Radiogenomic characterization of response to chemo-radiation therapy in glioblastoma is associated with PI3K/AKT/mTOR and apoptosis signaling pathways

Niha Beig, Prateek Prasanna, Virginia Hill, Ruchika Verma, Vinay Varadan, Anant Madabhushi, Pallavi Tiwari

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Over 40% of Glioblastoma (GBM) patients do not respond to conventional chemo-radiation therapy (chemo-RT) and relapse within 6-9 months, suggesting that they may have been better suited for other targeted therapies. Currently, there are no biomarkers that can reliably predict patients' response to chemo-RT in GBM. We seek to evaluate the role of radiomic markers on pre-treatment MRI to predict GBM patients' response to chemo-RT. Further, to establish a biological underpinning of the radiomic markers, we identified radiogenomic correlates of the radiomic markers with signaling pathways that are known to impact chemo-RT response. A total of 49 studies with Gd-T1w, T2w, FLAIR MRI protocols and corresponding gene expression were obtained from Ivy GAP (n=29) and TCIA (n=20) databases. Responders (n=22) were patients with progression-free survival (PFS) of at least ≥ 6 months, while non-responders (n=27) had PFS < 6 months. 13 molecular pathways were curated from the MSigDB Hallmark gene set. For each study, enhancing tumor on MRI was manually segmented by an expert reader. 1390 3D-radiomic features (Gabor, Haralick, and Laws energy) were extracted from this region across all MRI protocols. Joint mutual information identified the 3 most predictive radiomic features in the training set (n=29). This was followed by correlating these features with the gene set enrichment analysis (GSEA) score computed for every pathway. A support vector machine (SVM) classifier was trained using these 3 features and validated on a test set (n=20) that resulted in an Area Under Curve (AUC) of 0.71 to distinguish chemo-RT responders from non-responders. Laws energy descriptor (characterizing appearance of edges, spots, and ripples) from the enhancing region on Gd-T1w MR images were found to best predict chemo-RT response. Radiogenomic correlation with GSEA scores revealed that these radiomic features were significantly associated with PI3K/AKT/mTOR (promotes cell proliferation, survival) and apoptosis (programmed cell death) signaling pathways (p < 0.03, False Discovery Rate = 5%).

Original languageEnglish (US)
Title of host publicationMedical Imaging 2019
Subtitle of host publicationComputer-Aided Diagnosis
EditorsKensaku Mori, Horst K. Hahn
ISBN (Electronic)9781510625471
StatePublished - 2019
EventMedical Imaging 2019: Computer-Aided Diagnosis - San Diego, United States
Duration: Feb 17 2019Feb 20 2019

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2019: Computer-Aided Diagnosis
CountryUnited States
CitySan Diego


  • Chemo-radiation therapy (chemo-RT)
  • Glioblastoma
  • MRI
  • Personalized medicine
  • Radiogenomics

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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