TY - GEN
T1 - Collaborative learning of images and geometrics for predicting isocitrate dehydrogenase status of glioma
AU - Wei, Yiran
AU - Li, Chao
AU - Chen, Xi
AU - Schönlieb, Carola-Bibiane
AU - Price, Stephen J.
N1 - Accepted by International Symposium on Biomedical Imaging 2022
PY - 2022/4/26
Y1 - 2022/4/26
N2 - The isocitrate dehydrogenase (IDH) gene mutation status is an important biomarker for glioma patients. The gold standard of IDH mutation detection requires tumour tissue obtained via invasive approaches and is usually expensive. Recent advancement in radiogenomics provides a non-invasive approach for predicting IDH mutation based on MRI. Meanwhile, tumor geometrics encompass crucial information for tumour phenotyping. Here we propose a collaborative learning framework that learns both tumor images and tumor geometrics using convolutional neural networks (CNN) and graph neural networks (GNN), respectively. Our results show that the proposed model outperforms the baseline model of 3D-DenseNet121. Further, the collaborative learning model achieves better performance than either the CNN or the GNN alone. The model interpretation shows that the CNN and GNN could identify common and unique regions of interest for IDH mutation prediction. In conclusion, collaborating image and geometric learners provides a novel approach for predicting genotype and characterising glioma.
AB - The isocitrate dehydrogenase (IDH) gene mutation status is an important biomarker for glioma patients. The gold standard of IDH mutation detection requires tumour tissue obtained via invasive approaches and is usually expensive. Recent advancement in radiogenomics provides a non-invasive approach for predicting IDH mutation based on MRI. Meanwhile, tumor geometrics encompass crucial information for tumour phenotyping. Here we propose a collaborative learning framework that learns both tumor images and tumor geometrics using convolutional neural networks (CNN) and graph neural networks (GNN), respectively. Our results show that the proposed model outperforms the baseline model of 3D-DenseNet121. Further, the collaborative learning model achieves better performance than either the CNN or the GNN alone. The model interpretation shows that the CNN and GNN could identify common and unique regions of interest for IDH mutation prediction. In conclusion, collaborating image and geometric learners provides a novel approach for predicting genotype and characterising glioma.
KW - collaborative learning
KW - geometric deep learning
KW - glioma
KW - graph neural networks
KW - isocitrate dehydrogenase
UR - http://www.scopus.com/inward/record.url?scp=85129661066&partnerID=8YFLogxK
U2 - 10.1109/ISBI52829.2022.9761407
DO - 10.1109/ISBI52829.2022.9761407
M3 - Chapter in a published conference proceeding
SN - 9781665429245
T3 - Proceedings - International Symposium on Biomedical Imaging
BT - ISBI 2022 - Proceedings
PB - IEEE
CY - U. S. A.
ER -