Towards quantitative imaging biomarkers of tumor dissemination: a multi-scale parametric modeling of multiple myeloma

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Item Type:	Article
Title:	Towards quantitative imaging biomarkers of tumor dissemination: a multi-scale parametric modeling of multiple myeloma
Creators Name:	Piraud, M., Wennmann, M., Kintzelé, L., Hillengass, J., Keller, U., Langs, G., Weber, M.A. and Menze, B.H.
Abstract:	The advent of medical imaging and automatic image analysis is bringing the full quantitative assess- ment of lesions and tumor burden at every clinical examination within reach. This opens avenues for the development and testing of functional disease models, as well as their use in the clinical practice for per- sonalized medicine. In this paper, we introduce a Bayesian statistical framework, based on mixed-effects models, to quantitatively test and learn functional disease models at different scales, on population longi- tudinal data. We also derive an effective mathematical model for the crossover between initially detected lesions and tumor dissemination, based on the Iwata-Kawasaki-Shigesada model. We finally propose to leverage this descriptive disease progression model into model-aware biomarkers for personalized risk- assessment, taking all available examinations and relevant covariates into account. As a use case, we study Multiple Myeloma, a disseminated plasma cell cancer, in which proper diagnostics is essential, to differentiate frequent precursor state without end-organ damage from the rapidly developing disease re- quiring therapy. After learning the best biological models for local lesion growth and global tumor burden evolution on clinical data, and computing corresponding population priors, we use individual model pa- rameters as biomarkers, and can study them systematically for correlation with external covariates, such as sex or location of the lesion. On our cohort of 63 patients with smoldering Multiple Myeloma, we show that they perform substantially better than other radiological criteria, to predict progression into symptomatic Multiple Myeloma. Our study paves the way for modeling disease progression patterns for Multiple Myeloma, but also for other metastatic and disseminated tumor growth processes, and for an- alyzing large longitudinal image data sets acquired in oncological imaging. It shows the unprecedented potential of model-based biomarkers for better and more personalized treatment decisions and deserves being validated on larger cohorts to establish its role in clinical decision making.
Keywords:	Algorithms, Bayes Theorem, Computer-Assisted Image Interpretation, Disease Progression, Longitudinal Studies, Magnetic Resonance Imaging, Multiple Myeloma, Neoplasm Staging, Risk Assessment, Theoretical Models, Tumor Burden, Tumor Biomarkers, Whole Body Imaging
Source:	Medical Image Analysis
ISSN:	1361-8415
Publisher:	Elsevier
Volume:	57
Page Range:	214-225
Date:	October 2019
Official Publication:	https://doi.org/10.1016/j.media.2019.07.001
PubMed:	View item in PubMed

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