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Decomposition-based framework for tumor classification and prediction of treatment response from longitudinal MRI. | LitMetric

Decomposition-based framework for tumor classification and prediction of treatment response from longitudinal MRI.

Phys Med Biol

Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, DK-2650, Denmark.

Published: January 2023

AI Article Synopsis

  • MRI is a powerful tool in radiation oncology, surpassing its traditional use of providing detailed imaging for treatment planning by enabling mapping of physiological parameters throughout radiotherapy.
  • A new prediction framework has been developed to analyze changes in tissue features over time, using advanced techniques like monotonous slope non-negative matrix factorization (msNMF) to assess tumor heterogeneity and predict radiotherapy outcomes.
  • The framework successfully classified pancreatic tumor types with a high accuracy (AUC of 0.999) and demonstrated a moderate correlation for predicting tumor volume changes (0.513), while also classifying brain metastases into treatment responders and non-responders with an AUC of 0.74.

Article Abstract

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.Two previously published MRI datasets with multiple measurements during and after radiotherapy, were used for development and testing:-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.The framework was able to classify the two pancreatic tumor types with an area under curve (AUC) of 0.999,< 0.001 and predict the tumor volume change with a correlation coefficient of 0.513,= 0.034. A classification of the human brain metastases into responders and non-responders resulted in an AUC of 0.74,= 0.065.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.

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Source
http://dx.doi.org/10.1088/1361-6560/acaa85DOI Listing

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