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Shirokov A, Blokhina I, Fedosov I, Ilyukov E, Terskov A, Myagkov D, Tuktarov D, Tzoy M, Adushkina V, Zlatogosrkaya D, Evsyukova A, Telnova V, Dubrovsky A, Dmitrenko A, Manzhaeva M, Krupnova V, Tuzhilkin M, Elezarova I, Navolokin N, Saranceva E, Iskra T, Lykova E, Semyachkina-Glushkovskaya O. Different Effects of Phototherapy for Rat Glioma during Sleep and Wakefulness. Biomedicines 2024; 12:262. [PMID: 38397864 PMCID: PMC10886766 DOI: 10.3390/biomedicines12020262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 01/17/2024] [Accepted: 01/18/2024] [Indexed: 02/25/2024] Open
Abstract
There is an association between sleep quality and glioma-specific outcomes, including survival. The critical role of sleep in survival among subjects with glioma may be due to sleep-induced activation of brain drainage (BD), that is dramatically suppressed in subjects with glioma. Emerging evidence demonstrates that photobiomodulation (PBM) is an effective technology for both the stimulation of BD and as an add-on therapy for glioma. Emerging evidence suggests that PBM during sleep stimulates BD more strongly than when awake. In this study on male Wistar rats, we clearly demonstrate that the PBM course during sleep vs. when awake more effectively suppresses glioma growth and increases survival compared with the control. The study of the mechanisms of this phenomenon revealed stronger effects of the PBM course in sleeping vs. awake rats on the stimulation of BD and an immune response against glioma, including an increase in the number of CD8+ in glioma cells, activation of apoptosis, and blockage of the proliferation of glioma cells. Our new technology for sleep-phototherapy opens a new strategy to improve the quality of medical care for patients with brain cancer, using promising smart-sleep and non-invasive approaches of glioma treatment.
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Affiliation(s)
- Alexander Shirokov
- Institute of Biochemistry and Physiology of Plants and Microorganisms, Saratov Scientific Centre of the Russian Academy of Sciences, Prospekt Entuziastov 13, 410049 Saratov, Russia
- Department of Biology, Saratov State University, Astrakhanskaya Str. 83, 410012 Saratov, Russia; (I.B.); (A.T.); (V.A.); (D.Z.); (A.E.); (V.T.); (A.D.); (M.M.); (V.K.); (M.T.); (I.E.); (N.N.); (E.S.); (T.I.); (E.L.)
| | - Inna Blokhina
- Department of Biology, Saratov State University, Astrakhanskaya Str. 83, 410012 Saratov, Russia; (I.B.); (A.T.); (V.A.); (D.Z.); (A.E.); (V.T.); (A.D.); (M.M.); (V.K.); (M.T.); (I.E.); (N.N.); (E.S.); (T.I.); (E.L.)
| | - Ivan Fedosov
- Physics Department, Saratov State University, Astrakhanskaya Str. 83, 410012 Saratov, Russia; (I.F.); (E.I.); (D.M.); (D.T.); (M.T.); (A.D.)
| | - Egor Ilyukov
- Physics Department, Saratov State University, Astrakhanskaya Str. 83, 410012 Saratov, Russia; (I.F.); (E.I.); (D.M.); (D.T.); (M.T.); (A.D.)
| | - Andrey Terskov
- Department of Biology, Saratov State University, Astrakhanskaya Str. 83, 410012 Saratov, Russia; (I.B.); (A.T.); (V.A.); (D.Z.); (A.E.); (V.T.); (A.D.); (M.M.); (V.K.); (M.T.); (I.E.); (N.N.); (E.S.); (T.I.); (E.L.)
| | - Dmitry Myagkov
- Physics Department, Saratov State University, Astrakhanskaya Str. 83, 410012 Saratov, Russia; (I.F.); (E.I.); (D.M.); (D.T.); (M.T.); (A.D.)
| | - Dmitry Tuktarov
- Physics Department, Saratov State University, Astrakhanskaya Str. 83, 410012 Saratov, Russia; (I.F.); (E.I.); (D.M.); (D.T.); (M.T.); (A.D.)
| | - Maria Tzoy
- Physics Department, Saratov State University, Astrakhanskaya Str. 83, 410012 Saratov, Russia; (I.F.); (E.I.); (D.M.); (D.T.); (M.T.); (A.D.)
| | - Viktoria Adushkina
- Department of Biology, Saratov State University, Astrakhanskaya Str. 83, 410012 Saratov, Russia; (I.B.); (A.T.); (V.A.); (D.Z.); (A.E.); (V.T.); (A.D.); (M.M.); (V.K.); (M.T.); (I.E.); (N.N.); (E.S.); (T.I.); (E.L.)
| | - Daria Zlatogosrkaya
- Department of Biology, Saratov State University, Astrakhanskaya Str. 83, 410012 Saratov, Russia; (I.B.); (A.T.); (V.A.); (D.Z.); (A.E.); (V.T.); (A.D.); (M.M.); (V.K.); (M.T.); (I.E.); (N.N.); (E.S.); (T.I.); (E.L.)
| | - Arina Evsyukova
- Department of Biology, Saratov State University, Astrakhanskaya Str. 83, 410012 Saratov, Russia; (I.B.); (A.T.); (V.A.); (D.Z.); (A.E.); (V.T.); (A.D.); (M.M.); (V.K.); (M.T.); (I.E.); (N.N.); (E.S.); (T.I.); (E.L.)
| | - Valeria Telnova
- Department of Biology, Saratov State University, Astrakhanskaya Str. 83, 410012 Saratov, Russia; (I.B.); (A.T.); (V.A.); (D.Z.); (A.E.); (V.T.); (A.D.); (M.M.); (V.K.); (M.T.); (I.E.); (N.N.); (E.S.); (T.I.); (E.L.)
| | - Alexander Dubrovsky
- Physics Department, Saratov State University, Astrakhanskaya Str. 83, 410012 Saratov, Russia; (I.F.); (E.I.); (D.M.); (D.T.); (M.T.); (A.D.)
| | - Alexander Dmitrenko
- Department of Biology, Saratov State University, Astrakhanskaya Str. 83, 410012 Saratov, Russia; (I.B.); (A.T.); (V.A.); (D.Z.); (A.E.); (V.T.); (A.D.); (M.M.); (V.K.); (M.T.); (I.E.); (N.N.); (E.S.); (T.I.); (E.L.)
| | - Maria Manzhaeva
- Department of Biology, Saratov State University, Astrakhanskaya Str. 83, 410012 Saratov, Russia; (I.B.); (A.T.); (V.A.); (D.Z.); (A.E.); (V.T.); (A.D.); (M.M.); (V.K.); (M.T.); (I.E.); (N.N.); (E.S.); (T.I.); (E.L.)
| | - Valeria Krupnova
- Department of Biology, Saratov State University, Astrakhanskaya Str. 83, 410012 Saratov, Russia; (I.B.); (A.T.); (V.A.); (D.Z.); (A.E.); (V.T.); (A.D.); (M.M.); (V.K.); (M.T.); (I.E.); (N.N.); (E.S.); (T.I.); (E.L.)
| | - Matvey Tuzhilkin
- Department of Biology, Saratov State University, Astrakhanskaya Str. 83, 410012 Saratov, Russia; (I.B.); (A.T.); (V.A.); (D.Z.); (A.E.); (V.T.); (A.D.); (M.M.); (V.K.); (M.T.); (I.E.); (N.N.); (E.S.); (T.I.); (E.L.)
| | - Inna Elezarova
- Department of Biology, Saratov State University, Astrakhanskaya Str. 83, 410012 Saratov, Russia; (I.B.); (A.T.); (V.A.); (D.Z.); (A.E.); (V.T.); (A.D.); (M.M.); (V.K.); (M.T.); (I.E.); (N.N.); (E.S.); (T.I.); (E.L.)
| | - Nikita Navolokin
- Department of Biology, Saratov State University, Astrakhanskaya Str. 83, 410012 Saratov, Russia; (I.B.); (A.T.); (V.A.); (D.Z.); (A.E.); (V.T.); (A.D.); (M.M.); (V.K.); (M.T.); (I.E.); (N.N.); (E.S.); (T.I.); (E.L.)
- Department of Pathological Anatomy, Saratov Medical State University, Bolshaya Kazachaya Str. 112, 410012 Saratov, Russia
| | - Elena Saranceva
- Department of Biology, Saratov State University, Astrakhanskaya Str. 83, 410012 Saratov, Russia; (I.B.); (A.T.); (V.A.); (D.Z.); (A.E.); (V.T.); (A.D.); (M.M.); (V.K.); (M.T.); (I.E.); (N.N.); (E.S.); (T.I.); (E.L.)
| | - Tatyana Iskra
- Department of Biology, Saratov State University, Astrakhanskaya Str. 83, 410012 Saratov, Russia; (I.B.); (A.T.); (V.A.); (D.Z.); (A.E.); (V.T.); (A.D.); (M.M.); (V.K.); (M.T.); (I.E.); (N.N.); (E.S.); (T.I.); (E.L.)
| | - Ekaterina Lykova
- Department of Biology, Saratov State University, Astrakhanskaya Str. 83, 410012 Saratov, Russia; (I.B.); (A.T.); (V.A.); (D.Z.); (A.E.); (V.T.); (A.D.); (M.M.); (V.K.); (M.T.); (I.E.); (N.N.); (E.S.); (T.I.); (E.L.)
| | - Oxana Semyachkina-Glushkovskaya
- Department of Biology, Saratov State University, Astrakhanskaya Str. 83, 410012 Saratov, Russia; (I.B.); (A.T.); (V.A.); (D.Z.); (A.E.); (V.T.); (A.D.); (M.M.); (V.K.); (M.T.); (I.E.); (N.N.); (E.S.); (T.I.); (E.L.)
- Physics Department, Humboldt University, Newtonstrasse 15, 12489 Berlin, Germany
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Metz MC, Ezhov I, Peeken JC, Buchner JA, Lipkova J, Kofler F, Waldmannstetter D, Delbridge C, Diehl C, Bernhardt D, Schmidt-Graf F, Gempt J, Combs SE, Zimmer C, Menze B, Wiestler B. Toward image-based personalization of glioblastoma therapy: A clinical and biological validation study of a novel, deep learning-driven tumor growth model. Neurooncol Adv 2024; 6:vdad171. [PMID: 38435962 PMCID: PMC10907005 DOI: 10.1093/noajnl/vdad171] [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: 03/05/2024] Open
Abstract
Background The diffuse growth pattern of glioblastoma is one of the main challenges for accurate treatment. Computational tumor growth modeling has emerged as a promising tool to guide personalized therapy. Here, we performed clinical and biological validation of a novel growth model, aiming to close the gap between the experimental state and clinical implementation. Methods One hundred and twenty-four patients from The Cancer Genome Archive (TCGA) and 397 patients from the UCSF Glioma Dataset were assessed for significant correlations between clinical data, genetic pathway activation maps (generated with PARADIGM; TCGA only), and infiltration (Dw) as well as proliferation (ρ) parameters stemming from a Fisher-Kolmogorov growth model. To further evaluate clinical potential, we performed the same growth modeling on preoperative magnetic resonance imaging data from 30 patients of our institution and compared model-derived tumor volume and recurrence coverage with standard radiotherapy plans. Results The parameter ratio Dw/ρ (P < .05 in TCGA) as well as the simulated tumor volume (P < .05 in TCGA/UCSF) were significantly inversely correlated with overall survival. Interestingly, we found a significant correlation between 11 proliferation pathways and the estimated proliferation parameter. Depending on the cutoff value for tumor cell density, we observed a significant improvement in recurrence coverage without significantly increased radiation volume utilizing model-derived target volumes instead of standard radiation plans. Conclusions Identifying a significant correlation between computed growth parameters and clinical and biological data, we highlight the potential of tumor growth modeling for individualized therapy of glioblastoma. This might improve the accuracy of radiation planning in the near future.
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Affiliation(s)
- Marie-Christin Metz
- Department of Diagnostic and Interventional Neuroradiology, Technical University of Munich, Munich, Germany
| | - Ivan Ezhov
- Department of Informatics, Technical University of Munich, Munich, Germany
- TranslaTUM—Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Jan C Peeken
- Department of Radiation Oncology, Technical University of Munich, Munich, Germany
- Department of Radiation Sciences (DRS), Institute of Radiation Medicine (IRM), Helmholtz Zentrum München, Munich, Germany
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany
| | - Josef A Buchner
- Department of Radiation Oncology, Technical University of Munich, Munich, Germany
| | - Jana Lipkova
- Department of Pathology and Molecular Medicine, University of California, Irvine, Irvine, CA, USA
| | - Florian Kofler
- Department of Diagnostic and Interventional Neuroradiology, Technical University of Munich, Munich, Germany
- Department of Informatics, Technical University of Munich, Munich, Germany
- Helmholtz Artificial Intelligence Cooperation Unit, Helmholtz Zentrum Munich, Munich, Germany
- TranslaTUM—Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | | | - Claire Delbridge
- Department of Neuropathology, Institute of Pathology, Technical University of Munich, Munich, Germany
| | - Christian Diehl
- Department of Radiation Oncology, Technical University of Munich, Munich, Germany
| | - Denise Bernhardt
- Department of Radiation Oncology, Technical University of Munich, Munich, Germany
- Department of Radiation Sciences (DRS), Institute of Radiation Medicine (IRM), Helmholtz Zentrum München, Munich, Germany
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany
| | | | - Jens Gempt
- Department of Neurosurgery, Technical University of Munich, Munich, Germany
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Stephanie E Combs
- Department of Radiation Oncology, Technical University of Munich, Munich, Germany
- Department of Radiation Sciences (DRS), Institute of Radiation Medicine (IRM), Helmholtz Zentrum München, Munich, Germany
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, Technical University of Munich, Munich, Germany
| | - Bjoern Menze
- Department of Informatics, Technical University of Munich, Munich, Germany
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, Technical University of Munich, Munich, Germany
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Urcuyo JC, Curtin L, Langworthy JM, De Leon G, Anderies B, Singleton KW, Hawkins-Daarud A, Jackson PR, Bond KM, Ranjbar S, Lassiter-Morris Y, Clark-Swanson KR, Paulson LE, Sereduk C, Mrugala MM, Porter AB, Baxter L, Salomao M, Donev K, Hudson M, Meyer J, Zeeshan Q, Sattur M, Patra DP, Jones BA, Rahme RJ, Neal MT, Patel N, Kouloumberis P, Turkmani AH, Lyons M, Krishna C, Zimmerman RS, Bendok BR, Tran NL, Hu LS, Swanson KR. Image-localized biopsy mapping of brain tumor heterogeneity: A single-center study protocol. PLoS One 2023; 18:e0287767. [PMID: 38117803 PMCID: PMC10732423 DOI: 10.1371/journal.pone.0287767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 06/13/2023] [Indexed: 12/22/2023] Open
Abstract
Brain cancers pose a novel set of difficulties due to the limited accessibility of human brain tumor tissue. For this reason, clinical decision-making relies heavily on MR imaging interpretation, yet the mapping between MRI features and underlying biology remains ambiguous. Standard (clinical) tissue sampling fails to capture the full heterogeneity of the disease. Biopsies are required to obtain a pathological diagnosis and are predominantly taken from the tumor core, which often has different traits to the surrounding invasive tumor that typically leads to recurrent disease. One approach to solving this issue is to characterize the spatial heterogeneity of molecular, genetic, and cellular features of glioma through the intraoperative collection of multiple image-localized biopsy samples paired with multi-parametric MRIs. We have adopted this approach and are currently actively enrolling patients for our 'Image-Based Mapping of Brain Tumors' study. Patients are eligible for this research study (IRB #16-002424) if they are 18 years or older and undergoing surgical intervention for a brain lesion. Once identified, candidate patients receive dynamic susceptibility contrast (DSC) perfusion MRI and diffusion tensor imaging (DTI), in addition to standard sequences (T1, T1Gd, T2, T2-FLAIR) at their presurgical scan. During surgery, sample anatomical locations are tracked using neuronavigation. The collected specimens from this research study are used to capture the intra-tumoral heterogeneity across brain tumors including quantification of genetic aberrations through whole-exome and RNA sequencing as well as other tissue analysis techniques. To date, these data (made available through a public portal) have been used to generate, test, and validate predictive regional maps of the spatial distribution of tumor cell density and/or treatment-related key genetic marker status to identify biopsy and/or treatment targets based on insight from the entire tumor makeup. This type of methodology, when delivered within clinically feasible time frames, has the potential to further inform medical decision-making by improving surgical intervention, radiation, and targeted drug therapy for patients with glioma.
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Affiliation(s)
- Javier C Urcuyo
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Lee Curtin
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Jazlynn M. Langworthy
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Gustavo De Leon
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Barrett Anderies
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Kyle W. Singleton
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Andrea Hawkins-Daarud
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Pamela R. Jackson
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Kamila M. Bond
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Sara Ranjbar
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Yvette Lassiter-Morris
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Kamala R. Clark-Swanson
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Lisa E. Paulson
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Chris Sereduk
- Department of Cancer Biology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Maciej M. Mrugala
- Department of Neurology, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Oncology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Alyx B. Porter
- Department of Neurology, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Oncology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Leslie Baxter
- Department of Neurophysiology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Marcela Salomao
- Department of Pathology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Kliment Donev
- Department of Pathology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Miles Hudson
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Jenna Meyer
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Qazi Zeeshan
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Mithun Sattur
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Devi P. Patra
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Breck A. Jones
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Rudy J. Rahme
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Matthew T. Neal
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Naresh Patel
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Pelagia Kouloumberis
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Ali H. Turkmani
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Mark Lyons
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Chandan Krishna
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Richard S. Zimmerman
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Bernard R. Bendok
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Nhan L. Tran
- Department of Cancer Biology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Leland S. Hu
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, United States of America
| | - Kristin R. Swanson
- Mathematical NeuroOncology Lab, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Cancer Biology, Mayo Clinic, Phoenix, Arizona, United States of America
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, United States of America
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Christenson C, Wu C, Hormuth DA, Huang S, Bao A, Brenner A, Yankeelov TE. Predicting the spatio-temporal response of recurrent glioblastoma treated with rhenium-186 labelled nanoliposomes. BRAIN MULTIPHYSICS 2023; 5:100084. [PMID: 38187909 PMCID: PMC10768931 DOI: 10.1016/j.brain.2023.100084] [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: 01/09/2024] Open
Abstract
Rhenium-186 (186Re) labeled nanoliposome (RNL) therapy for recurrent glioblastoma patients has shown promise to improve outcomes by locally delivering radiation to affected areas. To optimize the delivery of RNL, we have developed a framework to predict patient-specific response to RNL using image-guided mathematical models. Methods We calibrated a family of reaction-diffusion type models with multi-modality imaging data from ten patients (NCR01906385) to predict the spatio-temporal dynamics of each patient's tumor. The data consisted of longitudinal magnetic resonance imaging (MRI) and single photon emission computed tomography (SPECT) to estimate tumor burden and local RNL activity, respectively. The optimal model from the family was selected and used to predict future growth. A simplified version of the model was used in a leave-one-out analysis to predict the development of an individual patient's tumor, based on cohort parameters. Results Across the cohort, predictions using patient-specific parameters with the selected model were able to achieve Spearman correlation coefficients (SCC) of 0.98 and 0.93 for tumor volume and total cell number, respectively, when compared to the measured data. Predictions utilizing the leave-one-out method achieved SCCs of 0.89 and 0.88 for volume and total cell number across the population, respectively. Conclusion We have shown that patient-specific calibrations of a biology-based mathematical model can be used to make early predictions of response to RNL therapy. Furthermore, the leave-one-out framework indicates that radiation doses determined by SPECT can be used to assign model parameters to make predictions directly following the conclusion of RNL treatment. Statement of Significance This manuscript explores the application of computational models to predict response to radionuclide therapy in glioblastoma. There are few, to our knowledge, examples of mathematical models used in clinical radionuclide therapy. We have tested a family of models to determine the applicability of different radiation coupling terms for response to the localized radiation delivery. We show that with patient-specific parameter estimation, we can make accurate predictions of future glioblastoma response to the treatment. As a comparison, we have shown that population trends in response can be used to forecast growth from the moment the treatment has been delivered.In addition to the high simulation and prediction accuracy our modeling methods have achieved, the evaluation of a family of models has given insight into the response dynamics of radionuclide therapy. These dynamics, while different than we had initially hypothesized, should encourage future imaging studies involving high dosage radiation treatments, with specific emphasis on the local immune and vascular response.
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Affiliation(s)
| | - Chengyue Wu
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - David A. Hormuth
- Livestrong Cancer Institutes, USA
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Shiliang Huang
- Department of Oncology, The University of Texas Health Sciences Center at San Antonio, San Antonio, TX 78229, USA
| | - Ande Bao
- Department of Radiation Oncology, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Andrew Brenner
- Department of Oncology, The University of Texas Health Sciences Center at San Antonio, San Antonio, TX 78229, USA
| | - Thomas E. Yankeelov
- Departments of Biomedical Engineering, USA
- Departments of Diagnostic Medicine, USA
- Departments of Oncology, USA
- Livestrong Cancer Institutes, USA
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
- The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA
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5
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Hillen T, Loy N, Painter KJ, Thiessen R. Modelling microtube driven invasion of glioma. J Math Biol 2023; 88:4. [PMID: 38015257 PMCID: PMC10684558 DOI: 10.1007/s00285-023-02025-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 10/20/2023] [Accepted: 10/29/2023] [Indexed: 11/29/2023]
Abstract
Malignant gliomas are notoriously invasive, a major impediment against their successful treatment. This invasive growth has motivated the use of predictive partial differential equation models, formulated at varying levels of detail, and including (i) "proliferation-infiltration" models, (ii) "go-or-grow" models, and (iii) anisotropic diffusion models. Often, these models use macroscopic observations of a diffuse tumour interface to motivate a phenomenological description of invasion, rather than performing a detailed and mechanistic modelling of glioma cell invasion processes. Here we close this gap. Based on experiments that support an important role played by long cellular protrusions, termed tumour microtubes, we formulate a new model for microtube-driven glioma invasion. In particular, we model a population of tumour cells that extend tissue-infiltrating microtubes. Mitosis leads to new nuclei that migrate along the microtubes and settle elsewhere. A combination of steady state analysis and numerical simulation is employed to show that the model can predict an expanding tumour, with travelling wave solutions led by microtube dynamics. A sequence of scaling arguments allows us reduce the detailed model into simpler formulations, including models falling into each of the general classes (i), (ii), and (iii) above. This analysis allows us to clearly identify the assumptions under which these various models can be a posteriori justified in the context of microtube-driven glioma invasion. Numerical simulations are used to compare the various model classes and we discuss their advantages and disadvantages.
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Affiliation(s)
- Thomas Hillen
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Canada.
| | - Nadia Loy
- Department of Mathematical Sciences (DISMA), Politecnico di Torino, Turin, Italy
| | - Kevin J Painter
- Interuniversity Department of Regional and Urban Studies and Planning (DIST), Politecnico di Torino, Turin, Italy
| | - Ryan Thiessen
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Canada
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6
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Wagner A, Schlicke P, Fritz M, Kuttler C, Oden JT, Schumann C, Wohlmuth B. A phase-field model for non-small cell lung cancer under the effects of immunotherapy. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:18670-18694. [PMID: 38052574 DOI: 10.3934/mbe.2023828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
Formulating mathematical models that estimate tumor growth under therapy is vital for improving patient-specific treatment plans. In this context, we present our recent work on simulating non-small-scale cell lung cancer (NSCLC) in a simple, deterministic setting for two different patients receiving an immunotherapeutic treatment. At its core, our model consists of a Cahn-Hilliard-based phase-field model describing the evolution of proliferative and necrotic tumor cells. These are coupled to a simplified nutrient model that drives the growth of the proliferative cells and their decay into necrotic cells. The applied immunotherapy decreases the proliferative cell concentration. Here, we model the immunotherapeutic agent concentration in the entire lung over time by an ordinary differential equation (ODE). Finally, reaction terms provide a coupling between all these equations. By assuming spherical, symmetric tumor growth and constant nutrient inflow, we simplify this full 3D cancer simulation model to a reduced 1D model. We can then resort to patient data gathered from computed tomography (CT) scans over several years to calibrate our model. Our model covers the case in which the immunotherapy is successful and limits the tumor size, as well as the case predicting a sudden relapse, leading to exponential tumor growth. Finally, we move from the reduced model back to the full 3D cancer simulation in the lung tissue. Thereby, we demonstrate the predictive benefits that a more detailed patient-specific simulation including spatial information as a possible generalization within our framework could yield in the future.
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Affiliation(s)
- Andreas Wagner
- School of Computation, Information and Technology, Technical University of Munich, Munich, Bavaria, Germany
| | - Pirmin Schlicke
- School of Computation, Information and Technology, Technical University of Munich, Munich, Bavaria, Germany
| | - Marvin Fritz
- Computational Methods for PDEs, Johann Radon Institute for Computational and Applied Mathematics, Linz, Upper Austria, Austria
| | - Christina Kuttler
- School of Computation, Information and Technology, Technical University of Munich, Munich, Bavaria, Germany
| | - J Tinsley Oden
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas, United States of America
| | - Christian Schumann
- Clinic of Pneumology, Thoracic Oncology, Sleep and Respiratory Critical Care, Klinikverbund Allgäu, Kempten, Bavaria, Germany
| | - Barbara Wohlmuth
- School of Computation, Information and Technology, Technical University of Munich, Munich, Bavaria, Germany
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7
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Allgood JE, Roe A, Sparks BB, Castillo M, Cruz A, Brooks AE, Brooks BD. The Correlation of Sleep Disturbance and Location of Glioma Tumors: A Narrative Review. J Clin Med 2023; 12:4058. [PMID: 37373751 DOI: 10.3390/jcm12124058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 06/08/2023] [Accepted: 06/14/2023] [Indexed: 06/29/2023] Open
Abstract
Sleep disturbance can occur when sleep centers of the brain, regions that are responsible for coordinating and generating healthy amounts of sleep, are disrupted by glioma growth or surgical resection. Several disorders cause disruptions to the average duration, quality, or patterns of sleep, resulting in sleep disturbance. It is unknown whether specific sleep disorders can be reliably correlated with glioma growth, but there are sufficient numbers of case reports to suggest that a connection is possible. In this manuscript, these case reports and retrospective chart reviews are considered in the context of the current primary literature on sleep disturbance and glioma diagnosis to identify a new and useful connection which warrants further systematic and scientific examination in preclinical animal models. Confirmation of the relationship between disruption of the sleep centers in the brain and glioma location could have significant implications for diagnostics, treatment, monitoring of metastasis/recurrence, and end-of-life considerations.
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Affiliation(s)
- JuliAnne E Allgood
- Department of Neuroscience, University of Wyoming, Laramie, WY 82071, USA
| | - Avery Roe
- College of Osteopathic Medicine, Rocky Vista University, Greenwood Village, CO 80112, USA
| | - Bridger B Sparks
- Department of Neuroscience, University of Wyoming, Laramie, WY 82071, USA
| | - Mercedes Castillo
- College of Osteopathic Medicine, Rocky Vista University, Greenwood Village, CO 80112, USA
| | - Angel Cruz
- College of Osteopathic Medicine, Rocky Vista University, Greenwood Village, CO 80112, USA
| | - Amanda E Brooks
- College of Osteopathic Medicine, Rocky Vista University, Greenwood Village, CO 80112, USA
| | - Benjamin D Brooks
- College of Osteopathic Medicine, Rocky Vista University, Greenwood Village, CO 80112, USA
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8
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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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9
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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] [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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10
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Ezhov I, Scibilia K, Franitza K, Steinbauer F, Shit S, Zimmer L, Lipkova J, Kofler F, Paetzold JC, Canalini L, Waldmannstetter D, Menten MJ, Metz M, Wiestler B, Menze B. Learn-Morph-Infer: A new way of solving the inverse problem for brain tumor modeling. Med Image Anal 2023; 83:102672. [PMID: 36395623 DOI: 10.1016/j.media.2022.102672] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 07/18/2022] [Accepted: 10/20/2022] [Indexed: 11/06/2022]
Abstract
Current treatment planning of patients diagnosed with a brain tumor, such as glioma, could significantly benefit by accessing the spatial distribution of tumor cell concentration. Existing diagnostic modalities, e.g. magnetic resonance imaging (MRI), contrast sufficiently well areas of high cell density. In gliomas, however, they do not portray areas of low cell concentration, which can often serve as a source for the secondary appearance of the tumor after treatment. To estimate tumor cell densities beyond the visible boundaries of the lesion, numerical simulations of tumor growth could complement imaging information by providing estimates of full spatial distributions of tumor cells. Over recent years a corpus of literature on medical image-based tumor modeling was published. It includes different mathematical formalisms describing the forward tumor growth model. Alongside, various parametric inference schemes were developed to perform an efficient tumor model personalization, i.e. solving the inverse problem. However, the unifying drawback of all existing approaches is the time complexity of the model personalization which prohibits a potential integration of the modeling into clinical settings. In this work, we introduce a deep learning based methodology for inferring the patient-specific spatial distribution of brain tumors from T1Gd and FLAIR MRI medical scans. Coined as Learn-Morph-Infer, the method achieves real-time performance in the order of minutes on widely available hardware and the compute time is stable across tumor models of different complexity, such as reaction-diffusion and reaction-advection-diffusion models. We believe the proposed inverse solution approach not only bridges the way for clinical translation of brain tumor personalization but can also be adopted to other scientific and engineering domains.
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Affiliation(s)
- Ivan Ezhov
- Department of Informatics, TUM, Munich, Germany; TranslaTUM - Central Institute for Translational Cancer Research, TUM, Munich, Germany.
| | | | | | | | - Suprosanna Shit
- Department of Informatics, TUM, Munich, Germany; TranslaTUM - Central Institute for Translational Cancer Research, TUM, Munich, Germany
| | - Lucas Zimmer
- TranslaTUM - Central Institute for Translational Cancer Research, TUM, Munich, Germany; Department of Quantitative Biomedicine, UZH, Zurich, Switzerland
| | - Jana Lipkova
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Broad Institute of Harvard and MIT, Cambridge, USA; Data Science Program, Dana-Farber Cancer Institute, Boston, USA
| | - Florian Kofler
- Department of Informatics, TUM, Munich, Germany; TranslaTUM - Central Institute for Translational Cancer Research, TUM, Munich, Germany; Neuroradiology Department of Klinikum Rechts der Isar, TUM, Munich, Germany
| | - Johannes C Paetzold
- Department of Informatics, TUM, Munich, Germany; TranslaTUM - Central Institute for Translational Cancer Research, TUM, Munich, Germany
| | | | | | - Martin J Menten
- Department of Informatics, TUM, Munich, Germany; TranslaTUM - Central Institute for Translational Cancer Research, TUM, Munich, Germany
| | - Marie Metz
- TranslaTUM - Central Institute for Translational Cancer Research, TUM, Munich, Germany; Neuroradiology Department of Klinikum Rechts der Isar, TUM, Munich, Germany
| | - Benedikt Wiestler
- TranslaTUM - Central Institute for Translational Cancer Research, TUM, Munich, Germany; Neuroradiology Department of Klinikum Rechts der Isar, TUM, Munich, Germany
| | - Bjoern Menze
- Department of Quantitative Biomedicine, UZH, Zurich, Switzerland
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11
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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 PMCID: PMC11165420 DOI: 10.1016/j.addr.2022.114367] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [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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12
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Simulating the behaviour of glioblastoma multiforme based on patient MRI during treatments. J Math Biol 2022; 84:44. [PMID: 35482133 DOI: 10.1007/s00285-022-01747-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 03/31/2022] [Accepted: 04/01/2022] [Indexed: 10/18/2022]
Abstract
Glioblastoma multiforme is a brain cancer that still shows poor prognosis for patients despite the active research for new treatments. In this work, the goal is to model and simulate the evolution of tumour associated angiogenesis and the therapeutic response to glioblastoma multiforme. Multiple phenomena are modelled in order to fit different biological pathways, such as the cellular cycle, apoptosis, hypoxia or angiogenesis. This leads to a nonlinear system with 4 equations and 4 unknowns: the density of tumour cells, the [Formula: see text] concentration, the density of endothelial cells and the vascular endothelial growth factor concentration. This system is solved numerically on a mesh fitting the geometry of the brain and the tumour of a patient based on a 2D slice of MRI. We show that our numerical scheme is positive, and we give the energy estimates on the discrete solution to ensure its existence. The numerical scheme uses nonlinear control volume finite elements in space and is implicit in time. Numerical simulations have been done using the different standard treatments: surgery, chemotherapy and radiotherapy, in order to conform to the behaviour of a tumour in response to treatments according to empirical clinical knowledge. We find that our theoretical model exhibits realistic behaviours.
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13
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Abstract
Cancer diseases lead to the second-highest death rate all over the world. For treating tumors, one of the most common schemes is chemotherapy, which can decrease the tumor size and control the progression of cancer diseases. To better understand the mechanisms of chemotherapy, we developed a mathematical model of tumor growth under chemotherapy. This model includes both immune system response and drug therapy. We characterize the symmetrical properties and dynamics of this differential equation model by finding the equilibrium points and exploring the stability and symmetry properties in a range of model parameters. Sensitivity analyses suggest that the chemotherapy drug-induced tumor mortality rate and the drug decay rate contribute significantly to the determination of treatment outcomes. Numerical simulations highlight the importance of CTL activation in tumor chemotherapy.
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14
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Lipková J, Menze B, Wiestler B, Koumoutsakos P, Lowengrub JS. Modelling glioma progression, mass effect and intracranial pressure in patient anatomy. J R Soc Interface 2022; 19:20210922. [PMID: 35317645 PMCID: PMC8941421 DOI: 10.1098/rsif.2021.0922] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 02/21/2022] [Indexed: 02/06/2023] Open
Abstract
Increased intracranial pressure is the source of most critical symptoms in patients with glioma, and often the main cause of death. Clinical interventions could benefit from non-invasive estimates of the pressure distribution in the patient's parenchyma provided by computational models. However, existing glioma models do not simulate the pressure distribution and they rely on a large number of model parameters, which complicates their calibration from available patient data. Here we present a novel model for glioma growth, pressure distribution and corresponding brain deformation. The distinct feature of our approach is that the pressure is directly derived from tumour dynamics and patient-specific anatomy, providing non-invasive insights into the patient's state. The model predictions allow estimation of critical conditions such as intracranial hypertension, brain midline shift or neurological and cognitive impairments. A diffuse-domain formalism is employed to allow for efficient numerical implementation of the model in the patient-specific brain anatomy. The model is tested on synthetic and clinical cases. To facilitate clinical deployment, a high-performance computing implementation of the model has been publicly released.
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Affiliation(s)
- Jana Lipková
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Bjoern Menze
- Department of Informatics, Technical University of Munich, Munich, Germany
- Department of Quantitative Biomedicine, University of Zürich, Zürich, Switzerland
- Department of Neuroradiology, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Benedikt Wiestler
- Department of Neuroradiology, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Petros Koumoutsakos
- Computational Science and Engineering Lab, ETH Zürich, Zürich, Switzerland
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
| | - John S. Lowengrub
- Department of Mathematics, University of California, Irvine, CA, USA
- Department of Biomedical Engineering, University of California, Irvine, CA, USA
- Center for Complex Biological Systems, Chao Family Comprehensive Cancer Center, University of California, Irvine, CA, USA
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15
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Harkos C, Svensson SF, Emblem KE, Stylianopoulos T. Inducing Biomechanical Heterogeneity in Brain Tumor Modeling by MR Elastography: Effects on Tumor Growth, Vascular Density and Delivery of Therapeutics. Cancers (Basel) 2022; 14:cancers14040884. [PMID: 35205632 PMCID: PMC8870149 DOI: 10.3390/cancers14040884] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 02/07/2022] [Indexed: 02/01/2023] Open
Abstract
Simple Summary Biomechanical forces aggravate brain tumor progression. In this study, magnetic resonance elastography (MRE) is employed to extract tissue biomechanical properties from five glioblastoma patients and a healthy subject, and data are incorporated in a mathematical model that simulates tumor growth. Mathematical modeling enables further understanding of glioblastoma development and allows patient-specific predictions for tumor vascularity and delivery of drugs. Incorporating MRE data results in a more realistic intratumoral distribution of mechanical stress and anisotropic tumor growth and a better description of subsequent events that are closely related to the development of stresses, including heterogeneity of the tumor vasculature and intrapatient variations in tumor perfusion and delivery of drugs. Abstract The purpose of this study is to develop a methodology that incorporates a more accurate assessment of tissue mechanical properties compared to current mathematical modeling by use of biomechanical data from magnetic resonance elastography. The elastography data were derived from five glioblastoma patients and a healthy subject and used in a model that simulates tumor growth, vascular changes due to mechanical stresses and delivery of therapeutic agents. The model investigates the effect of tumor-specific biomechanical properties on tumor anisotropic growth, vascular density heterogeneity and chemotherapy delivery. The results showed that including elastography data provides a more realistic distribution of the mechanical stresses in the tumor and induces anisotropic tumor growth. Solid stress distribution differs among patients, which, in turn, induces a distinct functional vascular density distribution—owing to the compression of tumor vessels—and intratumoral drug distribution for each patient. In conclusion, incorporating elastography data results in a more accurate calculation of intratumoral mechanical stresses and enables a better mathematical description of subsequent events, such as the heterogeneous development of the tumor vasculature and intrapatient variations in tumor perfusion and delivery of drugs.
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Affiliation(s)
- Constantinos Harkos
- Cancer Biophysics Laboratory, Department of Mechanical and Manufacturing Engineering, University of Cyprus, Nicosia 1678, Cyprus;
| | - Siri Fløgstad Svensson
- Division of Radiology and Nuclear Medicine, Department of Diagnostic Physics, Oslo University Hospital, 0372 Oslo, Norway; (S.F.S.); (K.E.E.)
- Department of Physics, The Faculty of Mathematics and Natural Sciences, University of Oslo, 0371 Oslo, Norway
| | - Kyrre E. Emblem
- Division of Radiology and Nuclear Medicine, Department of Diagnostic Physics, Oslo University Hospital, 0372 Oslo, Norway; (S.F.S.); (K.E.E.)
| | - Triantafyllos Stylianopoulos
- Cancer Biophysics Laboratory, Department of Mechanical and Manufacturing Engineering, University of Cyprus, Nicosia 1678, Cyprus;
- Correspondence:
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16
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Comparing the effects of linear and one-term Ogden elasticity in a model of glioblastoma invasion. BRAIN MULTIPHYSICS 2022. [DOI: 10.1016/j.brain.2022.100050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
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17
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Sousa F, Costa-Pereira AI, Cruz A, Ferreira FJ, Gouveia M, Bessa J, Sarmento B, Travasso RDM, Mendes Pinto I. Intratumoral VEGF nanotrapper reduces gliobastoma vascularization and tumor cell mass. J Control Release 2021; 339:381-390. [PMID: 34592385 DOI: 10.1016/j.jconrel.2021.09.031] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 09/21/2021] [Accepted: 09/22/2021] [Indexed: 01/05/2023]
Abstract
Glioblastoma multiforme (GBM) is the most aggressive and invasive malignant brain cancer. GBM is characterized by a dramatic metabolic imbalance leading to increased secretion of the pro-angiogenic factor VEGF and subsequent abnormal tumor vascularization. In 2009, FDA approved the intravenous administration of bevacizumab, an anti-VEGF monoclonal antibody, as a therapeutic agent for patients with GBM. However, the number of systemic side effects and reduced accessibility of bevacizumab to the central nervous system and consequently to the GBM tumor mass limited its effectiveness in improving patient survival. In this study, we combined experimental and computational modelling to quantitatively characterize the dynamics of VEGF secretion and turnover in GBM and in normal brain cells and simultaneous monitoring of vessel growth. We showed that sequestration of VEGF inside GBM cells, can be used as a novel target for improved bevacizumab-based therapy. We have engineered the VEGF nanotrapper, a cargo system that allows cellular uptake of bevacizumab and inhibits VEGF secretion required for angiogenesis activation and development. Here, we show the therapeutic efficacy of this nanocargo in reducing vascularization and tumor cell mass of GBM in vitro and in vivo cancer models.
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Affiliation(s)
- Flávia Sousa
- i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Rua Alfredo Allen 208, 4200-393 Porto, Portugal; INEB - Instituto Nacional de Engenharia Biomédica, Universidade do Porto, Rua Alfredo Allen 208, 4200-393 Porto, Portugal; ICBAS - Instituto Ciências Biomédicas Abel Salazar, Universidade do Porto, 4150-180 Porto, Portugal; CESPU - Instituto de Investigação e Formação Avançada em Ciências e Tecnologias da Saúde, Rua Central de Gandra 1317, 4585-116 Gandra, Portugal; INL - International Iberian Nanotechnology Laboratory, Avenida Mestre José Veiga, 4715-330 Braga, Portugal
| | | | - Andrea Cruz
- INL - International Iberian Nanotechnology Laboratory, Avenida Mestre José Veiga, 4715-330 Braga, Portugal
| | - Fábio Júnio Ferreira
- i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Rua Alfredo Allen 208, 4200-393 Porto, Portugal; IBMC - Instituto de Biologia Molecular e Celular, Universidade do Porto, Rua Alfredo Allen 208, 4200-393 Porto, Portugal
| | - Marcos Gouveia
- CFisUC - Department of Physics, University of Coimbra, Rua Larga, 3004-516 Coimbra, Portugal
| | - José Bessa
- i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Rua Alfredo Allen 208, 4200-393 Porto, Portugal; IBMC - Instituto de Biologia Molecular e Celular, Universidade do Porto, Rua Alfredo Allen 208, 4200-393 Porto, Portugal
| | - Bruno Sarmento
- i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Rua Alfredo Allen 208, 4200-393 Porto, Portugal; INEB - Instituto Nacional de Engenharia Biomédica, Universidade do Porto, Rua Alfredo Allen 208, 4200-393 Porto, Portugal; CESPU - Instituto de Investigação e Formação Avançada em Ciências e Tecnologias da Saúde, Rua Central de Gandra 1317, 4585-116 Gandra, Portugal
| | - Rui D M Travasso
- CFisUC - Department of Physics, University of Coimbra, Rua Larga, 3004-516 Coimbra, Portugal
| | - Inês Mendes Pinto
- i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Rua Alfredo Allen 208, 4200-393 Porto, Portugal; INL - International Iberian Nanotechnology Laboratory, Avenida Mestre José Veiga, 4715-330 Braga, Portugal.
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18
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Tunc B, Hormuth D, Biros G, Yankeelov TE. Modeling of Glioma Growth with Mass Effect by Longitudinal Magnetic Resonance Imaging. IEEE Trans Biomed Eng 2021; 68:3713-3724. [PMID: 34061731 DOI: 10.1109/tbme.2021.3085523] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
It is well-known that expanding glioblastomas typically induce significant deformations of the surrounding parenchyma (i.e., the so-called ?mass effect?). In this study, we evaluate the performance of three mathematical models of tumor growth: 1) a reaction-diffusion-advection model which accounts for mass effect (RDAM), 2) a reaction-diffusion model with mass effect that is consistent only in the case of small deformations (RDM), and 3) a reaction-diffusion model that does not include the mass effect (RD). The models were calibrated with magnetic resonance imaging (MRI) data obtained during tumor development in a murine model of glioma (n = 9). We obtained T2-weighted and contrast-enhanced T1-weighted MRI at 6 time points over 10 days to determine the spatiotemporal variation in the mass effect and tumor concentration, respectively. We calibrated the three models using data 1) at the first four, 2) only at the first and fourth, and 3) only at the third and fourth time points. Each of these calibrations were run forward in time to predict the volume fraction of tumor cells at the conclusion of the experiment. The diffusion coefficient for the RDAM model (median of 10.65 ? 10-3 mm2d-1) is significantly less than those for the RD and RDM models (17.46 ? 10-3 mm2d-1 and 19.38 ? 10-3 mm2d-1, respectively). The tumor concentrations for the RD, RDM, and RDAM models have medians of 40.2%, 32.1%, and 44.7%, respectively, for the calibration using data from the first four time points. The RDM model most accurately predicts tumor growth, while the RDAM model presents the least variation in its estimates of the diffusion coefficient and proliferation rate. This study demonstrates that the mathematical models capture both tumor development and mass effect observed in experiments.
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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] [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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20
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Scheufele K, Subramanian S, Biros G. Fully Automatic Calibration of Tumor-Growth Models Using a Single mpMRI Scan. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:193-204. [PMID: 32931431 PMCID: PMC8565678 DOI: 10.1109/tmi.2020.3024264] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
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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21
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A Mechanistic Investigation into Ischemia-Driven Distal Recurrence of Glioblastoma. Bull Math Biol 2020; 82:143. [PMID: 33159592 DOI: 10.1007/s11538-020-00814-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 09/25/2020] [Indexed: 10/23/2022]
Abstract
Glioblastoma (GBM) is the most aggressive primary brain tumor with a short median survival. Tumor recurrence is a clinical expectation of this disease and usually occurs along the resection cavity wall. However, previous clinical observations have suggested that in cases of ischemia following surgery, tumors are more likely to recur distally. Through the use of a previously established mechanistic model of GBM, the Proliferation Invasion Hypoxia Necrosis Angiogenesis (PIHNA) model, we explore the phenotypic drivers of this observed behavior. We have extended the PIHNA model to include a new nutrient-based vascular efficiency term that encodes the ability of local vasculature to provide nutrients to the simulated tumor. The extended model suggests sensitivity to a hypoxic microenvironment and the inherent migration and proliferation rates of the tumor cells are key factors that drive distal recurrence.
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Multiatlas Calibration of Biophysical Brain Tumor Growth Models with Mass Effect. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2020; 12262:551-560. [PMID: 34704089 DOI: 10.1007/978-3-030-59713-9_53] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
We present a 3D fully-automatic method for the calibration of partial differential equation (PDE) models of glioblastoma (GBM) growth with "mass effect", the deformation of brain tissue due to the tumor. We quantify the mass effect, tumor proliferation, tumor migration, and the localized tumor initial condition from a single multiparameteric Magnetic Resonance Imaging (mpMRI) patient scan. The PDE is a reaction-advection-diffusion partial differential equation coupled with linear elasticity equations to capture mass effect. The single-scan calibration model is notoriously difficult because the precancerous (healthy) brain anatomy is unknown. To solve this inherently ill-posed and illconditioned optimization problem, we introduce a novel inversion scheme that uses multiple brain atlases as proxies for the healthy precancer patient brain resulting in robust and reliable parameter estimation. We apply our method on both synthetic and clinical datasets representative of the heterogeneous spatial landscape typically observed in glioblastomas to demonstrate the validity and performance of our methods. In the synthetic data, we report calibration errors (due to the ill-posedness and our solution scheme) in the 10%-20% range. In the clinical data, we report good quantitative agreement with the observed tumor and qualitative agreement with the mass effect (for which we do not have a ground truth). Our method uses a minimal set of parameters and provides both global and local quantitative measures of tumor infiltration and mass effect.
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Mang A, Bakas S, Subramanian S, Davatzikos C, Biros G. Integrated Biophysical Modeling and Image Analysis: Application to Neuro-Oncology. Annu Rev Biomed Eng 2020; 22:309-341. [PMID: 32501772 PMCID: PMC7520881 DOI: 10.1146/annurev-bioeng-062117-121105] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Central nervous system (CNS) tumors come with vastly heterogeneous histologic, molecular, and radiographic landscapes, rendering their precise characterization challenging. The rapidly growing fields of biophysical modeling and radiomics have shown promise in better characterizing the molecular, spatial, and temporal heterogeneity of tumors. Integrative analysis of CNS tumors, including clinically acquired multi-parametric magnetic resonance imaging (mpMRI) and the inverse problem of calibrating biophysical models to mpMRI data, assists in identifying macroscopic quantifiable tumor patterns of invasion and proliferation, potentially leading to improved (a) detection/segmentation of tumor subregions and (b) computer-aided diagnostic/prognostic/predictive modeling. This article presents a summary of (a) biophysical growth modeling and simulation,(b) inverse problems for model calibration, (c) these models' integration with imaging workflows, and (d) their application to clinically relevant studies. We anticipate that such quantitative integrative analysis may even be beneficial in a future revision of the World Health Organization (WHO) classification for CNS tumors, ultimately improving patient survival prospects.
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Affiliation(s)
- Andreas Mang
- Department of Mathematics, University of Houston, Houston, Texas 77204, USA;
| | - Spyridon Bakas
- Department of Mathematics, University of Houston, Houston, Texas 77204, USA;
| | - Shashank Subramanian
- Oden Institute of Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, USA; ,
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA); Department of Radiology; and Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA; ,
| | - George Biros
- Oden Institute of Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, USA; ,
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Subramanian S, Scheufele K, Mehl M, Biros G. WHERE DID THE TUMOR START? AN INVERSE SOLVER WITH SPARSE LOCALIZATION FOR TUMOR GROWTH MODELS. INVERSE PROBLEMS 2020; 36:045006. [PMID: 33746330 PMCID: PMC7971430 DOI: 10.1088/1361-6420/ab649c] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
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.
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Affiliation(s)
- Shashank Subramanian
- Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, 201 E. 24th Street, Austin, Texas, USA
| | - Klaudius Scheufele
- Institute for Parallel and Distributed Systems, Universität Stuttgart, Universitatsstraßë38, Stuttgart, Germany
| | - Miriam Mehl
- Institute for Parallel and Distributed Systems, Universität Stuttgart, Universitatsstraßë38, Stuttgart, Germany
| | - George Biros
- Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, 201 E. 24th Street, Austin, Texas, USA
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