Epidemiologic Characteristics, Prognostic Factors and Treatment Outcomes in Primary Central Nervous System Lymphoma: A SEER-Based Study

Epidemiologic Characteristics, Prognostic Factors and Treatment Outcomes in Primary Central Nervous System Lymphoma: A SEER-Based Study | 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 Epidemiologic Characteristics, Prognostic Factors and Treatment Outcomes in Primary Central Nervous System Lymphoma: A SEER-Based Study Dongsheng Tang, Yue Chen, Yuye Shi, Hong Tao, Shandong Tao, Quan'e Zhang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-955053/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Feb, 2022 Read the published version in Frontiers in Oncology → Version 1 posted You are reading this latest preprint version Abstract Objective: To study the clinical characteristics, prognostic factors and treatment outcomes in patients with primary central nervous system lymphoma (PCNSL). Materials and Methods: The data of total 5166 PCNSL patients diagnosed between 2000 and 2018 from the Surveillance, Epidemiology, and End Results (SEER) database was obtained. Results: The mean age was 63.1±14.9 years, with a male to female of 1.1:1.0. The most common histologic subtype was diffuse large B-cell lymphoma (DLBCL) (84.6%). The 1-, 3-, and 5-year OS were 50.1, 36.0 and 27.2% and corresponding to DSS were 54.4, 41.3 and 33.5%, respectively. Multivariate analysis with Cox regression showed that race, sex, age, marital status, surgery, chemotherapy and radiotherapy were independent prognostic factors for OS, but radiotherapy no longer for DSS. Nomograms specially for DLBCL were established to predict the possibility of OS and DSS. The concordance index (C-index) of OS and DSS were 0.704 (95% CI 0.687-0.721) and 0.698 (95% CI 0.679-0.717), suggesting the high discrimination ability of the nomograms. Conclusion: Surgery or/and chemotherapy was favourably associated with better OS and DSS. However, radiotherapy did not benefit to OS and DSS in the long-term. A new predictive nomogram and a web-based survival rate calculator we developed showed favorable applicability and accuracy to predict the long-term OS for DLBCL patients specifically. Cancer Biology Primary Central Nervous System Lymphoma SEER Treatment Prognosis Nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Primary central nervous system lymphoma (PCNSL) is an uncommon and highly invasive tumor that involve the leptomeninges, brain, eyes or spinal cord without evidence of systemic disease[ 1 ]. PCNSL accounts for 1%-2% of non-Hodgkin lymphoma (NHL) and the most (over 90%) cases are DLBCL[ 2 , 3 ]. Immunocompromised individuals, such as HIV-infected or immunosuppressive patients, are deemed to have a higher risk in PCNSL[ 4 , 5 ]. PCNSL was historically associated with poor prognosis, with an overall survival (OS) of 1.5 months without additional treatment. The high-dose methotrexate (HD-MTX) systemic chemotherapy is deemed as the standard first-line treatment, however, few patients can achieve long-term survival, the median progression-free survival (PFS) and OS were only 24.0, and 36.9 months respectively[ 6 , 7 ], and the 5-year OS in the period 1992-1994 was increased only from 19.1–30.1% in the period 2004-2006[ 4 ]. Surgery is generally discouraged previously before 2010, but conventional view has been challenged as the advances in surgical techniques. Due to the high risk of neurotoxicity and lacking of sustainable response, PCNSL patients should avoid whole brain radiation (WBRT) in the first-line treatment, more research has focused on whether different radiotherapy regimens (including reduced-dose and partial‐brain radiotherapy) combination chemotherapy can bring benefits. However, the conclusions are inconsistent. In recent years, novel agents including immune checkpoint inhibitors, immunomodulatory drugs (IMiDs), bruton tyrosine kinase (BTK) inhibitor, PI3K/AKT/mTOR inhibitors and chimeric antigen receptor T cell (CAR-T cell) therapy have been applied in several clinic trials which exhibit promising clinic outcomes. Even with an impressive clinical response, more randomized clinical trials are still needed to verify and identify the optimal therapy for PCNSL patients. Two prognostic classification systems of PCNSL are widely used currently. The IELSG identified an age (>60 years), elevated lactate dehydrogenase (LDH) serum level, performance status (PS) (≥2), high CSF protein concentration, and extensive deep structure involved in the brain were independent predictors of negative prognosis[ 8 ]. In another prognostic model, the MSKCC prognostic score described three risk groups based on age and Karnofsky performance status (KPS)[ 9 ]. However, treatment information was not included in these prognostic systems, it is difficult to perform the treatment choose based on these prognostic systems. Therefore, a new, easily available prognostic system which include treatment information is needed to be developed. Duing to the rarity of PCNSL, large-scale clinical trials and prospective data are limited for us to investigate it. The Surveillance, Epidemiology, and End Results (SEER) contains a wealth of relevant information on different types of cancer patients based on the United States population which provides excellect resources for us to study. Therefore, a large population-based analysis was conducted to describe the clinical characteristics, prognostic factors and treatment outcomes of PCNSL using the SEER database. We also analyzed independent prognostic factors of PCNSL and established a nomogram to predict the prognosis in this population. Materials And Methods Study data was obtained using the SEER*Stat software (version 8.3.9). By “Incidence-SEER Research Plus Data, 18 Registires, Nov 2020 Sub (2000-2018)”, patients diagnosed with PCNSL between 2000 and 2018 were identified. International Classification of Diseases for Oncology (ICD-O-3) histologic codes (9590–9595, 9650–9699, 9702–9729) were used for lymphoma and primary sites limited to central nervous system were identified by site specific code (C71.0–C71.9). Unknown age of diagnosis, uncertain race, unknown sex, unknown marriage status, incomplete follow-up data and secondary to other tumors were excluded. The primary endpoint of this study were overall survival (OS) and disease-specific survival (DSS). Statistical Analyses The OS and DSS were estimated with the Kaplan-Meier method using the log-rank test, and Cox regression model was used for univariate and multivariate survival analysis. Nomograms were constructed to predict 1-, 3-, and 5-year OS and DSS specifically for DLBCL according to the results of multivariable Cox regression analysis. To evaluate the accuracy of the nomograms, Harrell's concordance index (C-index) was calculated to quantify the discrimination performance and calibration curve was plotted to identify whether the predicted survival was consistent with the actual survival. The data were analyzed using R software (R version 4.0.4). Statistical significance was set at P < 0.05 (two-sided). Results Epidemiologic Characteristics of PCNSL Patients The mean age at diagnosis was 63.1±14.9, ranging from 3 to 97 years. The population was comprised of 2679 (51.9%) males and 2487 (48.1%) females, and the highest incidence of age group was 70-79 years old. The characteristics of PCNSL patients were summarized in TABLE 1. Regarding the clinical aggressiveness and cell line of origin, there were 4429 (85.7%) patients of aggressive B cell NHLs, 166 (3.2%) indolent B cell NHLs, 474 (9.2%) NHL-NOSs and 85 (1.6%) T cell NHLs. As for the histological classification of PCNSL, the most common subtype was DLBCL (84.6 %), followed by not otherwise specified (NHL-NOS) (9.2%), follicular lymphoma (FL) (1.3%), peripheral T-cell lymphoma (PTCL) (1.2%), mucosal-associated lymphoid tissue (MALT) (1.2%), burkitt’s lymphoma (BL) (0.7%), lymphoplasmacytic lymphoma (LPL) (0.4%), anaplastic large cell lymphoma (ALCL) (0.4%), chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL) (0.4%). Except for ALCL (median age 39.0), the median age of all subtypes were over 60 years old. The epidemiologic characteristics and survival outcomes were summarized according to histological subtype in TABLE 2. Survival Analysis A total of 3660 patients died during the follow-up period, 3101 deaths were disease-specific. Kaplan-Meier curves illustrating OS and DSS were shown in Figures 1 A, B. The median OS and DSS was 13.0 and 19.0 months. The 1-, 3-, and 5-year OS were 50.1, 36.0 and 27.2% and corresponding to DSS were 54.4, 41.3 and 33.5%, respectively. Patients diagnosed in 2009 to 2018 showed better OS and DSS than patients diagnosed in 2000 to 2008 (P<0.0001) (Figures 1 C, D). In the whole cohort, the best 3-year OS and DSS were observed in MALT (OS: 78.3%, DSS: 86.0%) and FL (OS:53.8%, DSS: 57.4%). In addition, the Kaplan-Meier survival curves for OS and DSS of the main histological subtypes were presented in Figures 1 E, F. Furthermore, Kaplan-Meier survival analysis was also performed stratifying patients according to sex, age, race, marital status and treatment. It was found that increasing age was significantly associated with reduced OS and DSS (Figures 2 A, E). Females had significantly better OS and DSS than males (Figures 2 B, F). We also found that patients who were others (Figures 2 C, G) and married (Figures 2 D, H) had a better OS and DSS according to the univariate analysis. In terms of treatment, patients who underwent chemotherapy (Figures 3 A, D) or surgery (Figures 3 C, F) achieved significantly longer OS and DSS than who did not. However, radiotherapy led to worse OS and DSS in the long-term (Figures 3 B, E). We also explored the outcome in combination therapy and found that surgery combined with chemotherapy was associated with better OS and DSS (Figures 4 A, B), but radiotherapy combined with chemotherapy led to worse OS and DSS in the long-term (Figures 4 C, D). Then, we performed multivariate Cox-regression analysis to figure out the independent prognostic factors for OS and DSS and revealed race, sex, age, marital status, surgery, chemotherapy and radiotherapy were independent predictors of OS, but radiotherapy was no longer an independent prognostic factor for DSS (Table 3 ). Construction of Nomogram Considering the main histological subtype of PCNSL was DLBCL, we developed a prediction model specifically for DLBCL patients. 4373 patients with DLBCL were randomly divided into a training cohort (n=3061) and a validation cohort (n=1312) in a ratio of 7:3 for model construction and validation. Firstly, univariate and multivariate Cox regression analyses were conducted to select the independent prognostic factors for OS and DSS. Univariate and multivariate analyses results were displayed in Table 4. These significant factors from univariate Cox regression analysis were incorporated into multivariate analysis. Significant predictors of OS and DSS on multivariate analysis were used to establish the nomograms. The OS and DSS nomogram at the 1-, 3-, and 5- year were shown in Figure 5 . Then, the nomogram performance was assessed with discrimination and calibration by using the external validation cohort. The C-index of OS and DSS were 0.704 (95% CI 0.687-0.721) and 0.698 (95% CI 0.679-0.717), indicating the high discrimination ability of the nomograms. The calibration curves of the train cohort and the external validation cohort were presented in Figure 6 , which represented good agreement among the predicted survival and the actual survival at 1-, 3-, and 5-year. Web-Based Survival Rate Calculator A dynamic web-based survival rate calculator base on the nomogram was established to predict the long-term OS ( https://tangdongshengarticle.shinyapps.io/DynNomapp/ ). For instance, a 75-year-old white married man was diagnosed as PCNSL with DLBCL, if he refused surgery and chemotherapy, his 3-year OS rate is approximately only 4.6% (95% CI 0.022-0.101), if he was given surgery and chemotherapy, his 3-year OS rate is approximately 34.0% (95% CI 0.272-0.420) (Figure 7 ). Discussion PCNSL represents a rare but highly aggressive NHL with poor prognosis. In light of the rarity of PCNSL, current understanding of PCNSL is mainly based on retrospective analysis with small series. Therefore, we conducted a study based on a large population. In the current study, there were 5166 PCNSL patients from SEER database. The mean age at diagnosis was 63.1±14.9 years, and the male to female ratio was 1.1:1.0, which was largely consisting with population-based study from Australia[ 10 ]. Previous studies have demonstrated that age was a significant and adverse prognostic factor[ 11 – 13 ]. In the present study, inferior OS and DSS were significantly associated with older age, consisting with previous results. Interestingly, our study revealed that married patients tend to have better outcomes than single patients (including divorced/widowed/separated patients). The mechanism between marital status and survival is unclear, social-psychological factors may contribute to it. The married patients may have better socioeconomic status and more emotional support than single patients. Although the introduction of HD-MTX has significantly improved PCNSL prognosis, numerous patients still die attributed to treatment-related mortality, chemotherapy-resistant disease and relapse[ 14 ]. Recent progress in understanding the pathophysiology of PCNSL has led to novel therapeutics introduced into clinical trials and have shown promising clinical responses[ 15 ]. Based on population analysis, we found that patients diagnosed between 2009-2018 had better OS and DSS than those diagnosed 2000-2008, which reflected developments in treatment. PCNSL is characterized by a frequent early wide dissemination and the involvement of deep brain that leads to poor efficacy of surgery[ 16 ]. Previous research suggested that surgical resection (including complete and partial surgical resection) have no significant survival advantage and even be associated with higher mortality that should be avoided[ 17 , 18 ]. The role of surgery is to only establish a diagnosis by stereotactic biopsy. However, with the large number of applications of new techniques and practices in recent years, including increased use of MRI, frameless stereotyping and tumor visualization, the effectiveness and tolerability have greatly improved. Survival advantage was proven and the traditional view has been questioned in some studies[ 19 – 21 ]. Therefore, the role of surgery in PCNSL should be reevaluated. In our study, surgical excision was associated with significantly better OS and DSS, and was an independent risk factor for survival. Moreover, combining surgical excision and chemotherapy can bring favourable OS and DSS than chemotherapy alone, which suggested that multimodal therapy may be more beneficial. However, this results should be interpreted with caution because of unknown operation mode and variations in the technical level of operators, and more prospective research is needed to verify it. Due to the high sensitivity to radiation, newly diagnosed patients with PCNSL have historically received whole-brain radiotherapy (WBRT). However, WBRT-associated delayed neurotoxicity has limited its use, especially for age older than 60 years[ 7 , 22 , 23 ]. Given the higher risk of neurotoxicity and the limited durability of treatment responses, WBRT is not considered as the standard initial therapy for PCNSL patients[ 24 , 25 ]. Recently, many clinical studies have engaged in whether different radiotherapy regimens (including reduced-dose and partial‐brain radiotherapy) combination with chemotherapy can bring better clinical outcomes[ 26 – 30 ]. However, the results still remain controversial. Analysis based on a large population, radiotherapy did not improve long-term effects and associated with inferior OS and DSS compared with no radiotherapy according to univariate analysis. Multivariate analysis revealed radiotherapy was an independent prognostic factor for OS, but not for DSS. We further explored the combination of radiotherapy and chemotherapy, then found that patient may benefit from combination therapy in the early stage of the treatment, unfortunately, the long-term outcomes were not superior to chemotherapy alone because of the high incidence of delayed neurotoxicity and short-term responses. Giving above, the benefit of radiotherapy in establishing local control of tumors must be weighed against the increased risk of long‐term neurotoxicity. Due to unknown information about detailed radiotherapy and chemotherapy regimens, subgroup analysis could not be performed. Therefore, these results should be interpreted cautiously. The nomogram has become a useful tool for clinical decision-making and visualized and quick risk assessment for clinicians. In this study, it was found that race, age, sex, marital status, chemotherapy and surgery were independent prognostic factors for OS and DSS in DLBCL patients, and we constructed the nomograms to predict 1-, 3-, and 5-year survival based on these factors. The significantly higher C-index 0.704 and 0.698 of the nomograms proved discriminative power. Moreover, the calibration curve exhibited good consistency among the predicted survival and the actual survival. However, due to manual calculations, the nomograms are not easy to apply to clinical practice, so we further developed a dynamic web-based survival rate calculator that can predict the long-term OS dynamically at different time points for DLBCL patients based on the nomogram ( https://tangdongshengarticle.shinyapps.io/DynNomapp/ ). This study has several limitations. Firstly, potential biases were unavoidable as a retrospective study. Secondly, other potential prognostic factors, such as Karnofsky performance status score, size and number of lesions, LDH level are not registered in SEER database, and cannot combine these factors to predict prognosis. Thirdly, detail chemotherapy, surgical operation mode and radiotherapy regimens for patients are not available. We are unable to further analyze the impact of different treatment regimen on prognosis. Lastly, the nomograms were established and verified by using the same database, so it is necessary to prospectively verify the nomogram in another independent data set for reliable evaluation. Therefore, the results of the present study should be interpreted with caution given above limitations. Whereas, our study still provided useful information and important insights in PCNSL despite these limitations based on a large population. In conclusion, age, race, sex, use of chemotherapy, surgery and radiation were independent prognostic factors for OS, but radiotherapy was no longer an independent prognostic factor for DSS based on the SEER database. Surgery might be a therapeutic benefit for PCNSL patients. Radiotherapy was effective in therapeutic initial stage, but the long-term outcome was not satisfactory. We also developed a predictive nomogram and a web-based survival rate calculator predicting the long-term OS in DLBCL patients which showed favorable applicability and accuracy that could help in the prediction of mortality and the choice of treatment. Declarations Ethics statement Not applicable. Data availability statement The data from this study are available in the SEER database, https://seer.cancer.gov. Conflicts of Interest The authors declared that they have no competing interests. Fund Program: Funded by Jiangsu Commission of Health (H2019082, H2018085). References Holdhoff, M. et al. et, al. Challenges in the Treatment of Newly Diagnosed and Recurrent Primary Central Nervous System Lymphoma. J Natl Compr Canc Netw 2020;18(11):1571-1578. Deckert, M. et al. Modern concepts in the biology, diagnosis, differential diagnosis and treatment of primary central nervous system lymphoma. Leukemia. 2011;25(12):1797-807. Chihara, D. & Dunleavy, K. 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Is whole-brain radiotherapy still a standard treatment for primary central nervous system lymphomas? Current opinion in neurology 2018; 31(6): 733-739. Burton, E. C. et al. A Regional Multicenter Retrospective Analysis of Patients with Primary Central Nervous System Lymphoma Diagnosed from 2000-2012: Treatment Patterns and Clinical Outcomes. Cureus 2017; 9(7): e1512. Tables TABLE 1 | Patient and tumor characteristics of primary central nervous system lymphoma diagnosed in SEER 18 registries, 2000-2018. Characteristic No. of patients Percentage (%) total 5166 100 Age at diagnosis, years 63.1±14.9 65.0(3.0-97.0) Mean±SD Median(rang) Sex 2679 2487 51.9 48.1 Male Female Race 4182 364 81.0 7.0 White Black Others a 620 12.0 Years of diagnosis 2115 3051 916 884 1372 1415 579 3045 2121 40.9 59.1 17.7 17.1 26.6 27.4 11.2 58.9 41.1 2000-2008 2009-2018 Age <50 50-59 60-69 70-79 ≥80 Marital status Married Singleb Lineage 4429 166 85.7 3.2 Aggressive B cell NHLc Indolent B cell NHLd T cell NHL NHL-NOS Others 85 474 12 3040 2126 1.6 9.2 0.2 58.8 41.2 Surgery No Performed Radiation 3608 1558 69.8 30.2 No Performed Chemotherapy 1652 3514 32.0 68.0 No/unknown Performed NHL, non–Hodgkin lymphoma; NOS, not otherwise specified. a American Indian/Alaskan Native or Asian/Pacific Islander. b Included divorced/separated/widowed patients. c Included diffuse large B cell lymphoma, burkitt’s lymphoma, mantle cell lymphoma, and intravascular large B-cell lymphoma. d Included follicular lymphoma, chronic lymphocytic leukemia/small lymphocytic lymphoma, lymphoplasmacytic lymphoma, and mucosa associated lymphoid tissue lymphoma. TABLE 2 | Patient characteristics according to the histological subtypes Histology subtype n(%) Median age Male(%) Survival Median OS,m 3-year OS Median DSS,m 3-year DSS All patient 5166 DLBCL NHL-NOS FL PTCL MALT BL LPL ALCL CLL/SLL Others 4373(84.6) 474(9.2) 65(1.3) 63(1.2) 60(1.2) 34(0.7) 22(0.4) 21(0.4) 19(0.4) 35(0.7) 66.0 65.0 67.0 60.0 60.0 60.0 63.0 39.0 66.0 62.0 51.8 54.0 41.5 57.1 31.7 64.7 45.5 71.4 47.4 54.3 12 8 58 15 / 19 / 13 29 / 35.3 32.0 53.8 37.9 78.3 38.3 50.9 34.4 42.1 / 17 11 77 26 / 19 / 22 47 / 40.5 37.3 57.4 47.3 86.0 42.3 50.9 37.2 54.0 / n, number of cases; m, month; OS, overall survival; DSS, disease-specific survival; NHL, non–Hodgkin lymphoma; NOS, not otherwise specified; DLBCL, Diffuse large B-cell lymphoma; FL, Follicular lymphoma, BL, Burkitt’s lymphoma; CLL/SLL, Chronic lymphocytic leukemia/small lymphocytic lymphoma; MALT, Mucosal-associated lymphoid tissue. PTCL, Peripheral T-cell lymphoma, ALCL, Anaplastic large cell lymphoma. LPL, Lymphoplasmacytic lymphoma. TABLE 3 |Multivariable Cox regression analysis of the independent prognostic factors for OS and DSS among PCNSL patients. Variables Overall survival Disease-specific survival HR 95% CI P HR 95% CI P Race White Reference Reference Black 1.21 1.06-1.39 0.005 1.25 1.08-1.44 0.003 Others 0.92 0.83-1.03 0.138 0.91 0.81-1.02 0.094 Sex Male Reference Reference Female 0.84 0.78-0.89 <0.001 0.86 0.80-0.93 <0.001 Age <50 Reference Reference 50-59 1.48 1.31-1.67 <0.001 1.32 1.16-1.50 <0.001 60-69 1.90 1.70-2.13 <0.001 1.69 1.50-1.90 <0.001 70-79 2.65 2.37-2.96 <0.001 2.28 2.03-2.56 <0.001 ≥80 3.32 2.91-3.79 <0.001 2.76 2.39-3.19 <0.001 Years of diagnosis 2000-2008 Reference Reference 2009-2018 0.79 0.74-0.84 <0.001 0.77 0.72-0.83 <0.001 Marital status Married Reference Reference Single 1.21 1.13-1.30 <0.001 1.22 1.13-1.31 <0.001 Classification Aggressive B cell NHL Reference Reference Indolent B cell NHL 0.33 0.27-0.42 <0.001 0.31 0.24-0.40 <0.001 T cell NHL 0.82 0.63-1.07 0.148 0.80 0.59-1.07 0.132 NHL-NOS 0.96 0.86-1.08 0.501 0.95 0.85-1.07 0.432 Others 1.08 0.52-2.28 0.834 1.09 0.49-2.43 0.838 Surgery Performed Reference Reference No/unknown 1.34 1.25-1.43 <0.001 1.36 1.26-1.46 <0.001 Chemotherapy Performed Reference Reference No/unknown 2.73 2.53-2.94 <0.001 2.59 2.39-2.81 <0.001 Radiation Performed Reference Reference No/unknown 1.12 1.04-1.20 0.003 1.08 1.00-1.17 0.054 TABLE 4 | Univariate and multivariate Cox regression analysis of each factor’s ability in predicting OS and DSS among DLBCL patients. Overall Survival Disease-Specific Survival HR 95% CI P HR 95% CI P UNIVARIATE ANALYSES Race White vs Black 1.16 1.00-1.34 0.046 1.21 1.04-1.42 0.014 White vs Others 0.85 0.76-0.95 0.004 0.83 0.73-0.94 0.003 Sex Male vs Female 0.92 0.86-0.99 0.021 0.93 0.86-1.00 0.054 Age 0-50 vs 50-59 1.05 0.92-1.20 0.453 0.94 0.82-1.07 0.342 0-50 vs 60-69 1.39 1.23-1.55 <0.001 1.22 1.08-1.38 0.001 0-50 vs 70-79 2.19 1.96-2.45 <0.001 1.89 1.68-2.13 <0.001 0-50 vs ≥80 3.25 2.83-3.73 <0.001 2.73 2.35-3.16 <0.001 Marital status Married vs Single 1.23 1.14-1.32 <0.001 1.27 1.18-1.37 <0.001 Surgery Performed vs No/unknown 1.27 1.18-1.37 <0.001 1.30 1.21-1.41 <0.001 Chemotherapy Performed vs No/unknown 3.17 2.95-3.41 <0.001 3.03 2.80-3.28 <0.001 Radiation Performed vs No/unknown 0.83 0.77-0.89 <0.001 0.80 0.74-0.87 <0.001 MULTIVARIATE ANALYSES Race White vs Black 1.17 1.01-1.36 0.042 1.18 1.00-1.39 0.048 White vs Others Sex Male vs Female Age 0-50 vs 50-59 0.88 0.84 1.35 0.79-0.99 0.78-0.90 1.18-1.54 0.028 <0.001 <0.001 0.86 0.86 1.20 0.76-0.97 0.79-0.93 1.04-1.38 0.017 <0.001 0.013 0-50 vs 60-69 1.74 1.54-1.96 <0.001 1.53 1.35-1.74 <0.001 0-50 vs 70-79 2.43 2.16-2.74 <0.001 2.10 1.85-2.38 <0.001 0-50 vs ≥80 3.03 2.63-3.50 <0.001 2.55 2.18-2.97 <0.001 Marital status Married vs Single 1.21 1.12-1.31 <0.001 1.23 1.13-1.33 <0.001 Surgery Performed vs No/unknown 1.32 1.22-1.42 <0.001 1.34 1.24-1.45 <0.001 Chemotherapy Performed vs No/unknown 2.97 2.74-3.22 <0.001 2.80 2.56-3.05 <0.001 Radiation Performed vs No/unknown 1.08 1.00-1.17 0.053 1.04 0.95-1.13 0.394 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 Feb, 2022 Read the published version in Frontiers in Oncology → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-955053","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":55824561,"identity":"b800fb48-140f-4284-8c45-49cc8cdab18e","order_by":0,"name":"Dongsheng Tang","email":"","orcid":"","institution":"the Huai'an Clinical College of Xuzhou Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Dongsheng","middleName":"","lastName":"Tang","suffix":""},{"id":55824562,"identity":"329958d8-11f1-4520-aeae-3495ce854375","order_by":1,"name":"Yue Chen","email":"","orcid":"","institution":"the Huai'an Clinical College of Xuzhou Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Chen","suffix":""},{"id":55824563,"identity":"22d0e6ab-6166-4212-b5ff-2298af3cf076","order_by":2,"name":"Yuye Shi","email":"","orcid":"","institution":"the Huai'an Clinical College of Xuzhou Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuye","middleName":"","lastName":"Shi","suffix":""},{"id":55824564,"identity":"2a338852-af07-42e6-bcff-9312c381b1a7","order_by":3,"name":"Hong Tao","email":"","orcid":"","institution":"the Huai'an Clinical College of Xuzhou Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Tao","suffix":""},{"id":55824565,"identity":"2683412d-a2af-454e-9019-62d9fa39b3f7","order_by":4,"name":"Shandong Tao","email":"","orcid":"","institution":"the Huai'an Clinical College of Xuzhou Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shandong","middleName":"","lastName":"Tao","suffix":""},{"id":55824566,"identity":"c5e4093f-e251-488a-a1a5-86823391d51c","order_by":5,"name":"Quan'e Zhang","email":"","orcid":"","institution":"the Huai'an Clinical College of Xuzhou Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Quan'e","middleName":"","lastName":"Zhang","suffix":""},{"id":55824567,"identity":"2fe5259b-664a-48af-9313-b76901f0271d","order_by":6,"name":"Banghe Ding","email":"","orcid":"","institution":"the Huai'an Clinical College of Xuzhou Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Banghe","middleName":"","lastName":"Ding","suffix":""},{"id":55824568,"identity":"62fc37a2-6842-4fda-bfd0-08c44f968c5c","order_by":7,"name":"Zhengmei He","email":"","orcid":"","institution":"the Huai'an Clinical College of Xuzhou Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhengmei","middleName":"","lastName":"He","suffix":""},{"id":55824569,"identity":"798e38d7-66f4-4a2d-b2e4-768a360b1630","order_by":8,"name":"Liang Yu","email":"","orcid":"","institution":"the Huai'an Clinical College of Xuzhou Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Liang","middleName":"","lastName":"Yu","suffix":""},{"id":55824570,"identity":"c7327d05-c726-49a0-9eee-5fb1b8ffddee","order_by":9,"name":"Chunling Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYFAC5sbHPwxs5NjY2w8Qq4Wx2ZihIs2Yj+dMAtFa2oQZzhxKnCfhYECcBoPjB9uYC9sOpLdJMCQw/KjYRoSWM4ltj2e23cltk248wNhz5jZhLWY3GNsNeNue5bbJHEhgZmwjTkubBG/b4XQ2iQQD4rVI85w5nEC8Fvszic2GMyrSDNuAgXyQKL9Ith8++OCDgY28fHv7wQc/KojQggIOkKh+FIyCUTAKRgEuAABnLkChOZXJsAAAAABJRU5ErkJggg==","orcid":"","institution":"the Huai'an Clinical College of Xuzhou Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Chunling","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2021-10-04 08:44:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-955053/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-955053/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.3389/fonc.2022.817043","type":"published","date":"2022-02-10T10:48:27+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":14423484,"identity":"fe78b121-a9f6-4eea-97f9-8fe047ac8e7b","added_by":"auto","created_at":"2021-10-11 19:03:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":449455,"visible":true,"origin":"","legend":"Survival analysis of primary central nervous system lymphoma. OS (A) and DSS (B) curves for all patients. Survival analysis according to the year of diagnosis, OS (C) and DSS (D) have significantly improved in the past decades, P\u003c0.0001. Survival curves of OS (E) and DSS (F) according to the main histological subtypes. MALT, Mucosal-associated lymphoid tissue; DLBCL, Diffuse large B cell lymphoma; BL, Burkitt's lymphoma; FL, Follicular lymphoma; PTCL, Peripheral T-cell lymphoma; NHL, non-Hodgkin lymphoma; NOS, not otherwise specified.","description":"","filename":"OnlineFIGURE1.png","url":"https://assets-eu.researchsquare.com/files/rs-955053/v1/7f4bf76a2cee74d9e0f62e9d.png"},{"id":14423486,"identity":"775c2b0b-fc5e-4a1f-9369-a703cb2bcbe1","added_by":"auto","created_at":"2021-10-11 19:03:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":946699,"visible":true,"origin":"","legend":"Survival analysis of primary central nervous system lymphoma stratified by age, sex, race, marital status. Significant statistical difference was found in OS with age (A), P\u003c0.0001, sex (B), P=0.0082, race (C), P=0.0013, and marital status (D), P\u003c0.0001. Significant statistical difference was found in DSS with age (E), P\u003c0.0001, sex (F), P=0.036, race (G), P=0.0038, and marital status (H), P\u003c0.0001. Inferior OS and DSS was significantly associated with elder age, male, black, and single.","description":"","filename":"OnlineFIGURE2.png","url":"https://assets-eu.researchsquare.com/files/rs-955053/v1/ff866b8ef0f23ec99bd78ccc.png"},{"id":14423485,"identity":"2999d514-0f77-4bb2-93f8-1b506ee71628","added_by":"auto","created_at":"2021-10-11 19:03:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":314920,"visible":true,"origin":"","legend":"Survival analysis of primary central nervous system lymphoma stratified by treatment: chemotherapy, radiotherapy and surgery. Significant statistical difference was found in OS and DSS between patients with chemotherapy and no chemotherapy (A, D), radiotherapy and no radiotherapy (B, E), surgery and no surgery (C, F), P\u003c0.0001. Patients who underwent chemotherapy and surgery achieved significantly longer OS and DSS compared to who did not. However, radiotherapy led to worse OS and DSS in the long-term.","description":"","filename":"OnlineFIGURE3.png","url":"https://assets-eu.researchsquare.com/files/rs-955053/v1/8fd19a5b54ad3c17e41d72dd.png"},{"id":14423599,"identity":"ac47db2a-94ed-4294-85de-999386dcb84d","added_by":"auto","created_at":"2021-10-11 19:06:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":451599,"visible":true,"origin":"","legend":"Effect of combination therapy on primary central nervous system lymphoma. Kaplan-Meier survival curves of combined effect of chemotherapy and surgery: OS (A) and DSS (B), chemotherapy and radiotherapy: OS (C) and DSS (D). The surgery combined with chemotherapy was significantly associated with better OS and DSS, P\u003c0.0001. Chemotherapy combined with radiotherapy was associated with better OS and DSS in the early stage of the treatment, however, the long-term OS and DSS were not superior to chemotherapy alone, P\u003c0.0001. ","description":"","filename":"OnlineFIGURE4.png","url":"https://assets-eu.researchsquare.com/files/rs-955053/v1/0692c46a49a295f2f6071d3d.png"},{"id":14423490,"identity":"bb998aa6-9ac4-48a8-8229-dbf92dbbc95c","added_by":"auto","created_at":"2021-10-11 19:03:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":128172,"visible":true,"origin":"","legend":"Nomogram to predict 1-, 3-, and 5-year OS (A) and DSS (B) probability in patients with primary central nervous system lymphoma. The OS and DSS rates at 1-, 3-, and 5- year can be predicted by integrating scores related to race, age, sex, marital, surgery, chemotherapy, and projecting the total points to the bottom scale.","description":"","filename":"OnlineFIGURE5.png","url":"https://assets-eu.researchsquare.com/files/rs-955053/v1/a9f61824319c24173b95c0c8.png"},{"id":14423489,"identity":"70995a1b-f011-42bc-bcaa-c667d1e368a5","added_by":"auto","created_at":"2021-10-11 19:03:30","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":135550,"visible":true,"origin":"","legend":"Calibration curve of the nomogram for the prediction of 1-, 3- and 5-year OS (A-C) and DSS (D-F). The abscissa represents nomogram-predicted survival rate, ordinate represents actual survival rate, and the calibration curves for 1-, 3-, and 5-year survival rate showed satisfactory agreements between the predicted and actual values.","description":"","filename":"OnlineFIGURE6.png","url":"https://assets-eu.researchsquare.com/files/rs-955053/v1/59673251e114e42f56221672.png"},{"id":14423600,"identity":"1a4cebae-7ddc-44a3-a444-596973ac3907","added_by":"auto","created_at":"2021-10-11 19:06:29","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":224539,"visible":true,"origin":"","legend":"An example to illustrate the use of the web-based survival rate calculator. (A) a 75-year-old married white man was diagnosed as PCNSL with DLBCL, if he refused chemotherapy and surgery, his 3-year OS rate is approximately only 4.7% (95% CI 0.022-0.101), if he was given chemotherapy and surgery, his 3-year OS rate is approximately 34.0% (95% CI 0.272-0.420). (B) His survival curve depending on whether he was treated or not: received treatment (a), refused treatment (b). ","description":"","filename":"OnlineFIGURE7.png","url":"https://assets-eu.researchsquare.com/files/rs-955053/v1/d7c5a9528abec4aea53d49f1.png"},{"id":18091063,"identity":"91cfa3a9-ae21-456d-b291-e41be92178be","added_by":"auto","created_at":"2022-02-10 10:48:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2009346,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-955053/v1/0debca62-b934-4d30-8538-652a929d0f34.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eEpidemiologic Characteristics, Prognostic Factors and Treatment Outcomes in Primary Central Nervous System Lymphoma: A SEER-Based Study\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePrimary central nervous system lymphoma (PCNSL) is an uncommon and highly invasive tumor that involve the leptomeninges, brain, eyes or spinal cord without evidence of systemic disease[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. PCNSL accounts for 1%-2% of non-Hodgkin lymphoma (NHL) and the most (over 90%) cases are DLBCL[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Immunocompromised individuals, such as HIV-infected or immunosuppressive patients, are deemed to have a higher risk in PCNSL[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. PCNSL was historically associated with poor prognosis, with an overall survival (OS) of 1.5 months without additional treatment. The high-dose methotrexate (HD-MTX) systemic chemotherapy is deemed as the standard first-line treatment, however, few patients can achieve long-term survival, the median progression-free survival (PFS) and OS were only 24.0, and 36.9 months respectively[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and the 5-year OS in the period 1992-1994 was increased only from 19.1\u0026ndash;30.1% in the period 2004-2006[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Surgery is generally discouraged previously before 2010, but conventional view has been challenged as the advances in surgical techniques. Due to the high risk of neurotoxicity and lacking of sustainable response, PCNSL patients should avoid whole brain radiation (WBRT) in the first-line treatment, more research has focused on whether different radiotherapy regimens (including reduced-dose and partial‐brain radiotherapy) combination chemotherapy can bring benefits. However, the conclusions are inconsistent. In recent years, novel agents including immune checkpoint inhibitors, immunomodulatory drugs (IMiDs), bruton tyrosine kinase (BTK) inhibitor, PI3K/AKT/mTOR inhibitors and chimeric antigen receptor T cell (CAR-T cell) therapy have been applied in several clinic trials which exhibit promising clinic outcomes. Even with an impressive clinical response, more randomized clinical trials are still needed to verify and identify the optimal therapy for PCNSL patients.\u003c/p\u003e \u003cp\u003eTwo prognostic classification systems of PCNSL are widely used currently. The IELSG identified an age (\u0026gt;60 years), elevated lactate dehydrogenase (LDH) serum level, performance status (PS) (\u0026ge;2), high CSF protein concentration, and extensive deep structure involved in the brain were independent predictors of negative prognosis[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In another prognostic model, the MSKCC prognostic score described three risk groups based on age and Karnofsky performance status (KPS)[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, treatment information was not included in these prognostic systems, it is difficult to perform the treatment choose based on these prognostic systems. Therefore, a new, easily available prognostic system which include treatment information is needed to be developed.\u003c/p\u003e \u003cp\u003eDuing to the rarity of PCNSL, large-scale clinical trials and prospective data are limited for us to investigate it. The Surveillance, Epidemiology, and End Results (SEER) contains a wealth of relevant information on different types of cancer patients based on the United States population which provides excellect resources for us to study. Therefore, a large population-based analysis was conducted to describe the clinical characteristics, prognostic factors and treatment outcomes of PCNSL using the SEER database. We also analyzed independent prognostic factors of PCNSL and established a nomogram to predict the prognosis in this population.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003eStudy data was obtained using the SEER*Stat software (version 8.3.9). By \u0026ldquo;Incidence-SEER Research Plus Data, 18 Registires, Nov 2020 Sub (2000-2018)\u0026rdquo;, patients diagnosed with PCNSL between 2000 and 2018 were identified. International Classification of Diseases for Oncology (ICD-O-3) histologic codes (9590\u0026ndash;9595, 9650\u0026ndash;9699, 9702\u0026ndash;9729) were used for lymphoma and primary sites limited to central nervous system were identified by site specific code (C71.0\u0026ndash;C71.9). Unknown age of diagnosis, uncertain race, unknown sex, unknown marriage status, incomplete follow-up data and secondary to other tumors were excluded. The primary endpoint of this study were overall survival (OS) and disease-specific survival (DSS).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analyses\u003c/h2\u003e \u003cp\u003eThe OS and DSS were estimated with the Kaplan-Meier method using the log-rank test, and Cox regression model was used for univariate and multivariate survival analysis. Nomograms were constructed to predict 1-, 3-, and 5-year OS and DSS specifically for DLBCL according to the results of multivariable Cox regression analysis. To evaluate the accuracy of the nomograms, Harrell's concordance index (C-index) was calculated to quantify the discrimination performance and calibration curve was plotted to identify whether the predicted survival was consistent with the actual survival. The data were analyzed using R software (R version 4.0.4). Statistical significance was set at P \u0026lt; 0.05 (two-sided).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eEpidemiologic Characteristics of PCNSL Patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mean age at diagnosis was 63.1\u0026plusmn;14.9, ranging from 3 to 97 years. The population was comprised of 2679 (51.9%) males and 2487 (48.1%) females, and the highest incidence of age group was 70-79 years old. The characteristics of PCNSL patients were summarized in TABLE 1.\u003c/p\u003e\n\u003cp\u003eRegarding the clinical aggressiveness and cell line of origin, there were 4429 (85.7%) patients of aggressive B cell NHLs, 166 (3.2%) indolent B cell NHLs, 474 (9.2%) NHL-NOSs and 85 (1.6%) T cell NHLs. As for the histological classification of PCNSL, the most common subtype was DLBCL (84.6 %), followed by not otherwise specified (NHL-NOS) (9.2%), follicular lymphoma (FL) (1.3%), peripheral T-cell lymphoma (PTCL) (1.2%), mucosal-associated lymphoid tissue (MALT) (1.2%), burkitt\u0026rsquo;s lymphoma (BL) (0.7%), lymphoplasmacytic lymphoma (LPL) (0.4%), anaplastic large cell lymphoma (ALCL) (0.4%), chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL) (0.4%). Except for ALCL (median age 39.0), the median age of all subtypes were over 60 years old. The epidemiologic characteristics and survival outcomes were summarized according to histological subtype in TABLE 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSurvival Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 3660 patients died during the follow-up period, 3101 deaths were disease-specific. Kaplan-Meier curves illustrating OS and DSS were shown in Figures \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA, B. The median OS and DSS was 13.0 and 19.0 months. The 1-, 3-, and 5-year OS were 50.1, 36.0 and 27.2% and corresponding to DSS were 54.4, 41.3 and 33.5%, respectively. Patients diagnosed in 2009 to 2018 showed better OS and DSS than patients diagnosed in 2000 to 2008 (P\u0026lt;0.0001) (Figures \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC, D).\u003c/p\u003e\n\u003cp\u003eIn the whole cohort, the best 3-year OS and DSS were observed in MALT (OS: 78.3%, DSS: 86.0%) and FL (OS:53.8%, DSS: 57.4%). In addition, the Kaplan-Meier survival curves for OS and DSS of the main histological subtypes were presented in Figures \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eE, F. Furthermore, Kaplan-Meier survival analysis was also performed stratifying patients according to sex, age, race, marital status and treatment. It was found that increasing age was significantly associated with reduced OS and DSS (Figures \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA, E). Females had significantly better OS and DSS than males (Figures \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB, F). We also found that patients who were others (Figures \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC, G) and married (Figures \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD, H) had a better OS and DSS according to the univariate analysis.\u003c/p\u003e\n\u003cp\u003eIn terms of treatment, patients who underwent chemotherapy (Figures \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA, D) or surgery (Figures \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC, F) achieved significantly longer OS and DSS than who did not. However, radiotherapy led to worse OS and DSS in the long-term (Figures \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB, E). We also explored the outcome in combination therapy and found that surgery combined with chemotherapy was associated with better OS and DSS (Figures \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA, B), but radiotherapy combined with chemotherapy led to worse OS and DSS in the long-term (Figures \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC, D). Then, we performed multivariate Cox-regression analysis to figure out the independent prognostic factors for OS and DSS and revealed race, sex, age, marital status, surgery, chemotherapy and radiotherapy were independent predictors of OS, but radiotherapy was no longer an independent prognostic factor for DSS (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of Nomogram\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsidering the main histological subtype of PCNSL was DLBCL, we developed a prediction model specifically for DLBCL patients. 4373 patients with DLBCL were randomly divided into a training cohort (n=3061) and a validation cohort (n=1312) in a ratio of 7:3 for model construction and validation. Firstly, univariate and multivariate Cox regression analyses were conducted to select the independent prognostic factors for OS and DSS. Univariate and multivariate analyses results were displayed in Table 4. These significant factors from univariate Cox regression analysis were incorporated into multivariate analysis. Significant predictors of OS and DSS on multivariate analysis were used to establish the nomograms. The OS and DSS nomogram at the 1-, 3-, and 5- year were shown in Figure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. Then, the nomogram performance was assessed with discrimination and calibration by using the external validation cohort. The C-index of OS and DSS were 0.704 (95% CI 0.687-0.721) and 0.698 (95% CI 0.679-0.717), indicating the high discrimination ability of the nomograms. The calibration curves of the train cohort and the external validation cohort were presented in Figure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, which represented good agreement among the predicted survival and the actual survival at 1-, 3-, and 5-year.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWeb-Based Survival Rate Calculator\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA dynamic web-based survival rate calculator base on the nomogram was established to predict the long-term OS (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tangdongshengarticle.shinyapps.io/DynNomapp/\u003c/span\u003e\u003c/span\u003e). For instance, a 75-year-old white married man was diagnosed as PCNSL with DLBCL, if he refused surgery and chemotherapy, his 3-year OS rate is approximately only 4.6% (95% CI 0.022-0.101), if he was given surgery and chemotherapy, his 3-year OS rate is approximately 34.0% (95% CI 0.272-0.420) (Figure \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePCNSL represents a rare but highly aggressive NHL with poor prognosis. In light of the rarity of PCNSL, current understanding of PCNSL is mainly based on retrospective analysis with small series. Therefore, we conducted a study based on a large population. In the current study, there were 5166 PCNSL patients from SEER database.\u003c/p\u003e \u003cp\u003eThe mean age at diagnosis was 63.1\u0026plusmn;14.9 years, and the male to female ratio was 1.1:1.0, which was largely consisting with population-based study from Australia[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Previous studies have demonstrated that age was a significant and adverse prognostic factor[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In the present study, inferior OS and DSS were significantly associated with older age, consisting with previous results. Interestingly, our study revealed that married patients tend to have better outcomes than single patients (including divorced/widowed/separated patients). The mechanism between marital status and survival is unclear, social-psychological factors may contribute to it. The married patients may have better socioeconomic status and more emotional support than single patients.\u003c/p\u003e \u003cp\u003eAlthough the introduction of HD-MTX has significantly improved PCNSL prognosis, numerous patients still die attributed to treatment-related mortality, chemotherapy-resistant disease and relapse[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Recent progress in understanding the pathophysiology of PCNSL has led to novel therapeutics introduced into clinical trials and have shown promising clinical responses[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Based on population analysis, we found that patients diagnosed between 2009-2018 had better OS and DSS than those diagnosed 2000-2008, which reflected developments in treatment.\u003c/p\u003e \u003cp\u003ePCNSL is characterized by a frequent early wide dissemination and the involvement of deep brain that leads to poor efficacy of surgery[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Previous research suggested that surgical resection (including complete and partial surgical resection) have no significant survival advantage and even be associated with higher mortality that should be avoided[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The role of surgery is to only establish a diagnosis by stereotactic biopsy. However, with the large number of applications of new techniques and practices in recent years, including increased use of MRI, frameless stereotyping and tumor visualization, the effectiveness and tolerability have greatly improved. Survival advantage was proven and the traditional view has been questioned in some studies[\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Therefore, the role of surgery in PCNSL should be reevaluated. In our study, surgical excision was associated with significantly better OS and DSS, and was an independent risk factor for survival. Moreover, combining surgical excision and chemotherapy can bring favourable OS and DSS than chemotherapy alone, which suggested that multimodal therapy may be more beneficial. However, this results should be interpreted with caution because of unknown operation mode and variations in the technical level of operators, and more prospective research is needed to verify it.\u003c/p\u003e \u003cp\u003eDue to the high sensitivity to radiation, newly diagnosed patients with PCNSL have historically received whole-brain radiotherapy (WBRT). However, WBRT-associated delayed neurotoxicity has limited its use, especially for age older than 60 years[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Given the higher risk of neurotoxicity and the limited durability of treatment responses, WBRT is not considered as the standard initial therapy for PCNSL patients[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Recently, many clinical studies have engaged in whether different radiotherapy regimens (including reduced-dose and partial‐brain radiotherapy) combination with chemotherapy can bring better clinical outcomes[\u003cspan additionalcitationids=\"CR27 CR28 CR29\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. However, the results still remain controversial. Analysis based on a large population, radiotherapy did not improve long-term effects and associated with inferior OS and DSS compared with no radiotherapy according to univariate analysis. Multivariate analysis revealed radiotherapy was an independent prognostic factor for OS, but not for DSS. We further explored the combination of radiotherapy and chemotherapy, then found that patient may benefit from combination therapy in the early stage of the treatment, unfortunately, the long-term outcomes were not superior to chemotherapy alone because of the high incidence of delayed neurotoxicity and short-term responses. Giving above, the benefit of radiotherapy in establishing local control of tumors must be weighed against the increased risk of long‐term neurotoxicity. Due to unknown information about detailed radiotherapy and chemotherapy regimens, subgroup analysis could not be performed. Therefore, these results should be interpreted cautiously.\u003c/p\u003e \u003cp\u003eThe nomogram has become a useful tool for clinical decision-making and visualized and quick risk assessment for clinicians. In this study, it was found that race, age, sex, marital status, chemotherapy and surgery were independent prognostic factors for OS and DSS in DLBCL patients, and we constructed the nomograms to predict 1-, 3-, and 5-year survival based on these factors. The significantly higher C-index 0.704 and 0.698 of the nomograms proved discriminative power. Moreover, the calibration curve exhibited good consistency among the predicted survival and the actual survival. However, due to manual calculations, the nomograms are not easy to apply to clinical practice, so we further developed a dynamic web-based survival rate calculator that can predict the long-term OS dynamically at different time points for DLBCL patients based on the nomogram (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tangdongshengarticle.shinyapps.io/DynNomapp/\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study has several limitations. Firstly, potential biases were unavoidable as a retrospective study. Secondly, other potential prognostic factors, such as Karnofsky performance status score, size and number of lesions, LDH level are not registered in SEER database, and cannot combine these factors to predict prognosis. Thirdly, detail chemotherapy, surgical operation mode and radiotherapy regimens for patients are not available. We are unable to further analyze the impact of different treatment regimen on prognosis. Lastly, the nomograms were established and verified by using the same database, so it is necessary to prospectively verify the nomogram in another independent data set for reliable evaluation. Therefore, the results of the present study should be interpreted with caution given above limitations. Whereas, our study still provided useful information and important insights in PCNSL despite these limitations based on a large population.\u003c/p\u003e \u003cp\u003eIn conclusion, age, race, sex, use of chemotherapy, surgery and radiation were independent prognostic factors for OS, but radiotherapy was no longer an independent prognostic factor for DSS based on the SEER database. Surgery might be a therapeutic benefit for PCNSL patients. Radiotherapy was effective in therapeutic initial stage, but the long-term outcome was not satisfactory. We also developed a predictive nomogram and a web-based survival rate calculator predicting the long-term OS in DLBCL patients which showed favorable applicability and accuracy that could help in the prediction of mortality and the choice of treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data from this study are available in the SEER database, https://seer.cancer.gov.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFund Program:\u0026nbsp;\u003c/strong\u003eFunded by Jiangsu Commission of Health (H2019082, H2018085).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHoldhoff, M. \u003cem\u003eet al.\u003c/em\u003e et, al. Challenges in the Treatment of Newly Diagnosed and Recurrent Primary Central Nervous System Lymphoma. J Natl Compr Canc Netw 2020;18(11):1571-1578.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeckert, M. \u003cem\u003eet al.\u003c/em\u003e Modern concepts in the biology, diagnosis, differential diagnosis and treatment of primary central nervous system lymphoma. Leukemia. 2011;25(12):1797-807.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChihara, D. \u0026amp; Dunleavy, K. Primary Central Nervous System Lymphoma: Evolving Biologic Insights and Recent Therapeutic Advances. Clinical lymphoma, myeloma and leukemia 2021; 21(2): 73-79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShiels, M. S. \u003cem\u003eet al.\u003c/em\u003e Trends in primary central nervous system lymphoma incidence and survival in the U.S. British journal of haematology 2016; 174(3): 417-24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaldorsen, I. 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Cancer medicine 2020; 9(6): 2134-2145.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhn, Y. \u003cem\u003eet al.\u003c/em\u003e Primary central nervous system lymphoma: a new prognostic model for patients with diffuse large B-cell histology. Blood research 2017; 52(4): 285-292.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJang, J. E. \u003cem\u003eet al.\u003c/em\u003e A new prognostic model using absolute lymphocyte count in patients with primary central nervous system lymphoma. Eur J Cancer. 2016; 57:127-35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrommes, C., Rubenstein, J. L., DeAngelis, L. M., Ferreri, A. J. M. \u0026amp; Batchelor, T. T. Comprehensive approach to diagnosis and treatment of newly diagnosed primary CNS lymphoma. Neuro-oncology 2019; 21(3): 296-305.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrommes, C., Nayak, L., Tun, H. W. \u0026amp; Batchelor, T. T. Introduction of novel agents in the treatment of primary CNS lymphoma. Neuro-oncology 2019; 21(3): 306-313.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShankar, G. M. \u0026amp; Barker, F. G. 2 nd. Primary CNS lymphoma: the role of resection. Oncology (Williston Park) 2014; 28(7):637-8, 640, 642.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBellinzona, M., Roser, F., Ostertag, H., Gaab, R. M. \u0026amp; Saini, M. Surgical removal of primary central nervous system lymphomas (PCNSL) presenting as space occupying lesions: a series of 33 cases. European journal of surgical oncology 2005; 31(1): 100-5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBataille, B. \u003cem\u003eet al.\u003c/em\u003e Primary intracerebral malignant lymphoma: report of 248 cases. Journal of neurosurgery 2000; 92(2): 261-6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeller, M. \u003cem\u003eet al.\u003c/em\u003e Surgery for primary CNS lymphoma? Challenging a paradigm. Surgery for primary CNS lymphoma? Challenging a paradigm. Neuro-Oncology 2012; 14(12): 1481-1484.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVillalonga, J. F. \u003cem\u003eet al.\u003c/em\u003e The role of surgery in primary central nervous system lymphomas. Arquivos de neuro-psiquiatria 2018; 76(3): 139-144.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLabak, C. M. \u003cem\u003eet al.\u003c/em\u003e Surgical Resection for Primary Central Nervous System Lymphoma: A Systematic Review. World neurosurgery 2019; 126: e1436-e1448.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNelson, D. F. \u003cem\u003eet al.\u003c/em\u003e Non-Hodgkin's lymphoma of the brain: can high dose, large volume radiation therapy improve survival? Report on a prospective trial by the Radiation Therapy Oncology Group (RTOG): RTOG 8315. Int J Radiat Oncol Biol Phys. 1992; 23(1):9-17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGavrilovic, I. T., Hormigo, A., Yahalom, J., DeAngelis, L. M. \u0026amp; Abrey, L. E. Long-term follow-up of high-dose methotrexate-based therapy with and without whole brain irradiation for newly diagnosed primary CNS lymphoma. Journal of clinical oncology 2006; 24(28): 4570-4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrommes, C., DeAngelis, L. M., Primary, C. N. S. \u0026amp; Lymphoma Journal of clinical oncology 2017; 35(21): 2410-2418.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang, H., Xun, Y., Yang, A., Liu, F. \u0026amp; You, H. Advances and challenges in the treatment of primary central nervous system lymphoma. 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Journal of radiation research 2016; 57(2): 164-8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKobayashi, H. \u003cem\u003eet al.\u003c/em\u003e Long-Term Evaluation of Combination Treatment of Single Agent HD-MTX Chemotherapy up to Three Cycles and Moderate Dose Whole Brain Irradiation for Primary CNS Lymphoma. \u003cem\u003eJournal of chemotherapy (Florence, Italy)\u003c/em\u003e, \u003cb\u003e31\u003c/b\u003e (1), 35\u0026ndash;41 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdhikari, N. \u003cem\u003eet al.\u003c/em\u003e A prospective phase \u0026acirc;\u0026#133;\u0026iexcl; trial of response adapted whole brain radiotherapy after high dose methotrexate based chemotherapy in patients with newly diagnosed primary central nervous system lymphoma-analysis of acute toxicity profile and early clinical outcome. Journal of Neuro-Oncology 2018; 139(1): 153-166.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchlegel, U. \u0026amp; Korfel, A. Is whole-brain radiotherapy still a standard treatment for primary central nervous system lymphomas? Current opinion in neurology 2018; 31(6): 733-739.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurton, E. C. \u003cem\u003eet al.\u003c/em\u003e A Regional Multicenter Retrospective Analysis of Patients with Primary Central Nervous System Lymphoma Diagnosed from 2000-2012: Treatment Patterns and Clinical Outcomes. Cureus 2017; 9(7): e1512.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTABLE 1\u003c/strong\u003e | Patient and tumor characteristics of primary central nervous system lymphoma diagnosed in SEER 18 registries, 2000-2018.\u003c/p\u003e\n\u003ctable\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"29.6028880866426%\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.36823104693141%\"\u003e\n \u003cp\u003eNo. of patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.67509025270758%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.353790613718413%\"\u003e\n \u003cp\u003ePercentage (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"29.6028880866426%\"\u003e\n \u003cp\u003etotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.36823104693141%\"\u003e\n \u003cp\u003e5166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.67509025270758%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.353790613718413%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"29.6028880866426%\"\u003e\n \u003cp\u003eAge at diagnosis, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"24.36823104693141%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e63.1\u0026plusmn;14.9\u003c/p\u003e\n \u003cp\u003e65.0(3.0-97.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.67509025270758%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"26.353790613718413%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"60.07326007326007%\"\u003e\n \u003cp\u003eMean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.92673992673993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"60.07326007326007%\"\u003e\n \u003cp\u003eMedian(rang)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.92673992673993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"29.6028880866426%\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"24.36823104693141%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2679\u003c/p\u003e\n \u003cp\u003e2487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.67509025270758%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"26.353790613718413%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e51.9\u003c/p\u003e\n \u003cp\u003e48.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"60.07326007326007%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.92673992673993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"60.07326007326007%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.92673992673993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"29.6028880866426%\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"24.36823104693141%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4182\u003c/p\u003e\n \u003cp\u003e364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.67509025270758%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"26.353790613718413%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e81.0\u003c/p\u003e\n \u003cp\u003e7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"60.07326007326007%\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.92673992673993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"60.07326007326007%\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.92673992673993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"29.6028880866426%\"\u003e\n \u003cp\u003eOthers\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.36823104693141%\"\u003e\n \u003cp\u003e620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.67509025270758%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.353790613718413%\"\u003e\n \u003cp\u003e12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"29.6028880866426%\"\u003e\n \u003cp\u003eYears of diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" width=\"24.36823104693141%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2115\u003c/p\u003e\n \u003cp\u003e3051\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e916\u003c/p\u003e\n \u003cp\u003e884\u003c/p\u003e\n \u003cp\u003e1372\u003c/p\u003e\n \u003cp\u003e1415\u003c/p\u003e\n \u003cp\u003e579\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3045\u003c/p\u003e\n \u003cp\u003e2121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.67509025270758%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" width=\"26.353790613718413%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e40.9\u003c/p\u003e\n \u003cp\u003e59.1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e17.7\u003c/p\u003e\n \u003cp\u003e17.1\u003c/p\u003e\n \u003cp\u003e26.6\u003c/p\u003e\n \u003cp\u003e27.4\u003c/p\u003e\n \u003cp\u003e11.2\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e58.9\u003c/p\u003e\n \u003cp\u003e41.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"60.07326007326007%\"\u003e\n \u003cp\u003e2000-2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.92673992673993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"60.07326007326007%\"\u003e\n \u003cp\u003e2009-2018\u003c/p\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003cp\u003e\u0026lt;50\u003c/p\u003e\n \u003cp\u003e50-59\u003c/p\u003e\n \u003cp\u003e60-69\u003c/p\u003e\n \u003cp\u003e70-79\u003c/p\u003e\n \u003cp\u003e\u0026ge;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.92673992673993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"60.07326007326007%\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.92673992673993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"60.07326007326007%\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.92673992673993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"60.07326007326007%\"\u003e\n \u003cp\u003eSingleb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.92673992673993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"29.6028880866426%\"\u003e\n \u003cp\u003eLineage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"24.36823104693141%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4429\u003c/p\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.67509025270758%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"26.353790613718413%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e85.7\u003c/p\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"60.07326007326007%\"\u003e\n \u003cp\u003eAggressive B cell NHLc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.92673992673993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"60.07326007326007%\"\u003e\n \u003cp\u003eIndolent B cell NHLd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.92673992673993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"29.6028880866426%\"\u003e\n \u003cp\u003eT cell NHL\u003c/p\u003e\n \u003cp\u003eNHL-NOS\u003c/p\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" width=\"24.36823104693141%\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003cp\u003e474\u003c/p\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3040\u003c/p\u003e\n \u003cp\u003e2126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.67509025270758%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" width=\"26.353790613718413%\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003cp\u003e9.2\u003c/p\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e58.8\u003c/p\u003e\n \u003cp\u003e41.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"60.07326007326007%\"\u003e\n \u003cp\u003eSurgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.92673992673993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"60.07326007326007%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.92673992673993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"60.07326007326007%\"\u003e\n \u003cp\u003ePerformed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.92673992673993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"29.6028880866426%\"\u003e\n \u003cp\u003eRadiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"24.36823104693141%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3608\u003c/p\u003e\n \u003cp\u003e1558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.67509025270758%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"26.353790613718413%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e69.8\u003c/p\u003e\n \u003cp\u003e30.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"60.07326007326007%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.92673992673993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"60.07326007326007%\"\u003e\n \u003cp\u003ePerformed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.92673992673993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"29.6028880866426%\"\u003e\n \u003cp\u003eChemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"24.36823104693141%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1652\u003c/p\u003e\n \u003cp\u003e3514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.67509025270758%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" width=\"26.353790613718413%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e32.0\u003c/p\u003e\n \u003cp\u003e68.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"60.07326007326007%\"\u003e\n \u003cp\u003eNo/unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.92673992673993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"60.07326007326007%\"\u003e\n \u003cp\u003ePerformed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"39.92673992673993%\"\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\u003eNHL, non\u0026ndash;Hodgkin lymphoma; NOS, not otherwise specified.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eAmerican Indian/Alaskan Native or Asian/Pacific Islander.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003eIncluded\u0026nbsp;divorced/separated/widowed patients.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u003c/sup\u003eIncluded diffuse large B cell lymphoma, burkitt\u0026rsquo;s lymphoma, mantle cell lymphoma, and intravascular large B-cell lymphoma.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ed\u003c/sup\u003eIncluded follicular lymphoma, chronic lymphocytic leukemia/small lymphocytic lymphoma, lymphoplasmacytic lymphoma, and mucosa associated lymphoid tissue lymphoma.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 2\u003c/strong\u003e | Patient characteristics according to the histological subtypes\u003c/p\u003e\n\u003ctable\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"18.592057761732853%\"\u003e\n \u003cp\u003eHistology subtype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"13.898916967509026%\"\u003e\n \u003cp\u003en(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"16.60649819494585%\"\u003e\n \u003cp\u003eMedian age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003eMale(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" width=\"39.35018050541516%\"\u003e\n \u003cp\u003eSurvival\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"27.272727272727273%\"\u003e\n \u003cp\u003eMedian OS,m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.727272727272727%\"\u003e\n \u003cp\u003e3-year OS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"27.272727272727273%\"\u003e\n \u003cp\u003eMedian DSS,m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.727272727272727%\"\u003e\n \u003cp\u003e3-year DSS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.52517985611511%\"\u003e\n \u003cp\u003eAll patient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.848920863309353%\"\u003e\n \u003cp\u003e5166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.546762589928058%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.510791366906474%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.79136690647482%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.992805755395683%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.79136690647482%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.992805755395683%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.52517985611511%\"\u003e\n \u003cp\u003eDLBCL\u003c/p\u003e\n \u003cp\u003eNHL-NOS\u003c/p\u003e\n \u003cp\u003eFL\u003c/p\u003e\n \u003cp\u003ePTCL\u003c/p\u003e\n \u003cp\u003eMALT\u003c/p\u003e\n \u003cp\u003eBL\u003c/p\u003e\n \u003cp\u003eLPL\u003c/p\u003e\n \u003cp\u003eALCL\u003c/p\u003e\n \u003cp\u003eCLL/SLL\u003c/p\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.848920863309353%\"\u003e\n \u003cp\u003e4373(84.6)\u003c/p\u003e\n \u003cp\u003e474(9.2)\u003c/p\u003e\n \u003cp\u003e65(1.3)\u003c/p\u003e\n \u003cp\u003e63(1.2)\u003c/p\u003e\n \u003cp\u003e60(1.2)\u003c/p\u003e\n \u003cp\u003e34(0.7)\u003c/p\u003e\n \u003cp\u003e22(0.4)\u003c/p\u003e\n \u003cp\u003e21(0.4)\u003c/p\u003e\n \u003cp\u003e19(0.4)\u003c/p\u003e\n \u003cp\u003e35(0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.546762589928058%\"\u003e\n \u003cp\u003e66.0\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 65.0 \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 67.0\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 60.0\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 60.0\u003c/p\u003e\n \u003cp\u003e60.0\u003c/p\u003e\n \u003cp\u003e63.0\u003c/p\u003e\n \u003cp\u003e39.0\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 66.0\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 62.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.510791366906474%\"\u003e\n \u003cp\u003e51.8\u003c/p\u003e\n \u003cp\u003e54.0\u003c/p\u003e\n \u003cp\u003e41.5\u003c/p\u003e\n \u003cp\u003e57.1\u003c/p\u003e\n \u003cp\u003e31.7\u003c/p\u003e\n \u003cp\u003e64.7\u003c/p\u003e\n \u003cp\u003e45.5\u003c/p\u003e\n \u003cp\u003e71.4\u003c/p\u003e\n \u003cp\u003e47.4\u003c/p\u003e\n \u003cp\u003e54.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.79136690647482%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;/\u003c/p\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.992805755395683%\"\u003e\n \u003cp\u003e35.3\u003c/p\u003e\n \u003cp\u003e32.0\u003c/p\u003e\n \u003cp\u003e53.8\u003c/p\u003e\n \u003cp\u003e37.9\u003c/p\u003e\n \u003cp\u003e78.3\u003c/p\u003e\n \u003cp\u003e38.3\u003c/p\u003e\n \u003cp\u003e50.9\u003c/p\u003e\n \u003cp\u003e34.4\u003c/p\u003e\n \u003cp\u003e42.1\u003c/p\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.79136690647482%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;/\u003c/p\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.992805755395683%\"\u003e\n \u003cp\u003e40.5\u003c/p\u003e\n \u003cp\u003e37.3\u003c/p\u003e\n \u003cp\u003e57.4\u003c/p\u003e\n \u003cp\u003e47.3\u003c/p\u003e\n \u003cp\u003e86.0\u003c/p\u003e\n \u003cp\u003e42.3\u003c/p\u003e\n \u003cp\u003e50.9\u003c/p\u003e\n \u003cp\u003e37.2\u003c/p\u003e\n \u003cp\u003e54.0\u003c/p\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ctable\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003en, number of cases; m, month; OS, overall survival; DSS, disease-specific survival; NHL, non\u0026ndash;Hodgkin lymphoma; NOS, not otherwise specified; DLBCL, Diffuse large B-cell lymphoma; FL, Follicular lymphoma, BL, Burkitt\u0026rsquo;s lymphoma; CLL/SLL, Chronic lymphocytic leukemia/small lymphocytic lymphoma; MALT, Mucosal-associated lymphoid tissue. PTCL, Peripheral T-cell lymphoma, ALCL, Anaplastic large cell lymphoma. LPL, Lymphoplasmacytic lymphoma.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 3\u003c/strong\u003e |Multivariable Cox regression analysis of the independent prognostic factors for OS and DSS among PCNSL patients.\u003c/p\u003e\n\u003ctable\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"22.56317689530686%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" width=\"40.43321299638989%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall survival\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" width=\"37.00361010830325%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisease-specific survival\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" width=\"52.214452214452216%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;95% CI \u0026nbsp; \u0026nbsp; \u0026nbsp;P\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" width=\"47.785547785547784%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR \u0026nbsp; \u0026nbsp; \u0026nbsp; 95% CI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; P\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.56317689530686%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.16245487364621%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.815884476534295%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.71841155234657%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.469314079422382%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.56317689530686%\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.16245487364621%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.815884476534295%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.71841155234657%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.469314079422382%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.56317689530686%\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.16245487364621%\"\u003e\n \u003cp\u003e1.06-1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.815884476534295%\"\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.71841155234657%\"\u003e\n \u003cp\u003e1.08-1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.469314079422382%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.56317689530686%\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.16245487364621%\"\u003e\n \u003cp\u003e0.83-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.815884476534295%\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.71841155234657%\"\u003e\n \u003cp\u003e0.81-1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.469314079422382%\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.56317689530686%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.16245487364621%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.815884476534295%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.71841155234657%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.469314079422382%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.56317689530686%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.16245487364621%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.815884476534295%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.71841155234657%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.469314079422382%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.56317689530686%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.16245487364621%\"\u003e\n \u003cp\u003e0.78-0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.815884476534295%\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.71841155234657%\"\u003e\n \u003cp\u003e0.80-0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.469314079422382%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.56317689530686%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.16245487364621%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.815884476534295%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.71841155234657%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.469314079422382%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.56317689530686%\"\u003e\n \u003cp\u003e\u0026lt;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.16245487364621%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.815884476534295%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.71841155234657%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.469314079422382%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.56317689530686%\"\u003e\n \u003cp\u003e50-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.16245487364621%\"\u003e\n \u003cp\u003e1.31-1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.815884476534295%\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.71841155234657%\"\u003e\n \u003cp\u003e1.16-1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.469314079422382%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.56317689530686%\"\u003e\n \u003cp\u003e60-69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.16245487364621%\"\u003e\n \u003cp\u003e1.70-2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.815884476534295%\"\u003e\n \u003cp\u003e1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.71841155234657%\"\u003e\n \u003cp\u003e1.50-1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.469314079422382%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.56317689530686%\"\u003e\n \u003cp\u003e70-79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.16245487364621%\"\u003e\n \u003cp\u003e2.37-2.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.815884476534295%\"\u003e\n \u003cp\u003e2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.71841155234657%\"\u003e\n \u003cp\u003e2.03-2.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.469314079422382%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.56317689530686%\"\u003e\n \u003cp\u003e\u0026ge;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e3.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.16245487364621%\"\u003e\n \u003cp\u003e2.91-3.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.815884476534295%\"\u003e\n \u003cp\u003e2.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.71841155234657%\"\u003e\n \u003cp\u003e2.39-3.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.469314079422382%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.56317689530686%\"\u003e\n \u003cp\u003e\u003cstrong\u003eYears of diagnosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.16245487364621%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.815884476534295%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.71841155234657%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.469314079422382%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.56317689530686%\"\u003e\n \u003cp\u003e2000-2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.16245487364621%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.815884476534295%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.71841155234657%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.469314079422382%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.56317689530686%\"\u003e\n \u003cp\u003e2009-2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.16245487364621%\"\u003e\n \u003cp\u003e0.74-0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.815884476534295%\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.71841155234657%\"\u003e\n \u003cp\u003e0.72-0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.469314079422382%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.56317689530686%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.16245487364621%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.815884476534295%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.71841155234657%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.469314079422382%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.56317689530686%\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.16245487364621%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.815884476534295%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.71841155234657%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.469314079422382%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.56317689530686%\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.16245487364621%\"\u003e\n \u003cp\u003e1.13-1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.815884476534295%\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.71841155234657%\"\u003e\n \u003cp\u003e1.13-1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.469314079422382%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.56317689530686%\"\u003e\n \u003cp\u003e\u003cstrong\u003eClassification\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.16245487364621%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.815884476534295%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.71841155234657%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.469314079422382%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.56317689530686%\"\u003e\n \u003cp\u003eAggressive B cell NHL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.16245487364621%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.815884476534295%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.71841155234657%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.469314079422382%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.56317689530686%\"\u003e\n \u003cp\u003eIndolent B cell NHL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.16245487364621%\"\u003e\n \u003cp\u003e0.27-0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.815884476534295%\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.71841155234657%\"\u003e\n \u003cp\u003e0.24-0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.469314079422382%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.56317689530686%\"\u003e\n \u003cp\u003eT cell NHL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.16245487364621%\"\u003e\n \u003cp\u003e0.63-1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.815884476534295%\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.71841155234657%\"\u003e\n \u003cp\u003e0.59-1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.469314079422382%\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.56317689530686%\"\u003e\n \u003cp\u003eNHL-NOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.16245487364621%\"\u003e\n \u003cp\u003e0.86-1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e0.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.815884476534295%\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.71841155234657%\"\u003e\n \u003cp\u003e0.85-1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.469314079422382%\"\u003e\n \u003cp\u003e0.432\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.56317689530686%\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.16245487364621%\"\u003e\n \u003cp\u003e0.52-2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e0.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.815884476534295%\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.71841155234657%\"\u003e\n \u003cp\u003e0.49-2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.469314079422382%\"\u003e\n \u003cp\u003e0.838\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.56317689530686%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgery\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.16245487364621%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.815884476534295%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.71841155234657%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.469314079422382%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.56317689530686%\"\u003e\n \u003cp\u003ePerformed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.16245487364621%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.815884476534295%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.71841155234657%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.469314079422382%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.56317689530686%\"\u003e\n \u003cp\u003eNo/unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.16245487364621%\"\u003e\n \u003cp\u003e1.25-1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.815884476534295%\"\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.71841155234657%\"\u003e\n \u003cp\u003e1.26-1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.469314079422382%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.56317689530686%\"\u003e\n \u003cp\u003e\u003cstrong\u003eChemotherapy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.16245487364621%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.815884476534295%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.71841155234657%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.469314079422382%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.56317689530686%\"\u003e\n \u003cp\u003ePerformed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.16245487364621%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.815884476534295%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.71841155234657%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.469314079422382%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.56317689530686%\"\u003e\n \u003cp\u003eNo/unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e2.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.16245487364621%\"\u003e\n \u003cp\u003e2.53-2.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.815884476534295%\"\u003e\n \u003cp\u003e2.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.71841155234657%\"\u003e\n \u003cp\u003e2.39-2.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.469314079422382%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.56317689530686%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRadiation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.16245487364621%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.815884476534295%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.71841155234657%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.469314079422382%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.56317689530686%\"\u003e\n \u003cp\u003ePerformed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.16245487364621%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.815884476534295%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.71841155234657%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.469314079422382%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.56317689530686%\"\u003e\n \u003cp\u003eNo/unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.16245487364621%\"\u003e\n \u003cp\u003e1.04-1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.815884476534295%\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.71841155234657%\"\u003e\n \u003cp\u003e1.00-1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.469314079422382%\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 4\u003c/strong\u003e | Univariate and multivariate Cox regression analysis of each factor\u0026rsquo;s ability in predicting OS and DSS among DLBCL patients.\u003c/p\u003e\n\u003ctable\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" data-old-width=\"39.586919104991395\" valign=\"top\" width=\"39.85507246376812%\"\u003e\n \u003cp\u003eOverall Survival\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" data-old-width=\"34.76764199655766\" valign=\"top\" width=\"34.60144927536232%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Disease-Specific Survival\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"53.5279805352798%\"\u003e\n \u003cp\u003eHR \u0026nbsp; \u0026nbsp; \u0026nbsp; 95% CI \u0026nbsp; \u0026nbsp; \u0026nbsp;P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" data-old-width=\"34.76764199655766\" valign=\"top\" width=\"46.4720194647202%\"\u003e\n \u003cp\u003eHR \u0026nbsp; \u0026nbsp; \u0026nbsp;95% CI \u0026nbsp; \u0026nbsp; P\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd data-old-width=\"22.030981067125648\" valign=\"top\" width=\"22.20216606498195%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUNIVARIATE ANALYSES\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.876075731497417\" valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"16.179001721170398\" valign=\"top\" width=\"16.24548736462094%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.53184165232358\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"9.81067125645439\" valign=\"top\" width=\"9.747292418772563%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"13.253012048192772\" valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.703958691910499\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd data-old-width=\"22.030981067125648\" valign=\"top\" width=\"22.20216606498195%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.876075731497417\" valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"16.179001721170398\" valign=\"top\" width=\"16.24548736462094%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.53184165232358\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"9.81067125645439\" valign=\"top\" width=\"9.747292418772563%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"13.253012048192772\" valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.703958691910499\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd data-old-width=\"22.030981067125648\" valign=\"top\" width=\"22.20216606498195%\"\u003e\n \u003cp\u003eWhite vs Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.876075731497417\" valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"16.179001721170398\" valign=\"top\" width=\"16.24548736462094%\"\u003e\n \u003cp\u003e1.00-1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.53184165232358\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"9.81067125645439\" valign=\"top\" width=\"9.747292418772563%\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"13.253012048192772\" valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e1.04-1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.703958691910499\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd data-old-width=\"22.030981067125648\" valign=\"top\" width=\"22.20216606498195%\"\u003e\n \u003cp\u003eWhite vs Others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.876075731497417\" valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"16.179001721170398\" valign=\"top\" width=\"16.24548736462094%\"\u003e\n \u003cp\u003e0.76-0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.53184165232358\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"9.81067125645439\" valign=\"top\" width=\"9.747292418772563%\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"13.253012048192772\" valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e0.73-0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.703958691910499\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd data-old-width=\"22.030981067125648\" valign=\"top\" width=\"22.20216606498195%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.876075731497417\" valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"16.179001721170398\" valign=\"top\" width=\"16.24548736462094%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.53184165232358\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"9.81067125645439\" valign=\"top\" width=\"9.747292418772563%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"13.253012048192772\" valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.703958691910499\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd data-old-width=\"22.030981067125648\" valign=\"top\" width=\"22.20216606498195%\"\u003e\n \u003cp\u003eMale vs Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.876075731497417\" valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"16.179001721170398\" valign=\"top\" width=\"16.24548736462094%\"\u003e\n \u003cp\u003e0.86-0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.53184165232358\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"9.81067125645439\" valign=\"top\" width=\"9.747292418772563%\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"13.253012048192772\" valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e0.86-1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.703958691910499\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd data-old-width=\"22.030981067125648\" valign=\"top\" width=\"22.20216606498195%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.876075731497417\" valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"16.179001721170398\" valign=\"top\" width=\"16.24548736462094%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.53184165232358\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"9.81067125645439\" valign=\"top\" width=\"9.747292418772563%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"13.253012048192772\" valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.703958691910499\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd data-old-width=\"22.030981067125648\" valign=\"top\" width=\"22.20216606498195%\"\u003e\n \u003cp\u003e0-50 vs 50-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.876075731497417\" valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"16.179001721170398\" valign=\"top\" width=\"16.24548736462094%\"\u003e\n \u003cp\u003e0.92-1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.53184165232358\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e0.453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"9.81067125645439\" valign=\"top\" width=\"9.747292418772563%\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"13.253012048192772\" valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e0.82-1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.703958691910499\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e0.342\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd data-old-width=\"22.030981067125648\" valign=\"top\" width=\"22.20216606498195%\"\u003e\n \u003cp\u003e0-50 vs 60-69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.876075731497417\" valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"16.179001721170398\" valign=\"top\" width=\"16.24548736462094%\"\u003e\n \u003cp\u003e1.23-1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.53184165232358\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"9.81067125645439\" valign=\"top\" width=\"9.747292418772563%\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"13.253012048192772\" valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e1.08-1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.703958691910499\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd data-old-width=\"22.030981067125648\" valign=\"top\" width=\"22.20216606498195%\"\u003e\n \u003cp\u003e0-50 vs 70-79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.876075731497417\" valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"16.179001721170398\" valign=\"top\" width=\"16.24548736462094%\"\u003e\n \u003cp\u003e1.96-2.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.53184165232358\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"9.81067125645439\" valign=\"top\" width=\"9.747292418772563%\"\u003e\n \u003cp\u003e1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"13.253012048192772\" valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e1.68-2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.703958691910499\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd data-old-width=\"22.030981067125648\" valign=\"top\" width=\"22.20216606498195%\"\u003e\n \u003cp\u003e0-50 vs\u0026nbsp;\u0026ge;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.876075731497417\" valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e3.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"16.179001721170398\" valign=\"top\" width=\"16.24548736462094%\"\u003e\n \u003cp\u003e2.83-3.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.53184165232358\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"9.81067125645439\" valign=\"top\" width=\"9.747292418772563%\"\u003e\n \u003cp\u003e2.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"13.253012048192772\" valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e2.35-3.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.703958691910499\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd data-old-width=\"22.030981067125648\" valign=\"top\" width=\"22.20216606498195%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.876075731497417\" valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"16.179001721170398\" valign=\"top\" width=\"16.24548736462094%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.53184165232358\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"9.81067125645439\" valign=\"top\" width=\"9.747292418772563%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"13.253012048192772\" valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.703958691910499\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd data-old-width=\"22.030981067125648\" valign=\"top\" width=\"22.20216606498195%\"\u003e\n \u003cp\u003eMarried vs\u003c/p\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.876075731497417\" valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"16.179001721170398\" valign=\"top\" width=\"16.24548736462094%\"\u003e\n \u003cp\u003e1.14-1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.53184165232358\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"9.81067125645439\" valign=\"top\" width=\"9.747292418772563%\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"13.253012048192772\" valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e1.18-1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.703958691910499\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd data-old-width=\"22.030981067125648\" valign=\"top\" width=\"22.20216606498195%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgery\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.876075731497417\" valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"16.179001721170398\" valign=\"top\" width=\"16.24548736462094%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.53184165232358\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"9.81067125645439\" valign=\"top\" width=\"9.747292418772563%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"13.253012048192772\" valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.703958691910499\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd data-old-width=\"22.030981067125648\" valign=\"top\" width=\"22.20216606498195%\"\u003e\n \u003cp\u003ePerformed vs\u003c/p\u003e\n \u003cp\u003eNo/unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.876075731497417\" valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"16.179001721170398\" valign=\"top\" width=\"16.24548736462094%\"\u003e\n \u003cp\u003e1.18-1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.53184165232358\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"9.81067125645439\" valign=\"top\" width=\"9.747292418772563%\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"13.253012048192772\" valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e1.21-1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.703958691910499\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd data-old-width=\"22.030981067125648\" valign=\"top\" width=\"22.20216606498195%\"\u003e\n \u003cp\u003e\u003cstrong\u003eChemotherapy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.876075731497417\" valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"16.179001721170398\" valign=\"top\" width=\"16.24548736462094%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.53184165232358\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"9.81067125645439\" valign=\"top\" width=\"9.747292418772563%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"13.253012048192772\" valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.703958691910499\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd data-old-width=\"22.030981067125648\" valign=\"top\" width=\"22.20216606498195%\"\u003e\n \u003cp\u003ePerformed vs\u003c/p\u003e\n \u003cp\u003eNo/unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.876075731497417\" valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e3.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"16.179001721170398\" valign=\"top\" width=\"16.24548736462094%\"\u003e\n \u003cp\u003e2.95-3.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.53184165232358\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"9.81067125645439\" valign=\"top\" width=\"9.747292418772563%\"\u003e\n \u003cp\u003e3.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"13.253012048192772\" valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e2.80-3.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.703958691910499\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd data-old-width=\"22.030981067125648\" valign=\"top\" width=\"22.20216606498195%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRadiation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.876075731497417\" valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"16.179001721170398\" valign=\"top\" width=\"16.24548736462094%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.53184165232358\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"9.81067125645439\" valign=\"top\" width=\"9.747292418772563%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"13.253012048192772\" valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.703958691910499\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd data-old-width=\"22.030981067125648\" valign=\"top\" width=\"22.20216606498195%\"\u003e\n \u003cp\u003ePerformed vs\u003c/p\u003e\n \u003cp\u003eNo/unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.876075731497417\" valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"16.179001721170398\" valign=\"top\" width=\"16.24548736462094%\"\u003e\n \u003cp\u003e0.77-0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.53184165232358\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"9.81067125645439\" valign=\"top\" width=\"9.747292418772563%\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"13.253012048192772\" valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e0.74-0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.703958691910499\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd data-old-width=\"22.030981067125648\" valign=\"top\" width=\"22.20216606498195%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMULTIVARIATE ANALYSES\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.876075731497417\" valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"16.179001721170398\" valign=\"top\" width=\"16.24548736462094%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.53184165232358\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"9.81067125645439\" valign=\"top\" width=\"9.747292418772563%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"13.253012048192772\" valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.703958691910499\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd data-old-width=\"22.030981067125648\" valign=\"top\" width=\"22.20216606498195%\"\u003e\n \u003cp\u003eWhite vs Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.876075731497417\" valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"16.179001721170398\" valign=\"top\" width=\"16.24548736462094%\"\u003e\n \u003cp\u003e1.01-1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.53184165232358\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"9.81067125645439\" valign=\"top\" width=\"9.747292418772563%\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"13.253012048192772\" valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e1.00-1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.703958691910499\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd data-old-width=\"22.030981067125648\" valign=\"top\" width=\"22.20216606498195%\"\u003e\n \u003cp\u003eWhite vs Others\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMale vs Female\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0-50 vs 50-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.876075731497417\" valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"16.179001721170398\" valign=\"top\" width=\"16.24548736462094%\"\u003e\n \u003cp\u003e0.79-0.99\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.78-0.90\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.18-1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.53184165232358\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"9.81067125645439\" valign=\"top\" width=\"9.747292418772563%\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"13.253012048192772\" valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e0.76-0.97\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.79-0.93\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.04-1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.703958691910499\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd data-old-width=\"22.030981067125648\" valign=\"top\" width=\"22.20216606498195%\"\u003e\n \u003cp\u003e0-50 vs 60-69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.876075731497417\" valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"16.179001721170398\" valign=\"top\" width=\"16.24548736462094%\"\u003e\n \u003cp\u003e1.54-1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.53184165232358\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"9.81067125645439\" valign=\"top\" width=\"9.747292418772563%\"\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"13.253012048192772\" valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e1.35-1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.703958691910499\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd data-old-width=\"22.030981067125648\" valign=\"top\" width=\"22.20216606498195%\"\u003e\n \u003cp\u003e0-50 vs 70-79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.876075731497417\" valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"16.179001721170398\" valign=\"top\" width=\"16.24548736462094%\"\u003e\n \u003cp\u003e2.16-2.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.53184165232358\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"9.81067125645439\" valign=\"top\" width=\"9.747292418772563%\"\u003e\n \u003cp\u003e2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"13.253012048192772\" valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e1.85-2.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.703958691910499\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd data-old-width=\"22.030981067125648\" valign=\"top\" width=\"22.20216606498195%\"\u003e\n \u003cp\u003e0-50 vs\u0026nbsp;\u0026ge;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.876075731497417\" valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e3.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"16.179001721170398\" valign=\"top\" width=\"16.24548736462094%\"\u003e\n \u003cp\u003e2.63-3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.53184165232358\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"9.81067125645439\" valign=\"top\" width=\"9.747292418772563%\"\u003e\n \u003cp\u003e2.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"13.253012048192772\" valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e2.18-2.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.703958691910499\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd data-old-width=\"22.030981067125648\" valign=\"top\" width=\"22.20216606498195%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.876075731497417\" valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"16.179001721170398\" valign=\"top\" width=\"16.24548736462094%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.53184165232358\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"9.81067125645439\" valign=\"top\" width=\"9.747292418772563%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"13.253012048192772\" valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.703958691910499\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd data-old-width=\"22.030981067125648\" valign=\"top\" width=\"22.20216606498195%\"\u003e\n \u003cp\u003eMarried vs\u003c/p\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.876075731497417\" valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"16.179001721170398\" valign=\"top\" width=\"16.24548736462094%\"\u003e\n \u003cp\u003e1.12-1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.53184165232358\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"9.81067125645439\" valign=\"top\" width=\"9.747292418772563%\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"13.253012048192772\" valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e1.13-1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.703958691910499\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd data-old-width=\"22.030981067125648\" valign=\"top\" width=\"22.20216606498195%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgery\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.876075731497417\" valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"16.179001721170398\" valign=\"top\" width=\"16.24548736462094%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.53184165232358\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"9.81067125645439\" valign=\"top\" width=\"9.747292418772563%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"13.253012048192772\" valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.703958691910499\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd data-old-width=\"22.030981067125648\" valign=\"top\" width=\"22.20216606498195%\"\u003e\n \u003cp\u003ePerformed vs\u003c/p\u003e\n \u003cp\u003eNo/unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.876075731497417\" valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"16.179001721170398\" valign=\"top\" width=\"16.24548736462094%\"\u003e\n \u003cp\u003e1.22-1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.53184165232358\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"9.81067125645439\" valign=\"top\" width=\"9.747292418772563%\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"13.253012048192772\" valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e1.24-1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.703958691910499\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd data-old-width=\"22.030981067125648\" valign=\"top\" width=\"22.20216606498195%\"\u003e\n \u003cp\u003e\u003cstrong\u003eChemotherapy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.876075731497417\" valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"16.179001721170398\" valign=\"top\" width=\"16.24548736462094%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.53184165232358\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"9.81067125645439\" valign=\"top\" width=\"9.747292418772563%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"13.253012048192772\" valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.703958691910499\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd data-old-width=\"22.030981067125648\" valign=\"top\" width=\"22.20216606498195%\"\u003e\n \u003cp\u003ePerformed vs\u003c/p\u003e\n \u003cp\u003eNo/unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.876075731497417\" valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e2.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"16.179001721170398\" valign=\"top\" width=\"16.24548736462094%\"\u003e\n \u003cp\u003e2.74-3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.53184165232358\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"9.81067125645439\" valign=\"top\" width=\"9.747292418772563%\"\u003e\n \u003cp\u003e2.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"13.253012048192772\" valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e2.56-3.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.703958691910499\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd data-old-width=\"22.030981067125648\" valign=\"top\" width=\"22.20216606498195%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRadiation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.876075731497417\" valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"16.179001721170398\" valign=\"top\" width=\"16.24548736462094%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.53184165232358\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"9.81067125645439\" valign=\"top\" width=\"9.747292418772563%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"13.253012048192772\" valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.703958691910499\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd data-old-width=\"22.030981067125648\" valign=\"top\" width=\"22.20216606498195%\"\u003e\n \u003cp\u003ePerformed vs\u003c/p\u003e\n \u003cp\u003eNo/unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.876075731497417\" valign=\"top\" width=\"11.913357400722022%\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"16.179001721170398\" valign=\"top\" width=\"16.24548736462094%\"\u003e\n \u003cp\u003e1.00-1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.53184165232358\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"9.81067125645439\" valign=\"top\" width=\"9.747292418772563%\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"13.253012048192772\" valign=\"top\" width=\"13.35740072202166%\"\u003e\n \u003cp\u003e0.95-1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd data-old-width=\"11.703958691910499\" valign=\"top\" width=\"11.552346570397113%\"\u003e\n \u003cp\u003e0.394\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Primary Central Nervous System Lymphoma, SEER, Treatment, Prognosis, Nomogram","lastPublishedDoi":"10.21203/rs.3.rs-955053/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-955053/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e To study the clinical characteristics, prognostic factors and treatment outcomes in patients with primary central nervous system lymphoma (PCNSL).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMaterials and Methods:\u003c/strong\u003e The data of total 5166 PCNSL patients diagnosed between 2000 and 2018 from the Surveillance, Epidemiology, and End Results (SEER) database was obtained.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The mean age was 63.1±14.9 years, with a male to female of 1.1:1.0. The most common histologic subtype was diffuse large B-cell lymphoma (DLBCL) (84.6%). The 1-, 3-, and 5-year OS were 50.1, 36.0 and 27.2% and corresponding to DSS were 54.4, 41.3 and 33.5%, respectively. Multivariate analysis with Cox regression showed that race, sex, age, marital status, surgery, chemotherapy and radiotherapy were independent prognostic factors for OS, but radiotherapy no longer for DSS. Nomograms specially for DLBCL were established to predict the possibility of OS and DSS. The concordance index (C-index) of OS and DSS were 0.704 (95% CI 0.687-0.721) and 0.698 (95% CI 0.679-0.717), suggesting the high discrimination ability of the nomograms.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Surgery or/and chemotherapy was favourably associated with better OS and DSS. However, radiotherapy did not benefit to OS and DSS in the long-term. A new predictive nomogram and a web-based survival rate calculator we developed showed favorable applicability and accuracy to predict the long-term OS for DLBCL patients specifically.\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e","manuscriptTitle":"Epidemiologic Characteristics, Prognostic Factors and Treatment Outcomes in Primary Central Nervous System Lymphoma: A SEER-Based Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-10-11 19:03:27","doi":"10.21203/rs.3.rs-955053/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":"d4942228-9573-4449-9f1e-9758d717d27e","owner":[],"postedDate":"October 11th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":7732617,"name":"Cancer Biology"}],"tags":[],"updatedAt":"2022-02-10T10:48:27+00:00","versionOfRecord":{"articleIdentity":"rs-955053","link":"https://doi.org/10.3389/fonc.2022.817043","journal":{"identity":"frontiers-in-oncology","isVorOnly":true,"title":"Frontiers in Oncology"},"publishedOn":"2022-02-10 10:48:27","publishedOnDateReadable":"February 10th, 2022"},"versionCreatedAt":"2021-10-11 19:03:27","video":"","vorDoi":"10.3389/fonc.2022.817043","vorDoiUrl":"https://doi.org/10.3389/fonc.2022.817043","workflowStages":[]},"version":"v1","identity":"rs-955053","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-955053","identity":"rs-955053","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}