A Visually Interpretable, Dictionary-Based Approach to Imaging-Genomic Modeling, With Low-Grade Glioma as a Case Study.

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.

Histopathological Image Classification Using Discriminative Feature-Oriented Dictionary Learning.

In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structures. In this paper, we propose an automatic feature discovery framework via learning class-specific dictionaries and present a low-complexity method for classification and disease grading in histopathology. Essentially, our Discriminative Feature-oriented Dictionary Learning (DFDL) method learns class-specific dictionaries such that under a sparsity constraint, the learned dictionaries allow representing a new image sample parsimoniously via the dictionary corresponding to the class identity of the sample. At the same time, the dictionary is designed to be poorly capable of representing samples from other classes. Experiments on three challenging real-world image databases: 1) histopathological images of intraductal breast lesions, 2) mammalian kidney, lung and spleen images provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, reveal the merits of our proposal over state-of-the-art alternatives. Moreover, we demonstrate that DFDL exhibits a more graceful decay in classification accuracy against the number of training images which is highly desirable in practice where generous training is often not available.