Several machine learning approaches have been used to classify brain tumors using MR images and spectra. Here we explore the specific properties of convolutional neural networks (CNN) for this task. We designed a CNN that could be trained on combined MR image and spectroscopic image data by exploiting their specific properties (spatial and spectral locality). Using a ‘leave-one-out’ validation, we demonstrate that our method outperforms state-of-the-art classification methods to distinguish tumor grades. These results demonstrate that CNNs are a powerful approach for tumor classification using MRSI data.
MRSI and MRI data were acquired at the RUMC in the context of the INTERPRET project5 with approval of the local ethical committee from 25 patients with a brain tumor and 4 healthy volunteers. The data set encompassed 6 classes:
1. Normal brain tissue (8 persons);
2. Cerebral Spinal Fluid (8 patients);
3. Grade II gliomas (10 patients);
4. Grade III gliomas (5 patients);
5. Grade IV glioblastomas (7 patients);
6. Meningiomas (3 patients).
A strict quality control procedure was applied to each case and tumor types were determined by histopathological consensus6 . Of each patient water suppressed and unsuppressed 1H 2D MRSI data were acquired of a STEAM localized slab covering tumor and surrounding normal appearing tissue (TE=20ms, TR=2500ms, 16x16x1024 samples) avoiding signals from fat tissue at the skull. Additionally, four different MR images were acquired: T1-weighted, T2-weighted, PD-weighted and gadolinium (Gd)-enhanced T1-weighted images. The STEAM slab for MRSI was centered at the location of the Gd-enhanced T1-weighted or T1/T2 images that showed the largest tumor area. The MR images were registered with respect to the MRSI slice. After processing, including zero-filling to 32x32 voxels, several voxels, situated in the healthy, CSF and tumorous areas, were selected from the respective patients to obtain a sufficiently large data set (669 voxels). The dataset consists of 10 quantified metabolite peak areas from the processed MRSI spectra from the selected voxels, and the corresponding MR pixel intensities of the 4 MR images. The images and spectra are fed as distinct inputs to MRSI-CNN. After each input, there is one convolutional layer (1D and 2D shaped for spectral and image voxels respectively). More in detail, the convolutional layers construct new features from their inputs therefore changing how input data are represented. The output of both layers is then flattened and concatenated. The next and last layer of MRSI-CNN provides the class prediction for each input voxel (Figure 1). We compared the MRSI-CNN method with an SVM with RBF kernel (SVM-RBF) because SVM is a well-known method that generally achieves good accuracy7,8.
To demonstrate the MRSI-CNN method we selected two classification tasks: Grade II vs Grade III and Grade III vs Grade IV. For both, a double Leave One Patient Out (LOPO) cross-validation was used: an internal cross-validation to tune the hyper-parameters of MRSI-CNN and an external cross-validation to validate the obtained model. The MRSI-CNN and SVM-RBF were trained with different data sets:
a) integrals of 10 metabolite peaks and average intensity of MRI pixels9;
b) only complete MR spectra;
c) complete MR spectra and MRI data of the selected ROIs.
From the results reported in Tables 1,2 it is clear that MRSI with dataset c) performed best. A visual representation of the results is provided by t-SNE plots
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