A generative approach for image-based modeling of tumor growth

22nd International Conference, IPMI 2011, Kloster Irsee, Germany, July 3-8, 2011. ProceedingsExtensive imaging is routinely used in brain tumor patients to monitor the state of the disease and to evaluate therapeutic options. A large number of multi-modal and multi-temporal image volumes is acquired in standard clinical cases, requiring new approaches for comprehensive integration of information from different image sources and different time points. In this work we propose a joint generative model of tumor growth and of image observation that naturally handles multi-modal and longitudinal data. We use the model for analyzing imaging data in patients with glioma. The tumor growth model is based on a reaction-diffusion framework. Model personalization relies only on a forward model for the growth process and on image likelihood. We take advantage of an adaptive sparse grid approximation for efficient inference via Markov Chain Monte Carlo sampling. The approach can be used for integrating information from different multi-modal imaging protocols and can easily be adapted to other tumor growth models.German Academy of Sciences Leopoldina (Fellowship Programme LPDS 2009-10)Academy of Finland (133611)National Institutes of Health (U.S.) (NIBIB NAMIC U54-EB005149)National Institutes of Health (U.S.) (NCRR NAC P41- RR13218)National Institutes of Health (U.S.) (NINDS R01-NS051826)National Institutes of Health (U.S.) (NIH R01-NS052585)National Institutes of Health (U.S.) (NIH R01-EB006758)National Institutes of Health (U.S.) (NIH R01-EB009051)National Institutes of Health (U.S.) (NIH P41-RR014075)National Science Foundation (U.S.) (CAREER Award 0642971

Predicting the Location of Glioma Recurrence After a Resection Surgery

International audienceWe propose a method for estimating the location of glioma recurrence after surgical resection. This method consists of a pipeline including the registration of images at different time points, the estimation of the tumor infiltration map, and the prediction of tumor regrowth using a reaction-diffusion model. A data set acquired on a patient with a low-grade glioma and post surgery MRIs is considered to evaluate the accuracy of the estimated recurrence locations found using our method. We observed good agreement in tumor volume prediction and qualitative matching in regrowth locations. Therefore, the proposed method seems adequate for modeling low-grade glioma recurrence. This tool could help clinicians anticipate tumor regrowth and better characterize the radiologically non-visible infiltrative extent of the tumor. Such information could pave the way for model-based personalization of treatment planning in a near future

Tumor Growth Parameters Estimation and Source Localization From a Unique Time Point: Application to Low-grade Gliomas

International audienceCoupling time series of MR Images with reaction-di usion-based models has provided interesting ways to better understand the proliferative-invasive as- pect of glial cells in tumors. In this paper, we address a di erent formulation of the inverse problem: from a single time point image of a non-swollen brain tumor, estimate the tumor source location and the di usivity ratio between white and grey matter, while exploring the possibility to predict the further extent of the observed tumor at later time points in low-grade gliomas. The synthetic and clinical results show the stability of the located source and its varying distance from the tumor barycenter and how the estimated ratio controls the spikiness of the tumor

Longitudinal Brain Tumor Tracking, Tumor Grading, and Patient Survival Prediction Using MRI

This work aims to develop novel methods for brain tumor classification, longitudinal brain tumor tracking, and patient survival prediction. Consequently, this dissertation proposes three tasks. First, we develop a framework for brain tumor segmentation prediction in longitudinal multimodal magnetic resonance imaging (mMRI) scans, comprising two methods: feature fusion and joint label fusion (JLF). The first method fuses stochastic multi-resolution texture features with tumor cell density features, in order to obtain tumor segmentation predictions in follow-up scans from a baseline pre-operative timepoint. The second method utilizes JLF to combine segmentation labels obtained from (i) the stochastic texture feature-based and Random Forest (RF)-based tumor segmentation method; and (ii) another state-of-the-art tumor growth and segmentation method known as boosted Glioma Image Segmentation and Registration (GLISTRboost, or GB). With the advantages of feature fusion and label fusion, we achieve state-of-the-art brain tumor segmentation prediction. Second, we propose a deep neural network (DNN) learning-based method for brain tumor type and subtype grading using phenotypic and genotypic data, following the World Health Organization (WHO) criteria. In addition, the classification method integrates a cellularity feature which is derived from the morphology of a pathology image to improve classification performance. The proposed method achieves state-of-the-art performance for tumor grading following the new CNS tumor grading criteria. Finally, we investigate brain tumor volume segmentation, tumor subtype classification, and overall patient survival prediction, and then we propose a new context- aware deep learning method, known as the Context Aware Convolutional Neural Network (CANet). Using the proposed method, we participated in the Multimodal Brain Tumor Segmentation Challenge 2019 (BraTS 2019) for brain tumor volume segmentation and overall survival prediction tasks. In addition, we also participated in the Radiology-Pathology Challenge 2019 (CPM-RadPath 2019) for Brain Tumor Subtype Classification, organized by the Medical Image Computing & Computer Assisted Intervention (MICCAI) Society. The online evaluation results show that the proposed methods offer competitive performance from their use of state-of-the-art methods in tumor volume segmentation, promising performance on overall survival prediction, and state-of-the-art performance on tumor subtype classification. Moreover, our result was ranked second place in the testing phase of the CPM-RadPath 2019

Image-based modeling of tumor growth in patients with glioma.

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Experimental method and statistical analysis to fit tumor growth model using SPECT/CT imaging: A preclinical study

Background: Over the last decade, several theoretical tumor-models have been developed to describe tumor growth. Oncology imaging is performed using various modalities including computed tomography (CT), magnetic resonance imaging (MRI), single-photon emission computed tomography (SPECT) and fluorodeoxyglucose-positron emission tomography (FDG-PET). Our goal is to extract useful, otherwise hidden, quantitative biophysical parameters (such as growth-rate, tumor-necrotic-factor, etc.) from these serial images of tumors by fitting mathematical models to images. These biophysical features are intrinsic to the tumor types and specific to the study-subject, and expected to add valuable information on the tumor containment or spread and help treatment plans. Thus, fitting realistic but practical models and assessing parameter-errors and degree of fit is important. Methods: We implemented an existing theoretical ode-compartment model and variants and applied them for the first time, in vivo. We developed an inversion algorithm to fit the models for tumor growth for simulated as well as in vivo experimental data. Serial SPECT/CT scans of mice breast-tumors were acquired, and SPECT data was used to segment the proliferating-layers of tumors. Results: Results of noisy data simulation and inversion show that 5 out of 7 parameters were recovered to within 4.3% error. In particular, tumor growth-rate parameter was recovered to 0.07% error. For model fitting to in vivo mice-tumors, regression analysis on the P-layer volume showed R2 of 0.99 for logistic and Gompertzian while surface area model yielded R2=0.96. For the necrotic layer the R2 values were 0.95, 0.93 and 0.94 respectively for surface-area, logistic and Gompertzian. The Akaike Information Criterion (AIC) weights of the models (giving their relative probability of being the best Kullback-Leibler (K-L) model among the set of candidate models) were 0, 0.43 and 0.57 for surface-area, logistic and Gompertzian models. Conclusions: Model-fitting to mice tumor studies demonstrates feasibility of applying the models to in vivo imaging data to extract features. Akaike information criterion (AIC) evaluations show Gompertzian or logistic growth model fits in vivo breast-tumors better than surface-area based growth model