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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

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Abstract

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 < 0.001. Similarly, in assessing the impact of patient age on OS, the concordance index was 0.68 (95% CI 0.6–0.72). The proposed study concludes with the diagnostics remark of efficient HRS for MGMT prediction and conventional radiomics for OS prediction.

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Data Availability

The datasets generated during and/or analyzed during the current study are not available due to proprietary reasons.

Abbreviations

AI:

Artificial intelligence

ASNR:

American Society of Neuroradiology

AUC:

Area under the curve

BraTS:

Brain tumor segmentation

CapTk:

Cancer imaging phenomics toolkit

CI:

Confidence interval

CNN:

Convolutional neural network

CT:

Computed tomography

DL:

Deep learning

ED:

Edema

ET:

Enhancing tumor

FLAIR:

Fluid-attenuated inversion recovery

GBM:

Glioblastoma multiforme

HDL:

Hybrid deep learning

HRS:

Hybrid radiomics signatures

K-NN:

K-nearest neighbor

LDA:

Linear discriminant analysis

MGM:

O6-methylguanine methyltransferase

MICCAI:

Medical image computing and computer-assisted intervention society

ML:

Machine learning

mp-MRI:

Multi-parametric MRI

MRI:

Magnetic resonance imaging

NGTDM:

Neighborhood gray tone difference matrix

OS:

Overall survival

PET:

Positron emission tomography

ResNet:

Residual neural network

RF:

Random forest

RFE:

Recursive features elimination

ROC:

Receiver operating characteristics

ROI:

Region of interest

RSNA:

Radiological Society of North America

SVM:

Support vector machine

T1GD:

T1-Gadolinium

TC:

Tumor core

TCIA:

The cancer imaging archive

TMZ:

Temozolomide

WT:

Whole tumor

XGB:

Extreme gradient boost

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Saxena, S., Agrawal, A., Dash, P. et al. 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. Neural Comput & Applic 35, 13647–13663 (2023). https://doi.org/10.1007/s00521-023-08405-3

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