Decomposition-based framework for tumor classification and prediction of treatment response from longitudinal MRI. (21st January 2023)

• Record Type: Journal Article
• Authors: Rahbek, Sofie; Mahmood, Faisal; Tomaszewski, Michal R; Hanson, Lars G; Madsen, Kristoffer H
• Is Part Of: Physics in medicine & biology. Volume 68:Number 2(2023)
• Publication Date: 2023-01-21
• Subjects: radiotherapy -- prediction framework -- decomposition -- longitudinal MRI -- MR-Linac -- treatment response; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/acaa85
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: 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 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 area under curve (AUC) of 0.999, P < 0.001 and predict the tumor volume change with a correlation coefficient of 0.513, P = 0.034. A classification of the human brain metastases into responders and non-responders resulted in an AUC of 0.74, P = 0.065. Significance. A general data processing framework for analyses of longitudinal MRI data has been developed and applications were demonstrated by classification of tumor type and prediction of radiotherapy response. Further, as part of the assessment, the merits of msNMF for tumor tissue decomposition were demonstrated.