Abstract 2455: Human glioblastoma arises from the distant subventricular zone normal appearing but harboring tumor-initiating mutations

Lee, Joo Ho; Lee, Jeong Eun; Kahng, Jee Ye; Park, Junseong; Yoon, Sojung; Kim, Se Hoon; Chang, Jong Hee; Kang, Seok‐Gu; Lee, Jehee

doi:10.1158/1538-7445.am2017-2455

Cited by 3 publications

(3 citation statements)

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“…We observe an indication for correlation of short patient survival and tumor origin close to the ventricles (especially lower left ventricle), while tumors originating in white matter closer to the skull correlate with a longer life expectancy. This is in accordance with typically more severe brain damage caused by the tumor mass-effect for tumors originating close to the ventricles [9], [24], [25] and difficulties with resecting such tumors.…”

Section: B Construction Of Gbm Atlassupporting

confidence: 76%

Automatic MRI-Driven Model Calibration for Advanced Brain Tumor Progression Analysis

Scheufele,

Subramanian,

Biros

2020

Preprint

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Our objective is the calibration of mathematical tumor growth models from a single multiparametric scan. The target problem is the analysis of preoperative Glioblastoma (GBM) scans. To this end, we present a fully automatic tumorgrowth calibration methodology that integrates a singlespecies reaction-diffusion partial differential equation (PDE) model for tumor progression with multiparametric Magnetic Resonance Imaging (mpMRI) scans to robustly extract patient specific biomarkers i.e., estimates for (i) the tumor cell proliferation rate, (ii) the tumor cell migration rate, and (iii) the original, localized site(s) of tumor initiation. Our method is based on a sparse reconstruction algorithm for the tumor initial location (TIL). This problem is particularly challenging due to nonlinearity, ill-posedeness, and ill conditioning. We propose a coarse-to-fine multi-resolution continuation scheme with parameter decomposition to stabilize the inversion. We demonstrate robustness and practicality of our method by applying the proposed method to clinical data of 206 GBM patients. We analyze the extracted biomarkers and relate tumor origin with patient overall survival by mapping the former into a common atlas space. We present preliminary results that suggest improved accuracy for prediction of patient overall survival when a set of imaging features is augmented with estimated biophysical parameters. All extracted features, tumor initial positions, and biophysical growth parameters are made publicly available for further analysis. To our knowledge, this is the first fully automatic scheme that can handle multifocal tumors and can localize the TIL to a few millimeters.

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Section: B Construction Of Gbm Atlassupporting

confidence: 76%

Automatic MRI-Driven Model Calibration for Advanced Brain Tumor Progression Analysis

Scheufele,

Subramanian,

Biros

2020

Preprint

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“…We observe an indication for correlation of short patient survival and tumor origin close to the ventricles (especially lower left ventricle), while tumors originating in white matter closer to the skull correlate with a longer life expectancy. This is in with typically more severe brain damage caused by the tumor mass effect for tumors originating close to the ventricles [5], [25], [26] and difficulties with resecting such tumors.…”

Section: Construction Of Gbm Atlasmentioning

confidence: 99%

Fully Automatic Calibration of Tumor-Growth Models Using a Single mpMRI Scan

Scheufele

Subramanian

Biros

2021

IEEE Trans. Med. Imaging

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Our objective is the calibration of mathematical tumor growth models from a single multiparametric scan. The target problem is the analysis of preoperative Glioblastoma (GBM) scans. To this end, we present a fully automatic tumor-growth calibration methodology that integrates a single-species reaction-diffusion partial differential equation (PDE) model for tumor progression with multiparametric Magnetic Resonance Imaging (mpMRI) scans to robustly extract patient specific biomarkers i.e., estimates for (i) the tumor cell proliferation rate, (ii) the tumor cell migration rate, and (iii) the original, localized site(s) of tumor initiation. Our method is based on a sparse reconstruction algorithm for the tumor initial location (TIL). This problem is particularly challenging due to nonlinearity, ill-posedeness, and ill conditioning. We propose a coarse-to-fine multi-resolution continuation scheme with parameter decomposition to stabilize the inversion. We demonstrate robustness and practicality of our method by applying the proposed method to clinical data of 206 GBM patients. We analyze the extracted biomarkers and relate tumor origin with patient overall survival by mapping the former into a common atlas space. We present preliminary results that suggest improved accuracy for prediction of patient overall survival when a set of imaging features is augmented with estimated biophysical parameters. All extracted features, tumor initial positions, and biophysical growth parameters are made publicly available for further analysis. To our knowledge, this is the first fully automatic scheme that can handle multifocal tumors and can localize the TIL to a few millimeters.

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“…Additionally, biophysical models can advance our understanding of the disease by using imaging data to test model-driven hypotheses on disease progression and treatment. For example, one such hypothesis is that the location of tumor initiation relates to distinct tumor mutations and the identification of these cancer initiating sites can help further our understanding of the disease, thereby paving the way to molecularly-informed models of tumor growth [35,43,44,74]. The main problem we focus here is whether we can localize the initiation of the tumor while simultaneously estimating physics-based parameters that characterize infiltration (spreading of the tumor) and proliferation (growth).…”

Section: Introductionmentioning

confidence: 99%

Where did the tumor start? An inverse solver with sparse localization for tumor growth models

et al. 2020

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We present a numerical scheme for solving an inverse problem for parameter estimation in tumor growth models for glioblastomas, a form of aggressive primary brain tumor. The growth model is a reaction-diffusion partial differential equation (PDE) for the tumor concentration. We use a PDE-constrained optimization formulation for the inverse problem. The unknown parameters are the reaction coefficient (proliferation), the diffusion coefficient (infiltration), and the initial condition field for the tumor PDE. Segmentation of Magnetic Resonance Imaging (MRI) scans drive the inverse problem where segmented tumor regions serve as partial observations of the tumor concentration. Like most cases in clinical practice, we use data from a single time snapshot. Moreover, the precise time relative to the initiation of the tumor is unknown, which poses an additional difficulty for inversion. We perform a frozen-coefficient spectral analysis and show that the inverse problem is severely ill-posed. We introduce a biophysically motivated regularization on the structure and magnitude of the tumor initial condition. In particular, we assume that the tumor starts at a few locations (enforced with a sparsity constraint on the initial condition of the tumor) and that the initial condition magnitude in the maximum norm is equal to one. We solve the resulting optimization problem using an inexact quasi-Newton method combined with a compressive sampling algorithm for the sparsity constraint. Our implementation uses PETSc and AccFFT libraries. We conduct numerical experiments on synthetic and clinical images to highlight the improved performance of our solver over a previously existing solver that uses standard two-norm regularization for the calibration parameters. The existing solver is unable to localize the initial condition. Our new solver can localize the initial condition and recover infiltration and proliferation. In clinical datasets (for which the ground truth is unknown), our solver results in qualitatively different solutions compared to the two-norm regularized solver. Related work.Although there has been a lot of work on forward problems for tumor growth, there has been less work on inverse problems. The latter has different aspects. The first is the underlying biophysical model. The second is the inverse problem setup, observation operators and the existence of scans at multiple points, the noise models, inversion parameters and constraints. And the third one is the solution algorithm.Regarding the underlying model, like us, most researchers focus on parameter calibration of a handful of model parameters using single-species reaction-diffusion equations [7,22,24,28,33,39,51,58,59]. While more complex models describing processes like mass effect, angiogenesis and chemotaxis [23,26,48,54,60] exist, they have not been considered for calibration due to theoretical and computational challenges. However, several groups, including ours, are working to address these challenges.Regarding the inverse problem setup, in most studies t...

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Abstract 2455: Human glioblastoma arises from the distant subventricular zone normal appearing but harboring tumor-initiating mutations

Cited by 3 publications

References 0 publications

Automatic MRI-Driven Model Calibration for Advanced Brain Tumor Progression Analysis

Automatic MRI-Driven Model Calibration for Advanced Brain Tumor Progression Analysis

Fully Automatic Calibration of Tumor-Growth Models Using a Single mpMRI Scan

Where did the tumor start? An inverse solver with sparse localization for tumor growth models

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