| Creators: |
Hieber, Daniel and Prokop, Georg and Karthan, Maximilian and Holl, Felix and Kestler, Hans A. and Grambow, Gregor and Märkl, Bruno and Pryss, Rüdiger and Liesche-Starnecker, Friederike and Schobel, Johannes |
| Abstract (ENG): |
Glioblastoma is the most common malignant brain
tumor with a poor survival rate due to its high intra- and intertumor
heterogeneity. The current heterogeneity determination
is based on a microscopic analysis of Hematoxilyn and Eosinstained tumor slides carried out by experienced neuropathologists.
There is no standardized procedure yet, to quantify
heterogeneity, though. With the hypothesis that the amount of
heterogeneity impacts overall survival, we aim to develop an
objective method to capture heterogeneity. We were able to
successfully implement an initial Machine Learning classification model for determining heterogeneity. However, the available
dataset was insufficient to train a resilient and stable system.
Therefore, we propose an architecture for a semi-automatic data
collection and preprocessing framework easing the collection of large quantities of required data. While there exists a multitude of frameworks tackling parts of the tumor research area, no simple ready-to-use solution is present for the easy collection of tumor data in daily clinical routine, especially for high-resolution
pathological images.We plan to implement the proposed architecture at the University Hospital Augsburg, Germany in 2023. The dataset created in this process will then be used as a resilient basis for heterogeneity classification and for analyzing glioblastomas in general. |
| Citation: |
Hieber, Daniel and Prokop, Georg and Karthan, Maximilian and Holl, Felix and Kestler, Hans A. and Grambow, Gregor and Märkl, Bruno and Pryss, Rüdiger and Liesche-Starnecker, Friederike and Schobel, Johannes
(2023)
Towards an Architecture for Collecting a
Multidimensional Glioblastoma Dataset.
In: (Proceedings of the) IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS), June, 22-24, 2023, L'Aquila, Italy, pp. 904-909.
ISBN 9798350312249
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