%0 Journal Article
%T A Visually Interpretable, Dictionary-Based Approach to Imaging-Genomic Modeling, With Low-Grade Glioma as a Case Study
%A Arvind Rao
%A Chang Su
%A Harrison Bai
%A Srikanth Kuthuru
%A Tiep Vu
%A Vishal Monga
%A William Deaderick
%J Archive of "Cancer Informatics".
%D 2018
%R 10.1177/1176935118802796
%X Radiomics is a rapidly growing field in which sophisticated imaging features are extracted from radiology images to predict clinical outcomes/responses, genetic alterations, and other outcomes relevant to a patient¡¯s prognosis or response to therapy. This approach can effectively capture intratumor phenotypic heterogeneity by interrogating the ¡°larger¡± image field, which is not possible with traditional biopsy procedures that interrogate specific subregions alone. Most models in radiomics derive numerous imaging features (eg, texture, shape, size) from a radiology data set and then learn complex nonlinear hypotheses to solve a given prediction task. This presents the challenge of visual interpretability of radiomic features necessary for effective adoption of radiomic models into the clinical decision-making process. To this end, we employed a dictionary learning approach to derive visually interpretable imaging features relevant to genetic alterations in low-grade gliomas. This model can identify regions of a medical image that potentially influence the prediction process. Using a publicly available data set of magnetic resonance imaging images from patients diagnosed with low-grade gliomas, we demonstrated that the dictionary-based model performs well in predicting 2 biomarkers of interest (1p/19q codeletion and IDH1 mutation). Furthermore, the visual regions (atoms) associated with these dictionaries show association with key molecular pathways implicated in gliomagenesis. Our results show that dictionary learning is a promising approach to obtain insights into the diagnostic process and to potentially aid radiologists in selecting physiologically relevant biopsy locations
%K Radiomics
%K tumor heterogeneity
%K imaging
%K genomics
%K glioma
%U https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6174641/