Survival prediction of glioblastoma patients using modern deep learning and machine learning techniques

• Published in: Scientific reports 2024-01, Vol.14 (1), p.2371-12, Article 2371
• Main Authors: Babaei Rikan, Samin, Sorayaie Azar, Amir, Naemi, Amin, Bagherzadeh Mohasefi, Jamshid, Pirnejad, Habibollah, Wiil, Uffe Kock
• Format: Article
• Language: English
• Subjects: 631/114/1305; 631/114/1314; 631/114/2164; 631/114/2413; 631/67/1536; 631/67/70; 692/700/1750/1976; Brain cancer; Classification; Databases, Factual; Decision making; Deep Learning; Epidemiology; Glioblastoma; Glioblastoma - diagnosis; Humanities and Social Sciences; Humans; Hydrolases; Learning algorithms; Least squares method; Machine Learning; multidisciplinary; Neural networks; Predictions; Sampling; Science; Science (multidisciplinary); Skewness; Survival
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-53006-2

In this study, we utilized data from the Surveillance, Epidemiology, and End Results (SEER) database to predict the glioblastoma patients’ survival outcomes. To assess dataset skewness and detect feature importance, we applied Pearson's second coefficient test of skewness and the Ordinary Least Squares method, respectively. Using two sampling strategies, holdout and five-fold cross-validation, we developed five machine learning (ML) models alongside a feed-forward deep neural network (DNN) for the multiclass classification and regression prediction of glioblastoma patient survival. After balancing the classification and regression datasets, we obtained 46,340 and 28,573 samples, respectively. Shapley additive explanations (SHAP) were then used to explain the decision-making process of the best model. In both classification and regression tasks, as well as across holdout and cross-validation sampling strategies, the DNN consistently outperformed the ML models. Notably, the accuracy were 90.25% and 90.22% for holdout and five-fold cross-validation, respectively, while the corresponding R 2 values were 0.6565 and 0.6622. SHAP analysis revealed the importance of age at diagnosis as the most influential feature in the DNN's survival predictions. These findings suggest that the DNN holds promise as a practical auxiliary tool for clinicians, aiding them in optimal decision-making concerning the treatment and care trajectories for glioblastoma patients.