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Yankeelov TE, Hormuth DA, Lima EA, Lorenzo G, Wu C, Okereke LC, Rauch GM, Venkatesan AM, Chung C. Designing clinical trials for patients who are not average. iScience 2024; 27:108589. [PMID: 38169893 PMCID: PMC10758956 DOI: 10.1016/j.isci.2023.108589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2024] Open
Abstract
The heterogeneity inherent in cancer means that even a successful clinical trial merely results in a therapeutic regimen that achieves, on average, a positive result only in a subset of patients. The only way to optimize an intervention for an individual patient is to reframe their treatment as their own, personalized trial. Toward this goal, we formulate a computational framework for performing personalized trials that rely on four mathematical techniques. First, mathematical models that can be calibrated with patient-specific data to make accurate predictions of response. Second, digital twins built on these models capable of simulating the effects of interventions. Third, optimal control theory applied to the digital twins to optimize outcomes. Fourth, data assimilation to continually update and refine predictions in response to therapeutic interventions. In this perspective, we describe each of these techniques, quantify their "state of readiness", and identify use cases for personalized clinical trials.
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Affiliation(s)
- Thomas E. Yankeelov
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Oncology, The University of Texas at Austin, Austin, TX 78712, USA
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA
- Division of Diagnostic Imaging, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA
| | - David A. Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA
| | - Ernesto A.B.F. Lima
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
- Texas Advanced Computer Center, The University of Texas at Austin, Austin, TX 78712, USA
| | - Guillermo Lorenzo
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Civil Engineering and Architecture, University of Pavia, 27100 Pavia, Italy
| | - Chengyue Wu
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Lois C. Okereke
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Gaiane M. Rauch
- Department of Abdominal Imaging, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA
| | - Aradhana M. Venkatesan
- Department of Abdominal Imaging, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA
| | - Caroline Chung
- Department of Radiation Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA
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Subramanian S, Ghafouri A, Scheufele KM, Himthani N, Davatzikos C, Biros G. Ensemble Inversion for Brain Tumor Growth Models With Mass Effect. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:982-995. [PMID: 36378796 PMCID: PMC10201550 DOI: 10.1109/tmi.2022.3221913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
We propose a method for extracting physics-based biomarkers from a single multiparametric Magnetic Resonance Imaging (mpMRI) scan bearing a glioma tumor. We account for mass effect, the deformation of brain parenchyma due to the growing tumor, which on its own is an important radiographic feature but its automatic quantification remains an open problem. In particular, we calibrate a partial differential equation (PDE) tumor growth model that captures mass effect, parameterized by a single scalar parameter, tumor proliferation, migration, while localizing the tumor initiation site. The single-scan calibration problem is severely ill-posed because the precancerous, healthy, brain anatomy is unknown. To address the ill-posedness, we introduce an ensemble inversion scheme that uses a number of normal subject brain templates as proxies for the healthy precancer subject anatomy. We verify our solver on a synthetic dataset and perform a retrospective analysis on a clinical dataset of 216 glioblastoma (GBM) patients. We analyze the reconstructions using our calibrated biophysical model and demonstrate that our solver provides both global and local quantitative measures of tumor biophysics and mass effect. We further highlight the improved performance in model calibration through the inclusion of mass effect in tumor growth models-including mass effect in the model leads to 10% increase in average dice coefficients for patients with significant mass effect. We further evaluate our model by introducing novel biophysics-based features and using them for survival analysis. Our preliminary analysis suggests that including such features can improve patient stratification and survival prediction.
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Zhang Y, Liu PX, Hou W. Modeling of glioma growth using modified reaction-diffusion equation on brain MR images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 227:107233. [PMID: 36375418 DOI: 10.1016/j.cmpb.2022.107233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 11/02/2022] [Accepted: 11/04/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVE Modeling of glioma growth and evolution is of key importance for cancer diagnosis, predicting clinical progression and improving treatment outcomes of neurosurgery. However, existing models are unable to characterize spatial variations of the proliferation and infiltration of tumor cells, making it difficult to achieve accurate prediction of tumor growth. METHODS In this paper, a new growth model of brain tumor using a reaction-diffusion equation on brain magnetic resonance images is proposed. Both the heterogeneity of brain tissue and the density of tumor cells are used to estimate the proliferation and diffusion coefficients of brain tumor cells. The diffusion coefficient that characterizes tumor diffusion and infiltration is calculated based on the ratio of tissues (white and gray matter), while the proliferation coefficient is evaluated using the spatial gradient of tumor cells. In addition, a parameter space is constructed using inverse distance weighted interpolation to describe the spatial distribution of proliferation coefficient. RESULTS The glioma growth predicted by the proposed model were tested by comparing with the real magnetic resonance images of the patients. Experiments and simulation results show that the proposed method achieves accurate modeling of glioma growth. The interpolation-based growth model has higher average dice score of 0.0647 and 0.0545, and higher average Jaccard index of 0.0673 and 0.0573, respectively, compared to the uniform- and gradient-based growth models. CONCLUSIONS The experimental results demonstrate the feasibility of calculating the proliferation and diffusion coefficients of the growth model based on patient-specific anatomy. The parameter space that characterizes spatial distribution of proliferation and diffusion coefficients is established and incorporated into the simulation of glioma growth. It enables to obtain patient-specific models about glioma growth by estimating and calibrating only a few model parameters.
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Affiliation(s)
- Yanying Zhang
- School of Information Science and Engineering Zhejiang Sci-Tech University, Hangzhou,Zhejiang, China
| | - Peter X Liu
- School of Information Science and Engineering Zhejiang Sci-Tech University, Hangzhou,Zhejiang, China; Department of Systems and Computer Engineering Carleton University,Ottawa,ON KIS 5B6, Canada.
| | - Wenguo Hou
- Shenzhen Institute of Advanced Technology Chinese Academy of Sciences,Shenzhen, Guangdong,China.
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Wu C, Lorenzo G, Hormuth DA, Lima EABF, Slavkova KP, DiCarlo JC, Virostko J, Phillips CM, Patt D, Chung C, Yankeelov TE. Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology. BIOPHYSICS REVIEWS 2022; 3:021304. [PMID: 35602761 PMCID: PMC9119003 DOI: 10.1063/5.0086789] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 04/29/2022] [Indexed: 12/11/2022]
Abstract
Digital twins employ mathematical and computational models to virtually represent a physical object (e.g., planes and human organs), predict the behavior of the object, and enable decision-making to optimize the future behavior of the object. While digital twins have been widely used in engineering for decades, their applications to oncology are only just emerging. Due to advances in experimental techniques quantitatively characterizing cancer, as well as advances in the mathematical and computational sciences, the notion of building and applying digital twins to understand tumor dynamics and personalize the care of cancer patients has been increasingly appreciated. In this review, we present the opportunities and challenges of applying digital twins in clinical oncology, with a particular focus on integrating medical imaging with mechanism-based, tissue-scale mathematical modeling. Specifically, we first introduce the general digital twin framework and then illustrate existing applications of image-guided digital twins in healthcare. Next, we detail both the imaging and modeling techniques that provide practical opportunities to build patient-specific digital twins for oncology. We then describe the current challenges and limitations in developing image-guided, mechanism-based digital twins for oncology along with potential solutions. We conclude by outlining five fundamental questions that can serve as a roadmap when designing and building a practical digital twin for oncology and attempt to provide answers for a specific application to brain cancer. We hope that this contribution provides motivation for the imaging science, oncology, and computational communities to develop practical digital twin technologies to improve the care of patients battling cancer.
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Affiliation(s)
- Chengyue Wu
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, USA
| | | | | | | | - Kalina P. Slavkova
- Department of Physics, The University of Texas at Austin, Austin, Texas 78712, USA
| | | | | | - Caleb M. Phillips
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, USA
| | - Debra Patt
- Texas Oncology, Austin, Texas 78731, USA
| | - Caroline Chung
- Department of Radiation Oncology, MD Anderson Cancer Center, University of Texas, Houston, Texas 77030, USA
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Martens C, Rovai A, Bonatto D, Metens T, Debeir O, Decaestecker C, Goldman S, Van Simaeys G. Deep Learning for Reaction-Diffusion Glioma Growth Modeling: Towards a Fully Personalized Model? Cancers (Basel) 2022; 14:cancers14102530. [PMID: 35626134 PMCID: PMC9139770 DOI: 10.3390/cancers14102530] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 05/11/2022] [Accepted: 05/18/2022] [Indexed: 11/30/2022] Open
Abstract
Simple Summary Mathematical tumor growth models have been proposed for decades to capture the growth of gliomas, an aggressive form of brain tumor. However, the estimation of the tumor cell-density distribution at diagnosis and model parameters from partial observations provided by magnetic resonance imaging are ill-posed problems. In this work, we propose a deep learning-based approach to address these problems. 1200 synthetic tumors are first generated using the mathematical model over brain geometries of 6 volunteers. Two deep convolutional neural networks are then trained to (i) reconstruct a whole tumor cell-density distribution and (ii) evaluate the model parameters from partial observations provided in the form of threshold-like imaging contours, with state-of-the-art results. From the estimated cell-density distribution and parameter values, the spatio-temporal evolution of the tumor can ultimately be accurately captured by the mathematical model. Such an approach could be of great interest for glioma characterization and therapy planning. Abstract Reaction-diffusion models have been proposed for decades to capture the growth of gliomas, the most common primary brain tumors. However, ill-posedness of the initialization at diagnosis time and parameter estimation of such models have restrained their clinical use as a personalized predictive tool. In this work, we investigate the ability of deep convolutional neural networks (DCNNs) to address commonly encountered pitfalls in the field. Based on 1200 synthetic tumors grown over real brain geometries derived from magnetic resonance (MR) data of six healthy subjects, we demonstrate the ability of DCNNs to reconstruct a whole tumor cell-density distribution from only two imaging contours at a single time point. With an additional imaging contour extracted at a prior time point, we also demonstrate the ability of DCNNs to accurately estimate the individual diffusivity and proliferation parameters of the model. From this knowledge, the spatio-temporal evolution of the tumor cell-density distribution at later time points can ultimately be precisely captured using the model. We finally show the applicability of our approach to MR data of a real glioblastoma patient. This approach may open the perspective of a clinical application of reaction-diffusion growth models for tumor prognosis and treatment planning.
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Affiliation(s)
- Corentin Martens
- Department of Nuclear Medicine, Hôpital Erasme, Université libre de Bruxelles, Route de Lennik 808, 1070 Brussels, Belgium; (A.R.); (S.G.); (G.V.S.)
- Center for Microscopy and Molecular Imaging (CMMI), Université libre de Bruxelles, Rue Adrienne Bolland 8, 6041 Charleroi, Belgium; (O.D.); (C.D.)
- Laboratory of Image Synthesis and Analysis (LISA), École Polytechnique de Bruxelles, Université libre de Bruxelles, Avenue Franklin Roosevelt 50, 1050 Brussels, Belgium; (D.B.); (T.M.)
- Correspondence:
| | - Antonin Rovai
- Department of Nuclear Medicine, Hôpital Erasme, Université libre de Bruxelles, Route de Lennik 808, 1070 Brussels, Belgium; (A.R.); (S.G.); (G.V.S.)
| | - Daniele Bonatto
- Laboratory of Image Synthesis and Analysis (LISA), École Polytechnique de Bruxelles, Université libre de Bruxelles, Avenue Franklin Roosevelt 50, 1050 Brussels, Belgium; (D.B.); (T.M.)
| | - Thierry Metens
- Laboratory of Image Synthesis and Analysis (LISA), École Polytechnique de Bruxelles, Université libre de Bruxelles, Avenue Franklin Roosevelt 50, 1050 Brussels, Belgium; (D.B.); (T.M.)
- Department of Radiology, Hôpital Erasme, Université libre de Bruxelles, Route de Lennik 808, 1070 Brussels, Belgium
| | - Olivier Debeir
- Center for Microscopy and Molecular Imaging (CMMI), Université libre de Bruxelles, Rue Adrienne Bolland 8, 6041 Charleroi, Belgium; (O.D.); (C.D.)
- Laboratory of Image Synthesis and Analysis (LISA), École Polytechnique de Bruxelles, Université libre de Bruxelles, Avenue Franklin Roosevelt 50, 1050 Brussels, Belgium; (D.B.); (T.M.)
| | - Christine Decaestecker
- Center for Microscopy and Molecular Imaging (CMMI), Université libre de Bruxelles, Rue Adrienne Bolland 8, 6041 Charleroi, Belgium; (O.D.); (C.D.)
- Laboratory of Image Synthesis and Analysis (LISA), École Polytechnique de Bruxelles, Université libre de Bruxelles, Avenue Franklin Roosevelt 50, 1050 Brussels, Belgium; (D.B.); (T.M.)
| | - Serge Goldman
- Department of Nuclear Medicine, Hôpital Erasme, Université libre de Bruxelles, Route de Lennik 808, 1070 Brussels, Belgium; (A.R.); (S.G.); (G.V.S.)
- Center for Microscopy and Molecular Imaging (CMMI), Université libre de Bruxelles, Rue Adrienne Bolland 8, 6041 Charleroi, Belgium; (O.D.); (C.D.)
| | - Gaetan Van Simaeys
- Department of Nuclear Medicine, Hôpital Erasme, Université libre de Bruxelles, Route de Lennik 808, 1070 Brussels, Belgium; (A.R.); (S.G.); (G.V.S.)
- Center for Microscopy and Molecular Imaging (CMMI), Université libre de Bruxelles, Rue Adrienne Bolland 8, 6041 Charleroi, Belgium; (O.D.); (C.D.)
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Brunn M, Himthani N, Biros G, Mehl M, Mang A. CLAIRE: Constrained Large Deformation Diffeomorphic Image Registration on Parallel Computing Architectures. JOURNAL OF OPEN SOURCE SOFTWARE 2021; 6:3038. [PMID: 35295546 PMCID: PMC8923611 DOI: 10.21105/joss.03038] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
CLAIRE (Mang & Biros, 2019) is a computational framework for Constrained LArge deformation diffeomorphic Image REgistration (Mang et al., 2019). It supports highly-optimized, parallel computational kernels for (multi-node) CPU (Gholami et al., 2017; Mang et al., 2019; Mang & Biros, 2016) and (multi-node multi-)GPU architectures (Brunn et al., 2020, 2021). CLAIRE uses MPI for distributed-memory parallelism and can be scaled up to thousands of cores (Mang et al., 2019; Mang & Biros, 2016) and GPU devices (Brunn et al., 2020). The multi-GPU implementation uses device direct communication. The computational kernels are interpolation for semi-Lagrangian time integration, and a mixture of high-order finite difference operators and Fast-Fourier-Transforms (FFTs) for differentiation. CLAIRE uses a Newton-Krylov solver for numerical optimization (Mang & Biros, 2015, 2017). It features various schemes for regularization of the control problem (Mang & Biros, 2016) and different similarity measures. CLAIRE implements different preconditioners for the reduced space Hessian (Brunn et al., 2020; Mang et al., 2019) to optimize computational throughput and enable fast convergence. It uses PETSc (Balay et al., n.d.) for scalable and efficient linear algebra operations and solvers and TAO (Balay et al., n.d.; Munson et al., 2015) for numerical optimization. CLAIRE can be downloaded at https://github.com/andreasmang/claire.
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Affiliation(s)
- Malte Brunn
- Institute for Parallel and Distributed Systems, University Stuttgart
| | - Naveen Himthani
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin
| | - George Biros
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin
| | - Miriam Mehl
- Institute for Parallel and Distributed Systems, University Stuttgart
| | - Andreas Mang
- Department of Mathematics, University of Houston
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