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Radiogenomics: Correspondence Between Imaging and Genetic Features Based On Clustering Analysis

S Harmon

S Harmon1*, B Wendelberger1 , R Jeraj1,2 , (1) University of Wisconsin- Madison, Madison, WI, (2) University of Ljubljana, Slovenia


SU-E-J-98 Sunday 3:00PM - 6:00PM Room: Exhibit Hall

Radiogenomics aims to establish relationships between patient genotypes and imaging phenotypes. An open question remains on how best to integrate information from these distinct datasets. This work investigates if similarities in genetic features across patients correspond to similarities in PET-imaging features, assessed with various clustering algorithms.

[18F]FDG PET data was obtained for 26 NSCLC patients from a public database (TCIA). Tumors were contoured using an in-house segmentation algorithm combining gradient and region-growing techniques; resulting ROIs were used to extract 54 PET-based features. Corresponding genetic microarray data containing 48,778 elements were also obtained for each tumor. Given mismatch in feature sizes, two dimension reduction techniques were also applied to the genetic data: principle component analysis (PCA) and selective filtering of 25 NSCLC-associated genes-of-interest (GOI). Gene datasets (full, PCA, and GOI) and PET feature datasets were independently clustered using K-means and hierarchical clustering using variable number of clusters (K). Jaccard Index (JI) was used to score similarity of cluster assignments across different datasets.

Patient clusters from imaging data showed poor similarity to clusters from gene datasets, regardless of clustering algorithms or number of clusters (JImean= 0.3429±0.1623). Notably, we found clustering algorithms had different sensitivities to data reduction techniques. Using hierarchical clustering, the PCA dataset showed perfect cluster agreement to the full-gene set (JI =1) for all values of K, and the agreement between the GOI set and the full-gene set decreased as number of clusters increased (JI=0.9231 and 0.5769 for K=2 and 5, respectively). K-means clustering assignments were highly sensitive to data reduction and showed poor stability for different values of K (JIrange: 0.2301-1).

Using commonly-used clustering algorithms, we found poor agreement in patient characterization from genomic versus imaging data. Interpretation of radiogenomic associations was found to be dependent on clustering algorithms and sensitive to data reduction techniques.

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