A Neural Network Approach to Identify the Peritumoral Invasive Areas in Glioblastoma Patients by Using MR Radiomics

Abstract

the challenge in the treatment of glioblastoma is the failure to identify the cancer invasive area outside the contrast-enhancing tumour which leads to the high local progression rate. Our study aims to identify its progression from the preoperative MR radiomics. 57 newly diagnosed cerebral glioblastoma patients were included. All patients received 5-aminolevulinic acid (5-ALA) fluorescence guidance surgery and postoperative temozolomide concomitant chemoradiotherapy. Preoperative 3 T MRI data including structure MR, perfusion MR, and DTI were obtained. Voxel-based radiomics features extracted from 37 patients were used in the convolutional neural network to train and as internal validation. Another 20 patients of the cohort were tested blindly as external validation. Our results showed that the peritumoural progression areas had higher signal intensity in FLAIR (p = 0.02), rCBV (p = 0.038), and T1C (p = 0.0004), and lower intensity in ADC (p = 0.029) and DTI-p (p = 0.001) compared to non-progression area. The identification of the peritumoural progression area was done by using a supervised convolutional neural network. There was an overall accuracy of 92.6% in the training set and 78.5% in the validation set. Multimodal MR radiomics can demonstrate distinct characteristics in areas of potential progression on preoperative MRI.

Visualizing Glioma Infiltration by the Combination of Multimodality Imaging and Artificial Intelligence, a Systematic Review of the Literature

“…14 studies were eligible for this review. Eight of these were retrospective studies [ 21 , 22 , 23 , 27 , 30 , 31 , 32 , 37 ] and six were prospective [ 24 , 25 , 26 , 28 , 29 , 38 ]. Two of the reported six prospective studies had clear specification of being consecutive [ 24 , 29 ].…”

“…However, single imaging modalities have only achieved moderate accuracy for the detection of infiltration [ 20 ]. In acknowledgement of this, various groups have focused their attention on an integration of multiple imaging modalities in a predictive model showing the probability of tumor presence or later tumor recurrence in glioma patients [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ]. By the aid of artificial intelligence (AI) methods a variety of imaging parameters can be considered simultaneously as opposed to a manual visual assessment of tumor extent during treatment planning.…”

Quantitative Mapping of Individual Voxels in the Peritumoral Region of Glioblastoma to Distinguish Between Tumor Infiltration and Edema

“…While several studies have correlated the MRI ndings with targeted tissue specimens with reasonable radiology-histology agreement, temporal correlation with speci c areas of tumor progression has been lacking. Recently, studies have undertaken a quantitative or radiomics-based approach in mapping the PTR to generate probabilistic maps of tumor in ltration [17][18][19][20].…”

“…With the histopathological correlation being a signi cant contribution of the study, the manual placement of segments corresponding to the biopsy site (8×8×8 voxels) might include neighbouring voxels with different tumor content. In a recent report by Yan et al a neural network classi er was used to develop a radiomics model to map the PTR of pre-operative MRI, trained on extracting features from recurrence vs no-recurrence [20]. An accuracy of 78% was achieved on a validation group of 20 patients with GBM.…”

A functional artificial neural network for noninvasive presurgical evaluation of glioblastoma multiforme prognosis and radiosensitivity profiling

“…Aspects of deep learning (DL), a subfield of ML concerned with multi-layered artificial neural networks (ANNs), stand to benefit GBM research in particular given that applications in medical image processing allow the systematic extraction of image features desirable for use in predictive modeling [4][5][6].…”