| Creators: |
Prokop, Georg and Örtl, Michael and Fotteler, Marina L. and Schüffler, Peter and Schobel, Johannes and Swoboda, Walter and Schlegel, Jürgen and Liesche-Starnecker, Friederike |
| Title: |
Quantifying Heterogeneity in Tumors: Proposing a New Method Utilizing Convolutional Neuronal Networks |
| Item Type: |
Article or issue of a publication series |
| Projects: |
DigiHealth, n4n |
| Journal or Series Title: |
Studies in health technology and informatics |
| Page Range: |
pp. 397-400 |
| Additional Information: |
Open Access |
| Date: |
January 2022 |
| Divisions: |
Gesundheitsmanagement |
| Abstract (ENG): |
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. |
| Forthcoming: |
No |
| Language: |
English |
| Uncontrolled Keywords: |
Convolutional Neuronal Network; Digital Pathology; Glioblastoma; Neuropathology; Tumor heterogeneity |
| Link eMedia: |
Download |
| Citation: |
Prokop, Georg and Örtl, Michael and Fotteler, Marina L. and Schüffler, Peter and Schobel, Johannes and Swoboda, Walter and Schlegel, Jürgen and Liesche-Starnecker, Friederike
(2022)
Quantifying Heterogeneity in Tumors: Proposing a New Method Utilizing Convolutional Neuronal Networks.
Studies in health technology and informatics, 289.
pp. 397-400.
ISSN 0926-9630
|
