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Towards quantitative imaging biomarkers of tumor dissemination: A multi-scale parametric modeling of multiple myeloma.
Med Image Anal 2019; 57:214-225MI

Abstract

The advent of medical imaging and automatic image analysis is bringing the full quantitative assessment 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 personalized 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 longitudinal 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 requiring 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 parameters 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 analyzing 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.

Authors+Show Affiliations

Department of Computer Science, Technical University of Munich, Munich, Germany; Center for Translational Cancer Research (Translatum), Klinikum rechts der Isar, Technical University of Munich, Germany. Electronic address: marie.piraud@tum.de.Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany.Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany.Department of Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.Hematology and Oncology, Campus Benjamin Franklin, Charité - Universitätsmedizin Berlin, Berlin, Germany; Medical Department, Technical University of Munich, Munich, Germany.Department of Biomedical Imaging and Image-Guided Therapy, Computational Imaging Research Laboratory, Medical University of Vienna, Vienna, Austria.Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, University Medical Center Rostock, Rostock, Germany.Department of Computer Science, Technical University of Munich, Munich, Germany; Center for Translational Cancer Research (Translatum), Klinikum rechts der Isar, Technical University of Munich, Germany.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

31349146

Citation

Piraud, Marie, et al. "Towards Quantitative Imaging Biomarkers of Tumor Dissemination: a Multi-scale Parametric Modeling of Multiple Myeloma." Medical Image Analysis, vol. 57, 2019, pp. 214-225.
Piraud M, Wennmann M, Kintzelé L, et al. Towards quantitative imaging biomarkers of tumor dissemination: A multi-scale parametric modeling of multiple myeloma. Med Image Anal. 2019;57:214-225.
Piraud, M., Wennmann, M., Kintzelé, L., Hillengass, J., Keller, U., Langs, G., ... Menze, B. H. (2019). Towards quantitative imaging biomarkers of tumor dissemination: A multi-scale parametric modeling of multiple myeloma. Medical Image Analysis, 57, pp. 214-225. doi:10.1016/j.media.2019.07.001.
Piraud M, et al. Towards Quantitative Imaging Biomarkers of Tumor Dissemination: a Multi-scale Parametric Modeling of Multiple Myeloma. Med Image Anal. 2019;57:214-225. PubMed PMID: 31349146.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Towards quantitative imaging biomarkers of tumor dissemination: A multi-scale parametric modeling of multiple myeloma. AU - Piraud,Marie, AU - Wennmann,Markus, AU - Kintzelé,Laurent, AU - Hillengass,Jens, AU - Keller,Ulrich, AU - Langs,Georg, AU - Weber,Marc-André, AU - Menze,Björn H, Y1 - 2019/07/04/ PY - 2018/09/28/received PY - 2019/06/20/revised PY - 2019/07/02/accepted PY - 2019/7/28/pubmed PY - 2019/7/28/medline PY - 2019/7/27/entrez SP - 214 EP - 225 JF - Medical image analysis JO - Med Image Anal VL - 57 N2 - The advent of medical imaging and automatic image analysis is bringing the full quantitative assessment 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 personalized 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 longitudinal 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 requiring 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 parameters 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 analyzing 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. SN - 1361-8423 UR - https://www.unboundmedicine.com/medline/citation/31349146/Towards_quantitative_imaging_biomarkers_of_tumor_dissemination:_A_multi-scale_parametric_modeling_of_multiple_myeloma L2 - https://linkinghub.elsevier.com/retrieve/pii/S1361-8415(18)30764-3 DB - PRIME DP - Unbound Medicine ER -