Skip to main content

A fully automated artificial intelligence method for non-invasive, imaging-based identification of genetic alterations in glioblastomas.

Publication ,  Journal Article
Calabrese, E; Villanueva-Meyer, JE; Cha, S
Published in: Sci Rep
July 16, 2020

Glioblastoma is the most common malignant brain parenchymal tumor yet remains challenging to treat. The current standard of care-resection and chemoradiation-is limited in part due to the genetic heterogeneity of glioblastoma. Previous studies have identified several tumor genetic biomarkers that are frequently present in glioblastoma and can alter clinical management. Currently, genetic biomarker status is confirmed with tissue sampling, which is costly and only available after tumor resection or biopsy. The purpose of this study was to evaluate a fully automated artificial intelligence approach for predicting the status of several common glioblastoma genetic biomarkers on preoperative MRI. We retrospectively analyzed multisequence preoperative brain MRI from 199 adult patients with glioblastoma who subsequently underwent tumor resection and genetic testing. Radiomics features extracted from fully automated deep learning-based tumor segmentations were used to predict nine common glioblastoma genetic biomarkers with random forest regression. The proposed fully automated method was useful for predicting IDH mutations (sensitivity = 0.93, specificity = 0.88), ATRX mutations (sensitivity = 0.94, specificity = 0.92), chromosome 7/10 aneuploidies (sensitivity = 0.90, specificity = 0.88), and CDKN2 family mutations (sensitivity = 0.76, specificity = 0.86).

Duke Scholars

Altmetric Attention Stats
Dimensions Citation Stats

Published In

Sci Rep

DOI

EISSN

2045-2322

Publication Date

July 16, 2020

Volume

10

Issue

1

Start / End Page

11852

Location

England

Related Subject Headings

  • X-linked Nuclear Protein
  • Sensitivity and Specificity
  • Retrospective Studies
  • Preoperative Care
  • Mutation
  • Male
  • Magnetic Resonance Imaging
  • Isocitrate Dehydrogenase
  • Image Interpretation, Computer-Assisted
  • Humans
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Calabrese, E., Villanueva-Meyer, J. E., & Cha, S. (2020). A fully automated artificial intelligence method for non-invasive, imaging-based identification of genetic alterations in glioblastomas. Sci Rep, 10(1), 11852. https://doi.org/10.1038/s41598-020-68857-8
Calabrese, Evan, Javier E. Villanueva-Meyer, and Soonmee Cha. “A fully automated artificial intelligence method for non-invasive, imaging-based identification of genetic alterations in glioblastomas.Sci Rep 10, no. 1 (July 16, 2020): 11852. https://doi.org/10.1038/s41598-020-68857-8.
Calabrese, Evan, et al. “A fully automated artificial intelligence method for non-invasive, imaging-based identification of genetic alterations in glioblastomas.Sci Rep, vol. 10, no. 1, July 2020, p. 11852. Pubmed, doi:10.1038/s41598-020-68857-8.

Published In

Sci Rep

DOI

EISSN

2045-2322

Publication Date

July 16, 2020

Volume

10

Issue

1

Start / End Page

11852

Location

England

Related Subject Headings

  • X-linked Nuclear Protein
  • Sensitivity and Specificity
  • Retrospective Studies
  • Preoperative Care
  • Mutation
  • Male
  • Magnetic Resonance Imaging
  • Isocitrate Dehydrogenase
  • Image Interpretation, Computer-Assisted
  • Humans