Evidence map›Paper›PMID 40249416›Full record

ArticleDiscover oncology2025

Development and validation of nomogram model predicting overall survival and cancer specific survival in glioblastoma patients.

Yingming Mu, Junchi Luo, Tao Xiong, Junheng Zhang, Jinhai Lan, Jiqin Zhang, Ying Tan, Sha Yang

Abstract read
In one paragraph

Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Yingming MuDepartment of General Neurology, Ziyun Miao Buyi Autonomous County People's Hospital, Guiyang, China.
Junchi LuoDepartment of Neurosurgery, Guizhou Provincial People's Hospital, Guiyang, China.
Tao XiongDepartment of Neurosurgery, Guizhou Provincial People's Hospital, Guiyang, China.
Junheng ZhangDepartment of Neurosurgery, Guizhou Provincial People's Hospital, Guiyang, China.
Jinhai LanDepartment of Orthopedics, Ziyun Miao Buyi Autonomous County People's Hospital, Guiyang, China.
Jiqin ZhangDepartment of Anesthesiology, Guizhou Provincial People's Hospital, Guiyang, China.
Ying TanDepartment of Neurosurgery, Guizhou Provincial People's Hospital, Guiyang, China. tanyinggz5055@163.com.
Sha YangGuizhou University Medical College, Guiyang, 550025, Guizhou, China. shashayang520520@163.com.

Funding

Guizhou Province's funding for the cultivation of high-level innovative talents through the Thousand Talents Program gzwjrs2023-001Guizhou Province's funding for the cultivation of high-level innovative talents through the Thousand Talents Program gzwjrs2024-007Guizhou Provincial People's Hospital Youth Fund GZSYQN202202National Natural Science Foundation of China 82360376National Natural Science Foundation of China 82360482Science and Technology Fund of Guizhou Provincial Health Commission gzwkj2021-204
6 · The paper itself

Abstract

backgroundIdentifying the incidence and risk factors of Glioblastoma (GBM) and establishing effective predictive models will benefit the management of these patients.

methodsUsing 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.

resultsThe 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.

conclusionAt 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.

Indexed as

Cancer-specific survivalGlioblastomaOverall survivalPrognostic nomogramSEER

Identifiers

PMID40249416
PMCPMC12008090

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.