An online survival predictor in glioma patients using machine learning based on WHO CNS5 data

Liguo Ye; Lingui Gu; Zhiyao Zheng; Xin Zhang; Hao Xing; Xiaopeng Guo; Wenlin Chen; Yaning Wang; Yuekun Wang; Tingyu Liang; Hai Wang; Yilin Li; Shanmu Jin; Yixin Shi; Delin Liu; Tianrui Yang; Qianshu Liu; Congcong Deng; Yu Wang; Wenbin Ma

doi:10.3389/fneur.2023.1179761

An online survival predictor in glioma patients using machine learning based on WHO CNS5 data

Front Neurol. 2023 May 19:14:1179761. doi: 10.3389/fneur.2023.1179761. eCollection 2023.

Authors

Liguo Ye¹, Lingui Gu¹, Zhiyao Zheng^{1

2

3}, Xin Zhang¹, Hao Xing¹, Xiaopeng Guo^{1

4}, Wenlin Chen¹, Yaning Wang¹, Yuekun Wang¹, Tingyu Liang¹, Hai Wang¹, Yilin Li^{1

5}, Shanmu Jin^{1

5}, Yixin Shi^{1

6}, Delin Liu^{1

6}, Tianrui Yang^{1

6}, Qianshu Liu^{1

6}, Congcong Deng¹, Yu Wang^{1

4}, Wenbin Ma^{1

4}

Affiliations

¹ Department of Neurosurgery, Center for Malignant Brain Tumors, National Glioma MDT Alliance, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
² Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
³ Research Unit of Accurate Diagnosis, Treatment, and Translational Medicine of Brain Tumors (No. 2019RU011), Chinese Academy of Medical Sciences, Beijing, China.
⁴ China Anti-Cancer Association Specialty Committee of Glioma, Beijing, China.
⁵ 4+4 Medical Doctor Program, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
⁶ Eight-year Medical Doctor Program, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Abstract

Background: The World Health Organization (WHO) CNS5 classification system highlights the significance of molecular biomarkers in providing meaningful prognostic and therapeutic information for gliomas. However, predicting individual patient survival remains challenging due to the lack of integrated quantitative assessment tools. In this study, we aimed to design a WHO CNS5-related risk signature to predict the overall survival (OS) rate of glioma patients using machine learning algorithms.

Methods: We extracted data from patients who underwent an operation for histopathologically confirmed glioma from our hospital database (2011-2022) and split them into a training and hold-out test set in a 7/3 ratio. We used biological markers related to WHO CNS5, clinical data (age, sex, and WHO grade), and prognosis follow-up information to identify prognostic factors and construct a predictive dynamic nomograph to predict the survival rate of glioma patients using 4 kinds machine learning algorithms (RF, SVM, XGB, and GLM).

Results: A total of 198 patients with complete WHO5 molecular data and follow-up information were included in the study. The median OS time of all patients was 29.77 [95% confidence interval (CI): 21.19-38.34] months. Age, FGFR2, IDH1, CDK4, CDK6, KIT, and CDKN2A were considered vital indicators related to the prognosis and OS time of glioma. To better predict the prognosis of glioma patients, we constructed a WHO5-related risk signature and nomogram. The AUC values of the ROC curves of the nomogram for predicting the 1, 3, and 5-year OS were 0.849, 0.835, and 0.821 in training set, and, 0.844, 0.943, and 0.959 in validation set. The calibration plot confirmed the reliability of the nomogram, and the c-index was 0.742 in training set and 0.775 in validation set. Additionally, our nomogram showed a superior net benefit across a broader scale of threshold probabilities in decision curve analysis. Therefore, we selected it as the backend for the online survival prediction tool (Glioma Survival Calculator, https://who5pumch.shinyapps.io/DynNomapp/), which can calculate the survival probability for a specific time of the patients.

Conclusion: An online prognosis predictor based on WHO5-related biomarkers was constructed. This therapeutically promising tool may increase the precision of forecast therapy outcomes and assess prognosis.

Keywords: WHO CNS5; glioma; machine learning; predictive analytics; prognosis.