Competitive risk analysis of prognosis in patients with primary uveal melanoma

Competitive risk analysis of prognosis in patients with primary uveal melanoma | 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 Competitive risk analysis of prognosis in patients with primary uveal melanoma Ruisheng Huang, Jian Chen, Limin Lin, Jun Lyu, Qing Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5963224/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background The presence of competing risks suggests that the classic Cox proportional hazards model may yield biased results when assessing prognostic factors for primary uveal melanoma (PUM) patients. Objective The aim of this research is to utilize a competing risk model using the Surveillance, Epidemiology, and End Results (SEER) database in order to discover predictive factors for individuals with PUM and contrast them with the conventional Cox proportional hazards model. Methods We collected information on individuals who were diagnosed with PUM and registered in the SEER database from 2010 to 2015. The univariate analysis involved the application of the cumulative incidence function and Gray's test, while a multivariate analysis was conducted using the Fine-Gray, cause-specific (CS) and Cox proportional hazards models. Results Among the 1712 eligible patients diagnosed with PUM, 631 individuals passed away: 400 due to PUM and 231 from other causes. One-way Gray’s test indicated that seven variables significantly influenced the survival prognosis of PUM patients ( P < 0.05). Multivariate competing risk models indicated that age, race, histologic type, AJCC stage, surgery and chemotherapy were independent predictors for cause-specific survival of primary uveal melanoma competitive risk prognosis analysis Figures Figure 1 Figure 2 Figure 3 Introduction Melanoma is a common malignancy that has high disability and mortality. Melanoma can occur in many parts of the body, and the eye is one of them. Among adults, primary uveal melanoma (PUM) stands out as the prevailing primary intraocular tumo[ 1 , 2 ]. Common incidence sites of PUM include the choroid, ciliary body and iris[ 1 , 2 ]. Annually, uveal melanoma affects around 5 to 10 individuals per million globally[ 3 ]. Regrettably, the condition leads to metastatic disease in more than one-third of PUM patients, leading to a median survival time of less than twelve months[ 4 , 5 ]. In the majority of cases, a comprehensive examination of both the front and back parts of the eye using various imaging techniques such as ultrasound can lead to the diagnosis of PUM[ 6 , 7 ]. Through these multimodal imaging techniques, the early detection of PUM, which can significantly improve the survival rate of patients[ 6 , 7 ]. Treatment for PUM mainly includes enucleation, surgical local excision, radiotherapy and chemotherapy. In general, there has been a notable transition from enucleation and local tumor resection to the utilization of radiotherapy that preserves vision[ 8 ]. At present, there are more treatment methods for PUM, including targeted therapy, immunotherapy, etc., but the survival rate is still not significantly improved[ 9 ]. Therefore, many scholars have conducted extensive and thorough research on the diagnosis, treatment and prognostic survival of PUM. The Cox proportional hazards model is more commonly used to identify prognostic risk factors, survival analysis and predictive models for PUM. In the present era where personalized cancer treatments are given more importance, it is critical to assess the impact of cancer-related and other factors on patient mortality. For cancer patients, cancer is not the only cause of death, accidents and other diseases are also the main causes of death. When looking at factors that affect the prognosis of cancer patients, other factors that contribute to patient mortality are often considered competing risk events. In this case, multiple endpoints coexist and compete with each other, leading to the existence of competition risks[ 10 – 12 ]. The presence of multiple end events introduces bias in single end point analyses of estimated event probabilities due to competitive risks[ 13 , 14 ]. For this purpose, employing a competitive risk model for assessing the impact of risk factors on the prognosis of PUM patients and comparing outcomes with conventional survival analysis techniques can offer enhanced insights into uncovering the genuine influence of variables and accurately identifying relevant risk factors. This study employed data from the Surveillance, Epidemiology, and End Results (SEER) database to perform a competing risk analysis on individuals diagnosed with PUM. The main aim was to compare the results obtained from the Cox model with those derived from the competing risk model. This study aims to identify factors that have a more precise impact on PUM outcomes and better guide clinical decision making. Materials and Methods Data source and patient selection The SEER program, which was initiated by the National Cancer Institute, aimed at gathering and disseminating data on cancer occurrence and survival rate[ 15 ]. It encompassed a population-based database covering around 35% of the American population[ 15 , 16 ]. The SEER database, consisting of 17 separate registries, was utilized for patient selection in our study conducted between 2010 and 2015. The patients were chosen using the SEER*Stat software version 8.4.4 as per our application requirements[ 17 ]. The selection of individuals under investigation was based on the utilization of codes 8720–8790 from the third edition of the international classification of disease for oncology (ICD-O-3), with a specific focus on cases pertaining to malignant melanoma. The primary tumor site of patients was included C69.3 and C69.4. Inclusion criteria also included the identified 7th American Joint Committee on Cancer (AJCC) stage and that the uveal melanoma must be primary. The patients were excluded based on the following criteria: (1)unknown race(n = 15); (2)survival months < 1month(n = 5). The process of selecting cases can be observed in Fig. 1 , which displays the flow chart. Variable selection This study gathered the following variables: diagnosis of age, sex, race, marital status, histologic type, AJCC stage, surgery, radiotherapy, chemotherapy, SEER cause-specific death classification, SEER other cause of death classification, vital status recode (study cutoff used), survival months and patient ID. The optimal age cutoff was calculated by the X-tile software (v3.6.1, Yale University, USA). As such, age at diagnosis was classified into two groups: ≤60 years and ≥ 61 years (Fig. 2 ). The approach employed for incorporating histologic types of patients with PUM is founded on the criterion established by Liu et al and cao et al[ 17 , 18 ]. Therefore we categorized spindle melanoma, epithelioid melanoma and mixed melanoma into three groups separately. Additionally NOS melanoma and other types of melanomas were merged into a unified group. The AJCC staging system from 2010 to 2015 was utilized for determining the 7th AJCC stage in this study. We designated cause-specific death and other cause of death as variables for the outcome[ 17 ]. Cancer-specific deaths, competing events, and missing data were collected for all patients, based on records of cause-specific death classifications and other death classifications in the SEER database. Using these criteria, a total of 1712 patients were included in the study. Statistical Analyses Categorical variables were represented using frequencies and percentages, while the Chi-square test was employed to assess the variation in cause of death across different groups. The cumulative incidence function (CIF) was employed to determine the likelihood of cause-specific mortality in the presence of a competing risk event. Univariate Gray's test was performed to assess variations between sub-groups[ 19 ]. Relevant variables with statistical significance in univariate analyses were chosen for subsequent multivariate analyses, along with those deemed clinically significant. Multivariate analyses were performed using a COX proportional hazards model, a Fine-Gray subdistribution hazard model, and a cause-specific hazard (CS) model[ 14 , 20 , 21 ].Comparisons among these three models were conducted. All statistical analyses were conducted using the R 4.4.0 software package. A significance level of P < 0.05 was considered statistically significant. Results Patient Characteristics The 1712 eligible patients were diagnosed with PUM. There were 400(23.37%) patients died from PUM, 231(13.49%)patients died from other causes and 1081(63.14%) patients being alive. The majority of PUM patients who died were older than 61 years (n = 221, 55.25%), male (n = 209, 52.25%), white (n = 384, 96.00%), married (n = 241, 60.25%), NOS/Other (histologic type) (n = 295, 73.75%), AJCC stage II (n = 185, 46.25%), no surgery (n = 226, 56.50%), radiotherapy (n = 250, 62.50%), and no chemotherapy (n = 370, 92.50%). Baseline characteristics of the patients and more data are presented in Table 1 . Table 1. Baseline characteristics of patients. Variable All patients (%) Concerned (%) Competition (%) Censored (%) N umber 1712 400 231 1081 Age ≤60years 839(49.00) 179(44.75) 37(16.02) 623(57.63) ≥61years 873(51.00) 221(55.25) 194(83.98) 458(42.37) Sex Male 914(53.39) 209(52.25) 139(60.17) 566(52.36) Female 798(46.61) 191(47.75) 92(39.83) 515(47.64) Race White 1668(97.43) 384(96.00) 227(98.27) 1057(97.78) Black 15(0.88) 2(0.50) 1(0.43) 12(1.11) Other 29(1.69) 14(3.50) 3(1.30) 12(1.11) Marital status Married 1033(60.34) 241(60.25) 117(50.65) 675(62.44) Single 259(15.13) 52(13.00) 44(19.05) 163(15.08) Other/Unknown 420(24.53) 107(26.75) 70(30.30) 243(22.48) Histologic t ype Spindle 153(8.94) 25(6.25) 24(10.39) 104(9.62) Epithelioid 45(2.63) 22(5.50) 7(3.03) 16(1.48) Mixed 129(7.53) 58(14.50) 18(7.79) 53(4.90) NOS/Other 1385(80.80) 295(73.75) 182(78.79) 908(84.00) AJCC s tage I 515(30.08) 40(10.00) 64(27.70) 411(38.02) II 831(48.54) 185(46.25) 120(51.95) 526(48.66) III-IV 366(21.38) 175(43.75) 47(20.35) 144(13.32) Surgery No/Unknown 1253(73.19) 226(56.50) 157(67.97) 870(80.48) Yes 459(26.81) 174(43.50) 74(32.03) 211(19.52) R adiotherapy No/Unknown 400(23.36) 150(37.50) 77(33.33) 173(16.00) Yes 1312(76.64) 250(62.50) 154(66.67) 908(84.00) Chemotherapy No/Unknown 1658(96.85) 370(92.50) 230(99.57) 1058(97.87) Yes 54(3.15) 30(7.50) 1(0.43) 23(2.13) Abbreviations: AJCC, American Joint Committee on Cancer. Results of the Univariate Analysis In the univariate analyses, Fine-Gray’s test and CIF were applied. In the presence of competing risks, Gray’s test results indicated that seven factors significantly influenced the prognosis of PUM (P < 0.05), including diagnosis of age, race, histologic type, AJCC stage, surgery, radiotherapy and chemotherapy. The details on univariate Gray's test and 36-, 60-, and 96-months cumulative incidence are presented in Table 2 . The CIF curve of the seven selected variables is shown in Fig. 3 . Table 2 Univariate analysis of prognostic factors in patients with primary uveal melanoma. Variable Gray’s test p -value CIF 36-months 60-months 96-months Age 4.48 0.034* ≤ 60years 0.086 0.141 0.218 ≥ 61years 0.133 0.209 0.257 Sex 0.11 0.744 Male 0.124 0.184 0.234 Female 0.095 0.166 0.243 Race 10.19 0.006** White 0.110 0.174 0.234 Black 0.000 0.071 0.149 Other 0.172 0.310 0.476 Marital status 2.82 0.244 Married 0.105 0.173 0.236 Single 0.106 0.158 0.204 Other/Unknown 0.125 0.197 0.265 Histologic type 66.39 < 0.001*** Spindle 0.053 0.107 0.189 Epithelioid 0.289 0.378 0.512 Mixed 0.275 0.402 0.457 NOS/Other 0.095 0.156 0.215 AJCC stage 212.93 < 0.001*** I 0.023 0.051 0.080 II 0.093 0.147 0.230 III-IV 0.274 0.419 0.487 Surgery 81.00 < 0.001*** No/Unknown 0.074 0.127 0.187 Yes 0.209 0.309 0.378 Radiotherapy 67.76 < 0.001*** No/Unknown 0.217 0.322 0.377 Yes 0.078 0.131 0.196 Chemotherapy 27.96 < 0.001*** No/Unknown 0.108 0.169 0.229 Yes 0.187 0.376 0.512 Abbreviations: CIF, Cumulative incidence function; AJCC, American Joint Committee on Cancer. *p < 0.05; **p < 0.01; ***p < 0.001. Cumulative incidence was observed to be in lower white patients and black patients and higher in patients of other racial backgrounds at the 36-, 60-, and 96-months time points. The epithelioid uveal melanoma and mixed uveal melanoma also had higher cumulative incidence rates at the 36-, 60-, and 96-months time points. Notably, these data highlight that patients who were older, had later AJCC stage, had receive surgical treatment, did not receive radiation therapy and had receive chemotherapy had significantly higher cumulative incidence rates at the 36-, 60-, and 96-months time points. Results of the Multivariate Analysis The variables demonstrating statistical significance in the univariate analysis ( P < 0.05) were incorporated into the three models for subsequent multivariate analysis. In the COX proportional hazards model, we identified six variables that were found to be independent risk factors for PUM outcomes. The prognosis is most unfavorable for patients who are diagnosed with PUM and have an age of diagnosis ≥ 61 years (hazard ratio (HR) = 2.25). Of the race of PUM patients, black (HR = 0.33) had better outcomes and worse outcomes for other/unknown races (HR = 1.77). Regarding histologic type of PUM patients, epithelioid melanoma (HR = 1.75)and NOS/other melanoma (HR = 1.71) also had a worse prognosis. The AJCC stage showed a direct association with unfavorable prognosis. The patients diagnosed with PUM at the AJCC stage III-IV had the worst prognosis (HR = 3.74), followed by AJCC stage II(HR = 1.96). For individuals diagnosed with PUM, three frequently employed approaches for treatment include surgical intervention, radiotherapy and chemotherapy. The results showed that chemotherapy (HR = 1.74) had a negative impact on PUM outcomes and radiotherapy (HR = 0.58) had a positive impact on PUM outcome. Surgery was not statistically significant. Of the Fine-Gray model, six variables were identified as independent risk factors for patients with PUM. In the outcomes of the risk model that evaluates competition, age remained a distinct prognostic factor for patients with PUM, and patients aged ≥ 61 years at the time of diagnosis (HR = 1.25) exhibited an unfavorable prognosis. Of the race of PUM patients, other/unknown races (HR = 2.40) had worst outcomes. Regarding histologic type of PUM patients, epithelioid melanoma (HR = 2.84) and mixed melanoma (HR = 2.54) presented a more adverse prognosis. The prognosis of patients with PUM tends to be more favorable in cases characterized by a lower AJCC stage. The PUM patients in AJCC stage II (HR = 2.90) and AJCC stage III-IV (HR = 6.88) exhibited a lower survival rate. However, surgery (HR = 1.55) and chemotherapy (HR = 2.60) diminished the survival prognosis of patients diagnosed with PUM. Radiotherapy was not statistically significant. Regarding the CS model, six variables were identified as independent risk factors for patients with PUM. Of the outcomes of the risk model that evaluates competition, age remained a distinct prognostic factor for patients with PUM, and patients aged ≥ 61 years at the time of diagnosis (HR = 1.43) exhibited an unfavorable prognosis. Regarding race of PUM patients, other/unknown races (HR = 2.25) had worst outcomes. Of the histologic type of PUM patients, epithelioid melanoma (HR = 2.68) and mixed melanoma (HR = 2.56) presented a more adverse prognosis. The prognosis of patients with PUM tends to be more favorable in cases characterized by a lower AJCC stage. The PUM patients in AJCC stage II (HR = 2.98) and AJCC stage III-IV (HR = 7.46) exhibited a lower survival rate. However, surgery (HR = 1.54) and chemotherapy (HR = 2.43) diminished the survival prognosis of patients diagnosed with PUM. Radiotherapy was not statistically significant. Interestingly, the two competing risks models, Fine-Gray model and CS model, showed very similar results. The observed outcomes and impact risk factors of the Fine-Gray model and CS model demonstrated a consistent correlation direction, differing solely in terms of precise estimation levels. Additional details can be obtained by consulting the information presented in Table 3 . Table 3 Multivariate analysis of 3 Models of prognostic factors in patients with primary uveal melanoma. Prognostic factors Cox model Fine-gray model CS model P -value HR 95%CI P -value HR 95%CI P -value HR 95%CI Age ≤ 60years reference reference reference ≥ 61years < 0.001*** 2.25 1.91–2.66 0.031* 1.25 1.02–1.52 < 0.001*** 1.43 1.17–1.74 Race White reference reference reference Black 0.058 0.33 0.11–1.04 0.156 0.36 0.09–1.48 0.115 0.33 0.08–1.31 Other 0.022* 1.77 1.09–2.88 < 0.001*** 2.40 1.44–4.00 0.003** 2.25 1.31–3.85 Histologic type Spindle reference reference reference Epithelioid 0.019* 1.75 1.10–2.79 < 0.001*** 2.84 1.55–5.19 < 0.001*** 2.68 1.50–4.80 Mixed 0.005** 1.68 1.17– 2.41 < 0.001*** 2.54 1.58–4.09 < 0.001*** 2.56 1.59–4.10 NOS/Other < 0.001*** 1.71 1.24–2.35 < 0.001*** 2.21 1.43–3.41 < 0.001*** 2.30 1.49–3.56 AJCC stage I reference reference reference II < 0.001*** 1.96 1.56–2.45 < 0.001*** 2.90 2.06–4.09 < 0.001*** 2.98 2.11–4.20 III-IV < 0.001*** 3.74 2.92–4.80 < 0.001*** 6.88 4.82–9.82 < 0.001*** 7.46 5.21–10.69 Surgery No/Unknown reference reference reference Yes 0.126 1.24 0.94–1.63 0.012* 1.55 1.10–2.18 0.013* 1.54 1.10–2.15 Radiotherapy No/Unknown reference reference reference Yes < 0.001*** 0.58 0.44–0.78 0.435 0.86 0.60–1.25 0.172 0.78 0.54–1.12 Chemotherapy No/Unknown reference reference reference Yes 0.003** 1.74 1.21–2.51 < 0.001*** 2.60 1.88–3.60 < 0.001*** 2.43 1.66–3.54 Abbreviations : CIF, Cumulative incidence function; AJCC, American Joint Committee on Cancer; CI, confidence interval; CS, cause specific; HR, Hazard Ratio. * p < 0.05; ** p < 0.01; *** p < 0.001. Discussion Melanoma is a prevalent form of cancer globally, which consequently raises concerns on a global scale. Among them, PUM has a high mortality rate, which has attracted scholars' attention and in-depth research. In terms of long-term survival, many patients die from causes other than PUM. In studying cause-specific survival in patients with PUM, we consider death from other causes to be a competing event. In previous research, the utilization of Kaplan-Meier analysis and Cox proportional hazards models has been prevalent in studying disease survival. However, these methods are only suitable for analyzing a single outcome and may overlook competing risk events. By solely considering cause-specific death as the endpoint event, there is a possibility of expanding right censored data and potentially distorting the statistical significance and hazard ratios associated with prognostic factors[ 10 , 22 ]. In our study, we were interested not just in cause-specific death, but also death due to other causes as a competing event. Significant disparities were observed when comparing the Cox model with the two models addressing competing risks. The following is an analysis and discussion of meaningful independent factors. Different age brackets have a significant impact on the likelihood of survival. When diagnosed with the disease, older patients face a higher risk of mortality[ 4 ]. According to the Fine-Gray model, patients aged ≥ 61 years had an overall mortality rate of 1.25 (95% confidence interval(CI): 1.02–1.52). Nevertheless, it is evident that the Cox model excessively overestimated the impact of age on outcomes among individuals with PUM. All three models demonstrated a statistically significant difference between race groups, with white having a higher HR compared to black. This is consistent with previous literature reports. Previous literature reports that white are more likely to develop melanoma than black, possibly due to the protective effect of melanin in skin and eye pigmentation of black[ 23 ]. The other race included American Indian and Asian/Pacific Islander. They were two times more more likely to develop PUM than whites, which may be due to the specificity of race and requires further study and discussion. All three models showed that histologic type was also an independent risk factor, The two competing risk models suggest that spindle cell types have the best prognosis, while epithelial and mixed cell types have the worst prognosis. However, the Cox model suggestes that spindle cell types have the best prognosis, while epithelial and NOS/other cell types have the worst prognosis.The two competing risk models results align with previous literature reports[ 24 ]. This could be attributed to the fact that spindle cells adhere strongly to neighboring cells through intermediate junctions and filamentous cellular processes, thereby reducing the likelihood of metastasis. Additionally, spindle cell melanoma exhibits a relatively slow growth rate and displays less invasiveness[ 18 ]. On the other hand, epithelioid cells lack adhesion properties and possess high mobility, enabling them to easily infiltrate vascular lumens via gaps between endothelial cells and basement membranes, ultimately leading to hematogenous metastasis. Furthermore, epithelial melanomas are densely packed structures that sometimes formed nests or layered arrangements. They proliferate rapidly, display evident atypia, and are also prone to dissemination[ 18 ]. All three models indicated that the AJCC stage exhibited independent prognostic significance as well. All three models suggested a better prognosis for AJCC I and II, and a worse prognosis for AJCC III-IV.This outcome aligns with the discoveries made by Shields et al. and Cao et al[ 17 , 25 ]. Large-scale population studies reported in literature also suggest that AJCC stage can also be an important indicator of the presence of distant metastasis[ 26 , 27 ]. AJCC is an important indicator for predicting prognostic survival and for the development of treatment plans. Therefore, the emphasis on AJCC staging and early diagnosis and early treatment can enhance the standard of living and increase the likelihood of survival. However, it is evident that the risks are underestimated in all AJCC stages by the Cox model results. The result of the competitive risk model, although only an estimated point of difference, still represents a more precise form. The two competing risk models consistently demonstrated that surgery was an independent risk factor and associated with a unfavorable prognosis. The Cox model however showed that surgery is not an independent risk factor. The results of two competing risk models align with the existing literature reports. This may be correlated with the progression of PUM treatment. In the history of PUM treatment, surgical treatment included enucleation, surgical local excision, to radiotherapy and chemotherapy, and then to personalized treatment and biological immunotherapy, which also enables the treatment of ocular melanoma from enucleation to eye-preserving vision. The enucleation procedure has been widely employed as the predominant therapeutic approach for an extended period of time. For now, enucleation is limited to large intraocular melanoma or melanomas with serious intraocular complications. This result could potentially be attributed to the occurrence of distant metastasis resulting from the accelerated dissemination of tumor cells due to invasive surgical procedures and subsequent extrusion[ 28 ]. The Cox model showed that radiotherapy is an independent risk factor and associated with a favorable prognosis. However, the two competing risk models showed no statistical difference in radiotherapy. As can be seen from Table 1 , more than 60% of PUM patients are treated with radiotherapy, and radiotherapy has become the most important treatment method. This local treatment, known as proximity plaque radiotherapy, has gained remarkable popularity and is the mainstay of treatment for most patients with ocular melanoma. Plaque radiotherapy has been reported to rely mainly on radioactive implants that can provide a tip radiotherapy dose of about 80 Gy by insertion into the extrascleral tissue[ 25 , 29 ]. Although radiotherapy has become the main treatment method, the survival prognosis is not significantly improved. All three models consistently demonstrated that chemotherapy was an independent risk factor and associated with a unfavorable prognosis. The results of three models align with the existing literature reports. Chemotherapy has now been used in the treatment of metastatic PUM, but with a poor prognosis, limited treatment options, and low drug effectiveness[ 30 ]. Fane and Weeraratna observed that while chemotherapy may be beneficial during the early stages of treatment, it can potentially accelerate immune cell aging and increase mortality risk in patients during the middle and late stages of treatment[ 31 ]. In recent times, there has been a gradual shift towards more intricate and personalized approaches in PUM therapy, particularly when dealing with metastatic cases. New therapeutic strategies such as molecular targeted therapy and immunotherapy offer more effective avenues to enhance survival rates among patients diagnosed with metastatic PUM[ 9 , 32 ]. To the best of our knowledge, this study is the first to assess predictive factors of PUM using two competing risk models. To identify the most accurate prognostic factors, we compared these models with the Cox model and observed a superior predictive performance. Previous studies often relied on single endpoint analyses such as Cox models and Kaplan-Meier analyses, which may lack precision. With advancements in diagnostic technology leading to improved survival rates among patients with PUM, it is plausible that competing risk events may occur prior to reaching the study endpoint. Consequently, if such an event arises, the endpoint event will never transpire, resulting in an increased amount of accurately truncated data. This proliferation of statistical errors can generate both false positive and negative outcomes that have potential clinical implications detrimental to patients' well-being. In this investigation, while most findings from both the fine-gray model and CS model align with previous reports, there are some discrepancies noted as well. Moreover, these two competitive risk models aid in further distinguishing the role of risk factors under scrutiny. Generally speaking, CS models are commonly employed for exploring etiological matters whereas fine-Grey models focus on absolute incidence rates and serve as valuable tools for constructing clinical prediction models and risk scores[ 20 ]. Therefore, we primarily rely on conclusions drawn from two competing risk models in this study's context. Additionally, our results emphasize the importance of considering potential biases arising from patient mortality represented by competing risk events when investigating prognostic risk factors among cancer patients. Compared with previous PUM prediction models, the competing risk model of PUM in this study can not only more accurately predict survival prognosis, but also be used to explore various factors affecting survival prognosis. This is a unique model of innovation. There are some limitations to the study. As a retrospective study, there may have been bias of the selection process and inaccuracies in recorded data. Observational design limits drawing causal conclusions, and some important clinical variables may not have been adequately documented. Due to differences in data measurements, diagnostic methods, and treatment protocols, consistency may suffer over time. Although confounders were adjusted using multivariate models, factors such as gene expression profiles and chromosomal factors were not taken into account. And differences in follow-up time may lead to potential bias. Moreover, there was a lack of precise information regarding the therapeutic procedure, including the specific surgical approach employed and the dosage of radiotherapy administered. Therefore, it is important to validate these findings externally in different populations. Conclusion This research developed competing risk models to evaluate prognostic factors for PUM, demonstrating greater accuracy compared to conventional methods that fail to account for competing risk factors. The study's findings will assist clinicians in comprehending PUM, enhancing clinical decision making and creating personalized treatment plans. Declarations Ethics approval and consent to participate The clinical data in this retrospective study were collected from the publicly available SEER database, so there were no local or national ethical issues, and informed consent was not required. Author contributions Ruisheng Huang: Acquiring funding, initial drafting of the manuscript. Chen Jian: Analyzing the data. Limin Lin: Supervising and reviewing/editing the writing process. Jun Lyu: Developing the methodology, overseeing project administration and resources, reviewing and editing the writing. Qing Zhou: Supervising and reviewing/editing the writing process. Funding No funding. Acknowledgments The authors express their gratitude to the Surveillance, Epidemiology, and End Results (SEER) database for its valuable assistance. Conflict of interest The authors declare that the research was conducted of the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Publisher's note All opinions expressed in this article belong solely to the authors and do not necessarily reflect those of their affiliated organizations, or the publisher, editors, and reviewers. The publisher does not guarantee or endorse any product that may be evaluated in this article or any claims made by its manufacturer. Data availability statement Data used in this study are publicly available and can be accessed of the SEER program (https://seer.cancer.gov/). ORCID Ruisheng Huang https://orcid.org/0009-0003-6977-8443 References McLaughlin CC, Wu X-C, Jemal A et al. Incidence of noncutaneous melanomas in the u.S. Cancer 2005; 103 :1000-7. https://doi.org/10.1002/cncr.20866 Scott JF, Vyas R, Galvin J et al. Primary bilateral uveal melanoma: A population-based study and systematic review. Clinical & experimental ophthalmology 2018; 46 :502-10. https://doi.org/10.1111/ceo.13129 Kaliki S, Shields CL. Uveal melanoma: Relatively rare but deadly cancer. 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Evaluating health outcomes in the presence of competing risks: A review of statistical methods and clinical applications. Medical care 2010; 48 :S96-105. https://doi.org/10.1097/MLR.0b013e3181d99107 Putter H, Fiocco M, Geskus RB. Tutorial in biostatistics: Competing risks and multi-state models. Statistics in medicine 2007; 26 :2389-430. https://doi.org/10.1002/sim.2712 Che W-Q, Li Y-J, Tsang C-K et al. How to use the surveillance, epidemiology, and end results (seer) data: Research design and methodology. Military Medical Research 2023; 10 :50. https://doi.org/10.1186/s40779-023-00488-2 Xu Y, Zheng X, Li Y et al. Exploring patient medication adherence and data mining methods in clinical big data: A contemporary review. Journal of evidence-based medicine 2023; 16 :342-75. https://doi.org/10.1111/jebm.12548 Cao X, Zeng J, Ou Y et al. A prognostic nomogram for the cancer-specific survival rate of choroidal melanoma using the surveillance, epidemiology, and end results database. Frontiers in Medicine 2024; 11 . https://doi.org/10.3389/fmed.2024.1392336 Liu X, Liu C, Shang Y et al. Prognostic factors and nomograms for overall and cancer-specific survival of patients with uveal melanoma without metastases: A seer analysis of 4119 cases. Journal of ophthalmology 2022; 2022 :1874336. https://doi.org/10.1155/2022/1874336 Haller B, Schmidt G, Ulm K. Applying competing risks regression models: An overview. Lifetime data analysis 2013; 19 :33-58. https://doi.org/10.1007/s10985-012-9230-8 Nie ZQ, Ou YQ, Qu YJ et al. [a new perspective of survival data on clinical epidemiology: Introduction of competitive risk model]. Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi 2017; 38 :1127-31. https://doi.org/10.3760/cma.j.issn.0254-6450.2017.08.026 Berger M, Schmid M, Welchowski T et al. Subdistribution hazard models for competing risks in discrete time. Biostatistics (Oxford, England) 2020; 21 :449-66. https://doi.org/10.1093/biostatistics/kxy069 Koller MT, Raatz H, Steyerberg EW et al. Competing risks and the clinical community: Irrelevance or ignorance? Statistics in medicine 2012; 31 :1089-97. https://doi.org/10.1002/sim.4384 Rajeshuni N, Zubair T, Ludwig CA et al. Evaluation of racial, ethnic, and socioeconomic associations with treatment and survival in uveal melanoma, 2004-2014. JAMA ophthalmology 2020; 138 :876-84. https://doi.org/10.1001/jamaophthalmol.2020.2254 Foti PV, Inì C, Broggi G et al. Quantitative diffusion-weighted mr imaging: Is there a prognostic role in noninvasively predicting the histopathologic type of uveal melanomas? Cancers 2023; 15 . https://doi.org/10.3390/cancers15235627 Shields CL, Kaliki S, Furuta M et al. American joint committee on cancer classification of posterior uveal melanoma (tumor size category) predicts prognosis in 7731 patients. Ophthalmology 2013; 120 :2066-71. https://doi.org/10.1016/j.ophtha.2013.03.012 Force AOOT. International validation of the american joint committee on cancer's 7th edition classification of uveal melanoma. JAMA ophthalmology 2015; 133 :376-83. https://doi.org/10.1001/jamaophthalmol.2014.5395 Delgado-Ramos GM, Thomas F, VanderWalde A et al. Risk factors, clinical outcomes, and natural history of uveal melanoma: A single-institution analysis. Medical oncology (Northwood, London, England) 2019; 36 :17. https://doi.org/10.1007/s12032-018-1230-4 Zimmerman LE, McLean IW, Foster WD. Does enucleation of the eye containing a malignant melanoma prevent or accelerate the dissemination of tumour cells. The British journal of ophthalmology 1978; 62 :420-5. https://doi.org/10.1136/bjo.62.6.420 Sandinha MT, Farquharson MA, McKay IC et al. Monosomy 3 predicts death but not time until death in choroidal melanoma. Investigative ophthalmology & visual science 2005; 46 :3497-501. https://doi.org/10.1167/iovs.05-0613 Shields CL, Furuta M, Thangappan A et al. Metastasis of uveal melanoma millimeter-by-millimeter in 8033 consecutive eyes. Archives of ophthalmology (Chicago, Ill : 1960) 2009; 127 :989-98. https://doi.org/10.1001/archophthalmol.2009.208 Fane M, Weeraratna AT. How the ageing microenvironment influences tumour progression. Nature reviews Cancer 2020; 20 :89-106. https://doi.org/10.1038/s41568-019-0222-9 Synoradzki KJ, Paduszyńska N, Solnik M et al. From molecular biology to novel immunotherapies and nanomedicine in uveal melanoma. Current oncology (Toronto, Ont) 2024; 31 :778-800. https://doi.org/10.3390/curroncol31020058 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 05 May, 2025 Editor assigned by journal 25 Apr, 2025 Reviews received at journal 17 Apr, 2025 Reviewers agreed at journal 17 Apr, 2025 Reviews received at journal 16 Apr, 2025 Reviewers agreed at journal 16 Apr, 2025 Reviewers invited by journal 16 Apr, 2025 Submission checks completed at journal 15 Apr, 2025 First submitted to journal 31 Mar, 2025 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5963224","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":443766933,"identity":"b7ee28c5-9901-4545-b752-36caca277d90","order_by":0,"name":"Ruisheng Huang","email":"","orcid":"","institution":"The First Affiliated Hospital of Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Ruisheng","middleName":"","lastName":"Huang","suffix":""},{"id":443766934,"identity":"bb525ac9-7388-4c3a-9b29-5598afebeddc","order_by":1,"name":"Jian Chen","email":"","orcid":"","institution":"The First Affiliated Hospital of Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Chen","suffix":""},{"id":443766935,"identity":"9b8e48c2-522c-464c-bd14-077b8061738e","order_by":2,"name":"Limin Lin","email":"","orcid":"","institution":"Shantou Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Limin","middleName":"","lastName":"Lin","suffix":""},{"id":443766936,"identity":"eeac7bb4-f3e4-433d-9cb6-07d48b65998b","order_by":3,"name":"Jun Lyu","email":"","orcid":"","institution":"The First Affiliated Hospital of Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Lyu","suffix":""},{"id":443766937,"identity":"4f101822-84ac-4cf4-94a5-c558db9dd769","order_by":4,"name":"Qing Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYBACCQh1QI6Nvf0AaVqM+XjOJJCmJXGehIMBcVok23uffeb5cye9TYIhgeFHxTbCWqR5jhvP5uF5ltsm3XiAsefMbcJa5CTSmJl5JA7ntskcSGBmbCNGi/wzoBaDw+lsEgkGxGmRlmADakk4nEC8FsmeNGbGOQcOG7YBA/kgUX6ROH6MmeHNn8Py8u3tBx/8qCBCCwgw8UAZB4hTDwSMP4hWOgpGwSgYBSMSAAAupDew6h8h9wAAAABJRU5ErkJggg==","orcid":"","institution":"The First Affiliated Hospital of Jinan University","correspondingAuthor":true,"prefix":"","firstName":"Qing","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2025-02-05 07:23:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5963224/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5963224/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81004230,"identity":"ed146a66-7677-42e4-bd30-894fc209b50a","added_by":"auto","created_at":"2025-04-21 06:45:38","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":52110,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of case selection process.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5963224/v1/e24e95863045f991bd99c3ea.jpg"},{"id":81002809,"identity":"1a30ba41-d702-4179-a351-28800dd8f56c","added_by":"auto","created_at":"2025-04-21 06:37:38","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":16271,"visible":true,"origin":"","legend":"\u003cp\u003eOptimal cutoff values of age at diagnosis calculated by X-tile software. (A) Histogram of patient distribution based on age. (B) Kaplan–Meier curve on cause-specific survival of two age groups.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5963224/v1/2faf01692bdb546460174f28.jpg"},{"id":81004232,"identity":"c013aa3f-4706-4a9e-be0f-76b3341be1d5","added_by":"auto","created_at":"2025-04-21 06:45:38","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":163448,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative incidence curves of cause-specific death in (A) Age subgroups; (B) Race subgroups; (C) Histologic type subgroups; (D) AJCC stage subgroups; (E) Surgery subgroups; (F) Radiotherapy subgroups; (G) Chemotherapy subgroups. AJCC, American Joint Committee on Cancer.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5963224/v1/6a689d8162d6b63c7d4c7067.jpg"},{"id":81005505,"identity":"44696300-185f-43db-ac85-8597f6380ac3","added_by":"auto","created_at":"2025-04-21 07:01:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1427028,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5963224/v1/b2669b85-bb7f-41b6-98ad-d3e1ffb00fb6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Competitive risk analysis of prognosis in patients with primary uveal melanoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMelanoma is a common malignancy that has high disability and mortality. Melanoma can occur in many parts of the body, and the eye is one of them. Among adults, primary uveal melanoma (PUM) stands out as the prevailing primary intraocular tumo[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Common incidence sites of PUM include the choroid, ciliary body and iris[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Annually, uveal melanoma affects around 5 to 10 individuals per million globally[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Regrettably, the condition leads to metastatic disease in more than one-third of PUM patients, leading to a median survival time of less than twelve months[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the majority of cases, a comprehensive examination of both the front and back parts of the eye using various imaging techniques such as ultrasound can lead to the diagnosis of PUM[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Through these multimodal imaging techniques, the early detection of PUM, which can significantly improve the survival rate of patients[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Treatment for PUM mainly includes enucleation, surgical local excision, radiotherapy and chemotherapy. In general, there has been a notable transition from enucleation and local tumor resection to the utilization of radiotherapy that preserves vision[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. At present, there are more treatment methods for PUM, including targeted therapy, immunotherapy, etc., but the survival rate is still not significantly improved[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therefore, many scholars have conducted extensive and thorough research on the diagnosis, treatment and prognostic survival of PUM.\u003c/p\u003e \u003cp\u003eThe Cox proportional hazards model is more commonly used to identify prognostic risk factors, survival analysis and predictive models for PUM. In the present era where personalized cancer treatments are given more importance, it is critical to assess the impact of cancer-related and other factors on patient mortality. For cancer patients, cancer is not the only cause of death, accidents and other diseases are also the main causes of death. When looking at factors that affect the prognosis of cancer patients, other factors that contribute to patient mortality are often considered competing risk events. In this case, multiple endpoints coexist and compete with each other, leading to the existence of competition risks[\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The presence of multiple end events introduces bias in single end point analyses of estimated event probabilities due to competitive risks[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. For this purpose, employing a competitive risk model for assessing the impact of risk factors on the prognosis of PUM patients and comparing outcomes with conventional survival analysis techniques can offer enhanced insights into uncovering the genuine influence of variables and accurately identifying relevant risk factors. This study employed data from the Surveillance, Epidemiology, and End Results (SEER) database to perform a competing risk analysis on individuals diagnosed with PUM. The main aim was to compare the results obtained from the Cox model with those derived from the competing risk model. This study aims to identify factors that have a more precise impact on PUM outcomes and better guide clinical decision making.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source and patient selection\u003c/h2\u003e \u003cp\u003eThe SEER program, which was initiated by the National Cancer Institute, aimed at gathering and disseminating data on cancer occurrence and survival rate[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. It encompassed a population-based database covering around 35% of the American population[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The SEER database, consisting of 17 separate registries, was utilized for patient selection in our study conducted between 2010 and 2015. The patients were chosen using the SEER*Stat software version 8.4.4 as per our application requirements[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The selection of individuals under investigation was based on the utilization of codes 8720\u0026ndash;8790 from the third edition of the international classification of disease for oncology (ICD-O-3), with a specific focus on cases pertaining to malignant melanoma. The primary tumor site of patients was included C69.3 and C69.4. Inclusion criteria also included the identified 7th American Joint Committee on Cancer (AJCC) stage and that the uveal melanoma must be primary. The patients were excluded based on the following criteria: (1)unknown race(n\u0026thinsp;=\u0026thinsp;15); (2)survival months\u0026thinsp;\u0026lt;\u0026thinsp;1month(n\u0026thinsp;=\u0026thinsp;5). The process of selecting cases can be observed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, which displays the flow chart.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eVariable selection\u003c/h3\u003e\n\u003cp\u003eThis study gathered the following variables: diagnosis of age, sex, race, marital status, histologic type, AJCC stage, surgery, radiotherapy, chemotherapy, SEER cause-specific death classification, SEER other cause of death classification, vital status recode (study cutoff used), survival months and patient ID. The optimal age cutoff was calculated by the X-tile software (v3.6.1, Yale University, USA). As such, age at diagnosis was classified into two groups: \u0026le;60 years and \u0026ge;\u0026thinsp;61 years (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The approach employed for incorporating histologic types of patients with PUM is founded on the criterion established by Liu et al and cao et al[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Therefore we categorized spindle melanoma, epithelioid melanoma and mixed melanoma into three groups separately. Additionally NOS melanoma and other types of melanomas were merged into a unified group. The AJCC staging system from 2010 to 2015 was utilized for determining the 7th AJCC stage in this study. We designated cause-specific death and other cause of death as variables for the outcome[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Cancer-specific deaths, competing events, and missing data were collected for all patients, based on records of cause-specific death classifications and other death classifications in the SEER database. Using these criteria, a total of 1712 patients were included in the study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eStatistical Analyses\u003c/h3\u003e\n\u003cp\u003eCategorical variables were represented using frequencies and percentages, while the Chi-square test was employed to assess the variation in cause of death across different groups. The cumulative incidence function (CIF) was employed to determine the likelihood of cause-specific mortality in the presence of a competing risk event. Univariate Gray's test was performed to assess variations between sub-groups[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Relevant variables with statistical significance in univariate analyses were chosen for subsequent multivariate analyses, along with those deemed clinically significant. Multivariate analyses were performed using a COX proportional hazards model, a Fine-Gray subdistribution hazard model, and a cause-specific hazard (CS) model[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].Comparisons among these three models were conducted. All statistical analyses were conducted using the R 4.4.0 software package. A significance level of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePatient Characteristics\u003c/h2\u003e \u003cp\u003eThe 1712 eligible patients were diagnosed with PUM. There were 400(23.37%) patients died from PUM, 231(13.49%)patients died from other causes and 1081(63.14%) patients being alive. The majority of PUM patients who died were older than 61 years (n\u0026thinsp;=\u0026thinsp;221, 55.25%), male (n\u0026thinsp;=\u0026thinsp;209, 52.25%), white (n\u0026thinsp;=\u0026thinsp;384, 96.00%), married (n\u0026thinsp;=\u0026thinsp;241, 60.25%), NOS/Other (histologic type) (n\u0026thinsp;=\u0026thinsp;295, 73.75%), AJCC stage II (n\u0026thinsp;=\u0026thinsp;185, 46.25%), no surgery (n\u0026thinsp;=\u0026thinsp;226, 56.50%), radiotherapy (n\u0026thinsp;=\u0026thinsp;250, 62.50%), and no chemotherapy (n\u0026thinsp;=\u0026thinsp;370, 92.50%). Baseline characteristics of the patients and more data are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eTable 1. Baseline characteristics of patients.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll patients (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConcerned (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompetition (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCensored (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003cstrong\u003eumber\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1081\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 568px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026le;60years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e839(49.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e179(44.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e37(16.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e623(57.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026ge;61years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e873(51.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e221(55.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e194(83.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e458(42.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 568px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e914(53.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e209(52.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e139(60.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e566(52.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e798(46.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e191(47.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e92(39.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e515(47.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 568px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1668(97.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e384(96.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e227(98.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1057(97.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e15(0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e2(0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1(0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e12(1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e29(1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e14(3.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e3(1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e12(1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 568px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1033(60.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e241(60.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e117(50.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e675(62.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e259(15.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e52(13.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e44(19.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e163(15.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eOther/Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e420(24.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e107(26.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e70(30.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e243(22.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 568px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistologic\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003et\u003c/strong\u003e\u003cstrong\u003eype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eSpindle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e153(8.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e25(6.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e24(10.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e104(9.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eEpithelioid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e45(2.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e22(5.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e7(3.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e16(1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eMixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e129(7.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e58(14.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e18(7.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e53(4.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eNOS/Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1385(80.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e295(73.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e182(78.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e908(84.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 568px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAJCC\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003cstrong\u003etage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e515(30.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e40(10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e64(27.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e411(38.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e831(48.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e185(46.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e120(51.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e526(48.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eIII-IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e366(21.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e175(43.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e47(20.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e144(13.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 568px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgery\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eNo/Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1253(73.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e226(56.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e157(67.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e870(80.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e459(26.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e174(43.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e74(32.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e211(19.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 568px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003cstrong\u003eadiotherapy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eNo/Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e400(23.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e150(37.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e77(33.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e173(16.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1312(76.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e250(62.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e154(66.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e908(84.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 568px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChemotherapy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eNo/Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1658(96.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e370(92.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e230(99.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1058(97.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e54(3.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e30(7.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e1(0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e23(2.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003eAJCC, American Joint Committee on Cancer.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eResults of the Univariate Analysis\u003c/h2\u003e \u003cp\u003eIn the univariate analyses, Fine-Gray\u0026rsquo;s test and CIF were applied. In the presence of competing risks, Gray\u0026rsquo;s test results indicated that seven factors significantly influenced the prognosis of PUM (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), including diagnosis of age, race, histologic type, AJCC stage, surgery, radiotherapy and chemotherapy. The details on univariate Gray's test and 36-, 60-, and 96-months cumulative incidence are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The CIF curve of the seven selected variables is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate analysis of prognostic factors in patients with primary uveal melanoma.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGray\u0026rsquo;s test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eCIF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36-months\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60-months\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96-months\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.034*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;60years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;61years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.476\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistologic type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpindle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpithelioid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNOS/Other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAJCC stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e212.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.230\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII-IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgery\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e81.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.187\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.378\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRadiotherapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.377\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChemotherapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAbbreviations: CIF, Cumulative incidence function; AJCC, American Joint Committee on Cancer.\n*p \u003c 0.05; **p \u003c 0.01; ***p \u003c 0.001.\n\u003c/p\u003e \u003cp\u003eCumulative incidence was observed to be in lower white patients and black patients and higher in patients of other racial backgrounds at the 36-, 60-, and 96-months time points. The epithelioid uveal melanoma and mixed uveal melanoma also had higher cumulative incidence rates at the 36-, 60-, and 96-months time points. Notably, these data highlight that patients who were older, had later AJCC stage, had receive surgical treatment, did not receive radiation therapy and had receive chemotherapy had significantly higher cumulative incidence rates at the 36-, 60-, and 96-months time points.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eResults of the Multivariate Analysis\u003c/h3\u003e\n\u003cp\u003eThe variables demonstrating statistical significance in the univariate analysis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were incorporated into the three models for subsequent multivariate analysis. In the COX proportional hazards model, we identified six variables that were found to be independent risk factors for PUM outcomes. The prognosis is most unfavorable for patients who are diagnosed with PUM and have an age of diagnosis\u0026thinsp;\u0026ge;\u0026thinsp;61 years (hazard ratio (HR)\u0026thinsp;=\u0026thinsp;2.25). Of the race of PUM patients, black (HR\u0026thinsp;=\u0026thinsp;0.33) had better outcomes and worse outcomes for other/unknown races (HR\u0026thinsp;=\u0026thinsp;1.77). Regarding histologic type of PUM patients, epithelioid melanoma (HR\u0026thinsp;=\u0026thinsp;1.75)and NOS/other melanoma (HR\u0026thinsp;=\u0026thinsp;1.71) also had a worse prognosis. The AJCC stage showed a direct association with unfavorable prognosis. The patients diagnosed with PUM at the AJCC stage III-IV had the worst prognosis (HR\u0026thinsp;=\u0026thinsp;3.74), followed by AJCC stage II(HR\u0026thinsp;=\u0026thinsp;1.96). For individuals diagnosed with PUM, three frequently employed approaches for treatment include surgical intervention, radiotherapy and chemotherapy. The results showed that chemotherapy (HR\u0026thinsp;=\u0026thinsp;1.74) had a negative impact on PUM outcomes and radiotherapy (HR\u0026thinsp;=\u0026thinsp;0.58) had a positive impact on PUM outcome. Surgery was not statistically significant.\u003c/p\u003e \u003cp\u003eOf the Fine-Gray model, six variables were identified as independent risk factors for patients with PUM. In the outcomes of the risk model that evaluates competition, age remained a distinct prognostic factor for patients with PUM, and patients aged\u0026thinsp;\u0026ge;\u0026thinsp;61 years at the time of diagnosis (HR\u0026thinsp;=\u0026thinsp;1.25) exhibited an unfavorable prognosis. Of the race of PUM patients, other/unknown races (HR\u0026thinsp;=\u0026thinsp;2.40) had worst outcomes. Regarding histologic type of PUM patients, epithelioid melanoma (HR\u0026thinsp;=\u0026thinsp;2.84) and mixed melanoma (HR\u0026thinsp;=\u0026thinsp;2.54) presented a more adverse prognosis. The prognosis of patients with PUM tends to be more favorable in cases characterized by a lower AJCC stage. The PUM patients in AJCC stage II (HR\u0026thinsp;=\u0026thinsp;2.90) and AJCC stage III-IV (HR\u0026thinsp;=\u0026thinsp;6.88) exhibited a lower survival rate. However, surgery (HR\u0026thinsp;=\u0026thinsp;1.55) and chemotherapy (HR\u0026thinsp;=\u0026thinsp;2.60) diminished the survival prognosis of patients diagnosed with PUM. Radiotherapy was not statistically significant.\u003c/p\u003e \u003cp\u003eRegarding the CS model, six variables were identified as independent risk factors for patients with PUM. Of the outcomes of the risk model that evaluates competition, age remained a distinct prognostic factor for patients with PUM, and patients aged\u0026thinsp;\u0026ge;\u0026thinsp;61 years at the time of diagnosis (HR\u0026thinsp;=\u0026thinsp;1.43) exhibited an unfavorable prognosis. Regarding race of PUM patients, other/unknown races (HR\u0026thinsp;=\u0026thinsp;2.25) had worst outcomes. Of the histologic type of PUM patients, epithelioid melanoma (HR\u0026thinsp;=\u0026thinsp;2.68) and mixed melanoma (HR\u0026thinsp;=\u0026thinsp;2.56) presented a more adverse prognosis. The prognosis of patients with PUM tends to be more favorable in cases characterized by a lower AJCC stage. The PUM patients in AJCC stage II (HR\u0026thinsp;=\u0026thinsp;2.98) and AJCC stage III-IV (HR\u0026thinsp;=\u0026thinsp;7.46) exhibited a lower survival rate. However, surgery (HR\u0026thinsp;=\u0026thinsp;1.54) and chemotherapy (HR\u0026thinsp;=\u0026thinsp;2.43) diminished the survival prognosis of patients diagnosed with PUM. Radiotherapy was not statistically significant.\u003c/p\u003e \u003cp\u003eInterestingly, the two competing risks models, Fine-Gray model and CS model, showed very similar results. The observed outcomes and impact risk factors of the Fine-Gray model and CS model demonstrated a consistent correlation direction, differing solely in terms of precise estimation levels. Additional details can be obtained by consulting the information presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate analysis of 3 Models of prognostic factors in patients with primary uveal melanoma.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePrognostic factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eCox model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eFine-gray model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eCS model\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;60years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;61years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.91\u0026ndash;2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.031*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.02\u0026ndash;1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.17\u0026ndash;1.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11\u0026ndash;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.09\u0026ndash;1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.08\u0026ndash;1.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.022*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09\u0026ndash;2.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.44\u0026ndash;4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.003**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.31\u0026ndash;3.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistologic type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpindle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpithelioid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.019*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.10\u0026ndash;2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.55\u0026ndash;5.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.50\u0026ndash;4.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.005**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17\u0026ndash; 2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.58\u0026ndash;4.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.59\u0026ndash;4.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNOS/Other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.24\u0026ndash;2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.43\u0026ndash;3.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.49\u0026ndash;3.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAJCC stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.56\u0026ndash;2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.06\u0026ndash;4.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.11\u0026ndash;4.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII-IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.92\u0026ndash;4.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.82\u0026ndash;9.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.21\u0026ndash;10.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgery\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94\u0026ndash;1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.012*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.10\u0026ndash;2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.013*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.10\u0026ndash;2.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRadiotherapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.44\u0026ndash;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.60\u0026ndash;1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.54\u0026ndash;1.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChemotherapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.003**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.21\u0026ndash;2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.88\u0026ndash;3.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.66\u0026ndash;3.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cb\u003eAbbreviations\u003c/b\u003e: CIF, Cumulative incidence function; AJCC, American Joint Committee on Cancer; CI, confidence interval; CS, cause specific; HR, Hazard Ratio.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e*\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMelanoma is a prevalent form of cancer globally, which consequently raises concerns on a global scale. Among them, PUM has a high mortality rate, which has attracted scholars' attention and in-depth research. In terms of long-term survival, many patients die from causes other than PUM. In studying cause-specific survival in patients with PUM, we consider death from other causes to be a competing event. In previous research, the utilization of Kaplan-Meier analysis and Cox proportional hazards models has been prevalent in studying disease survival. However, these methods are only suitable for analyzing a single outcome and may overlook competing risk events. By solely considering cause-specific death as the endpoint event, there is a possibility of expanding right censored data and potentially distorting the statistical significance and hazard ratios associated with prognostic factors[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In our study, we were interested not just in cause-specific death, but also death due to other causes as a competing event. Significant disparities were observed when comparing the Cox model with the two models addressing competing risks. The following is an analysis and discussion of meaningful independent factors.\u003c/p\u003e \u003cp\u003eDifferent age brackets have a significant impact on the likelihood of survival. When diagnosed with the disease, older patients face a higher risk of mortality[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. According to the Fine-Gray model, patients aged\u0026thinsp;\u0026ge;\u0026thinsp;61 years had an overall mortality rate of 1.25 (95% confidence interval(CI): 1.02\u0026ndash;1.52). Nevertheless, it is evident that the Cox model excessively overestimated the impact of age on outcomes among individuals with PUM.\u003c/p\u003e \u003cp\u003eAll three models demonstrated a statistically significant difference between race groups, with white having a higher HR compared to black. This is consistent with previous literature reports. Previous literature reports that white are more likely to develop melanoma than black, possibly due to the protective effect of melanin in skin and eye pigmentation of black[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The other race included American Indian and Asian/Pacific Islander. They were two times more more likely to develop PUM than whites, which may be due to the specificity of race and requires further study and discussion.\u003c/p\u003e \u003cp\u003eAll three models showed that histologic type was also an independent risk factor, The two competing risk models suggest that spindle cell types have the best prognosis, while epithelial and mixed cell types have the worst prognosis. However, the Cox model suggestes that spindle cell types have the best prognosis, while epithelial and NOS/other cell types have the worst prognosis.The two competing risk models results align with previous literature reports[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This could be attributed to the fact that spindle cells adhere strongly to neighboring cells through intermediate junctions and filamentous cellular processes, thereby reducing the likelihood of metastasis. Additionally, spindle cell melanoma exhibits a relatively slow growth rate and displays less invasiveness[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. On the other hand, epithelioid cells lack adhesion properties and possess high mobility, enabling them to easily infiltrate vascular lumens via gaps between endothelial cells and basement membranes, ultimately leading to hematogenous metastasis. Furthermore, epithelial melanomas are densely packed structures that sometimes formed nests or layered arrangements. They proliferate rapidly, display evident atypia, and are also prone to dissemination[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAll three models indicated that the AJCC stage exhibited independent prognostic significance as well. All three models suggested a better prognosis for AJCC I and II, and a worse prognosis for AJCC III-IV.This outcome aligns with the discoveries made by Shields et al. and Cao et al[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Large-scale population studies reported in literature also suggest that AJCC stage can also be an important indicator of the presence of distant metastasis[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. AJCC is an important indicator for predicting prognostic survival and for the development of treatment plans. Therefore, the emphasis on AJCC staging and early diagnosis and early treatment can enhance the standard of living and increase the likelihood of survival. However, it is evident that the risks are underestimated in all AJCC stages by the Cox model results. The result of the competitive risk model, although only an estimated point of difference, still represents a more precise form.\u003c/p\u003e \u003cp\u003eThe two competing risk models consistently demonstrated that surgery was an independent risk factor and associated with a unfavorable prognosis. The Cox model however showed that surgery is not an independent risk factor. The results of two competing risk models align with the existing literature reports. This may be correlated with the progression of PUM treatment. In the history of PUM treatment, surgical treatment included enucleation, surgical local excision, to radiotherapy and chemotherapy, and then to personalized treatment and biological immunotherapy, which also enables the treatment of ocular melanoma from enucleation to eye-preserving vision. The enucleation procedure has been widely employed as the predominant therapeutic approach for an extended period of time. For now, enucleation is limited to large intraocular melanoma or melanomas with serious intraocular complications. This result could potentially be attributed to the occurrence of distant metastasis resulting from the accelerated dissemination of tumor cells due to invasive surgical procedures and subsequent extrusion[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Cox model showed that radiotherapy is an independent risk factor and associated with a favorable prognosis. However, the two competing risk models showed no statistical difference in radiotherapy. As can be seen from Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, more than 60% of PUM patients are treated with radiotherapy, and radiotherapy has become the most important treatment method. This local treatment, known as proximity plaque radiotherapy, has gained remarkable popularity and is the mainstay of treatment for most patients with ocular melanoma. Plaque radiotherapy has been reported to rely mainly on radioactive implants that can provide a tip radiotherapy dose of about 80 Gy by insertion into the extrascleral tissue[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Although radiotherapy has become the main treatment method, the survival prognosis is not significantly improved.\u003c/p\u003e \u003cp\u003eAll three models consistently demonstrated that chemotherapy was an independent risk factor and associated with a unfavorable prognosis. The results of three models align with the existing literature reports. Chemotherapy has now been used in the treatment of metastatic PUM, but with a poor prognosis, limited treatment options, and low drug effectiveness[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Fane and Weeraratna observed that while chemotherapy may be beneficial during the early stages of treatment, it can potentially accelerate immune cell aging and increase mortality risk in patients during the middle and late stages of treatment[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In recent times, there has been a gradual shift towards more intricate and personalized approaches in PUM therapy, particularly when dealing with metastatic cases. New therapeutic strategies such as molecular targeted therapy and immunotherapy offer more effective avenues to enhance survival rates among patients diagnosed with metastatic PUM[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, this study is the first to assess predictive factors of PUM using two competing risk models. To identify the most accurate prognostic factors, we compared these models with the Cox model and observed a superior predictive performance. Previous studies often relied on single endpoint analyses such as Cox models and Kaplan-Meier analyses, which may lack precision. With advancements in diagnostic technology leading to improved survival rates among patients with PUM, it is plausible that competing risk events may occur prior to reaching the study endpoint. Consequently, if such an event arises, the endpoint event will never transpire, resulting in an increased amount of accurately truncated data. This proliferation of statistical errors can generate both false positive and negative outcomes that have potential clinical implications detrimental to patients' well-being. In this investigation, while most findings from both the fine-gray model and CS model align with previous reports, there are some discrepancies noted as well. Moreover, these two competitive risk models aid in further distinguishing the role of risk factors under scrutiny. Generally speaking, CS models are commonly employed for exploring etiological matters whereas fine-Grey models focus on absolute incidence rates and serve as valuable tools for constructing clinical prediction models and risk scores[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Therefore, we primarily rely on conclusions drawn from two competing risk models in this study's context. Additionally, our results emphasize the importance of considering potential biases arising from patient mortality represented by competing risk events when investigating prognostic risk factors among cancer patients.\u003c/p\u003e \u003cp\u003eCompared with previous PUM prediction models, the competing risk model of PUM in this study can not only more accurately predict survival prognosis, but also be used to explore various factors affecting survival prognosis. This is a unique model of innovation.\u003c/p\u003e \u003cp\u003eThere are some limitations to the study. As a retrospective study, there may have been bias of the selection process and inaccuracies in recorded data. Observational design limits drawing causal conclusions, and some important clinical variables may not have been adequately documented. Due to differences in data measurements, diagnostic methods, and treatment protocols, consistency may suffer over time. Although confounders were adjusted using multivariate models, factors such as gene expression profiles and chromosomal factors were not taken into account. And differences in follow-up time may lead to potential bias. Moreover, there was a lack of precise information regarding the therapeutic procedure, including the specific surgical approach employed and the dosage of radiotherapy administered. Therefore, it is important to validate these findings externally in different populations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis research developed competing risk models to evaluate prognostic factors for PUM, demonstrating greater accuracy compared to conventional methods that fail to account for competing risk factors. The study's findings will assist clinicians in comprehending PUM, enhancing clinical decision making and creating personalized treatment plans.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe clinical data in this retrospective study were collected from the publicly available SEER database, so there were no local or national ethical issues, and informed consent was not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRuisheng Huang:\u003c/strong\u003e Acquiring funding, initial drafting of the manuscript. \u003cstrong\u003eChen Jian:\u0026nbsp;\u003c/strong\u003eAnalyzing the data. \u003cstrong\u003eLimin Lin:\u0026nbsp;\u003c/strong\u003eSupervising and reviewing/editing the writing process. \u003cstrong\u003eJun Lyu:\u003c/strong\u003e Developing the methodology, overseeing project administration and resources, reviewing and editing the writing. \u003cstrong\u003eQing Zhou:\u003c/strong\u003e Supervising and reviewing/editing the writing process.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors express their gratitude to the Surveillance, Epidemiology, and End Results (SEER) database for its valuable assistance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted of the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublisher's note\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll opinions expressed in this article belong solely to the authors and do not necessarily reflect those of their affiliated organizations, or the publisher, editors, and reviewers. The publisher does not guarantee or endorse any product that may be evaluated in this article or any claims made by its manufacturer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData used in this study are publicly available and can be accessed of the SEER program (https://seer.cancer.gov/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eORCID\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRuisheng Huang \u0026nbsp; \u0026nbsp;https://orcid.org/0009-0003-6977-8443\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMcLaughlin CC, Wu X-C, Jemal A\u003cem\u003e et al.\u003c/em\u003e Incidence of noncutaneous melanomas in the u.S. \u003cem\u003eCancer \u003c/em\u003e2005;\u003cstrong\u003e103\u003c/strong\u003e:1000-7. https://doi.org/10.1002/cncr.20866\u003c/li\u003e\n\u003cli\u003eScott JF, Vyas R, Galvin J\u003cem\u003e et al.\u003c/em\u003e Primary bilateral uveal melanoma: A population-based study and systematic review. \u003cem\u003eClinical \u0026amp; experimental ophthalmology \u003c/em\u003e2018;\u003cstrong\u003e46\u003c/strong\u003e:502-10. https://doi.org/10.1111/ceo.13129\u003c/li\u003e\n\u003cli\u003eKaliki S, Shields CL. 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How the ageing microenvironment influences tumour progression. \u003cem\u003eNature reviews Cancer \u003c/em\u003e2020;\u003cstrong\u003e20\u003c/strong\u003e:89-106. https://doi.org/10.1038/s41568-019-0222-9\u003c/li\u003e\n\u003cli\u003eSynoradzki KJ, Paduszyńska N, Solnik M\u003cem\u003e et al.\u003c/em\u003e From molecular biology to novel immunotherapies and nanomedicine in uveal melanoma. \u003cem\u003eCurrent oncology (Toronto, Ont) \u003c/em\u003e2024;\u003cstrong\u003e31\u003c/strong\u003e:778-800. https://doi.org/10.3390/curroncol31020058\u003c/li\u003e\n\u003c/ol\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":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"primary uveal melanoma, competitive risk, prognosis, analysis","lastPublishedDoi":"10.21203/rs.3.rs-5963224/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5963224/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe presence of competing risks suggests that the classic Cox proportional hazards model may yield biased results when assessing prognostic factors for primary uveal melanoma (PUM) patients.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThe aim of this research is to utilize a competing risk model using the Surveillance, Epidemiology, and End Results (SEER) database in order to discover predictive factors for individuals with PUM and contrast them with the conventional Cox proportional hazards model.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe collected information on individuals who were diagnosed with PUM and registered in the SEER database from 2010 to 2015. The univariate analysis involved the application of the cumulative incidence function and Gray's test, while a multivariate analysis was conducted using the Fine-Gray, cause-specific (CS) and Cox proportional hazards models.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong the 1712 eligible patients diagnosed with PUM, 631 individuals passed away: 400 due to PUM and 231 from other causes. One-way Gray\u0026rsquo;s test indicated that seven variables significantly influenced the survival prognosis of PUM patients (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Multivariate competing risk models indicated that age, race, histologic type, AJCC stage, surgery and chemotherapy were independent predictors for cause-specific survival of\u003c/p\u003e","manuscriptTitle":"Competitive risk analysis of prognosis in patients with primary uveal melanoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-21 06:37:33","doi":"10.21203/rs.3.rs-5963224/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-05T14:17:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-25T09:09:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-17T18:49:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"205591653791475023677958836024284989991","date":"2025-04-17T18:31:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-16T10:12:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"89892276339855818191024601944281349717","date":"2025-04-16T08:27:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-16T06:57:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-15T11:41:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2025-03-31T08:53:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fbfa1502-e6fe-4305-ab5a-d946ceee4050","owner":[],"postedDate":"April 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T14:54:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-21 06:37:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5963224","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5963224","identity":"rs-5963224","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}