Blood-Based Prognostic Prediction Model for Glioblastoma: Construction and Validation - Gao S; Liu Y; Kong J; Huangfu L; Yang Y; Cui H; Sun X; Shi S; Yang D (Cancer Management and Research)
Shibo Gao,* Yukun Liu,* Jinglin Kong, Linkuan Huangfu, Yuchuan Yang, Haiyang Cui, Xiaocong Sun, Shuling Shi, Daoke Yang Department of Radiation Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, People’s Republic of China*These authors contributed equally to this workCorrespondence: Daoke Yang, Department of Radiation Oncology, The First Affiliated Hospital of Zhengzhou University, Henan, People’s Republic of China, Email 15903650068@163.comObjective: Objective: To explore the prognostic factors affecting patients with glioblastoma (GBM) treated with the Stupp regimen and establish a prediction model based on hematological indicators to guide future clinical decision - making.Methods: A total of 271 GBM patients meeting the screening criteria were recruited. They were randomly divided into a training set (190 cases) and a validation set (81 cases) at a 7:3 ratio. The training set was utilized to establish a comprehensive hematology prognostic scoring system (CHPSS), and the validation set was employed to verify the CHPSS. A Risk Score (RS) was computed from the CHPSS, and a nomogram model was constructed to predict patients’ overall survival (OS) based on the RS. Additionally, the relationship between RS and the surgery - to - radiotherapy interval (SRI) was analyzed.Results: Patients were categorized into low - risk and high - risk groups according to the RS calculated by CHPSS. The overall survival of patients in these two groups differed significantly. The C - indices of the nomogram model constructed based on RS and clinical features were 0.79 and 0.73 in the training and validation sets, respectively. The clinical decision curve showed that when the threshold probability exceeded 20%, the model’s prediction provided the greatest net benefit for GBM patients receiving the Stupp regimen. In the overall cohort, a correlation between RS and SRI was observed, allowing for the classification of SRI into different risk subgroups based on RS.Conclusion: The nomogram model based on CHPSS can effectively evaluate the prognosis of glioblastoma patients.Keywords: prognostic scoring system, risk score, preoperative hematologic indicators, surgery-to-radiotherapy interval