Deep learning links localized digital pathology phenotypes with transcriptional subtype and patient outcome in glioblastoma

Author:

Roetzer-Pejrimovsky Thomas12ORCID,Nenning Karl-Heinz34ORCID,Kiesel Barbara5ORCID,Klughammer Johanna6ORCID,Rajchl Martin7ORCID,Baumann Bernhard8ORCID,Langs Georg4ORCID,Woehrer Adelheid129ORCID

Affiliation:

1. Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna , 1090 Vienna , Austria

2. Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna , 1090 Vienna , Austria

3. Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute , Orangeburg, NY 10962 , USA

4. Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna , 1090 Vienna , Austria

5. Department of Neurosurgery, Medical University of Vienna , 1090 Vienna , Austria

6. Gene Center and Department of Biochemistry, Ludwig-Maximilians-Universität München , 80539 Munich , Germany

7. Department of Computing and Medicine, Imperial College London , London SW7 2AZ , UK

8. Center for Medical Physics and Biomedical Engineering, Medical University of Vienna , 1090 Vienna , Austria

9. Department of Pathology, Neuropathology and Molecular Pathology, Medical University of Innsbruck , 6020 Innsbruck , Austria

Abstract

Abstract Background Deep learning has revolutionized medical image analysis in cancer pathology, where it had a substantial clinical impact by supporting the diagnosis and prognostic rating of cancer. Among the first available digital resources in the field of brain cancer is glioblastoma, the most common and fatal brain cancer. At the histologic level, glioblastoma is characterized by abundant phenotypic variability that is poorly linked with patient prognosis. At the transcriptional level, 3 molecular subtypes are distinguished with mesenchymal-subtype tumors being associated with increased immune cell infiltration and worse outcome. Results We address genotype–phenotype correlations by applying an Xception convolutional neural network to a discovery set of 276 digital hematozylin and eosin (H&E) slides with molecular subtype annotation and an independent The Cancer Genome Atlas–based validation cohort of 178 cases. Using this approach, we achieve high accuracy in H&E-based mapping of molecular subtypes (area under the curve for classical, mesenchymal, and proneural = 0.84, 0.81, and 0.71, respectively; P < 0.001) and regions associated with worse outcome (univariable survival model P < 0.001, multivariable P = 0.01). The latter were characterized by higher tumor cell density (P < 0.001), phenotypic variability of tumor cells (P < 0.001), and decreased T-cell infiltration (P = 0.017). Conclusions We modify a well-known convolutional neural network architecture for glioblastoma digital slides to accurately map the spatial distribution of transcriptional subtypes and regions predictive of worse outcome, thereby showcasing the relevance of artificial intelligence–enabled image mining in brain cancer.

Funder

Austrian Science Fund

Publisher

Oxford University Press (OUP)

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