@inproceedings{08e6888674ca495b94cd066edcac9fa4,
title = "Quantifying Heterogeneity in Tumors: Proposing a New Method Utilizing Convolutional Neuronal Networks",
abstract = "Heterogeneity is a hallmark of glioblastoma (GBM), the most common malignant brain tumor, and a key reason for the poor survival rate of patients. However, establishing a clinically applicable, cost-efficient tool to measure and quantify heterogeneity is challenging. We present a novel method in an ongoing study utilizing two convolutional neuronal networks (CNN). After digitizing tumor samples, the first CNN delimitates GBM from normal tissue, the second quantifies heterogeneity within the tumor. Since neuronal networks can detect and interpret underlying and hidden information within images and have the ability to incorporate different information sets (i.e. clinical data and mutational status), this approach might venture towards a next level of integrated diagnosis. It may be applicable to other tumors as well and lead to a more precision-based medicine.",
keywords = "Convolutional Neuronal Network, Digital Pathology, Glioblastoma, Neuropathology, Tumor heterogeneity",
author = "Georg Prokop and Michael {\"O}rtl and Marina Fotteler and Peter Sch{\"u}ffler and Johannes Schobel and Walter Swoboda and J{\"u}rgen Schlegel and Friederike Liesche-Starnecker",
note = "Publisher Copyright: {\textcopyright} 2022 The authors and IOS Press.",
year = "2022",
doi = "10.3233/SHTI210942",
language = "English",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press BV",
pages = "397--400",
editor = "John Mantas and Arie Hasman and Househ, {Mowafa S.} and Parisis Gallos and Emmanouil Zoulias and Joseph Liasko",
booktitle = "Informatics and Technology in Clinical Care and Public Health",
}