Spatial Lesion Graphs: Analyzing Liver Metastases with Geometric Deep Learning for Cancer Survival Regression

Rist L, Taubmann O, Muhlberg A, Denzinger F, Thamm F, Sühling M, Norenberg D, Holch JW, Maurus S, Gebauer L, Huber T, Maier A (2023)

Publication Language: English

Publication Type: Conference contribution

Publication year: 2023

Journal

Proceedings - International Symposium on Biomedical Imaging

Publisher: IEEE Computer Society

Book Volume: 2023-April

Conference Proceedings Title: Proceedings - International Symposium on Biomedical Imaging

Event location: Cartagena CO

ISBN: 9781665473583

DOI: 10.1109/ISBI53787.2023.10230367

Abstract

The degree of metastatic progress is a crucial factor in cancer assessment and is often estimated from few target lesions. However, features of the overall configuration of metastases, such as spread distance in the liver, offer further predictive information. Graphs can represent both local characteristics of individual lesions and their global spatial distribution. In this work, liver metastases stemming from a primary pancreatic tumor are transformed patient-wise into spatial lesion graphs and processed by geometric deep learning to estimate hazard ratios. The influence of lesion features and graph topologies is investigated in a data set of 78 patients to provide model design guidelines. Graph attention and differential pooling networks were selected as architectures to capitalize on the proposed topologies and provide meaningful visualizations of the decision process. Compared with conventional survival analysis methods and deep learning models, both networks show improvement with a maximum concordance of 68.9 %.

Authors with CRIS profile

Leonhard Rist Lehrstuhl für Informatik 5 (Mustererkennung) Felix Denzinger Lehrstuhl für Informatik 5 (Mustererkennung) Florian Thamm Lehrstuhl für Informatik 5 (Mustererkennung) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

Involved external institutions

Klinikum der Universität München DE Germany (DE) Siemens AG, Healthcare Sector DE Germany (DE)

How to cite

APA:

Rist, L., Taubmann, O., Muhlberg, A., Denzinger, F., Thamm, F., Sühling, M.,... Maier, A. (2023). Spatial Lesion Graphs: Analyzing Liver Metastases with Geometric Deep Learning for Cancer Survival Regression. In Proceedings - International Symposium on Biomedical Imaging. Cartagena, CO: IEEE Computer Society.

MLA:

Rist, Leonhard, et al. "Spatial Lesion Graphs: Analyzing Liver Metastases with Geometric Deep Learning for Cancer Survival Regression." Proceedings of the 20th IEEE International Symposium on Biomedical Imaging, ISBI 2023, Cartagena IEEE Computer Society, 2023.

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