Glioma constitutes 80% of malignant primary brain tumors and is usually classified as HGG and LGG. The LGG tumors are less aggressive, with slower growth rate as compared to HGG, and are responsive to therapy. Tumor biopsy being challenging for brain tumor patients, noninvasive imaging techniques like Magnetic Resonance Imaging (MRI) have been extensively employed in diagnosing brain tumors. Therefore automated systems for the detection and prediction of the grade of tumors based on MRI data becomes necessary for assisting doctors in the framework of augmented intelligence. In this paper, we thoroughly investigate the power of Deep ConvNets for classification of brain tumors using multi-sequence MR images. We propose novel ConvNet models, which are trained from scratch, on MRI patches, slices, and multi-planar volumetric slices. The suitability of transfer learning for the task is next studied by applying two existing ConvNets models (VGGNet and ResNet) trained on ImageNet dataset, through fine-tuning of the last few layers. LOPO testing, and testing on the holdout dataset are used to evaluate the performance of the ConvNets. Results demonstrate that the proposed ConvNets achieve better accuracy in all cases where the model is trained on the multi-planar volumetric dataset. Unlike conventional models, it obtains a testing accuracy of 95% for the low/high grade glioma classification problem. A score of 97% is generated for classification of LGG with/without 1p/19q codeletion, without any additional effort towards extraction and selection of features. We study the properties of self-learned kernels/ filters in different layers, through visualization of the intermediate layer outputs. We also compare the results with that of state-of-the-art methods, demonstrating a maximum improvement of 7% on the grading performance of ConvNets and 9% on the prediction of 1p/19q codeletion status.

Deep Radiomics for Brain Tumor Detection and Classification from Multi-Sequence MRI

Glioma constitutes $$80\%$$
 of malignant primary brain tumors in adults, and is usually classified as high-grade glioma (HGG) and low-grade glioma (LGG) The LGG tumors are less aggressive, with slower growth rate as compared to HGG, and are responsive to therapy Tumor biopsy being challenging for brain tumor patients, noninvasive imaging techniques like magnetic resonance imaging (MRI) have been extensively employed in diagnosing brain tumors Therefore, development of automated systems for the detection and prediction of the grade of tumors based on MRI data becomes necessary for assisting doctors in the framework of augmented intelligence In this paper, we thoroughly investigate the power of deep convolutional neural networks (ConvNets) for classification of brain tumors using multi-sequence MR images We propose novel ConvNet models, which are trained from scratch, on MRI patches, slices, and multi-planar volumetric slices The suitability of transfer learning for the task is next studied by applying two existing ConvNets models (VGGNet and ResNet) trained on ImageNet dataset, through fine-tuning of the last few layers Leave-one-patient-out testing, and testing on the holdout dataset are used to evaluate the performance of the ConvNets The results demonstrate that the proposed ConvNets achieve better accuracy in all cases where the model is trained on the multi-planar volumetric dataset Unlike conventional models, it obtains a testing accuracy of $$95\%$$
 for the low/high grade glioma classification problem A score of $$97\%$$
 is generated for classification of LGG with/without 1p/19q codeletion, without any additional effort toward extraction and selection of features We study the properties of self-learned kernels/ filters in different layers, through visualization of the intermediate layer outputs We also compare the results with that of state-of-the-art methods, demonstrating a maximum improvement of $$7\%$$
 on the grading performance of ConvNets and $$9\%$$
 on the prediction of 1p/19q codeletion status

https://link.springer.com/content/pdf/10.1007/s42979-020-00214-y.pdf

Glioma Classification Using Deep Radiomics

Radiomics is a nascent field in quantitative imaging that uses advanced algorithms and considerable computing power to describe tumor phenotypes, monitor treatment response, and assess normal tissue toxicity quantifiably. Remarkable interest has been drawn to the field due to its noninvasive nature and potential for diagnosing and predicting patient prognosis. This review will attempt to comprehensively and critically discuss the various aspects of radiomics including its workflow, applications to different modalities, potential applications in cancer radiotherapy, and limitations.

Radiomics in Cancer Radiotherapy: a Review

Computerized detection and diagnosis of cancer, based on medical image analysis, promises to be a good alternative to the conventional biopsy test. Since repeated biopsy in the brain is challenging and lead to severe side effects, the role of noninvasive image guided diagnostic techniques become necessary; with Magnetic Resonance Imaging being the standard diagnostic tool typically used. This paper discusses the development of computer-aided systems based on deep Convolutional Neural Network (CNN) for improved detection, diagnosis, and prognosis of brain cancer. An encoder-decoder type strategy, to produce the final volumetric segmentation of the tumor and its subregions. The power of deep CNN models is explored for the detection and classification of brain tumors from multisequence magnetic resonance images. The suitability of transfer learning is also studied for this task through fine-tuning of pretrained models. 

Deep learning for noninvasive management of brain tumors

A new deep learning method is introduced for the automatic delineation/segmentation of brain tumors from multi-sequence MR images. A Radiomic model for predicting the Overall Survival (OS) is designed, based on the features extracted from the segmented Volume of Interest (VOI). An encoder-decoder type ConvNet model is designed for pixel-wise segmentation of the tumor along three anatomical planes (axial, sagittal and coronal) at the slice level. These are then combined, using a consensus fusion strategy, to produce the final volumetric segmentation of the tumor and its sub-regions. Novel concepts such as spatial-pooling and unpooling are introduced to preserve the spatial locations of the edge pixels for reducing segmentation error around the boundaries. We also incorporate shortcut connections to copy and concatenate the receptive fields from the encoder to the decoder part, for helping the decoder network localize and recover the object details more effectively. These connections allow the network to simultaneously incorporate high-level features along with pixel-level details. A new aggregated loss function helps in effectively handling data imbalance. The integrated segmentation and OS prediction system is trained and validated on the BraTS 2018 dataset.

Multi-planar Spatial-ConvNet for Segmentation and Survival Prediction in Brain Cancer

Purpose:
The clinical management of meningioma is guided by its grade and biologic behavior. Currently, diagnosis of tumor grade follows surgical resection and histopathologic review. Reliable techniques for pre-operative determination of tumor behavior are needed. We investigated the association between imaging features extracted from preoperative gadolinium-enhanced T1-weighted MRI and meningioma grade.

Methods:
We retrospectively examined the pre-operative MRI for 139 patients with de novo WHO grade I (63%) and grade II (37%) meningiomas. We investigated the predictive power of ten semantic radiologic features as determined by a neuroradiologist, fifteen radiomic features, and tumor location. Conventional (volume and diameter) imaging features were added for comparison. AUC was computed for continuous and χ2 for discrete variables. Classification was done using random forest. Performance was evaluated using cross validation (1000 iterations, 75% training and 25% validation). All p-values were adjusted for multiple testing.

Results:
Significant association was observed between meningioma grade and tumor location (p<0.001) and two semantic features including intra-tumoral heterogeneity (p<0.001) and overt hemorrhage (p=0.01). Conventional (AUC 0.61–0.67) and eleven radiomic (AUC 0.60–0.70) features were significant from random (p<0.05, Noether test). Median AUC values for classification of tumor grade were 0.57, 0.71, 0.72 and 0.77 respectively for conventional, radiomic, location, and semantic features after using random forest. By combining all imaging data (semantic, radiomic, and location), the median AUC was 0.81, which offers superior predicting power to that of conventional imaging descriptors for meningioma as well as radiomic features alone (p<0.05, permutation test).

Conclusion:
We demonstrate a strong association between radiologic features and meningioma grade. Pre-operative prediction of tumor behavior based on imaging features offers promise for guiding personalized medicine and improving patient management.

SU-D-207B-02: Early Grade Classification in Meningioma Patients Combining Radiomics and Semantics Data

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SU-D-207B-02: Early Grade Classification in Meningioma Patients Combining Radiomics and Semantics Data