6 research outputs found
Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival Prediction
This paper introduces a novel methodology to integrate human brain
connectomics and parcellation for brain tumor segmentation and survival
prediction. For segmentation, we utilize an existing brain parcellation atlas
in the MNI152 1mm space and map this parcellation to each individual subject
data. We use deep neural network architectures together with hard negative
mining to achieve the final voxel level classification. For survival
prediction, we present a new method for combining features from connectomics
data, brain parcellation information, and the brain tumor mask. We leverage the
average connectome information from the Human Connectome Project and map each
subject brain volume onto this common connectome space. From this, we compute
tractographic features that describe potential neural disruptions due to the
brain tumor. These features are then used to predict the overall survival of
the subjects. The main novelty in the proposed methods is the use of normalized
brain parcellation data and tractography data from the human connectome project
for analyzing MR images for segmentation and survival prediction. Experimental
results are reported on the BraTS2018 dataset.Comment: 14 pages, 5 figures, 4 tables, accepted by BrainLes 2018 MICCAI
worksho
Incorporating radiomics into clinical trials: expert consensus on considerations for data-driven compared to biologically-driven quantitative biomarkers
Existing Quantitative Imaging Biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials