Tumor image-derived texture features are associated with CD3 T-cell infiltration status in glioblastoma

Shivali Narang, Donnie Kim, Sathvik Aithala, Amy B. Heimberger, Salmaan Ahmed, Dinesh Rao, Ganesh Rao, Arvind Rao*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


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 noninvasive inference of intratumoral CD3 T-cell infiltration in GB patients, with potential value for the radiological assessment of response to immune therapeutics.

Original languageEnglish (US)
Pages (from-to)101244-101254
Number of pages11
Issue number60
StatePublished - 2017
Externally publishedYes


  • Glioblastoma
  • Imaging-genomics analysis
  • Immune activity
  • Texture analysis

ASJC Scopus subject areas

  • Oncology


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