Towards quantitative imaging biomarkers of tumor dissemination

• Verfasst von: Piraud, Marie [VerfasserIn]
• Verfasst von: Wennmann, Markus [VerfasserIn]
• Verfasst von: Kintzelé, Laurent [VerfasserIn]
• Verfasst von: Hillengass, Jens [VerfasserIn]
• Verfasst von: Keller, Ulrich [VerfasserIn]
• Verfasst von: Langs, Georg [VerfasserIn]
• Verfasst von: Weber, Marc-André [VerfasserIn]
• Verfasst von: Menze, Björn H. [VerfasserIn]
• Titel: Towards quantitative imaging biomarkers of tumor dissemination
• Titelzusatz: A multi-scale parametric modeling of multiple myeloma
• Verf.angabe: Marie Piraud, Markus Wennmann, Laurent Kintzelé, Jens Hillengass, Ulrich Keller, Georg Langs, Marc-André Weber, Björn H. Menze
• E-Jahr: 2019
• Jahr: 4 July 2019
• Umfang: 12 S.
• Fussnoten: Gesehen am 16.12.2019
• Titel Quelle: Enthalten in: Medical image analysis
• Ort Quelle: Amsterdam [u.a.] : Elsevier Science, 1996
• Jahr Quelle: 2019
• Band/Heft Quelle: 57(2019), Seite 214-225
• ISSN Quelle: 1361-8423
• DOI: doi:10.1016/j.media.2019.07.001
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1685705219

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.