Personalized Pancreatic Tumor Growth Prediction via Group Learning

Tumor growth prediction, a highly challenging task, has long been viewed as a mathematical modeling problem, where the tumor growth pattern is personalized based on imaging and clinical data of a target patient. Though mathematical models yield promising results, their prediction accuracy may be limited by the absence of population trend data and personalized clinical characteristics. In this paper, we propose a statistical group learning approach to predict the tumor growth pattern that incorporates both the population trend and personalized data, in order to discover high-level features from multimodal imaging data. A deep convolutional neural network approach is developed to model the voxel-wise spatio-temporal tumor progression. The deep features are combined with the time intervals and the clinical factors to feed a process of feature selection. Our predictive model is pretrained on a group data set and personalized on the target patient data to estimate the future spatio-temporal progression of the patient's tumor. Multimodal imaging data at multiple time points are used in the learning, personalization and inference stages. Our method achieves a Dice coefficient of 86.8% +- 3.6% and RVD of 7.9% +- 5.4% on a pancreatic tumor data set, outperforming the DSC of 84.4% +- 4.0% and RVD 13.9% +- 9.8% obtained by a previous state-of-the-art model-based method

Personalization of Reaction-Diffusion Tumor Growth Models in MR Images: Application to Brain Gliomas Characterization and Radiotherapy Planning

International audienceReaction-diffusion based tumor growth models have been widely used in the literature for modeling the growth of brain gliomas. Lately, recent models have started integrating medical images, specifically anatomical and diffusion images, in their formulation. On the other hand, the adaptation of the general model to the specific patient cases has not been studied thoroughly yet. In this chapter we address this adaptation. This chapter is a short summary of the articles (Konukoglu 2009a), (Konukoglu 2009b) and the thesis (Konukoglu 2009c) that we have submitted recently. In the first part, we describe a parameter estimation method for reaction-diffusion tumor growth models using time series of medical (Magnetic Resonance) images. This method estimates the patient specific parameters of the model using the images of the patient taken at different successive time instances. In the second part of the chapter we focus on an application of the personalized models aimed to improve the tumor targeting in radiation therapy. Specifically we address the problem of limited visualization of medical images. We describe a method for extrapolating the invisible infiltration margins of gliomas in the MR images and the usage of these margins in constructing irradiation margins taking into account the growth dynamics of the tumor. Finally for both parts we show preliminary results demonstrating the power and the potential benefits of the personalizatio

Accurate state estimation from uncertain data and models: an application of data assimilation to mathematical models of human brain tumors

<p>Abstract</p> <p>Background</p> <p>Data assimilation refers to methods for updating the state vector (initial condition) of a complex spatiotemporal model (such as a numerical weather model) by combining new observations with one or more prior forecasts. We consider the potential feasibility of this approach for making short-term (60-day) forecasts of the growth and spread of a malignant brain cancer (glioblastoma multiforme) in individual patient cases, where the observations are synthetic magnetic resonance images of a hypothetical tumor.</p> <p>Results</p> <p>We apply a modern state estimation algorithm (the Local Ensemble Transform Kalman Filter), previously developed for numerical weather prediction, to two different mathematical models of glioblastoma, taking into account likely errors in model parameters and measurement uncertainties in magnetic resonance imaging. The filter can accurately shadow the growth of a representative synthetic tumor for 360 days (six 60-day forecast/update cycles) in the presence of a moderate degree of systematic model error and measurement noise.</p> <p>Conclusions</p> <p>The mathematical methodology described here may prove useful for other modeling efforts in biology and oncology. An accurate forecast system for glioblastoma may prove useful in clinical settings for treatment planning and patient counseling.</p> <p>Reviewers</p> <p>This article was reviewed by Anthony Almudevar, Tomas Radivoyevitch, and Kristin Swanson (nominated by Georg Luebeck).</p