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Ramakrishnan D, Brüningk SC, von Reppert M, Memon F, Maleki N, Aneja S, Kazerooni AF, Nabavizadeh A, Lin M, Bousabarah K, Molinaro A, Nicolaides T, Prados M, Mueller S, Aboian MS. Comparison of Volumetric and 2D Measurements and Longitudinal Trajectories in the Response Assessment of BRAF V600E-Mutant Pediatric Gliomas in the Pacific Pediatric Neuro-Oncology Consortium Clinical Trial. AJNR Am J Neuroradiol 2024; 45:475-482. [PMID: 38453411 DOI: 10.3174/ajnr.a8189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 12/03/2023] [Indexed: 03/09/2024]
Abstract
BACKGROUND AND PURPOSE Response on imaging is widely used to evaluate treatment efficacy in clinical trials of pediatric gliomas. While conventional criteria rely on 2D measurements, volumetric analysis may provide a more comprehensive response assessment. There is sparse research on the role of volumetrics in pediatric gliomas. Our purpose was to compare 2D and volumetric analysis with the assessment of neuroradiologists using the Brain Tumor Reporting and Data System (BT-RADS) in BRAF V600E-mutant pediatric gliomas. MATERIALS AND METHODS Manual volumetric segmentations of whole and solid tumors were compared with 2D measurements in 31 participants (292 follow-up studies) in the Pacific Pediatric Neuro-Oncology Consortium 002 trial (NCT01748149). Two neuroradiologists evaluated responses using BT-RADS. Receiver operating characteristic analysis compared classification performance of 2D and volumetrics for partial response. Agreement between volumetric and 2D mathematically modeled longitudinal trajectories for 25 participants was determined using the model-estimated time to best response. RESULTS Of 31 participants, 20 had partial responses according to BT-RADS criteria. Receiver operating characteristic curves for the classification of partial responders at the time of first detection (median = 2 months) yielded an area under the curve of 0.84 (95% CI, 0.69-0.99) for 2D area, 0.91 (95% CI, 0.80-1.00) for whole-volume, and 0.92 (95% CI, 0.82-1.00) for solid volume change. There was no significant difference in the area under the curve between 2D and solid (P = .34) or whole volume (P = .39). There was no significant correlation in model-estimated time to best response (ρ = 0.39, P >.05) between 2D and whole-volume trajectories. Eight of the 25 participants had a difference of ≥90 days in transition from partial response to stable disease between their 2D and whole-volume modeled trajectories. CONCLUSIONS Although there was no overall difference between volumetrics and 2D in classifying partial response assessment using BT-RADS, further prospective studies will be critical to elucidate how the observed differences in tumor 2D and volumetric trajectories affect clinical decision-making and outcomes in some individuals.
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Affiliation(s)
- Divya Ramakrishnan
- From the Department of Radiology and Biomedical Imaging (D.R., M.v.R., F.M., N.M., M.L., M.S.A.), Yale School of Medicine, New Haven, Connecticut
| | - Sarah C Brüningk
- Department of Biosystems Science and Engineering (S.C.B.), ETH Zürich, Basel, Switzerland
- Swiss Institute for Bioinformatics (S.C.B.), Lausanne, Switzerland
| | - Marc von Reppert
- From the Department of Radiology and Biomedical Imaging (D.R., M.v.R., F.M., N.M., M.L., M.S.A.), Yale School of Medicine, New Haven, Connecticut
- Department of Neuroradiology (M.v.R.), Leipzig University Hospital, Leipzig, Germany
| | - Fatima Memon
- From the Department of Radiology and Biomedical Imaging (D.R., M.v.R., F.M., N.M., M.L., M.S.A.), Yale School of Medicine, New Haven, Connecticut
| | - Nazanin Maleki
- From the Department of Radiology and Biomedical Imaging (D.R., M.v.R., F.M., N.M., M.L., M.S.A.), Yale School of Medicine, New Haven, Connecticut
| | - Sanjay Aneja
- Department of Therapeutic Radiology (S.A.), Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation (S.A.), Yale School of Medicine, New Haven, Connecticut
| | - Anahita Fathi Kazerooni
- Center for Biomedical Image Computing and Analytics (A.F.K.), University of Pennsylvania, Philadelphia, Pennsylvania
| | - Ali Nabavizadeh
- Center for Data-Driven Discovery in Biomedicine (A.N.), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - MingDe Lin
- From the Department of Radiology and Biomedical Imaging (D.R., M.v.R., F.M., N.M., M.L., M.S.A.), Yale School of Medicine, New Haven, Connecticut
- Visage Imaging (M.L.), San Diego, Calfornia
| | | | - Annette Molinaro
- Department of Neurological Surgery (A.M.), University of California San Francisco, San Francisco, Calfornia
| | | | - Michael Prados
- Department of Neurology (M.P., S.M.), Neurosurgery, and Pediatrics, University of California San Francisco, San Francisco, Calfornia
| | - Sabine Mueller
- Department of Neurology (M.P., S.M.), Neurosurgery, and Pediatrics, University of California San Francisco, San Francisco, Calfornia
- Children's University Hospital Zürich (S.M.), Zürich, Switzerland
| | - Mariam S Aboian
- From the Department of Radiology and Biomedical Imaging (D.R., M.v.R., F.M., N.M., M.L., M.S.A.), Yale School of Medicine, New Haven, Connecticut
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Enderling H. Fractal calculus in tumor growth simulations: The proof is in the pudding. Biosystems 2024; 237:105141. [PMID: 38355079 DOI: 10.1016/j.biosystems.2024.105141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 02/05/2024] [Accepted: 02/06/2024] [Indexed: 02/16/2024]
Abstract
Mathematical modeling in oncology has a long history. Recently, mathematical models and their predictions have made inroads into prospective clinical trials with encouraging results. The goal of many such modeling efforts is to make predictions, either to clinician's choice therapy or into "optimal" therapy - often for individual patients. The mathematical oncology community rightfully puts great hope into predictive modeling and mechanistic digital twins - but with this great opportunity comes great responsibility. Mathematical models need to be rigorously calibrated and validated, and their predictive performance ascertained, before conclusions about predictions into the unknown can be drawn. The recent article "Modeling tumor growth using fractal calculus: Insights into tumor dynamics" (Golmankhaneh et al., 2023), applied fractal calculus to tumor growth data. In this short commentary, I raise concerns about the study design and interpretation. In its current form, this study is poised to put cancer patients at risk if interpreted as concluded by the authors.
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Affiliation(s)
- Heiko Enderling
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Institute for Data Science in Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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3
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von Reppert M, Ramakrishnan D, Brüningk SC, Memon F, Abi Fadel S, Maleki N, Bahar R, Avesta AE, Jekel L, Sala M, Lost J, Tillmanns N, Kaur M, Aneja S, Fathi Kazerooni A, Nabavizadeh A, Lin M, Hoffmann KT, Bousabarah K, Swanson KR, Haas-Kogan D, Mueller S, Aboian MS. Comparison of volumetric and 2D-based response methods in the PNOC-001 pediatric low-grade glioma clinical trial. Neurooncol Adv 2024; 6:vdad172. [PMID: 38221978 PMCID: PMC10785766 DOI: 10.1093/noajnl/vdad172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2024] Open
Abstract
Background Although response in pediatric low-grade glioma (pLGG) includes volumetric assessment, more simplified 2D-based methods are often used in clinical trials. The study's purpose was to compare volumetric to 2D methods. Methods An expert neuroradiologist performed solid and whole tumor (including cyst and edema) volumetric measurements on MR images using a PACS-based manual segmentation tool in 43 pLGG participants (213 total follow-up images) from the Pacific Pediatric Neuro-Oncology Consortium (PNOC-001) trial. Classification based on changes in volumetric and 2D measurements of solid tumor were compared to neuroradiologist visual response assessment using the Brain Tumor Reporting and Data System (BT-RADS) criteria for a subset of 65 images using receiver operating characteristic (ROC) analysis. Longitudinal modeling of solid tumor volume was used to predict BT-RADS classification in 54 of the 65 images. Results There was a significant difference in ROC area under the curve between 3D solid tumor volume and 2D area (0.96 vs 0.78, P = .005) and between 3D solid and 3D whole volume (0.96 vs 0.84, P = .006) when classifying BT-RADS progressive disease (PD). Thresholds of 15-25% increase in 3D solid tumor volume had an 80% sensitivity in classifying BT-RADS PD included in their 95% confidence intervals. The longitudinal model of solid volume response had a sensitivity of 82% and a positive predictive value of 67% for detecting BT-RADS PD. Conclusions Volumetric analysis of solid tumor was significantly better than 2D measurements in classifying tumor progression as determined by BT-RADS criteria and will enable more comprehensive clinical management.
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Affiliation(s)
- Marc von Reppert
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
- Department of Neuroradiology, Leipzig University Hospital, Leipzig, Germany
| | - Divya Ramakrishnan
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Sarah C Brüningk
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
- Swiss Institute for Bioinformatics (SIB), Lausanne, Switzerland
| | - Fatima Memon
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Sandra Abi Fadel
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Nazanin Maleki
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Ryan Bahar
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Arman E Avesta
- Department of Therapeutic Radiology, Yale School of Medicine, New Haven, Connecticut, USA
- Center for Outcomes Research and Evaluation (CORE), Yale School of Medicine, New Haven, Connecticut, USA
- Department of Neuroradiology, Harvard Medical School—Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Leon Jekel
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
- University of Duisburg-Essen, Essen, Germany
| | - Matthew Sala
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
- Tulane School of Medicine, New Orleans, Louisiana, USA
| | - Jan Lost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
- Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany
| | - Niklas Tillmanns
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
- Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany
| | - Manpreet Kaur
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
- Ludwig Maximilian University, Munich, Germany
| | - Sanjay Aneja
- Department of Therapeutic Radiology, Yale School of Medicine, New Haven, Connecticut, USA
- Center for Outcomes Research and Evaluation (CORE), Yale School of Medicine, New Haven, Connecticut, USA
| | - Anahita Fathi Kazerooni
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ali Nabavizadeh
- Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - MingDe Lin
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
- Visage Imaging, Inc., San Diego, California, USA
| | | | | | - Kristin R Swanson
- Mathematical Neuro-Oncology Lab, Department of Neurological Surgery, Mayo Clinic, Phoenix, Arizona, USA
| | - Daphne Haas-Kogan
- Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Sabine Mueller
- Department of Neurology, Neurosurgery, and Pediatrics, UCSF, San Francisco, California, USA
- Children’s University Hospital Zürich, Zürich, Switzerland
| | - Mariam S Aboian
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
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Ravi H, Hawkins SH, Stringfield O, Pereira M, Chen DT, Enderling H, Michael Yu HH, Arrington JA, Sahebjam S, Raghunand N. Rules-based Volumetric Segmentation of Multiparametric MRI for Response Assessment in Recurrent High-Grade Glioma. Res Sq 2023:rs.3.rs-3318286. [PMID: 37790451 PMCID: PMC10543497 DOI: 10.21203/rs.3.rs-3318286/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
We report domain knowledge-based rules for assigning voxels in brain multiparametric MRI (mpMRI) to distinct tissuetypes based on their appearance on Apparent Diffusion Coefficient of water (ADC) maps, T1-weighted unenhanced and contrast-enhanced, T2-weighted, and Fluid-Attenuated Inversion Recovery images. The development dataset comprised mpMRI of 18 participants with preoperative high-grade glioma (HGG), recurrent HGG (rHGG), and brain metastases. External validation was performed on mpMRI of 235 HGG participants in the BraTS 2020 training dataset. The treatment dataset comprised serial mpMRI of 32 participants (total 231 scan dates) in a clinical trial of immunoradiotherapy in rHGG (NCT02313272). Pixel intensity-based rules for segmenting contrast-enhancing tumor (CE), hemorrhage, Fluid, non-enhancing tumor (Edema1), and leukoaraiosis (Edema2) were identified on calibrated, co-registered mpMRI images in the development dataset. On validation, rule-based CE and High FLAIR (Edema1 + Edema2) volumes were significantly correlated with ground truth volumes of enhancing tumor (R = 0.85;p < 0.001) and peritumoral edema (R = 0.87;p < 0.001), respectively. In the treatment dataset, a model combining time-on-treatment and rule-based volumes of CE and intratumoral Fluid was 82.5% accurate for predicting progression within 30 days of the scan date. An explainable decision tree applied to brain mpMRI yields validated, consistent, intratumoral tissuetype volumes suitable for quantitative response assessment in clinical trials of rHGG.
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Slavkova KP, Patel SH, Cacini Z, Kazerouni AS, Gardner AL, Yankeelov TE, Hormuth DA. Mathematical modelling of the dynamics of image-informed tumor habitats in a murine model of glioma. Sci Rep 2023; 13:2916. [PMID: 36804605 PMCID: PMC9941120 DOI: 10.1038/s41598-023-30010-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 02/14/2023] [Indexed: 02/22/2023] Open
Abstract
Tumors exhibit high molecular, phenotypic, and physiological heterogeneity. In this effort, we employ quantitative magnetic resonance imaging (MRI) data to capture this heterogeneity through imaging-based subregions or "habitats" in a murine model of glioma. We then demonstrate the ability to model and predict the growth of the habitats using coupled ordinary differential equations (ODEs) in the presence and absence of radiotherapy. Female Wistar rats (N = 21) were inoculated intracranially with 106 C6 glioma cells, a subset of which received 20 Gy (N = 5) or 40 Gy (N = 8) of radiation. All rats underwent diffusion-weighted and dynamic contrast-enhanced MRI at up to seven time points. All MRI data at each visit were subsequently clustered using k-means to identify physiological tumor habitats. A family of four models consisting of three coupled ODEs were developed and calibrated to the habitat time series of control and treated rats and evaluated for predictive capability. The Akaike Information Criterion was used for model selection, and the normalized sum-of-square-error (SSE) was used to evaluate goodness-of-fit in model calibration and prediction. Three tumor habitats with significantly different imaging data characteristics (p < 0.05) were identified: high-vascularity high-cellularity, low-vascularity high-cellularity, and low-vascularity low-cellularity. Model selection resulted in a five-parameter model whose predictions of habitat dynamics yielded SSEs that were similar to the SSEs from the calibrated model. It is thus feasible to mathematically describe habitat dynamics in a preclinical model of glioma using biology-based ODEs, showing promise for forecasting heterogeneous tumor behavior.
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Affiliation(s)
- Kalina P. Slavkova
- grid.89336.370000 0004 1936 9924Department of Physics, The University of Texas at Austin, Austin, TX USA
| | - Sahil H. Patel
- grid.67105.350000 0001 2164 3847 Department of Computer Science, Case Western Reserve University, Cleveland, OH USA
| | - Zachary Cacini
- grid.35403.310000 0004 1936 9991 Department of Bioengineering, University of Illinois, Urbana-Champaign, IL USA
| | - Anum S. Kazerouni
- grid.34477.330000000122986657Department of Radiology, The University of Washington, Seattle, WA USA
| | - Andrea L. Gardner
- grid.89336.370000 0004 1936 9924Department of Biomedical Engineering, The University of Texas at Austin, Austin, USA
| | - Thomas E. Yankeelov
- grid.89336.370000 0004 1936 9924Department of Biomedical Engineering, The University of Texas at Austin, Austin, USA ,grid.89336.370000 0004 1936 9924Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX USA ,grid.89336.370000 0004 1936 9924Department of Oncology, The University of Texas at Austin, Austin, TX USA ,grid.89336.370000 0004 1936 9924The Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 E 24th Street, Austin, TX 78712 USA ,grid.89336.370000 0004 1936 9924Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX USA ,grid.240145.60000 0001 2291 4776Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - David A. Hormuth
- grid.89336.370000 0004 1936 9924The Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 E 24th Street, Austin, TX 78712 USA ,grid.89336.370000 0004 1936 9924Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX USA
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Hormuth DA, Farhat M, Christenson C, Curl B, Chad Quarles C, Chung C, Yankeelov TE. Opportunities for improving brain cancer treatment outcomes through imaging-based mathematical modeling of the delivery of radiotherapy and immunotherapy. Adv Drug Deliv Rev 2022; 187:114367. [PMID: 35654212 DOI: 10.1016/j.addr.2022.114367] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/25/2022] [Accepted: 05/25/2022] [Indexed: 11/01/2022]
Abstract
Immunotherapy has become a fourth pillar in the treatment of brain tumors and, when combined with radiation therapy, may improve patient outcomes and reduce the neurotoxicity. As with other combination therapies, the identification of a treatment schedule that maximizes the synergistic effect of radiation- and immune-therapy is a fundamental challenge. Mechanism-based mathematical modeling is one promising approach to systematically investigate therapeutic combinations to maximize positive outcomes within a rigorous framework. However, successful clinical translation of model-generated combinations of treatment requires patient-specific data to allow the models to be meaningfully initialized and parameterized. Quantitative imaging techniques have emerged as a promising source of high quality, spatially and temporally resolved data for the development and validation of mathematical models. In this review, we will present approaches to personalize mechanism-based modeling frameworks with patient data, and then discuss how these techniques could be leveraged to improve brain cancer outcomes through patient-specific modeling and optimization of treatment strategies.
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Affiliation(s)
- David A Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Maguy Farhat
- Departments of Radiation Oncology, MD Anderson Cancer Center, Houston, TX 77230, USA
| | - Chase Christenson
- Departments of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
| | - Brandon Curl
- Departments of Radiation Oncology, MD Anderson Cancer Center, Houston, TX 77230, USA
| | - C Chad Quarles
- Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, AZ 85013, USA
| | - Caroline Chung
- Departments of Radiation Oncology, MD Anderson Cancer Center, Houston, TX 77230, USA
| | - Thomas E Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Oncology, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Imaging Physics, MD Anderson Cancer Center, Houston, TX 77230, USA
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Bekker RA, Kim S, Pilon-Thomas S, Enderling H. Mathematical modeling of radiotherapy and its impact on tumor interactions with the immune system. Neoplasia 2022; 28:100796. [PMID: 35447601 PMCID: PMC9043662 DOI: 10.1016/j.neo.2022.100796] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 03/27/2022] [Accepted: 04/01/2022] [Indexed: 11/01/2022]
Abstract
Radiotherapy is a primary therapeutic modality widely utilized with curative intent. Traditionally tumor response was hypothesized to be due to high levels of cell death induced by irreparable DNA damage. However, the immunomodulatory aspect of radiation is now widely accepted. As such, interest into the combination of radiotherapy and immunotherapy is increasing, the synergy of which has the potential to improve tumor regression beyond that observed after either treatment alone. However, questions regarding the timing (sequential vs concurrent) and dose fractionation (hyper-, standard-, or hypo-fractionation) that result in improved anti-tumor immune responses, and thus potentially enhanced tumor inhibition, remain. Here we discuss the biological response to radiotherapy and its immunomodulatory properties before giving an overview of pre-clinical data and clinical trials concerned with answering these questions. Finally, we review published mathematical models of the impact of radiotherapy on tumor-immune interactions. Ranging from considering the impact of properties of the tumor microenvironment on the induction of anti-tumor responses, to the impact of choice of radiation site in the setting of metastatic disease, these models all have an underlying feature in common: the push towards personalized therapy.
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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. Biophys Rev (Melville) 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] [What about the content of this article? (0)] [Affiliation(s)] [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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Glazar DJ, Johnson M, Farinhas J, Steuer CE, Saba NF, Bonomi M, Chung CH, Enderling H. Early response dynamics predict treatment failure in patients with recurrent and/or metastatic head and neck squamous cell carcinoma treated with cetuximab and nivolumab. Oral Oncol 2022; 127:105787. [DOI: 10.1016/j.oraloncology.2022.105787] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 02/09/2022] [Accepted: 02/20/2022] [Indexed: 12/18/2022]
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Brüningk SC, Peacock J, Whelan CJ, Brady-Nicholls R, Yu HM, Sahebjam S, Enderling H. Intermittent radiotherapy as alternative treatment for recurrent high grade glioma: a modeling study based on longitudinal tumor measurements. Sci Rep 2021; 11:20219. [PMID: 34642366 DOI: 10.1038/s41598-021-99507-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 09/20/2021] [Indexed: 12/30/2022] Open
Abstract
Recurrent high grade glioma patients face a poor prognosis for which no curative treatment option currently exists. In contrast to prescribing high dose hypofractionated stereotactic radiotherapy (HFSRT, [Formula: see text] Gy [Formula: see text] 5 in daily fractions) with debulking intent, we suggest a personalized treatment strategy to improve tumor control by delivering high dose intermittent radiation treatment (iRT, [Formula: see text] Gy [Formula: see text] 1 every 6 weeks). We performed a simulation analysis to compare HFSRT, iRT and iRT plus boost ([Formula: see text] Gy [Formula: see text] 3 in daily fractions at time of progression) based on a mathematical model of tumor growth, radiation response and patient-specific evolution of resistance to additional treatments (pembrolizumab and bevacizumab). Model parameters were fitted from tumor growth curves of 16 patients enrolled in the phase 1 NCT02313272 trial that combined HFSRT with bevacizumab and pembrolizumab. Then, iRT +/- boost treatments were simulated and compared to HFSRT based on time to tumor regrowth. The modeling results demonstrated that iRT + boost(- boost) treatment was equal or superior to HFSRT in 15(11) out of 16 cases and that patients that remained responsive to pembrolizumab and bevacizumab would benefit most from iRT. Time to progression could be prolonged through the application of additional, intermittently delivered fractions. iRT hence provides a promising treatment option for recurrent high grade glioma patients for prospective clinical evaluation.
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Hormuth DA, Al Feghali KA, Elliott AM, Yankeelov TE, Chung C. Image-based personalization of computational models for predicting response of high-grade glioma to chemoradiation. Sci Rep 2021; 11:8520. [PMID: 33875739 PMCID: PMC8055874 DOI: 10.1038/s41598-021-87887-4] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 03/30/2021] [Indexed: 12/16/2022] Open
Abstract
High-grade gliomas are an aggressive and invasive malignancy which are susceptible to treatment resistance due to heterogeneity in intratumoral properties such as cell proliferation and density and perfusion. Non-invasive imaging approaches can measure these properties, which can then be used to calibrate patient-specific mathematical models of tumor growth and response. We employed multiparametric magnetic resonance imaging (MRI) to identify tumor extent (via contrast-enhanced T1-weighted, and T2-FLAIR) and capture intratumoral heterogeneity in cell density (via diffusion-weighted imaging) to calibrate a family of mathematical models of chemoradiation response in nine patients with unresected or partially resected disease. The calibrated model parameters were used to forecast spatially-mapped individual tumor response at future imaging visits. We then employed the Akaike information criteria to select the most parsimonious member from the family, a novel two-species model describing the enhancing and non-enhancing components of the tumor. Using this model, we achieved low error in predictions of the enhancing volume (median: - 2.5%, interquartile range: 10.0%) and a strong correlation in total cell count (Kendall correlation coefficient 0.79) at 3-months post-treatment. These preliminary results demonstrate the plausibility of using multiparametric MRI data to inform spatially-informative, biologically-based predictive models of tumor response in the setting of clinical high-grade gliomas.
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Affiliation(s)
- David A Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, 201 E. 24th Street, POB 4.102, 1 University Station (C0200), Austin, TX, 78712-1229, USA.
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, Austin, TX, USA.
| | - Karine A Al Feghali
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Andrew M Elliott
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Thomas E Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, 201 E. 24th Street, POB 4.102, 1 University Station (C0200), Austin, TX, 78712-1229, USA
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Austin, TX, USA
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, Austin, TX, USA
- Department of Oncology, The University of Texas at Austin, Austin, Austin, TX, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, Austin, TX, USA
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA
| | - Caroline Chung
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
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Kirshtein A, Akbarinejad S, Hao W, Le T, Su S, Aronow RA, Shahriyari L. Data Driven Mathematical Model of Colon Cancer Progression. J Clin Med 2020; 9:E3947. [PMID: 33291412 PMCID: PMC7762015 DOI: 10.3390/jcm9123947] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 11/28/2020] [Accepted: 12/02/2020] [Indexed: 12/13/2022] Open
Abstract
Every colon cancer has its own unique characteristics, and therefore may respond differently to identical treatments. Here, we develop a data driven mathematical model for the interaction network of key components of immune microenvironment in colon cancer. We estimate the relative abundance of each immune cell from gene expression profiles of tumors, and group patients based on their immune patterns. Then we compare the tumor sensitivity and progression in each of these groups of patients, and observe differences in the patterns of tumor growth between the groups. For instance, in tumors with a smaller density of naive macrophages than activated macrophages, a higher activation rate of macrophages leads to an increase in cancer cell density, demonstrating a negative effect of macrophages. Other tumors however, exhibit an opposite trend, showing a positive effect of macrophages in controlling tumor size. Although the results indicate that for all patients the size of the tumor is sensitive to the parameters related to macrophages, such as their activation and death rate, this research demonstrates that no single biomarker could predict the dynamics of tumors.
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Affiliation(s)
- Arkadz Kirshtein
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003-9305, USA; (A.K.); (S.A.); (T.L.); (S.S.); (R.A.A.)
| | - Shaya Akbarinejad
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003-9305, USA; (A.K.); (S.A.); (T.L.); (S.S.); (R.A.A.)
| | - Wenrui Hao
- Department of Mathematics, Pennsylvania State University, University Park, State College, PA 16802, USA;
| | - Trang Le
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003-9305, USA; (A.K.); (S.A.); (T.L.); (S.S.); (R.A.A.)
| | - Sumeyye Su
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003-9305, USA; (A.K.); (S.A.); (T.L.); (S.S.); (R.A.A.)
| | - Rachel A. Aronow
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003-9305, USA; (A.K.); (S.A.); (T.L.); (S.S.); (R.A.A.)
| | - Leili Shahriyari
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003-9305, USA; (A.K.); (S.A.); (T.L.); (S.S.); (R.A.A.)
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