Reza Babaei
Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, 15418-49611, Iran
Armin Bonakdar
Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, 15418-49611, Iran
Nastaran Shakourifar
Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, 15418-49611, Iran
Madjid Soltani
Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, 15418-49611, Iran
Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada
Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada
Advanced Bioengineering Initiative Center, Computational Medicine Center, K. N. Toosi University of Technology, Tehran, 15418-49611, Iran
Optometry & Vision Science Department, University of Waterloo, Waterloo, ON, N2L 3G1, Canada
Kaamran Raahemifar
College of Information Sciences and Technology (IST), Data Science and Artificial Intelligence Program, Penn State University, State College, Pennsylvania, PA, 16801, United States of America
Chemical Engineering Department, University of Waterloo, Waterloo, ON, N2L 3G1, Canada
Optometry & Vision Science Department, University of Waterloo, Waterloo, ON, N2L 3G1, Canada
In recent decades, there have been significant advancements in medical diagnosis and treatment techniques. However, there is still much progress to be made in effectively managing a wide range of diseases, particularly cancer. Timely diagnosis of cancer remains a critical step towards successful treatment, as it significantly impacts patients’ chances of survival. Among various types of cancer, glioma stands out as the most common primary brain tumor, exhibiting different levels of aggressiveness. One of the monitoring techniques is magnetic resonance imaging (MRI) that provides a precise visual representation of the tumor and its sub-regions (edema (ED), enhancing tumor (ET), and non-enhancing necrotic tumor core (NEC)), enabling monitoring of its location, shape, and sub- regional characteristics. In this study, we aim to investigate the underlying relationship between the maximumdiameters of tumor sub-regions and patients’ overall survival (OS) in glioblastoma cases. Using an MRI dataset of glioblastoma patients, we categorized them based on resection status: gross total resection (GTR) and unknown (NA). By employing the Euclidean distance algorithm, we estimated sub-regions’ maximum diameters. Machine learning algorithms were used to explore the correlation between sub-regions’ maximum diameters and survival outcomes. The results of the univariate prediction models showed that tumor sub-regions’ maximum diameters have a noticeable correlation with the survival rates among patients with unknown resection status with the average spearman correlation of -0.254. Also, addition of the sub-regions’ maximum diameter feature to the radiomics increased the accuracy of ML algorithms in predicting the survival rates with an average of 4.58%.
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