Development and validation of nomogram model predicting overall survival and cancer specific survival in glioblastoma patients
Abstract Background Identifying the incidence and risk factors of Glioblastoma (GBM) and establishing effective predictive models will benefit the management of these patients. Methods Using GBM data from the Surveillance, Epidemiology, and End Results (SEER) database, we used Joinpoint software to...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-04-01
|
| Series: | Discover Oncology |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s12672-025-02331-7 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850145199897968640 |
|---|---|
| author | Yingming Mu Junchi Luo Tao Xiong Junheng Zhang Jinhai Lan Jiqin Zhang Ying Tan Sha Yang |
| author_facet | Yingming Mu Junchi Luo Tao Xiong Junheng Zhang Jinhai Lan Jiqin Zhang Ying Tan Sha Yang |
| author_sort | Yingming Mu |
| collection | DOAJ |
| description | Abstract Background Identifying the incidence and risk factors of Glioblastoma (GBM) and establishing effective predictive models will benefit the management of these patients. Methods Using GBM data from the Surveillance, Epidemiology, and End Results (SEER) database, we used Joinpoint software to assess trends in GBM incidence across populations of different age groups. Subsequently, we identified important prognostic factors by stepwise regression and multivariate Cox regression analysis, and established a Nomogram mathematical model. COX regression model combined with restricted cubic splines (RCS) model was used to analyze the relationship between tumor size and prognosis of GBM patients. Results The incidence of GBM has been on the rise since 1978, especially in the age group of 65–84 years. 11498 patients with GBM were included in our study. The multivariate Cox analysis revealed that age, tumor size, sex, primary tumor site, laterality, number of primary tumors, surgery, chemotherapy, radiotherapy, systematic therapy, marital status, median household income, first malignant primary indicator were independent prognostic factors of overall survival (OS) for GBMs. For cancer-specific survival (CSS), race is also independent prognostic factors. Additionally, risk of poor prognosis increased significantly with tumor size in patients with tumors smaller than 49 mm. Moreover, our nomogram model showed favorable discriminative ability. Conclusion At the population level, the incidence of GBM is on the rise. The relationship between tumor size and patient prognosis is still worthy of further study. Moreover, the proposed nomogram with good performance was constructed and verified to predict the OS and CSS of patients with GBM. |
| format | Article |
| id | doaj-art-90e20df1c3ea469ba33ff41e560eae83 |
| institution | OA Journals |
| issn | 2730-6011 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Oncology |
| spelling | doaj-art-90e20df1c3ea469ba33ff41e560eae832025-08-20T02:28:09ZengSpringerDiscover Oncology2730-60112025-04-0116111810.1007/s12672-025-02331-7Development and validation of nomogram model predicting overall survival and cancer specific survival in glioblastoma patientsYingming Mu0Junchi Luo1Tao Xiong2Junheng Zhang3Jinhai Lan4Jiqin Zhang5Ying Tan6Sha Yang7Department of General Neurology, Ziyun Miao Buyi Autonomous County People’s HospitalDepartment of Neurosurgery, Guizhou Provincial People’s HospitalDepartment of Neurosurgery, Guizhou Provincial People’s HospitalDepartment of Neurosurgery, Guizhou Provincial People’s HospitalDepartment of Orthopedics, Ziyun Miao Buyi Autonomous County People’s HospitalDepartment of Anesthesiology, Guizhou Provincial People’s HospitalDepartment of Neurosurgery, Guizhou Provincial People’s HospitalGuizhou University Medical CollegeAbstract Background Identifying the incidence and risk factors of Glioblastoma (GBM) and establishing effective predictive models will benefit the management of these patients. Methods Using GBM data from the Surveillance, Epidemiology, and End Results (SEER) database, we used Joinpoint software to assess trends in GBM incidence across populations of different age groups. Subsequently, we identified important prognostic factors by stepwise regression and multivariate Cox regression analysis, and established a Nomogram mathematical model. COX regression model combined with restricted cubic splines (RCS) model was used to analyze the relationship between tumor size and prognosis of GBM patients. Results The incidence of GBM has been on the rise since 1978, especially in the age group of 65–84 years. 11498 patients with GBM were included in our study. The multivariate Cox analysis revealed that age, tumor size, sex, primary tumor site, laterality, number of primary tumors, surgery, chemotherapy, radiotherapy, systematic therapy, marital status, median household income, first malignant primary indicator were independent prognostic factors of overall survival (OS) for GBMs. For cancer-specific survival (CSS), race is also independent prognostic factors. Additionally, risk of poor prognosis increased significantly with tumor size in patients with tumors smaller than 49 mm. Moreover, our nomogram model showed favorable discriminative ability. Conclusion At the population level, the incidence of GBM is on the rise. The relationship between tumor size and patient prognosis is still worthy of further study. Moreover, the proposed nomogram with good performance was constructed and verified to predict the OS and CSS of patients with GBM.https://doi.org/10.1007/s12672-025-02331-7GlioblastomaPrognostic nomogramOverall survivalCancer-specific survivalSEER |
| spellingShingle | Yingming Mu Junchi Luo Tao Xiong Junheng Zhang Jinhai Lan Jiqin Zhang Ying Tan Sha Yang Development and validation of nomogram model predicting overall survival and cancer specific survival in glioblastoma patients Discover Oncology Glioblastoma Prognostic nomogram Overall survival Cancer-specific survival SEER |
| title | Development and validation of nomogram model predicting overall survival and cancer specific survival in glioblastoma patients |
| title_full | Development and validation of nomogram model predicting overall survival and cancer specific survival in glioblastoma patients |
| title_fullStr | Development and validation of nomogram model predicting overall survival and cancer specific survival in glioblastoma patients |
| title_full_unstemmed | Development and validation of nomogram model predicting overall survival and cancer specific survival in glioblastoma patients |
| title_short | Development and validation of nomogram model predicting overall survival and cancer specific survival in glioblastoma patients |
| title_sort | development and validation of nomogram model predicting overall survival and cancer specific survival in glioblastoma patients |
| topic | Glioblastoma Prognostic nomogram Overall survival Cancer-specific survival SEER |
| url | https://doi.org/10.1007/s12672-025-02331-7 |
| work_keys_str_mv | AT yingmingmu developmentandvalidationofnomogrammodelpredictingoverallsurvivalandcancerspecificsurvivalinglioblastomapatients AT junchiluo developmentandvalidationofnomogrammodelpredictingoverallsurvivalandcancerspecificsurvivalinglioblastomapatients AT taoxiong developmentandvalidationofnomogrammodelpredictingoverallsurvivalandcancerspecificsurvivalinglioblastomapatients AT junhengzhang developmentandvalidationofnomogrammodelpredictingoverallsurvivalandcancerspecificsurvivalinglioblastomapatients AT jinhailan developmentandvalidationofnomogrammodelpredictingoverallsurvivalandcancerspecificsurvivalinglioblastomapatients AT jiqinzhang developmentandvalidationofnomogrammodelpredictingoverallsurvivalandcancerspecificsurvivalinglioblastomapatients AT yingtan developmentandvalidationofnomogrammodelpredictingoverallsurvivalandcancerspecificsurvivalinglioblastomapatients AT shayang developmentandvalidationofnomogrammodelpredictingoverallsurvivalandcancerspecificsurvivalinglioblastomapatients |