L. Petrick, and H.B. El-Serag, Epidemiology of Hepatocellular Carcinoma. Hepatology, 2021. 73 Suppl 1(Suppl 1): p. 4-13.\u003c/li\u003e\n\u003cli\u003eReig, M., et al., BCLC strategy for prognosis prediction and treatment recommendation: The 2022 update. J Hepatol, 2022. 76(3): p. 681-693.\u003c/li\u003e\n\u003cli\u003eBenson, A.B., et al., Hepatobiliary Cancers, Version 2.2021, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw, 2021. 19(5): p. 541-565.\u003c/li\u003e\n\u003cli\u003eChoi, Y., et al., Dose escalation using helical tomotherapy improves local control in spine metastases from primary hepatic malignancies. Liver Int, 2014. 34(3): p. 462-8.\u003c/li\u003e\n\u003cli\u003eLam, C.M., et al., Prolonged survival in selected patients following surgical resection for pulmonary metastasis from hepatocellular carcinoma. 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J Gastroenterol Hepatol, 2005. 20(11): p. 1781-7.\u003c/li\u003e\n\u003cli\u003eLu, J., et al., Management of patients with hepatocellular carcinoma and portal vein tumour thrombosis: comparing east and west. Lancet Gastroenterol Hepatol, 2019. 4(9): p. 721-730.\u003c/li\u003e\n\u003cli\u003eEuropean Association for the Study of the Liver. Electronic address, e.e.e. and L. European Association for the Study of the, EASL Clinical Practice Guidelines: Management of hepatocellular carcinoma. J Hepatol, 2018. 69(1): p. 182-236.\u003c/li\u003e\n\u003cli\u003eHeimbach, J.K., et al., AASLD guidelines for the treatment of hepatocellular carcinoma. Hepatology, 2018. 67(1): p. 358-380.\u003c/li\u003e\n\u003cli\u003eMartins-Filho, S.N., et al., Histological Grading of Hepatocellular Carcinoma-A Systematic Review of Literature. Front Med (Lausanne), 2017. 4: p. 193.\u003c/li\u003e\n\u003cli\u003eLin, B., et al., Structural basis for alpha fetoprotein-mediated inhibition of caspase-3 activity in hepatocellular carcinoma cells. Int J Cancer, 2017. 141(7): p. 1413-1421.\u003c/li\u003e\n\u003cli\u003eSuriapranata, I.M., et al., Alpha-fetoprotein gene polymorphisms and risk of HCC and cirrhosis. Clin Chim Acta, 2010. 411(5-6): p. 351-8.\u003c/li\u003e\n\u003cli\u003eTian, G., et al., Comparative efficacy of treatment strategies for hepatocellular carcinoma: systematic review and network meta-analysis. BMJ Open, 2018. 8(10): p. e021269.\u003c/li\u003e\n\u003cli\u003eBenson, A.B., 3rd, et al., NCCN Guidelines Insights: Hepatobiliary Cancers, Version 1.2017. J Natl Compr Canc Netw, 2017. 15(5): p. 563-573.\u003c/li\u003e\n\u003cli\u003eRoayaie, S., et al., The role of hepatic resection in the treatment of hepatocellular cancer. Hepatology, 2015. 62(2): p. 440-51.\u003c/li\u003e\n\u003cli\u003eMao, K., et al., The impact of liver resection on survival outcomes of hepatocellular carcinoma patients with extrahepatic metastases: A propensity score matching study. Cancer Med, 2018. 7(9): p. 4475-4484.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 The baseline clinical characteristics of the HCC in training cohort, internal validation cohort, and external validation cohort.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTraining cohort\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=8662)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInternal Validation cohort (N=3713)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eExternal validation cohort (N=196)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026le;58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e2486 (28.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e1079 (29.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e87 (44.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e6176 (71.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e2634 (70.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e109 (55.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e2036 (23.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e881 (23.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e33 (16.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e6626 (76.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e2832 (76.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e163 (83.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e5864 (67.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e2521 (67.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003eAsian or Pacific\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e1497 (17.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e651 (17.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e196 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003eOthers\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e1301 (15.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e541 (14.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e4638 (53.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e2010 (54.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e191 (97.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003eUnmarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e1697 (19.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e760 (20.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e5 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003eOthers\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e2327 (26.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e943 (25.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003eT1-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e7209 (83.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e3077 (82.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e159 (81.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003eT3-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e1453 (16.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e636 (17.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e37 (18.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003eN0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e8176 (94.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e3520 (94.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e188 (95.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003eN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e486 (5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e193 (5.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e8 (4.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistological grade\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003eH-M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e4437 (51.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e1916 (51.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e110 (56.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003eL-U\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e1108 (12.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e466 (12.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e55 (28.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003eUnknow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e3117 (36.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e1331 (35.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e31 (15.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTumor size (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026le;3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e3712 (42.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e1616 (43.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e67 (34.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e4950 (57.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e2097 (56.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e129 (65.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTumor number\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e2702 (31.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e1144 (30.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e154 (78.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003eMultiple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e1286 (14.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e543 (14.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e42 (21.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003eUnknow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e4674 (54.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e2026 (54.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAFP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e2484 (28.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e1038 (28.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e63 (32.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e4611 (53.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e1940 (52.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e129 (65.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003eUnknow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e1567 (18.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e735 (19.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e4 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIshak\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003eIshak 0-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e773 (8.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e307 (8.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e80 (40.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003eIshak 5-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e2005 (23.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e846 (22.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e100 (51.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003eUnknow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e5884 (67.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e2560 (68.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e16 (8.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e6221 (71.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e2671 (71.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e128 (65.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003emVI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e1503 (17.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e610 (16.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e55 (28.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003ePVTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e938 (10.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e432 (11.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e13 (6.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgery\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e4529 (52.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e1918 (51.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e4 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003eLocal destruction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e1294 (14.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e567 (15.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e1 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003eResection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e2839 (32.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e1228 (33.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e191 (97.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChemotherapy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e5158 (59.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e2261 (60.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e97 (49.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e3504 (40.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e1452 (39.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e99 (50.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRadiotherapy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e7610 (87.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e3271 (88.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e186 (94.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e1052 (12.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e442 (11.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e10 (5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eExtrahepatic Metastases\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e7878 (90.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e3387 (91.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e171(87.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.685920577617328%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e784 (9.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e326 (8.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.104693140794225%\" valign=\"top\"\u003e\n \u003cp\u003e25 (12.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviation: AFP, alpha fetoprotein.\u003c/p\u003e\n\u003cp\u003eOthers\u003csup\u003ea\u003c/sup\u003e: include Black, American Indian/Alaskan Native.\u003c/p\u003e\n\u003cp\u003eOthers\u003csup\u003eb\u003c/sup\u003e:\u0026nbsp;include divorced, separated, widowed, and unknown.\u003c/p\u003e\n\u003cp\u003eH-M: Well or Moderately differentiated.\u003c/p\u003e\n\u003cp\u003eL-U: Poorly differentiated or Undifferentiated.\u003c/p\u003e\n\u003cp\u003e*p \u0026lt; 0.05; **p \u0026lt; 0.01; ***p \u0026lt; 0.001.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2 Sites of extrahepatic metastasis of hepatocellular carcinoma.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.17467248908297%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetastasis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.912663755458514%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.912663755458514%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.17467248908297%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal patients\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.912663755458514%\" valign=\"top\"\u003e\n \u003cp\u003e3759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.912663755458514%\" valign=\"top\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.17467248908297%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Lung\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.912663755458514%\" valign=\"top\"\u003e\n \u003cp\u003e1422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.912663755458514%\" valign=\"top\"\u003e\n \u003cp\u003e37.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.17467248908297%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Bone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.912663755458514%\" valign=\"top\"\u003e\n \u003cp\u003e1354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.912663755458514%\" valign=\"top\"\u003e\n \u003cp\u003e36.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.17467248908297%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Brain and other sites\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.912663755458514%\" valign=\"top\"\u003e\n \u003cp\u003e912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.912663755458514%\" valign=\"top\"\u003e\n \u003cp\u003e24.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.17467248908297%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Lymph nodes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.912663755458514%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;1151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.912663755458514%\" valign=\"top\"\u003e\n \u003cp\u003e30.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.17467248908297%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTwo sites\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.912663755458514%\" valign=\"top\"\u003e\n \u003cp\u003e775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.912663755458514%\" valign=\"top\"\u003e\n \u003cp\u003e20.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.17467248908297%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eThree sites\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.912663755458514%\" valign=\"top\"\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.912663755458514%\" valign=\"top\"\u003e\n \u003cp\u003e3.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.17467248908297%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003efour sites\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.912663755458514%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.912663755458514%\" valign=\"top\"\u003e\n \u003cp\u003e0.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3 Univariate and multivariate logistic analyses of the clinicopathological parameters using the SEER training cohort.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.88969258589512%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Univariate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.80470162748644%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR(95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026le;58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026gt;58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\n \u003cp\u003e1.11 (0.94-1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\n \u003cp\u003e1.32 (1.10-1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e0.003**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e1.23 (1.01-1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e0.046*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Asian or Pacific\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\n \u003cp\u003e0.80 (0.64- 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e0.03*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e1.06 (0.84-1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\n \u003cp\u003e1.04 (0.84-1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e0.90 (0.71-1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Unmarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\n \u003cp\u003e1.30 (1.07-1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e0.007**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e1.22 (0.99-1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\n \u003cp\u003e1.27 (1.07-1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e0.007**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e1.17 (0.96-1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; T1-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; T3-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\n \u003cp\u003e3.80 (3.25-4.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e1.12 (0.75-1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; N0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; N1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\n \u003cp\u003e10.08 (8.26-12.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e4.74 (3.82-5.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrade\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; H-M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; L-U\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\n \u003cp\u003e2.38 (1.89-2.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e1.79 (1.39-2.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Unknow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\n \u003cp\u003e2.92 (2.48-3.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e1.94 (1.62-2.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTumor size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026le;3.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026gt;3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\n \u003cp\u003e3.46 (2.89-4.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e1.80 (1.46-2.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTumor number\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Single\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Multiple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\n \u003cp\u003e1.31 (1.04-1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e0.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e1.10 (0.77-1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Unknow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\n \u003cp\u003e1.08 (0.92-1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e0.77 (0.62-0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAFP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Negative\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\n \u003cp\u003e2.18 (1.80-2.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e1.46 (1.18-1.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Unknow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\n \u003cp\u003e1.94 (1.53-2.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e1.69 (1.30-2.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIshak\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Ishak 0-4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Ishak 5-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\n \u003cp\u003e0.88 (0.62-1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Unknow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\n \u003cp\u003e1.80 (1.34-2.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; mVI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\n \u003cp\u003e0.99 (0.80-1.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e1.26 (0.98-1.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; PVTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\n \u003cp\u003e3.92 (3.27-4.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e1.84 (1.15-2.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e0.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgery\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Local destruction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\n \u003cp\u003e0.10 (0.06-0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e0.19 (0.12-0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Resection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\n \u003cp\u003e0.05 (0.04-0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e0.09 (0.06-0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChemotherapy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.355072463768117%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.471014492753625%\" valign=\"top\"\u003e\n \u003cp\u003e1.69 (1.45-1.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e1.02 (0.87-1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4 Univariate and multivariate cox logistic analyses of the clinicopathological parameters using the SEER training cohort.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.88969258589512%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Univariate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.80470162748644%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR(95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026nbsp;\u0026le;58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026gt;58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e0.896 (0.759-1.057)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e1.033 (0.856-1.248)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e0.735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Asian or Pacific\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e1.211 (0.980-1.497)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e0.969 (0.790-1.188)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e0.762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Unmarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e0.948 (0.785-1.145)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e0.578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e0.936 (0.789-1.111)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e0.451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; T1-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; T3-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e1.393 (1.199-1.618)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e1.12 (0.75-1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\n \u003cp\u003e0.892\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; N0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; N1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e1.195 (1.012-1.412)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e0.036*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e4.74 (3.82-5.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrade\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; H-M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; L-U\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e1.277 (1.020-1.598)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e0.033*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e1.79 (1.39-2.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Unknow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e1.057 (0.894-1.251)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e0.514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e1.94 (1.62-2.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\n \u003cp\u003e0.758\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTumor size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026nbsp;\u0026le;3.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026gt;3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e1.279 (1.054-1.552)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e0.013*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e1.80 (1.46-2.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTumor number\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Single\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Multiple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e1.213 (0.972-1.514)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e1.10 (0.77-1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\n \u003cp\u003e0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Unknow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e1.247 (1.049-1.482)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e0.012*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e0.77 (0.62-0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\n \u003cp\u003e0.608\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAFP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Negative\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e1.349 (1.097-1.658)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e0.004**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e1.46 (1.18-1.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\n \u003cp\u003e0.002**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Unknow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e1.422 (1.110-1.821)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e0.005**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e1.69 (1.30-2.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\n \u003cp\u003e0.011*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIshak\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Ishak 0-4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Ishak 5-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e1.286 (0.892-1.856)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Unknow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e1.341 (0.973-1.848)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; mVI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e0.928 (0.739-1.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e0.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e1.26 (0.98-1.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\n \u003cp\u003e0.809\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; PVTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e1.401 (1.185-1.656)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e1.84 (1.15-2.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgery\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Local destruction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e0.452 (0.282-0.723)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e0.19 (0.12-0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Resection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e0.359 (0.233-0.551)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e0.09 (0.06-0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChemotherapy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e0.635 (0.548-0.736)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e1.02 (0.87-1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRadiotherapy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e0.770 (0.655-0.906)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e0.002**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e1.02 (0.87-1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLung Metastases\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e1.382 (1.183-1.614)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e1.02 (0.87-1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\n \u003cp\u003e0.002**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBone Metastases\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e0.979 (0.840-1.141)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e0.786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLN metastases\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e1.130 (0.967-1.320)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOther Metastases\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.305605786618443%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.24231464737794%\" valign=\"top\"\u003e\n \u003cp\u003e0.877 (0.731-1.052)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.64737793851718%\" valign=\"top\"\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.529837251356238%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.2748643761302%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"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":"","lastPublishedDoi":"10.21203/rs.3.rs-3823499/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3823499/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThis study aimed to identify risk factors associated with the occurrence of extrahepatic metastases (EHM) in patients with hepatocellular carcinoma (HCC) and to establish an effective predictive nomogram.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe extracted eligible data of HCC patients from the Surveillance, Epidemiology, and End Results (SEER) database. This study also included 196 HCC patients from the Zhejiang Cancer Hospital in China. A nomogram for predicting extrahepatic metastases in patients with hepatocellular carcinoma was developed according to the independent variables that were found by univariate and multivariate logistic analysis analyses. The effective performance of the nomogram was evaluated using the areas under the curves (AUC), receiver operating characteristic curve (ROC), and calibration curves. The clinical practicability was evaluated using decision curve analysis (DCA).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSex, N stage, histological grade, tumor size, AFP, vascular Invasion (VI), and surgery were all included as independent predictors in a nomogram to predict HCC patients for extrahepatic metastases. In the training cohort, internal validation cohort, and external validation cohort, the AUC of the prediction model were 0.830, 0.834, and 0.831, respectively, while the AUC of the AJCC Stage were 0.692, 0.693, and 0.650. Among patients with extrahepatic metastases, the most common metastasis site was lung (37.38%), followed by bone (36.0%), and lymph nodes (30.6%).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eBased on the SEER database and the Chinese single-institutional cohort, we have developed and validated a nomogram to forecast EHM in HCC patients. The AUC indicated that the nomogram showed adequate accuracy in discriminating EHM. Additionally, the nomogram fared well in the validation cohort and could support clinical decision-making.\u003c/p\u003e","manuscriptTitle":"A New Nomogram for Predicting Extrahepatic Metastases in Patients With Hepatocellular Carcinoma： A population-based study of the SEER database and a Chinese single-institutional cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-05 17:52:14","doi":"10.21203/rs.3.rs-3823499/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":"48a91597-f00e-46f6-94ab-b2df267c8b7e","owner":[],"postedDate":"January 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-01-16T18:30:00+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-05 17:52:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3823499","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3823499","identity":"rs-3823499","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}