Abstract 2633: Radiogenomics of breast cancer using DCE-MRI and gene expression profiling
Authors: Albert C. Yeh, Stephanie McGregor, Hui Li, Yuan Ji, Yitan Zhu, Tatyana Grushko, Alexandra Edwards, Fan Lui, Jing Zhang, Qiu Niu, Yonglan Zheng, Toshio Yoshimatsu, Galina Khramtsova, Karen Drukker, Gregory Karczmar, Hiroyuki Abe, Jeffrey Mueller, Maryellen Giger, Olufunmilayo Olopade
Journal: Cancer Research (2016)

Abstract The utilization of radiomics (high-throughput extraction and analysis of imaging features) to abstract underlying genomic features of an evolving malignancy is an emerging field that has the potential to detail biological information of a tumor in a non-invasive and more accessible manner. Furthermore, applying radiomics confers a comprehensive spatial view of the tumor, a potential advantage over the limitations of sampling a small region of tissue that may not accurately represent the underlying complexity of the entire tumor. Most studies to date that incorporate radiogenomics in the analysis of breast cancers have focused on few basic clinical data or individual genetic mutations such as BRCA or HER2 status. Here, we use an automated quantitative radiomics analysis platform developed at the University of Chicago that enables computerized feature extraction of tumors to analyze magnetic resonant imaging scans of 50 breast cancer patients (mean age of diagnosis [range]: 54 [24-89]; receptor status: HER2+: 14, Triple negative: 7; stage [1 through 4]: 10%, 40%, 42%, 8%) who have had comprehensive gene expression profiling performed using Agilent Human Gene Expression arrays. Our imaging platform extracts 38 features across six major phenotypes (size, shape, morphology, enhancement texture, kinetic curve assessment, and enhancement variance kinetics) (see Table for listing of 24 selected features). Existing radiomic analysis derived from a TCGA/TCIA dataset suggests that there are many correlations between imaging phenotypes and various genetic pathways, such as VEGF signaling and volume of enhancing voxels, base excision repair and enhancement texture entropy, and TGF-beta signaling and enhancement texture variance. We confirm these relationships as well as establish novel associations using a robust imaging dataset. By associating specific radiomic features with gene expression profile of tumors, we have the opportunity to extract detailed biological information non-invasively through clinical imaging. Selected imaging phenotypes extracted from MRI scansPhenotype categoryImage phenotypeDescriptionSizeVolumeVolume of lesionSizeEffective diameterDiameter of a sphere with the same volume as the lesionSizeSurface areaLesion surface areaShapeSphericitySimilarity of the lesion shape to a sphereShapeIrregularityDeviation of the lesion surface from the surface of a sphereShapeSurface area / volumeRatio of surface area to volumeMorphologyMargin sharpnessMean of the image gradient at the lesion marginMorphologyVariance of margin sharpnessVariance of the image gradient at the lesion marginMorphologyVariance of radial gradient histogramDegree to which the enhancement structure extends in a radial pattern originating from the center of the lesionEnhancement TextureContrastLocal image variationsEnhancement TextureEntropyRandomness of the gray-levelsEnhancement TextureDifference varianceVariations of difference of gray-levels between voxel-pairsEnhancement TextureAngular second momentImage homogeneityEnhancement TextureMaximum correlation coefficientNonlinear gray-level dependenceEnhancement TextureSum averageOverall brightnessEnhancement TextureSum of squaresSpread in the gray-level distributionKinetic Curve AssessmentMaximum enhancementMaximum contrast enhancementKinetic Curve AssessmentTime to peakTime at which the maximum enhancement occursKinetic Curve AssessmentUptake rateUptake speed of the contrast enhancementKinetic Curve AssessmentCurve shape indexDifference between late and early enhancementKinetic Curve AssessmentTotal rate variationHow rapidly the contrast will enter and exit from the lesionEnhancement-Variance KineticsMaximum variance of enhancementMaximum spatial variance of contrast enhancement over timeEnhancement-Variance KineticsTime to peak maximum varianceTime at which the maximum variance occursEnhancement-Variance KineticsEnhancement variance increasing rateRate of increase of the enhancement-variance during uptake Citation Format: Albert C. Yeh, Stephanie McGregor, Hui Li, Yuan Ji, Yitan Zhu, Tatyana Grushko, Alexandra Edwards, Fan Lui, Jing Zhang, Qiu Niu, Yonglan Zheng, Toshio Yoshimatsu, Galina Khramtsova, Karen Drukker, Gregory Karczmar, Hiroyuki Abe, Jeffrey Mueller, Maryellen Giger, Olufunmilayo Olopade. Radiogenomics of breast cancer using DCE-MRI and gene expression profiling. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2633.