The benefits of decision tree to predict survival in patients with glioblastoma multiforme with the use of clinical and imaging features

Abstract

Background: Machine learning is a type of artificial intelligence which aims to improve machine with the ability of extracting knowledge from the environment. Glioblastoma multiforme (GBM) is one of the most common and aggressive primary malignant brain tumors in adults. Due to a low rate of survival in patients with these tumors, machine learning can help physicians for better decision-making. The aim of this paper is to develop a machine learning model for predicting the survival rate of patients with GBM based on clinical features and magnetic resonance imaging (MRI). Materials and Methods: The present investigation is an observational study conducted to predict the survival rate in patients with GBM in 12 months. Fifty-five patients who were registered in five Iranian Hospitals (Tehran) during 2012–2014 were selected in this study. Results: This study used Cox and C5.0 decision tree models based on clinical features and combined them with MRI. Accuracy, sensitivity, and specification parameters used to evaluate the models. The result of Cox and C5.0 for clinical feature was <32.73%, 22.5%, 45.83%>, <72.73%, 67.74%, 79.19%>, respectively; also, the result of Cox and C5.0 for both features was <60%, 48.58%, 75%>, <90.91%, 96.77%, 88.33%>, respectively. Conclusion: Using C5.0 decision tree model in both survival models including clinical features, both the imaging features and the clinical features as the covariates, shows additional predictive values and better results. The tumor width and Karnofsky performance status scores were determined as the most important parameters in the survival prediction of these types of patients.

Neurosurgical Management of Cerebrospinal Tumors in the Era of Artificial Intelligence : A Scoping Review

“…Emblem et al [ 15 ] used a SVM and a whole- tumor cerebral blood volume for this purpose, while Nematollahi et al [ 40 ] proposed the C5.0 decision tree model, with tumor width and Karnofsky performance status scores being the most critical parameters for prediction of survival.…”

Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI

“…This study was based on the hypothesis that connectomics-based features could capture tumor-induced network level alterations that can influence prognosis, underlining the importance of including rs-fMRI in the pre-surgical workout of patients with glioma. Nematollahi et al [ 71 ] showed the impact of a decision tree trained using both clinical and radiomic features. They obtained an accuracy of 90.9% for OS classification, using the C5.0 decision tree algorithm.…”

“…For the SC task, Single Layer Neural Network [ 93 ], SVM [ 64 ], Decision Trees [ 71 ], and 3D-CNN plus a SVM [ 75 ] achieved the highest accuracy (>90.7%). Characteristics and performance of those four methods are summarised in Table 2 , a graphical representation of the accuracy of the best four methods is displayed in Figure 4 .…”

“…Overall, the best four algorithms have accuracy values in the range 90.7–98.0%. More in details, the best performance was shown by a Single Layer Neural Network using PWI and DWI features [ 93 ], followed by SVM with volumetric, texture, shape features and multimodal imaging [ 64 ], a decision tree-based method [ 71 ], and a DL method, based on a 3D-CNN [ 75 ].…”

“… Performance of SC task: Graphical representation of the performance of the four best methods for the Survival Classification (SC) task, as evaluated by accuracy [ 64 , 71 , 75 , 93 ]. …”