Literature DB >> 33927308

Competing risk model to determine the prognostic factors and treatment strategies for elderly patients with glioblastoma.

Zhuo-Yi Liu1,2,3, Song-Shan Feng2,4,5, Yi-Hao Zhang2,3, Li-Yang Zhang2,3, Sheng-Chao Xu2,3, Jing Li6, Hui Cao7, Jun Huang2,3, Fan Fan2,3,8, Li Cheng9, Jun-Yi Jiang10, Quan Cheng11,12, Zhi-Xiong Liu13,14.   

Abstract

The prognostic factors and optimal treatment for the elderly patient with glioblastoma (GBM) were poorly understood. This study extracted 4975 elderly patients (≥ 65 years old) with histologically confirmed GBM from Surveillance, Epidemiology and End Results (SEER) database. Firstly, Cumulative incidence function and cox proportional model were utilized to illustrate the interference of non-GBM related mortality in our cohort. Then, the Fine-Gray competing risk model was applied to determine the prognostic factors for GBM related mortality. Age ≥ 75 years old, white race, size > 5.4 cm, frontal lobe tumor, and overlapping lesion were independently associated with more GBM related death, while Gross total resection (GTR) (HR 0.87, 95%CI 0.80-0.94, P = 0.010), radiotherapy (HR 0.64, 95%CI 0.55-0.74, P < 0.001), chemotherapy (HR 0.72, 95%CI 0.59-0.90, P = 0.003), and chemoRT (HR 0.43, 95%CI 0.38-0.48, P < 0.001) were identified as independently protective factors of GBM related death. Based on this, a corresponding nomogram was conducted to predict 3-, 6- and 12-month GBM related mortality, the C-index of which were 0.763, 0.718, and 0.694 respectively. The calibration curve showed that there was a good consistency between the predicted and the actual mortality probability. Concerning treatment options, GTR followed by chemoRT is suggested as optimal treatment. Radiotherapy and chemotherapy alone also provide moderate clinical benefits.

Entities:  

Year:  2021        PMID: 33927308     DOI: 10.1038/s41598-021-88820-5

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.379


  2 in total

1.  Nomograms for Predicting the Overall and Cancer-Specific Survival of Patients with High-Grade Glioma: A Surveillance, Epidemiology, and End Results Study.

Authors:  Yuhan Xia; Weixin Liao; Shaozhuo Huang; Zhicheng Liu; Xiaowen Huang; Chen Yang; Chao Ye; Yingjie Jiang; Jun Wang
Journal:  Turk Neurosurg       Date:  2020       Impact factor: 1.003

2.  Prognostic Nomograms for Primary High-Grade Glioma Patients in Adult: A Retrospective Study Based on the SEER Database.

Authors:  Yi Yang; Mingze Yao; Shengrong Long; Chengran Xu; Lun Li; Yinghui Li; Guangyu Li
Journal:  Biomed Res Int       Date:  2020-07-23       Impact factor: 3.411

  2 in total
  3 in total

1.  Optimizing management of the elderly patient with glioblastoma: Survival prediction online tool based on BC Cancer Registry real-world data.

Authors:  Rachel Zhao; Jonathan Zeng; Kimberly DeVries; Ryan Proulx; Andra Valentina Krauze
Journal:  Neurooncol Adv       Date:  2022-04-13

2.  Functions of RNF Family in the Tumor Microenvironment and Drugs Prediction in Grade II/III Gliomas.

Authors:  Jingwei Zhang; Zeyu Wang; Hao Zhang; Ziyu Dai; Xisong Liang; Shuwang Li; Xun Zhang; Fangkun Liu; Zhixiong Liu; Kui Yang; Quan Cheng
Journal:  Front Cell Dev Biol       Date:  2022-02-09

3.  Factors Influencing the Improvement of Activities of Daily Living during Inpatient Rehabilitation in Newly Diagnosed Patients with Glioblastoma Multiforme.

Authors:  Keisuke Natsume; Harutoshi Sakakima; Kentaro Kawamura; Akira Yoshida; Shintaro Akihiro; Hajime Yonezawa; Koji Yoshimoto; Megumi Shimodozono
Journal:  J Clin Med       Date:  2022-01-14       Impact factor: 4.241

  3 in total

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