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Extracting Radiomic features from pre-operative and segmented MRI scans improved survival prognosis of glioblastoma Multiforme patients through machine learning: a retrospective study

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Abstract

The combination of radiomics and artificial intelligence has emerged as a strong technique for building predictive models in radiology. This study aims to address the clinically important issue of whether a radiomic profile can predict the overall survival (OS) time of glioblastoma multiforme (GBM) patients having gross tumor resection (GTR) through pre-operative structural magnetic resonance imaging (MRI) scans. A retrospective analysis was made using data of glioma patients made publicly available by the University of Pennsylvania. The radiomic characteristics were extracted from pre-operative structural multiparametric MRI (mpMRI) sequences after pre-processing and 3D segmentation using deep learning (DL). After removing irrelevant features, regression models based on machine learning (ML) were developed by considering selected features to predict the OS time of GBM patients within a period of days only. The patients were divided into three survivor groups depending on their projected survival time. To validate the significance of the selected feature set, statistical analysis was performed. As many as 494 patients were considered to improve survival prediction (SP) by using more effective feature extraction and selection techniques. The ridge regressor acquired the highest SpearmanR Rank correlation of 0.635 with an accuracy of 69%, the greatest of all the previous works for categorical predictions of such patients. The researchers in the past who used radiomic characteristics for the OS prognosis of GBM patients could yield limited results only. However, the current research work recorded an enhanced accuracy and SpearmanR rank for the three survivor classes of GBM patients using ML, feature selection, and radiomics. The significance of this work lies in the selection of patients with GTR and the extraction of radiomic characteristics through the use of radiomics and artificial intelligence.

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  1. Computer Science & Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Gurinderjeet Kaur, Prashant Singh Rana & Vinay Arora

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Conceptualization—Gurinderjeet Kaur.

Methodology—Gurinderjeet Kaur.

Formal analysis and investigation—Gurinderjeet Kaur, Prashant Singh Rana, Vinay Arora.

Writing (original draft preparation)— Gurinderjeet Kaur.

Writing (review and editing)— Gurinderjeet Kaur, Prashant Singh Rana, Vinay Arora.

Resources— Gurinderjeet Kaur, Prashant Singh Rana, Vinay Arora.

Supervision—Prashant Singh Rana, Vinay Arora.

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Correspondence to Gurinderjeet Kaur.

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Kaur, G., Rana, P.S. & Arora, V. Extracting Radiomic features from pre-operative and segmented MRI scans improved survival prognosis of glioblastoma Multiforme patients through machine learning: a retrospective study. Multimed Tools Appl 82, 30003–30038 (2023). https://doi.org/10.1007/s11042-022-14223-x

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