Journal article
Decomposition-based framework for tumor classification and prediction of treatment response from longitudinal MRI
MRI Acquisition, Magnetic Resonance, Department of Health Technology, Technical University of Denmark1
Magnetic Resonance, Department of Health Technology, Technical University of Denmark2
Department of Health Technology, Technical University of Denmark3
University of Southern Denmark4
H. Lee Moffitt Cancer Center and Research Institute5
Hyperpolarization & Metabolism, Magnetic Resonance, Department of Health Technology, Technical University of Denmark6
Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark7
Department of Applied Mathematics and Computer Science, Technical University of Denmark8
Objective: In the field of radiation oncology, the benefit of MRI goes beyond that of providing high soft-tissue contrast images for staging and treatment planning. With the recent clinical introduction of hybrid MRI linear accelerators (MR-Linacs) it has become feasible to map physiological parameters describing diffusion, perfusion, and relaxation during the entire course of radiotherapy, for example.
However, advanced data analysis tools are required for extracting qualified prognostic and predictive imaging biomarkers from longitudinal MRI data. In this study, we propose a new prediction framework tailored to exploit temporal dynamics of tissue features from repeated measurements. We demonstrate the framework using a newly developed decomposition method for tumor characterization.
Approach: Two previously published MRI datasets with multiple measurements during and after radiotherapy, were used for development and testing: T$_2$-weighted multi-echo images obtained for two mouse models of pancreatic cancer, and diffusion-weighted images for patients with brain metastases. Initially, the data was decomposed using the novel monotonous slope non-negative matrix factorization (msNMF) tailored for MR data.
The following processing consisted of a tumor heterogeneity assessment using descriptive statistical measures, robust linear modelling to capture temporal changes of these, and finally logistic regression analysis for stratification of tumors and volumetric outcome. Main Results: The framework was able to classify the two pancreatic tumor types with an \textit{area under curve} AUC of 0.999, $P
Language: | English |
---|---|
Publisher: | IOP Publishing |
Year: | 2023 |
ISSN: | 13616560 and 00319155 |
Types: | Journal article |
DOI: | 10.1088/1361-6560/acaa85 |
ORCIDs: | Rahbek, Sofie , Hanson, Lars G. , Madsen, Kristoffer H. , 0000-0002-7270-7967 and 0000-0002-3194-9492 |