Primary central nervous system lymphoma prognosis analysis marker, prognosis analysis system and application thereof

Abstract

The invention belongs to the technical field of disease prognosis and molecular biology, and particularly relates to a primary central nervous system lymphoma prognosis analysis marker, a prognosis analysis system and application of the primary central nervous system lymphoma prognosis analysis marker. Specifically, the invention provides the prognosis analysis marker for the primary central nervous system lymphoma, the prognosis analysis marker comprises age, ECOG score and MYC protein, and a prognosis prediction model suitable for the primary central nervous system lymphoma is further constructed. According to the model, the three key independent variables are integrated, the influence of the three key independent variables on the total lifetime of the patient is defined, the capability of the model is superior to that of a current common clinical staging system in the aspect of risk stratification, and a scientific basis can be provided for prognosis evaluation and treatment decision of the patient with primary central nervous system lymphoma; the overall treatment effect and the life quality of the patient can be improved, so that the method has a good practical application value.

Description

Technical Field

[0001] This invention belongs to the field of disease prognosis and molecular biology technology, specifically relating to a prognostic biomarker, prognostic analysis system and its application for primary central nervous system lymphoma. Background Technology

[0002] The information disclosed in this background section is intended only to enhance understanding of the overall background of the invention and is not necessarily to be construed as an admission or in any way implying that such information constitutes prior art known to those skilled in the art.

[0003] Primary central nervous system lymphoma (PCNSL) is a rare extranodal non-Hodgkin lymphoma. The lesions are primarily confined to the brain parenchyma, spinal cord, pia mater, and eye. The annual incidence is approximately 0.4 to 0.5 cases per 100,000 people, accounting for 3%-4% of newly diagnosed brain tumors and 4%-6% of extranodal non-Hodgkin lymphomas. This pathological type has the worst prognosis among all non-Hodgkin lymphomas; therefore, early and accurate prognostic assessment and the development of individualized treatment strategies are crucial for improving patient outcomes.

[0004] Currently, commonly used prognostic scoring systems for PCNSL include the scoring system proposed by the International Extranodal Lymphoma Working Group (IELSG), the scoring system proposed by Memorial Sloan Kettering Cancer Center (MSKCC), and the Taipei scoring system, which has been proposed in recent years. However, the inventors have found that all three scoring systems have significant limitations: the IELSG scoring system relies on lactate dehydrogenase (LDH) and cerebrospinal fluid protein test results, but the clinical accessibility of these tests varies across different medical institutions, limiting its application; the MSKCC scoring system only includes age and Karnofsky Performance Status (KPS) scores, making its assessment dimension singular and unable to comprehensively reflect the severity of the disease and prognostic risk, resulting in insufficient predictive accuracy; the Taipei scoring system sets the age limit at 80 years, limiting its ability to differentiate between low-risk and intermediate-risk patients. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a prognostic biomarker, a prognostic analysis system, and their applications for PCNSL. Specifically, this invention constructs a prognostic prediction model EAAM for PCNSL based on high-risk immunohistochemical biomarkers. This model integrates three key independent variables and clarifies their impact on overall survival (OS), demonstrating superior ability in risk stratification compared to commonly used clinical staging systems (including MSKCC, IELSG, and Taipei scores). This invention is based on the above research findings.

[0006] To achieve the above technical objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a prognostic biomarker for PCNSL, the prognostic biomarker including age, ECOG score and MYC protein.

[0007] The prognostic analysis includes the analysis and assessment of overall survival (OS) and / or progression-free survival (PFS) in PCNSL patients; OS is preferred.

[0008] A second aspect of the present invention provides the use of reagents for detecting the above-mentioned prognostic biomarkers in the preparation of products for prognostic analysis of PCNSL patients.

[0009] A third aspect of the present invention provides a PCNSL prognostic analysis system EAAM, the prognostic analysis system comprising: The acquisition unit is configured to acquire the aforementioned prognostic analysis biomarkers of the subjects; An assessment unit is configured to predict the prognosis of the PCNSL patient based on prognostic biomarkers obtained by the acquisition unit. The output unit is configured to output the prognostic analysis results based on the evaluation unit.

[0010] In a fourth aspect, the present invention provides a computer-readable storage medium having a program stored thereon that, when executed by a processor, performs the functions of the PCNSL prognostic analysis system as described in the third aspect of the present invention.

[0011] In a fifth aspect, the present invention provides an electronic device including a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to perform the functions of the PCNSL prognostic analysis system as described in the third aspect of the present invention.

[0012] Compared with existing technical solutions, one or more of the above technical solutions have the following beneficial effects: The above-mentioned technical solution is constructed and validated based on large-scale real-world clinical data. The included predictive factors (age, ECOG score, and MYC protein expression) are all routine clinical indicators, with high accessibility and low testing costs, overcoming the IELSG scoring system's dependence on specific testing items. Simultaneously, the solution integrates clinical characteristics with immunohistochemical biomarkers, providing a more comprehensive assessment dimension and addressing the issue of the MSKCC scoring system's single assessment indicator, significantly improving predictive accuracy. Precise risk stratification is achieved through quantitative scoring, revealing significant differences in survival among patients in different risk groups. Furthermore, the model demonstrates good stability and applicability across different age subgroups, indicating high clinical practical value. The assessment tools built upon the model (such as nomograms) are easy to operate, requiring no complex calculations, and are convenient for clinical application. They provide a scientific basis for prognostic assessment and treatment decisions for PCNSL patients, contributing to improved overall treatment outcomes and quality of life, thus possessing significant practical application value. Attached Figure Description

[0013] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0014] Figure 1 This invention investigates the KM survival curves for median overall survival (OS) and median progression-free survival (PFS) in a cohort dataset, as well as the nomogram and ROC curve of the prognostic prediction model EAAM. In the figures, A represents the KM survival curve for OS in the training and validation sets; B represents the KM survival curve for PFS in the training and validation sets; C represents the nomogram of EAAM predicting OS in PCNSL patients based on the training set; D represents the ROC curve of EAAM in the training set; and E represents the ROC curve of EAAM in the validation set.

[0015] Figure 2 These are the calibration curves and decision curves for the EAAM model of this invention on the training and validation sets. Specifically, A, B, and C are the calibration curves for EAAM predicting 1-year, 2-year, and 3-year overall survival (OS) on the training set, respectively; D, E, and F are the calibration curves for EAAM predicting 1-year, 2-year, and 3-year OS on the validation set, respectively; G, H, and I are the decision curves for the clinical applicability of EAAM predicting 1-year, 2-year, and 3-year OS on the training set, respectively; and J, K, and L are the decision curves for the clinical applicability of EAAM predicting 1-year, 2-year, and 3-year OS on the validation set, respectively.

[0016] Figure 3These are the OS and PFS survival curves for different risk strata and the OS survival curves for different age subgroups in the EAAM model of this invention. Specifically, A is the KM survival curve for OS in different risk strata of the EAAM model; B is the KM survival curve for PFS in different risk strata of the EAAM model; C is the KM survival curve for OS in the <65 years age subgroup in different risk strata of the EAAM model; and D is the KM survival curve for OS in the ≥65 years age subgroup in different risk strata of the EAAM model.

[0017] Figure 4 This diagram compares the prognostic efficacy of the EAAM model of this invention with existing MSKCC, IELSG, and Taipei scoring systems, and presents a risk stratification distribution impact diagram. Specifically, A represents the KM survival curves for OS prediction under different risk strata in the EAAM model; B represents the KM survival curves for OS prediction under different risk strata in the MSKCC model; C represents the KM survival curves for OS prediction under different risk strata in the Taipei model; D represents the KM survival curves for OS prediction under different risk strata in the IELSG model; and E represents the risk stratification distribution impact diagrams for the MSKCC, Taipei, and EAAM models. Detailed Implementation

[0018] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0019] It should be noted that the terminology used herein is for descriptive purposes only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof. Additionally, the molecular biology methods not detailed in the embodiments are conventional methods in the art; specific operations can be found in molecular biology guides or product manuals.

[0020] In a typical embodiment of the present invention, a prognostic biomarker for PCNSL is provided, the prognostic biomarker including age, ECOG score and MYC protein.

[0021] The prognostic analysis includes the analysis and evaluation of OS and / or PFS in PCNSL patients; OS is preferred.

[0022] In another specific embodiment of the present invention, the use of reagents for detecting the above-mentioned prognostic biomarkers in the preparation of products for prognostic analysis of PCNSL patients is provided.

[0023] The prognostic analysis includes the analysis and assessment of OS and / or PFS in DLBCL patients.

[0024] In another specific embodiment of the present invention, a PCNSL prognostic analysis system is provided, the prognostic analysis system comprising: The acquisition unit is configured to acquire the aforementioned prognostic analysis biomarkers of the subjects; An assessment unit is configured to predict the prognosis of the PCNSL patient based on prognostic biomarkers obtained by the acquisition unit. The output unit is configured to output the prognostic analysis results based on the evaluation unit.

[0025] The assessment unit includes at least one PCNSL prognostic assessment model, and the predictive factors of the prognostic assessment model include: age, ECOG, and MYC protein; wherein the risk assignment rules for each predictive factor are as follows: age ≥ 60 years is assigned 1 point, age < 60 years is assigned 0 points; ECOG score ≥ 2 points is assigned 2 points, ECOG score < 2 points is assigned 0 points; MYC protein expression positive is assigned 1 point, and MYC protein expression negative is assigned 0 points.

[0026] Furthermore, the criteria for determining positive MYC protein expression are that the proportion of tumor cells with positive MYC-specific antibody staining observed under a microscope during immunohistochemistry is ≥40% of the total number of tumor cells, and <40% is considered negative.

[0027] The risk stratification criteria of the prognostic assessment model are as follows: a total score of 0 indicates a low-risk group, a total score of 1-2 indicates an intermediate-risk group, and a total score of 3-4 indicates a high-risk group; among them, the overall survival and progression-free survival of patients in the low-risk group, intermediate-risk group, and high-risk group decrease in that order, and the differences between the groups are statistically significant.

[0028] Therefore, when the total score of the prognostic assessment model of the subject is 0, they belong to the low-risk group, and their overall survival and progression-free survival are relatively long; when the total score of the prognostic assessment model of the subject is 1-2, they belong to the intermediate-risk group, and their overall survival and progression-free survival are in the middle; when the total score of the prognostic assessment model of the subject is 3-4, they belong to the high-risk group, and their overall survival and progression-free survival are relatively short.

[0029] In another specific embodiment of the present invention, a computer-readable storage medium is provided having a program stored thereon, which, when executed by a processor, implements the functions of the PCNSL prognostic analysis system as described in the present invention.

[0030] In another specific embodiment of the present invention, an electronic device is provided, including a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the functions of the PCNSL prognostic analysis system as described in the present invention.

[0031] The present invention will be further illustrated below with specific examples. These examples are for illustrative purposes only and do not limit the scope of the invention. Experimental conditions not specifically specified in the examples are generally performed under conventional conditions or as recommended by the sales company; unless otherwise specified in the present invention, these conditions are commercially available.

[0032] Example 1 1 Research Methods 1.1 Research Subjects This retrospective cohort study selected 180 newly diagnosed PCNSL patients who visited Shandong Provincial Hospital between January 2012 and January 2025. Patients were randomly assigned to the training and validation sets at a ratio of 7:3. Patients included in this study met the following criteria: (1) pathologically confirmed PCNSL according to the 2016 World Health Organization Classification of Tumors of Hematopoietic and Lymphoid Tissues; (2) no history of immunosuppression or organ transplantation; (3) HIV serological negativity; (4) no other malignant tumors; and (5) sufficient clinical, laboratory, and follow-up data. In accordance with the principles outlined in the Declaration of Helsinki, all participants were informed of the purpose of the study and provided signed informed consent forms before participating.

[0033] 1.2 Data Collection Baseline demographic and clinical data were collected from all study participants prior to the start of treatment. Parameters collected included age, sex, primary tumor location, number of lesions, histological type, and functional status assessed using the Karnofsky Performance Status (KPS) score and the Eastern Cooperative Oncology Group (ECOG) Functional Status Scale. In addition, risk stratification scores (MSKCC, IELSG, and Taipei scores) and a comprehensive range of laboratory values ​​were obtained, including complete blood counts (white blood cells, red blood cells, hemoglobin, platelets, monocytes, neutrophils), creatinine, total bilirubin, total cholesterol, serum glucose, albumin, lactate dehydrogenase (LDH), and β2-microglobulin (β2-MG).

[0034] 1.3 Patient follow-up The primary endpoint of this study was overall survival (OS), defined as the time from initial diagnosis to death from any cause or the last follow-up. The secondary endpoint was progression-free survival (PFS), defined as the duration from initial diagnosis to disease progression, death from any cause, or the last follow-up. Patient follow-up was conducted via telephone or outpatient visits to confirm their survival status, with a cutoff date of April 2025.

[0035] 1.4 Statistical Analysis The following statistical methods were used to analyze and model the data. Continuous variables, after normality testing, were expressed as mean ± standard deviation if they conformed to a normal distribution, and compared between groups using a t-test; otherwise, they were expressed as median [interquartile range (IQR)], and compared using the Mann-Whitney U test. Categorical variables were described as frequencies (percentages), and comparisons between groups were performed using a chi-square test or Fisher's exact test. Survival analysis used the Kaplan-Meier method to plot OS and PFS curves, and the log-rank test was used to compare differences. Univariate Cox regression was used to screen variables significantly associated with survival (…). p <0.05, and was incorporated into a multivariate Cox proportional hazards regression model to identify independent prognostic factors. Results were expressed as hazard ratios (HR) and their 95% confidence intervals (CI). A prognostic prediction model was constructed based on the multivariate analysis results, and a nomogram was plotted. The model's discrimination, calibration ability, and clinical applicability were evaluated using time-dependent receiver operating characteristic (ROC) curve analysis, calibration curve analysis, and decision curve analysis (DCA). Furthermore, impact plots were used to visually demonstrate the distribution relationship between the risk stratification defined by the new model and the included risk factors in the training and validation sets. All hypothesis tests were two-sided. p A value <0.05 was considered statistically significant. Data analysis was performed using SPSS 27.0 software and R language (version 4.5.1).

[0036] 2. Research Results 2.1 Baseline clinical characteristics of patients This retrospective study included 180 patients diagnosed with PCNSL between January 2012 and January 2025. These patients were randomly assigned to a training set (n = 126) and a validation set (n = 54). As shown in Table 1, the baseline characteristics of the two cohorts were comparable. Specifically, the proportion of patients with an ECOG score ≥2 was 78.2% in the training set and 78.6% in the validation set. Analysis of the treatment regimens used by all patients revealed that: 80 patients (44.4%) underwent surgical resection; 36 patients (20.0%) received chemotherapy based on rituximab combined with methotrexate (R-MTX); 30 patients (16.7%) received combination chemotherapy and radiotherapy; 11 patients (6.1%) received radiotherapy alone; 5 patients (2.8%) received single-agent MTX chemotherapy; 9 patients (5.0%) received other chemotherapy regimens; and 9 patients (5.0%) received only supportive care. Regarding survival outcomes, the median OS and median PFS on the training set were 51 months and 46 months, respectively. The corresponding values ​​on the validation set were 46 months and 44 months, respectively. Figure 1 (AB). These data indicate that the baseline feature distribution is relatively balanced between the two cohorts.

[0037] 2.2 Construction of PCNSL prognostic model To identify the independent influencing factors of survival outcomes, we performed univariate and multivariate Cox regression analyses in the PCNSL cohort. In the training set, univariate analysis showed that age ≥60 years (hazard ratio [HR] = 2.54) was a significant factor influencing survival outcomes. p <0.001), ECOG score ≥2 (HR=2.34, p =0.004), KPS score <70% (HR=2.16, p =0.017) and MYC protein expression (the proportion of tumor cells positive for MYC-specific antibody staining observed under immunohistochemical microscopy was ≥40% of the total number of tumor cells) (HR=1.80, p All of these factors (=0.03) significantly influenced overall survival (OS). These results were largely reproduced in the validation set, but the prognostic significance of a KPS score <70% did not reach statistical significance (HR=2.14, p =0.06 (Table 2). Further univariate analysis showed a significant correlation with OS ( pAfter including variables with values ​​<0.05 in multivariate Cox regression analysis, the results showed that age ≥60 years, ECOG score ≥2, and MYC protein expression were all independent influencing factors for overall survival (OS) in both the training and validation sets (Table 3). Based on the independent prognostic factors identified in the aforementioned multivariate analysis, this study successfully constructed a novel prognostic prediction model, EAAM, and plotted the corresponding clinical nomogram accordingly. Figure 1 C).

[0038] 2.3 Validation of the PCNSL prognostic model To evaluate the predictive power of EAAM, we validated its discrimination and calibration on both the training and validation sets. The results showed that EAAM's prediction of OS had a C-statistic of 0.68 on the training set and 0.86 on the validation set. Meanwhile, the time-dependent area under the receiver operating characteristic (AUC) curves at 1, 2, and 3 years were 0.74 (95% CI: 0.63–0.86), 0.77 (95% CI: 0.67–0.87), and 0.74 (95% CI: 0.63–0.84) on the training set, respectively, while these values ​​improved to 0.89 (95% CI: 0.73–1.04), 0.92 (95% CI: 0.81–1.03), and 0.94 (95% CI: 0.88–1.02) on the validation set, respectively. Figure 1 DE). Further calibration analysis showed that the calibration curves of the OS for 1, 2, and 3 years in both the training and validation sets were roughly consistent with the ideal reference line, suggesting that the predicted probability was consistent with the observed results. Figure 2 (A–F). In summary, EAAM demonstrates excellent performance in both discrimination and calibration, exhibiting robust predictive performance and good generalization ability. Decision curve analysis was used to evaluate the clinical applicability of EAAM. This analysis graphically displays the continuous decision thresholds for mortality risk on the X-axis and shows the net benefit of using the model for risk stratification compared to the assumption of "no deaths" on the Y-axis. The results indicate that EAAM demonstrates good clinical net benefit in predicting OS in PCNSL patients on both the training and validation sets. Figure 2 GL).

[0039] 2.4 A Novel Predictive Framework for Precise Risk Stratification Based on the risk hazard ratio (HR) values ​​for each factor in the multivariate Cox analysis, this study assigned a score of 1 for both age ≥60 years and immunohistochemical MYC protein expression (due to their similar HR values), and a score of 2 for ECOG ≥2. A new risk stratification model was constructed by accumulating the risk scores. The model's total score ranged from 0 to 4, dividing the entire cohort into three risk levels: low-risk (0 points, 16.1%), intermediate-risk (1–2 points, 67.2%), and high-risk (3–4 points, 16.7%). Compared to the low-risk group, the mortality risk in the intermediate-risk and high-risk groups was approximately 4 times and 11 times, respectively (Table 4). The median overall survival (OS) for the low-risk, intermediate-risk, and high-risk groups were 84.0 months (95% CI: 81.0–96.0), 47.0 months (95% CI: 43.0–60.0), and 19.0 months (95% CI: 9.0–40.0), respectively; the median progression-free survival (PFS) were 76.0 months (95% CI: 73.0–86.0), 44.0 months (95% CI: 39.0–52.0), and 10.0 months (95% CI: 7.0–40.0), respectively. The differences in OS and PFS among the different risk stratification groups were statistically significant. Figure 3 AB). Secondly, to evaluate the practicality of EAAM, we conducted analyses in different age subgroups (<65 years and ≥65 years). The results showed that EAAM's risk stratification effectively distinguished patient prognosis across different age groups, further validating the practicality and reproducibility of the model. Figure 3 CD). Furthermore, to further evaluate the performance of EAAM, we conducted a comprehensive comparison with the MSKCC, Taipei, and IELSG prognostic scoring models. Comparison of the KM curves on overall survival (OS) of each model shows that the new model has the highest discriminative power for patient prognosis (CD). Figure 4 Furthermore, we systematically compared the discriminative abilities of the MSKCC model, the Taipei model, and EAAM by analyzing the risk stratification distribution displayed in the impact plot. The MSKCC prognostic score risk groups were relatively dispersed in the plot, with some intermediate-risk patients overlapping with the high-risk interval, indicating some ambiguity in the intermediate-risk segment. The Taipei model's risk stratification had a wider distribution in the intermediate-risk group and overlapped with the high- and low-risk groups, suggesting that its stratification boundaries were not clear enough. In contrast, the EAAM model established in this study showed concentrated distributions in the low-, intermediate-, and high-risk groups, with significantly reduced inter-stratification overlap and clearer risk boundaries. Figure 4 E). The above results indicate that the new model outperforms the MSKCC and Taipei scoring systems in risk discrimination, demonstrating better accuracy and clinical applicability.

[0040] Table 1. Clinical characteristics of the training and validation sets.

[0041] Table 2 Univariate Cox regression analysis of OS-related risk factors in PCNSL patients

[0042] Table 3. Multivariate Cox regression analysis of OS-related risk factors in PCNSL patients

[0043] Table 4 Comparison of different hazard stratification groups in the new model EAAM

[0044] Example 2 This embodiment provides a PCNSL prognostic analysis system, the prognostic analysis system comprising: The acquisition unit is configured to acquire the aforementioned prognostic analysis biomarkers of the subjects; An assessment unit is configured to predict progression-free survival of the PCNSL patients based on prognostic biomarkers obtained by the acquisition unit. The output unit is configured to output the progression-free survival prediction results based on the evaluation unit.

[0045] The assessment unit includes at least one prognostic assessment model for primary central nervous system lymphoma.

[0046] The system can be operated in accordance with the method for prognostic analysis (OS and PFS) of patients with primary central nervous system lymphoma as described in Embodiment 1 of the present invention.

[0047] Matters not covered in this invention are common knowledge.

[0048] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A prognostic biomarker for primary central nervous system lymphoma, characterized in that, The prognostic biomarkers include age, ECOG score, and MYC protein.

2. The use of the reagent for detecting the prognostic biomarker of claim 1 in the preparation of a prognostic analysis product for patients with primary central nervous system lymphoma.

3. The application as described in claim 2, characterized in that, The prognostic analysis includes the analysis and assessment of OS and / or PFS in DLBCL patients.

4. A prognostic analysis system for primary central nervous system lymphoma, characterized in that, The prognostic analysis system includes: An acquisition unit, configured to: acquire the prognostic analysis biomarker of the subject as described in claim 1; An assessment unit is configured to predict the prognosis of the patient with primary central nervous system lymphoma based on prognostic biomarkers obtained by the acquisition unit. The output unit is configured to output the prognostic analysis results based on the evaluation unit.

5. The system as described in claim 4, characterized in that, The assessment unit includes at least one prognostic assessment model for primary central nervous system lymphoma, and the predictive factors of the prognostic assessment model include: age, ECOG, and MYC protein; The risk assignment rules for each predictor are as follows: 1 point for age ≥ 60 years and 0 points for age < 60 years; 2 points for ECOG score ≥ 2 and 0 points for ECOG score < 2; 1 point for positive MYC protein expression and 0 points for negative MYC protein expression.

6. The system as described in claim 4, characterized in that, The criterion for determining MYC protein expression positivity is that the proportion of tumor cells showing positive staining for MYC-specific antibodies under immunohistochemical microscopy is ≥40% of the total number of tumor cells.

7. The system as described in claim 4, characterized in that, The risk stratification criteria of the prognostic assessment model are as follows: a total score of 0 indicates a low-risk group, a total score of 1-2 indicates an intermediate-risk group, and a total score of 3-4 indicates a high-risk group; among them, the overall survival and progression-free survival of patients in the low-risk group, intermediate-risk group, and high-risk group decrease in that order.

8. The system as described in claim 4, characterized in that, When the total score of the prognostic assessment model of the subject is 0, the subject belongs to the low-risk group, and the overall survival and progression-free survival are relatively long; when the total score of the prognostic assessment model of the subject is 1-2, the subject belongs to the intermediate-risk group, and the overall survival and progression-free survival are in the middle; when the total score of the prognostic assessment model of the subject is 3-4, the subject belongs to the high-risk group, and the overall survival and progression-free survival are relatively short.

9. A computer-readable storage medium having a program stored thereon that, when executed by a processor, performs the functions of the prognostic analysis system for primary central nervous system lymphoma as claimed in any one of claims 4-8.

10. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor, when executing the program, performs the functions of the prognostic analysis system for primary central nervous system lymphoma as described in any one of claims 4-8.

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