Prediction of O-6-methylguanine-DNA methyltransferase and overall survival of the patients suffering from glioblastoma using MRI-based hybrid radiomics signatures in machine and deep learning framework

O-6-methylguanine-DNA methyltransferase (MGMT) is one of the most salient gene promoters that correlates with the effectiveness of standard therapy for patients suffering from glioblastoma (GBM). Non-invasive estimation of MGMT and overall survival (OS) in GBM patients could provide a particular dir...

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Published in:Neural computing & applications 2023-06, Vol.35 (18), p.13647-13663
Main Authors: Saxena, Sanjay, Agrawal, Aaditya, Dash, Prasad, Jena, Biswajit, Khanna, Narendra N., Paul, Sudip, Kalra, Mannudeep M., Viskovic, Klaudija, Fouda, Mostafa M., Saba, Luca, Suri, Jasjit S.
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Deep learning
Deoxyribonucleic acid
DNA
Image Processing and Computer Vision
Machine learning
Original Article
Probability and Statistics in Computer Science
Radiomics
Rank tests
Signatures
Survival
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Summary:O-6-methylguanine-DNA methyltransferase (MGMT) is one of the most salient gene promoters that correlates with the effectiveness of standard therapy for patients suffering from glioblastoma (GBM). Non-invasive estimation of MGMT and overall survival (OS) in GBM patients could provide a particular direction to neuro-oncologists and surgeons for precise treatment and surgical planning. This study investigated hybrid radiomics signatures (HRS) for the prediction of (i) MGMT status (methylated/unmethylated) and (ii) OS (short survivors 12 months and long survivors >  = 12 months) using both conventional and deep radiomic features derived from multi-parametric MRI (mp-MRI). Further, for the OS, Kaplan–Meier analysis was carried out to analyze the difference between two groups of survivors. Additionally, Cox-PH modeling was adapted to investigate the impact of clinical observation on OS. Two cohorts of 555 and 209 GBM patients have been used to analyze HRS for MGMT and OS, respectively. (i) For MGMT status prediction employing conventional machine learning radiomics features along with deep learning features using VGG16 and VGG19, the HRS obtained an AUC of 0.76 (95% CI: 0.70–0.80). (ii) For OS prediction employing the log-rank test, the conventional radiomic signature showed an AUC of 0.78 (95% CI: 0.75–0.83) with a p-value 
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-023-08405-3