Special Section on Radiomics and Imaging Genomics

Imaging-genomic pipeline for identifying gene mutations using three-dimensional intra-tumor heterogeneity features

[+] Author Affiliations
Payel Ghosh

University of Texas MD Anderson Cancer Center, Department of Diagnostic Radiology, 1400 Pressler Street, Unit 1459, Houston, Texas 77030, United States

University of Texas MD Anderson Cancer Center, Department of Bioinformatics and Computational Biology, 1400 Pressler Street, Unit 1410, Houston, Texas 77030, United States

Pheroze Tamboli

University of Texas MD Anderson Cancer Center, Department of Pathology, 1515 Holcombe Boulevard, Unit 0085, Houston, Texas 77030, United States

Raghu Vikram

University of Texas MD Anderson Cancer Center, Department of Diagnostic Radiology, 1400 Pressler Street, Unit 1459, Houston, Texas 77030, United States

Arvind Rao

University of Texas MD Anderson Cancer Center, Department of Bioinformatics and Computational Biology, 1400 Pressler Street, Unit 1410, Houston, Texas 77030, United States

J. Med. Imag. 2(4), 041009 (Oct 06, 2015). doi:10.1117/1.JMI.2.4.041009
History: Received February 28, 2015; Accepted September 10, 2015
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Abstract.  This paper presents an imaging-genomic pipeline to derive three-dimensional intra-tumor heterogeneity features from contrast-enhanced CT images and correlates them with gene mutation status. The pipeline has been demonstrated using CT scans of patients with clear cell renal cell carcinoma (ccRCC) from The Cancer Genome Atlas. About 15% of ccRCC cases reported have BRCA1-associated protein 1 (BAP1) gene alterations that are associated with high tumor grade and poor prognosis. We hypothesized that BAP1 mutation status can be detected using computationally derived image features. The molecular data pertaining to gene mutation status were obtained from the cBioPortal. Correlation of the image features with gene mutation status was assessed using the Mann-Whitney-Wilcoxon rank-sum test. We also used the random forests classifier in the Waikato Environment for Knowledge Analysis software to assess the predictive ability of the computationally derived image features to discriminate cases with BAP1 mutations for ccRCC. Receiver operating characteristics were obtained using a leave-one-out-cross-validation procedure. Our results show that our model can predict BAP1 mutation status with a high degree of sensitivity and specificity. This framework demonstrates a methodology for noninvasive disease biomarker detection from contrast-enhanced CT images.

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Citation

Payel Ghosh ; Pheroze Tamboli ; Raghu Vikram and Arvind Rao
"Imaging-genomic pipeline for identifying gene mutations using three-dimensional intra-tumor heterogeneity features", J. Med. Imag. 2(4), 041009 (Oct 06, 2015). ; http://dx.doi.org/10.1117/1.JMI.2.4.041009


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