Abstract IA-23: Multi-scale modeling of cancer patients

Recently biomedical analysis in both imaging and non-imaging data has advanced rapidly aided by the modeling power of deep learning networks. Complex diseases such as cancer may reveal different aspects of disease characteristics in different data modalities, and thus integrating imaging with non-imaging biomarkers and clinical data has the potential to discover information that is not obvious in any single data modality. We are developing methods for integrating image, genomic and clinical data to improve precision medicine applications for cancer patients. Our results suggest that the integration strategies achieve improved prediction results than using each single data modality alone. We further show that information is transferrable across age groups or tumor subtypes under the data fusion framework.

Citation Format: Olivier Gevaert. Multi-scale modeling of cancer patients [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr IA-23.