Predicting Overall Survival Time in Glioblastoma Patients Using Gradient Boosting Machines Algorithm and Recursive Feature Elimination Technique

• Published in: Cancers Vol. 13; no. 19; p. 4976
• Main Authors: Karami, Golestan, Giuseppe Orlando, Marco, Delli Pizzi, Andrea, Caulo, Massimo, Del Gratta, Cosimo
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 04.10.2021; MDPI
• Subjects: Accuracy; Algorithms; Angiogenesis; Anisotropy; Biomarkers; Biopsy; Brain cancer; Classification; Feature selection; Glioblastoma; Glioblastoma multiforme; Histology; Image processing; Learning algorithms; Machine learning; Magnetic resonance imaging; Medical prognosis; Mortality; Neural networks; Patients; Perfusion; Segmentation; Support vector machines; Surgery; Survival; Tumors; GBM; gradient boosting; recursive feature elimination; random forest; survival time
• ISSN: 2072-6694; 2072-6694
• DOI: 10.3390/cancers13194976

Despite advances in tumor treatment, the inconsistent response is a major challenge among glioblastoma multiform (GBM) that lead to different survival time. Our aim was to integrate multimodal MRI with non-supervised and supervised machine learning methods to predict GBM patients’ survival time. To this end, we identified different compartments of the tumor and extracted their features. Next, we applied Random Forest-Recursive Feature Elimination (RF-RFE) to identify the most relevant features to feed into a GBoost machine. This study included 29 GBM patients with known survival time. RF-RFE GBoost model was evaluated to assess the survival prediction performance using optimal features. Furthermore, overall survival (OS) was analyzed using univariate and multivariate Cox regression analyses, to evaluate the effect of ROIs and their features on survival. The results showed that a RF-RFE Gboost machine was able to predict survival time with 75% accuracy. The results also revealed that the rCBV in the low perfusion area was significantly different between groups and had the greatest effect size in terms of the rate of change of the response variable (survival time). In conclusion, not only integration of multi-modality MRI but also feature selection method can enhance the classifier performance.