Tumor image-derived texture features are associated with CD3 T-cell infiltration status in glioblastoma
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Shivali Narang1, Donnie Kim1, Sathvik Aithala1, Amy B. Heimberger2, Salmaan Ahmed3, Dinesh Rao4, Ganesh Rao2 and Arvind Rao1,5
1Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston 77030, TX, USA
2Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston 77030, TX, USA
3Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston 77030, TX, USA
4Department of Radiology, The University of Florida College of Medicine, Jacksonville 32209, Florida, USA
5Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston 77030, TX, USA
Arvind Rao, email: firstname.lastname@example.org
Keywords: imaging-genomics analysis; texture analysis; immune activity; glioblastoma
Received: February 10, 2017 Accepted: August 07, 2017 Published: September 05, 2017
This study analyzed magnetic resonance imaging (MRI) scans of Glioblastoma (GB) patients to develop an imaging-derived predictive model for assessing the extent of intratumoral CD3 T-cell infiltration. Pre-surgical T1-weighted post-contrast and T2-weighted Fluid-Attenuated-Inversion-Recovery (FLAIR) MRI scans, with corresponding mRNA expression of CD3D/E/G were obtained through The Cancer Genome Atlas (TCGA) for 79 GB patients. The tumor region was contoured and 86 image-derived features were extracted across the T1-post contrast and FLAIR images. Six imaging features—kurtosis, contrast, small zone size emphasis, low gray level zone size emphasis, high gray level zone size emphasis, small zone high gray level emphasis—were found associated with CD3 activity and used to build a predictive model for CD3 infiltration in an independent data set of 69 GB patients (using a 50-50 split for training and testing). For the training set, the image-based prediction model for CD3 infiltration achieved accuracy of 97.1% and area under the curve (AUC) of 0.993. For the test set, the model achieved accuracy of 76.5% and AUC of 0.847. This suggests a relationship between image-derived textural features and CD3 T-cell infiltration enabling the non-invasive inference of intratumoral CD3 T-cell infiltration in GB patients, with potential value for the radiological assessment of response to immune therapeutics.
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