Showing papers by "B. Uma Shankar published in 2014"

Robust Radiomics feature quantification using semiautomatic volumetric segmentation.

Abstract: Due to advances in the acquisition and analysis of medical imaging, it is currently possible to quantify the tumor phenotype. The emerging field of Radiomics addresses this issue by converting medical images into minable data by extracting a large number of quantitative imaging features. One of the main challenges of Radiomics is tumor segmentation. Where manual delineation is time consuming and prone to inter-observer variability, it has been shown that semi-automated approaches are fast and reduce inter-observer variability. In this study, a semiautomatic region growing volumetric segmentation algorithm, implemented in the free and publicly available 3D-Slicer platform, was investigated in terms of its robustness for quantitative imaging feature extraction. Fifty-six 3D-radiomic features, quantifying phenotypic differences based on tumor intensity, shape and texture, were extracted from the computed tomography images of twenty lung cancer patients. These radiomic features were derived from the 3D-tumor volumes defined by three independent observers twice using 3D-Slicer, and compared to manual slice-by-slice delineations of five independent physicians in terms of intra-class correlation coefficient (ICC) and feature range. Radiomic features extracted from 3D-Slicer segmentations had significantly higher reproducibility (ICC = 0.85±0.15, p = 0.0009) compared to the features extracted from the manual segmentations (ICC = 0.77±0.17). Furthermore, we found that features extracted from 3D-Slicer segmentations were more robust, as the range was significantly smaller across observers (p = 3.819e-07), and overlapping with the feature ranges extracted from manual contouring (boundary lower: p = 0.007, higher: p = 5.863e-06). Our results show that 3D-Slicer segmented tumor volumes provide a better alternative to the manual delineation for feature quantification, as they yield more reproducible imaging descriptors. Therefore, 3D-Slicer can be employed for quantitative image feature extraction and image data mining research in large patient cohorts.

Integrating Radio Imaging With Gene Expressions Toward a Personalized Management of Cancer

Abstract: Radiographic-imaging modalities like computerized tomography, positron emission tomography, and magnetic resonance imaging are playing a major role in the diagnosis and prognosis of cancer. Gene and protein expression patterns, from the tumor genome, are seen to facilitate individualized selection of therapies. Along with breakthroughs in biotechnology, applicable within cancer radiation biology, a new research field called Radiogenomics has been born in radiation oncology. Associating genotypes with imaging phenotypes holds promise for personalized optimal treatment. Segmentation and feature selection from the region of interest in an image are followed by correlation with the gene expression profile of the tumor in order to determine its noninvasive surrogates. This paper highlights the roles of quantitative imaging, genomics, and radiogenomics for a patient-specific tumor management.