NCI Workshop Report: Clinical and Computational Requirements for Correlating Imaging Phenotypes with Genomics Signatures

Abstract

The National Cancer Institute (NCI) Cancer Imaging Program organized two related workshops on June 26-27, 2013, entitled "Correlating Imaging Phenotypes with Genomics Signatures Research" and "Scalable Computational Resources as Required for Imaging-Genomics Decision Support Systems." The first workshop focused on clinical and scientific requirements, exploring our knowledge of phenotypic characteristics of cancer biological properties to determine whether the field is sufficiently advanced to correlate with imaging phenotypes that underpin genomics and clinical outcomes, and exploring new scientific methods to extract phenotypic features from medical images and relate them to genomics analyses. The second workshop focused on computational methods that explore informatics and computational requirements to extract phenotypic features from medical images and relate them to genomics analyses and improve the accessibility and speed of dissemination of existing NIH resources. These workshops linked clinical and scientific requirements of currently known phenotypic and genotypic cancer biology characteristics with imaging phenotypes that underpin genomics and clinical outcomes. The group generated a set of recommendations to NCI leadership and the research community that encourage and support development of the emerging radiogenomics research field to address short-and longer-term goals in cancer research.

Figure 1

A logic diagram of an example of the field of radiogenomics for breast cancer using digital mammography and DCE MRI.

Figure 2

Radiological and histological feature extraction and correlation to genomic data. Following identification of the contrast enhancing region (top-left) or cancer nuclei (right) various imaging features can be extracted. Using corresponding genomic data such as DNA expression, mutations, and methylation, these features can be correlated with molecular data and underlying biological characteristics of tumorigenesis can be identified.

Figure 3

MR images fused with CBV map showing a contrast-enhancing GBM (red ROI) with central necrosis in the medial left parieto-occipital lobe in a 56-years old male. Surrounding non-enhancing component of the tumor is outlined by multiple ROIs showing different grades of increased CBV, suggesting tumor infiltration beyond the contrast enhancing component.

Figure 4

Integrated morphologic analysis for the identification and characterization of disease subtypes. Approximately 500 whole slide images representing 162 glioblastoma cases were analyzed and cellular features were extracted and analyzed. Clustering results identified three morphologically driven subtypes which differed from each other based on prognosis, pathology, genetics, methylation, and TCGA subtype associations (for example, neural subtypes were particularly enriched in the chromatin modification subtype).

Figure 5

Relationship of MRI-based phenotypes in distinguishing breast cancer subtypes (big data analyses). Performance is given in terms of AUC (y-axis) for various MRI-based Phenotypes (x-axis) as well as a Tumor Signature formulated from merging selected MRI-based phenotypes using linear discriminant analysis. Features 1 to 11 are kinetic features, 12 to 25 are texture features, 26 to 30 are morphological features, 31 to 34 are size features, and the far right data point refers to the tumor signature from the merged features.

Figure 6

Image-based phenotype array of computer-extracted characteristics of breast cancer tumors on MRI for ER − and ER + tumors from the TCGA/TCIA breast cancer dataset. Bottom row gives values and the corresponding AUC (area under the receiver operating characteristic curve) of the image-based signature in the task of distinguishing between ER − and ER + tumors.

Figure 7

Enhancement analysis of renal cell carcinoma. Axial multidetector CT images obtained during the A) noncontrast, B) corticomedullary, C) nephrographic and D) excretory phases show regions of interest (red circles) drawn within a renal cell carcinoma of the right kidney. Attenuation values measured by the regions of interest are used to investigate the enhancement characteristics of the tumor during dynamic imaging.

Figure 8

Diagnostic CT scans from two patients with non-small cell lung cancer, and gradient maps of image intensity.

Figure 9

Example of a contrast enhanced T1 map and a diffusion ADC map clustered into high (red) and low (green) values using Otsu thresholding. These can be combined to yield regions with low ADC (high cell density) and low T1 (low contrast enhancement) in violet; and regions with high ADC (edema) and low T1 in green.

Figure 10

Glioblastoma morphology pipeline: (A) Image analysis algorithms segment cell nuclei and calculate a feature set for each nucleus describing its shape and texture. Nuclear features are aggregated over the hundreds of millions of cells in whole-slide images to produce a morphometric profile for each patient. (B) Analysis of morphometric profiles reveals clusters of patients with cohesive morphologic characteristics. (C) The correlates of morphologic patient clusters are identified through deeper analysis of clinical and genomic data.

Figure 11

Multi-scale deep annotation and population-based atlas: by employing advanced segmentation on MRI we can segment (A) prostate (yellow) on MRI. However, to “see” more on routine MRI we need to rely on coregistering MRI with (d, f) pathology to appreciate the (B) central gland (purple) and peripheral zones (cyan), anatomic landmarks such as the ejaculatory ducts (green), urethra (blue), and neurovascular bundles (yellow) and (C) cancer extent (red). Immuno-histochemically stained pathology (D, F) can inform on cancer aggressiveness; population-based prostate atlas (H) shows the 3D distribution of cancer relative to the prostate anatomic regions.

Figure 12

Computer extracted MRI markers of aggressive prostate cancer. (A) DCE-MRI, corresponding (B) CD31 (vascular) stained slice with PCa annotations (red), (C) histology-MRI registration, (D) DCE-MRI feature map, (E) microvessel architecture used for histologic feature extraction, (F) correlation heat map of histology and DCE MRI features, (G) imaging biomarkers identified in (F) allowed for separation of Gleason grade 3 from 4 tumors, with an AUC = 0.92.

Figure 13

Upstream fusion of Big Data streams may improve prediction of 5 year PSA failure in prostate cancer patients following surgery. Panels (a)–(c) show survival curves for distinguishing men with (red) without 5 year PSA recurrence (blue) via (A) histologic image features from excised specimens, (B) proteomics from mass spectrometry from dominant nodule on the excised specimen, and (C) combination of histologic image and proteomic features.