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Kolokotroni E, Abler D, Ghosh A, Tzamali E, Grogan J, Georgiadi E, Büchler P, Radhakrishnan R, Byrne H, Sakkalis V, Nikiforaki K, Karatzanis I, McFarlane NJB, Kaba D, Dong F, Bohle RM, Meese E, Graf N, Stamatakos G. A Multidisciplinary Hyper-Modeling Scheme in Personalized In Silico Oncology: Coupling Cell Kinetics with Metabolism, Signaling Networks, and Biomechanics as Plug-In Component Models of a Cancer Digital Twin. J Pers Med 2024; 14:475. [PMID: 38793058 PMCID: PMC11122096 DOI: 10.3390/jpm14050475] [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: 03/03/2024] [Revised: 04/11/2024] [Accepted: 04/17/2024] [Indexed: 05/26/2024] Open
Abstract
The massive amount of human biological, imaging, and clinical data produced by multiple and diverse sources necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell-signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels, as well as their orchestration and links, are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof-of-concept study cases. Personalized simulations of the actual anatomy of a patient have been conducted. The hypermodel has also been applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin-based clinical decision support system and as the core of future in silico trial platforms, although additional retrospective adaptation and validation are necessary.
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Affiliation(s)
- Eleni Kolokotroni
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, 157 80 Zografos, Greece;
| | - Daniel Abler
- Department of Oncology, Geneva University Hospitals and University of Geneva, 1205 Geneva, Switzerland;
- Department of Oncology, Lausanne University Hospital and University of Lausanne, 1011 Lausanne, Switzerland
| | - Alokendra Ghosh
- Department of Chemical and Biomolecular Engineering, Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; (A.G.); (R.R.)
| | - Eleftheria Tzamali
- Institute of Computer Science, Foundation for Research and Technology—Hellas, 70013 Heraklion, Greece; (E.T.); (V.S.); (K.N.); (I.K.)
| | - James Grogan
- Irish Centre for High End Computing, University of Galway, H91 TK33 Galway, Ireland;
| | - Eleni Georgiadi
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, 157 80 Zografos, Greece;
- Biomedical Engineering Department, University of West Attica, 12243 Egaleo, Greece
| | | | - Ravi Radhakrishnan
- Department of Chemical and Biomolecular Engineering, Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; (A.G.); (R.R.)
| | - Helen Byrne
- Mathematical Institute, University of Oxford, Oxford OX1 2JD, UK;
| | - Vangelis Sakkalis
- Institute of Computer Science, Foundation for Research and Technology—Hellas, 70013 Heraklion, Greece; (E.T.); (V.S.); (K.N.); (I.K.)
| | - Katerina Nikiforaki
- Institute of Computer Science, Foundation for Research and Technology—Hellas, 70013 Heraklion, Greece; (E.T.); (V.S.); (K.N.); (I.K.)
| | - Ioannis Karatzanis
- Institute of Computer Science, Foundation for Research and Technology—Hellas, 70013 Heraklion, Greece; (E.T.); (V.S.); (K.N.); (I.K.)
| | | | - Djibril Kaba
- Department of Computer Science and Technology, University of Bedfordshire, Luton LU1 3JU, UK;
| | - Feng Dong
- Department of Computer & Information Sciences, University of Strathclyde, Glasgow G1 1XH, UK;
| | - Rainer M. Bohle
- Department of Pathology, Saarland University, 66421 Homburg, Germany;
| | - Eckart Meese
- Department of Human Genetics, Saarland University, 66421 Homburg, Germany;
| | - Norbert Graf
- Department of Paediatric Oncology and Haematology, Saarland University, 66421 Homburg, Germany;
| | - Georgios Stamatakos
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, 157 80 Zografos, Greece;
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Du C, Li J, Zhang B, Feng W, Zhang T, Li D. Intraoperative navigation system with a multi-modality fusion of 3D virtual model and laparoscopic real-time images in laparoscopic pancreatic surgery: a preclinical study. BMC Surg 2022; 22:139. [PMID: 35410155 PMCID: PMC9004060 DOI: 10.1186/s12893-022-01585-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 03/31/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Laparoscopy is widely used in pancreatic surgeries nowadays. The efficient and correct judgment of the location of the anatomical structures is crucial for a safe laparoscopic pancreatic surgery. The technologies of 3-dimensional(3D) virtual model and image fusion are widely used for preoperative planning and intraoperative navigation in the medical field, but not in laparoscopic pancreatic surgery up to now. We aimed to develop an intraoperative navigation system with an accurate multi-modality fusion of 3D virtual model and laparoscopic real-time images for laparoscopic pancreatic surgery. METHODS The software for the navigation system was developed ad hoc. The preclinical study included tests with the laparoscopic simulator and pilot cases. The 3D virtual models were built using preoperative Computed Tomography (CT) Digital Imaging and Communications in Medicine (DICOM) data. Manual and automatic real-time image fusions were tested. The practicality of the navigation system was evaluated by the operators using the National Aeronautics and Space Administration-Task Load Index (NASA-TLX) method. RESULTS The 3D virtual models were successfully built using the navigation system. The 3D model was correctly fused with the real-time laparoscopic images both manually and automatically optical orientation in the preclinical tests. The statistical comparative tests showed no statistically significant differences between the scores of the rigid model and those of the phantom model(P > 0.05). There was statistically significant difference between the total scores of automatic fusion function and those of manual fusion function (P = 0.026). In pilot cases, the 3D model was correctly fused with the real-time laparoscopic images manually. The Intraoperative navigation system was easy to use. The automatic fusion function brought more convenience to the user. CONCLUSIONS The intraoperative navigation system applied in laparoscopic pancreatic surgery clearly and correctly showed the covered anatomical structures. It has the potentiality of helping achieve a more safe and efficient laparoscopic pancreatic surgery.
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Affiliation(s)
- Chengxu Du
- Department of Hepatobiliary Surgery, The Second Hospital of Hebei Medical University, 215 Heping West Road, Hebei, 050000, Shijiazhuang, China
| | - Jiaxuan Li
- Hebei Medical University, Shijiazhuang, Hebei, China
| | - Bin Zhang
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Wenfeng Feng
- Department of Medical Imaging, The Second Hospital of Hebei Medical University, Hebei, Shijiazhuang, China
| | - Tengfei Zhang
- Department of Hepatobiliary Surgery, The Second Hospital of Hebei Medical University, 215 Heping West Road, Hebei, 050000, Shijiazhuang, China
| | - Dongrui Li
- Department of Hepatobiliary Surgery, The Second Hospital of Hebei Medical University, 215 Heping West Road, Hebei, 050000, Shijiazhuang, China.
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A Fractional Analysis of Hyperthermia Therapy on Breast Cancer in a Porous Medium along with Radiative Microwave Heating. FRACTAL AND FRACTIONAL 2022. [DOI: 10.3390/fractalfract6020082] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Cancer is a prominent source of mortality and morbidity globally, but little is known about how it develops and spreads. Tumor cells are unable to thrive in high-temperature environments, according to recent research. Hyperthermia is the name for this therapy method. This study provides insights into hyperthermia therapy on breast cancer in the presence of a porous material with fractional derivative access when using radiative microwave heating. The mathematical model is formulated by PDE, while the time-fractional Caputo derivative is applied to make our equation more general as compared to the classical model. To produce a more efficient analysis of blood temperature distributions inside the tissues of the breast, the unsteady state is calculated by using the Laplace transform technique. The Laplace inversion is found by Durbin’s and Zakian’s algorithms. The treatment involves mild temperature hyperthermia, which causes cell death by enhancing cell sensitivity to radiation therapy and blood flow in the tumor. The variations of different parameters to control the temperate profile during therapy are discussed; we can also see how a fractional parameter makes our study more realistic for further experimental study.
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Falco J, Agosti A, Vetrano IG, Bizzi A, Restelli F, Broggi M, Schiariti M, DiMeco F, Ferroli P, Ciarletta P, Acerbi F. In Silico Mathematical Modelling for Glioblastoma: A Critical Review and a Patient-Specific Case. J Clin Med 2021; 10:2169. [PMID: 34067871 PMCID: PMC8156762 DOI: 10.3390/jcm10102169] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 04/24/2021] [Accepted: 05/14/2021] [Indexed: 01/28/2023] Open
Abstract
Glioblastoma extensively infiltrates the brain; despite surgery and aggressive therapies, the prognosis is poor. A multidisciplinary approach combining mathematical, clinical and radiological data has the potential to foster our understanding of glioblastoma evolution in every single patient, with the aim of tailoring therapeutic weapons. In particular, the ultimate goal of biomathematics for cancer is the identification of the most suitable theoretical models and simulation tools, both to describe the biological complexity of carcinogenesis and to predict tumor evolution. In this report, we describe the results of a critical review about different mathematical models in neuro-oncology with their clinical implications. A comprehensive literature search and review for English-language articles concerning mathematical modelling in glioblastoma has been conducted. The review explored the different proposed models, classifying them and indicating the significative advances of each one. Furthermore, we present a specific case of a glioblastoma patient in which our recently proposed innovative mechanical model has been applied. The results of the mathematical models have the potential to provide a relevant benefit for clinicians and, more importantly, they might drive progress towards improving tumor control and patient's prognosis. Further prospective comparative trials, however, are still necessary to prove the impact of mathematical neuro-oncology in clinical practice.
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Affiliation(s)
- Jacopo Falco
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (J.F.); (I.G.V.); (F.R.); (M.B.); (M.S.); (F.D.); (P.F.)
| | - Abramo Agosti
- MOX, Department of Mathematics, Politecnico di Milano, 20133 Milan, Italy; (A.A.); (P.C.)
| | - Ignazio G. Vetrano
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (J.F.); (I.G.V.); (F.R.); (M.B.); (M.S.); (F.D.); (P.F.)
| | - Alberto Bizzi
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy;
| | - Francesco Restelli
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (J.F.); (I.G.V.); (F.R.); (M.B.); (M.S.); (F.D.); (P.F.)
| | - Morgan Broggi
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (J.F.); (I.G.V.); (F.R.); (M.B.); (M.S.); (F.D.); (P.F.)
| | - Marco Schiariti
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (J.F.); (I.G.V.); (F.R.); (M.B.); (M.S.); (F.D.); (P.F.)
| | - Francesco DiMeco
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (J.F.); (I.G.V.); (F.R.); (M.B.); (M.S.); (F.D.); (P.F.)
- Department of Pathophysiology and Transplantation, Università degli Studi di Milano, 20122 Milan, Italy
- Department of Neurological Surgery, Johns Hopkins Medical School, Baltimora, MD 21205, USA
| | - Paolo Ferroli
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (J.F.); (I.G.V.); (F.R.); (M.B.); (M.S.); (F.D.); (P.F.)
| | - Pasquale Ciarletta
- MOX, Department of Mathematics, Politecnico di Milano, 20133 Milan, Italy; (A.A.); (P.C.)
| | - Francesco Acerbi
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (J.F.); (I.G.V.); (F.R.); (M.B.); (M.S.); (F.D.); (P.F.)
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Antonopoulos M, Dionysiou D, Stamatakos G, Uzunoglu N. Three-dimensional tumor growth in time-varying chemical fields: a modeling framework and theoretical study. BMC Bioinformatics 2019; 20:442. [PMID: 31455206 PMCID: PMC6712764 DOI: 10.1186/s12859-019-2997-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Accepted: 07/16/2019] [Indexed: 01/10/2023] Open
Abstract
Background Contemporary biological observations have revealed a large variety of mechanisms acting during the expansion of a tumor. However, there are still many qualitative and quantitative aspects of the phenomenon that remain largely unknown. In this context, mathematical and computational modeling appears as an invaluable tool providing the means for conducting in silico experiments, which are cheaper and less tedious than real laboratory experiments. Results This paper aims at developing an extensible and computationally efficient framework for in silico modeling of tumor growth in a 3-dimensional, inhomogeneous and time-varying chemical environment. The resulting model consists of a set of mathematically derived and algorithmically defined operators, each one addressing the effects of a particular biological mechanism on the state of the system. These operators may be extended or re-adjusted, in case a different set of starting assumptions or a different simulation scenario needs to be considered. Conclusion In silico modeling provides an alternative means for testing hypotheses and simulating scenarios for which exact biological knowledge remains elusive. However, finer tuning of pertinent methods presupposes qualitative and quantitative enrichment of available biological evidence. Validation in a strict sense would further require comprehensive, case-specific simulations and detailed comparisons with biomedical observations.
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Affiliation(s)
- Markos Antonopoulos
- Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece.
| | - Dimitra Dionysiou
- Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
| | - Georgios Stamatakos
- Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
| | - Nikolaos Uzunoglu
- Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
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Kyroudis CA, Dionysiou DD, Kolokotroni EA, Stamatakos GS. Studying the regression profiles of cervical tumours during radiotherapy treatment using a patient-specific multiscale model. Sci Rep 2019; 9:1081. [PMID: 30705291 PMCID: PMC6355788 DOI: 10.1038/s41598-018-37155-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Accepted: 12/03/2018] [Indexed: 12/24/2022] Open
Abstract
Apart from offering insight into the biomechanisms involved in cancer, many recent mathematical modeling efforts aspire to the ultimate goal of clinical translation, wherein models are designed to be used in the future as clinical decision support systems in the patient-individualized context. Most significant challenges are the integration of multiscale biodata and the patient-specific model parameterization. A central aim of this study was the design of a clinically-relevant parameterization methodology for a patient-specific computational model of cervical cancer response to radiotherapy treatment with concomitant cisplatin, built around a tumour features-based search of the parameter space. Additionally, a methodological framework for the predictive use of the model was designed, including a scoring method to quantitatively reflect the similarity and bilateral predictive ability of any two tumours in terms of their regression profile. The methodology was applied to the datasets of eight patients. Tumour scenarios in accordance with the available longitudinal data have been determined. Predictive investigations identified three patient cases, anyone of which can be used to predict the volumetric evolution throughout therapy of the tumours of the other two with very good results. Our observations show that the presented approach is promising in quantifiably differentiating tumours with distinct regression profiles.
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Affiliation(s)
- Christos A Kyroudis
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
| | - Dimitra D Dionysiou
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece.
| | - Eleni A Kolokotroni
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
| | - Georgios S Stamatakos
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
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Bruni C, Conte F, Papa F, Sinisgalli C. Optimal number and sizes of the doses in fractionated radiotherapy according to the LQ model. MATHEMATICAL MEDICINE AND BIOLOGY-A JOURNAL OF THE IMA 2018; 36:1-53. [DOI: 10.1093/imammb/dqx020] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2017] [Accepted: 12/10/2017] [Indexed: 01/18/2023]
Affiliation(s)
- C Bruni
- Istituto di Analisi dei Sistemi ed Informatica “A. Ruberti” – CNR, Via dei Taurini 19, Rome, Italy
| | - F Conte
- Istituto di Analisi dei Sistemi ed Informatica “A. Ruberti” – CNR, Via dei Taurini 19, Rome, Italy
| | - F Papa
- Istituto di Analisi dei Sistemi ed Informatica “A. Ruberti” – CNR, Via dei Taurini 19, Rome, Italy
| | - C Sinisgalli
- Istituto di Analisi dei Sistemi ed Informatica “A. Ruberti” – CNR, Via dei Taurini 19, Rome, Italy
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Mathematical Modeling of the Effects of Tumor Heterogeneity on the Efficiency of Radiation Treatment Schedule. Bull Math Biol 2017; 80:283-293. [PMID: 29218592 DOI: 10.1007/s11538-017-0371-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2016] [Accepted: 11/22/2017] [Indexed: 01/08/2023]
Abstract
Radiotherapy uses high doses of energy to eradicate cancer cells and control tumors. Various treatment schedules have been developed and tried in clinical trials, yet significant obstacles remain to improving the radiotherapy fractionation. Genetic and non-genetic cellular diversity within tumors can lead to different radiosensitivity among cancer cells that can affect radiation treatment outcome. We propose a minimal mathematical model to study the effect of tumor heterogeneity and repair in different radiation treatment schedules. We perform stochastic and deterministic simulations to estimate model parameters using available experimental data. Our results suggest that gross tumor volume reduction is insufficient to control the disease if a fraction of radioresistant cells survives therapy. If cure cannot be achieved, protocols should balance volume reduction with minimal selection for radioresistant cells. We show that the most efficient treatment schedule is dependent on biology and model parameter values and, therefore, emphasize the need for careful tumor-specific model calibration before clinically actionable conclusions can be drawn.
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Protopapa M, Zygogianni A, Stamatakos GS, Antypas C, Armpilia C, Uzunoglu NK, Kouloulias V. Clinical implications of in silico mathematical modeling for glioblastoma: a critical review. J Neurooncol 2017; 136:1-11. [PMID: 29081039 DOI: 10.1007/s11060-017-2650-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2017] [Accepted: 10/22/2017] [Indexed: 01/22/2023]
Abstract
Glioblastoma remains a clinical challenge in spite of years of extensive research. Novel approaches are needed in order to integrate the existing knowledge. This is the potential role of mathematical oncology. This paper reviews mathematical models on glioblastoma from the clinical doctor's point of view, with focus on 3D modeling approaches of radiation response of in vivo glioblastomas based on contemporary imaging techniques. As these models aim to provide a clinically useful tool in the era of personalized medicine, the integration of the latest advances in molecular and imaging science and in clinical practice by the in silico models is crucial for their clinical relevance. Our aim is to indicate areas of GBM research that have not yet been addressed by in silico models and to point out evidence that has come up from in silico experiments, which may be worth considering in the clinic. This review examines how close these models have come in predicting the outcome of treatment protocols and in shaping the future of radiotherapy treatments.
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Affiliation(s)
- Maria Protopapa
- Radiation Oncology Unit, 1st Department of Radiology, Aretaieio University Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Anna Zygogianni
- Radiation Oncology Unit, 1st Department of Radiology, Aretaieio University Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Georgios S Stamatakos
- Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
| | - Christos Antypas
- Radiation Oncology Unit, 1st Department of Radiology, Aretaieio University Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Christina Armpilia
- Radiation Oncology Unit, 1st Department of Radiology, Aretaieio University Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Nikolaos K Uzunoglu
- Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
| | - Vassilis Kouloulias
- Radiation Oncology Unit, 2nd Department of Radiology, Attikon University General Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece. .,Radiotherapy Unit, 2nd Department of Radiology, ATTIKON University Hospital, National and Kapodistrian University of Athens, Medical School, Athens, Greece.
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Dionysiou DD, Stamatakos GS. Applying a 4D Multiscale In Vivo Tumor Growth Model to the Exploration of Radiotherapy Scheduling: The Effects of Weekend Treatment Gaps and P53 Gene Status on the Response of Fast Growing Solid Tumors. Cancer Inform 2017. [DOI: 10.1177/117693510600200001] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
The present paper aims at demonstrating clinically oriented applications of the multiscale four dimensional in vivo tumor growth simulation model previously developed by our research group. To this end the effect of weekend radiotherapy treatment gaps and p53 gene status on two virtual glioblastoma tumors differing only in p53 gene status is investigated in silico. Tumor response predictions concerning two rather extreme dose fractionation schedules (daily dose of 4.5 Gy administered in 3 equal fractions) namely HART (Hyperfractionated Accelerated Radiotherapy weekend less) 54 Gy and CHART (Continuous HART) 54 Gy are presented and compared. The model predictions suggest that, for the same p53 status, HART 54 Gy and CHART 54 Gy have almost the same long term effects on locoregional tumor control. However, no data have been located in the literature concerning a comparison of HART and CHART radiotherapy schedules for glioblastoma. As non small cell lung carcinoma (NSCLC) may also be a fast growing and radiosensitive tumor, a comparison of the model predictions with the outcome of clinical studies concerning the response of NSCLC to HART 54 Gy and CHART 54 Gy is made. The model predictions are in accordance with corresponding clinical observations, thus strengthening the potential of the model.
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Affiliation(s)
- Dimitra D. Dionysiou
- Laboratory of Microwaves and Fiber Optics, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, GR-157 80 Zografos, Greece
- Department of Mathematics, School of Applied Sciences, National Technical University of Athens, GR-157 80 Zografos, Greece
| | - Georgios S. Stamatakos
- Laboratory of Microwaves and Fiber Optics, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, GR-157 80 Zografos, Greece
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11
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Forouzannia F, Sivaloganathan S. Cancer Stem Cells, the Tipping Point: Minority Rules? CURRENT STEM CELL REPORTS 2017. [DOI: 10.1007/s40778-017-0095-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Stamatakos GS, Giatili SG. A Numerical Handling of the Boundary Conditions Imposed by the Skull on an Inhomogeneous Diffusion-Reaction Model of Glioblastoma Invasion Into the Brain: Clinical Validation Aspects. Cancer Inform 2017; 16:1176935116684824. [PMID: 28469383 PMCID: PMC5392020 DOI: 10.1177/1176935116684824] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2016] [Accepted: 11/24/2016] [Indexed: 12/22/2022] Open
Abstract
A novel explicit triscale reaction-diffusion numerical model of glioblastoma multiforme tumor growth is presented. The model incorporates the handling of Neumann boundary conditions imposed by the cranium and takes into account both the inhomogeneous nature of human brain and the complexity of the skull geometry. The finite-difference time-domain method is adopted. To demonstrate the workflow of a possible clinical validation procedure, a clinical case/scenario is addressed. A good agreement of the in silico calculated value of the doubling time (ie, the time for tumor volume to double) with the value of the same quantity based on tomographic imaging data has been observed. A theoretical exploration suggests that a rough but still quite informative value of the doubling time may be calculated based on a homogeneous brain model. The model could serve as the main component of a continuous mathematics-based glioblastoma oncosimulator aiming at supporting the clinician in the optimal patient-individualized design of treatment using the patient's multiscale data and experimenting in silico (ie, on the computer).
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Affiliation(s)
- Georgios S Stamatakos
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, National Technical University of Athens, Zografou, Greece
| | - Stavroula G Giatili
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, National Technical University of Athens, Zografou, Greece
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Belfatto A, Vidal Urbinati AM, Ciardo D, Franchi D, Cattani F, Lazzari R, Jereczek-Fossa BA, Orecchia R, Baroni G, Cerveri P. Comparison between model-predicted tumor oxygenation dynamics and vascular-/flow-related Doppler indices. Med Phys 2017; 44:2011-2019. [PMID: 28273332 DOI: 10.1002/mp.12192] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Revised: 01/25/2017] [Accepted: 02/24/2017] [Indexed: 11/06/2022] Open
Abstract
PURPOSE Mathematical modeling is a powerful and flexible method to investigate complex phenomena. It discloses the possibility of reproducing expensive as well as invasive experiments in a safe environment with limited costs. This makes it suitable to mimic tumor evolution and response to radiotherapy although the reliability of the results remains an issue. Complexity reduction is therefore a critical aspect in order to be able to compare model outcomes to clinical data. Among the factors affecting treatment efficacy, tumor oxygenation is known to play a key role in radiotherapy response. In this work, we aim at relating the oxygenation dynamics, predicted by a macroscale model trained on tumor volumetric data of uterine cervical cancer patients, to vascularization and blood flux indices assessed on Ultrasound Doppler images. METHODS We propose a macroscale model of tumor evolution based on three dynamics, namely active portion, necrotic portion, and oxygenation. The model parameters were assessed on the volume size of seven cervical cancer patients administered with 28 fractions of intensity modulated radiation therapy (IMRT) (1.8 Gy/fraction). For each patient, five Doppler ultrasound tests were acquired before, during, and after the treatment. The lesion was manually contoured by an expert physician using 4D View® (General Electric Company - Fairfield, Connecticut, United States), which automatically provided the overall tumor volume size along with three vascularization and/or blood flow indices. Volume data only were fed to the model for training purpose, while the predicted oxygenation was compared a posteriori to the measured Doppler indices. RESULTS The model was able to fit the tumor volume evolution within 8% error (range: 3-8%). A strong correlation between the intrapatient longitudinal indices from Doppler measurements and oxygen predicted by the model (about 90% or above) was found in three cases. Two patients showed an average correlation value (50-70%) and the remaining two presented poor correlations. The latter patients were the ones featuring the smallest tumor reduction throughout the treatment, typical of hypoxic conditions. Moreover, the average oxygenation value predicted by the model was close to the average vascularization-flow index (average difference: 7%). CONCLUSIONS The results suggest that the modeled relation between tumor evolution and oxygen dynamics was reasonable enough to provide realistic oxygenation curves in five cases (correlation greater than 50%) out of seven. In case of nonresponsive tumors, the model failed in predicting the oxygenation trend while succeeded in reproducing the average oxygenation value according to the mean vascularization-flow index. Despite the need for deeper investigations, the outcomes of the present work support the hypothesis that a simple macroscale model of tumor response to radiotherapy is able to predict the tumor oxygenation. The possibility of an objective and quantitative validation on imaging data discloses the possibility to translate them as decision support tools in clinical practice and to move a step forward in the treatment personalization.
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Affiliation(s)
- Antonella Belfatto
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano University, Piazza Leonardo da Vinci, 32 - 20133, Milan, Italy
| | - Ailyn M Vidal Urbinati
- Preventive Gynecology Unit, Division of Gynecology, European Institute of Oncology, Via Giuseppe Ripamonti, 435 - 20141, Milan, Italy
| | - Delia Ciardo
- Department of Radiation Oncology, European Institute of Oncology, Via Giuseppe Ripamonti, 435 - 20141, Milan, Italy
| | - Dorella Franchi
- Preventive Gynecology Unit, Division of Gynecology, European Institute of Oncology, Via Giuseppe Ripamonti, 435 - 20141, Milan, Italy
| | - Federica Cattani
- Unit of Medical Physics, European Institute of Oncology, Via Giuseppe Ripamonti, 435 - 20141, Milan, Italy
| | - Roberta Lazzari
- Department of Radiation Oncology, European Institute of Oncology, Via Giuseppe Ripamonti, 435 - 20141, Milan, Italy
| | - Barbara A Jereczek-Fossa
- Department of Radiation Oncology, European Institute of Oncology, Via Giuseppe Ripamonti, 435 - 20141, Milan, Italy.,Department of Oncology and Hemato-oncology, University of Milan, Via Festa del Perdono, 7 - 20122, Milan, Italy
| | - Roberto Orecchia
- Department of Oncology and Hemato-oncology, University of Milan, Via Festa del Perdono, 7 - 20122, Milan, Italy.,Department of Medical Imaging and Radiation Sciences, European Institute of Oncology, Via Giuseppe Ripamonti, 435 - 20141, Milan, Italy
| | - Guido Baroni
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano University, Piazza Leonardo da Vinci, 32 - 20133, Milan, Italy
| | - Pietro Cerveri
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano University, Piazza Leonardo da Vinci, 32 - 20133, Milan, Italy
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Crispin-Ortuzar M, Jeong J, Fontanella AN, Deasy JO. A radiobiological model of radiotherapy response and its correlation with prognostic imaging variables. Phys Med Biol 2017; 62:2658-2674. [PMID: 28140359 DOI: 10.1088/1361-6560/aa5d42] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Radiobiological models of tumour control probability (TCP) can be personalized using imaging data. We propose an extension to a voxel-level radiobiological TCP model in order to describe patient-specific differences and intra-tumour heterogeneity. In the proposed model, tumour shrinkage is described by means of a novel kinetic Monte Carlo method for inter-voxel cell migration and tumour deformation. The model captures the spatiotemporal evolution of the tumour at the voxel level, and is designed to take imaging data as input. To test the performance of the model, three image-derived variables found to be predictive of outcome in the literature have been identified and calculated using the model's own parameters. Simulating multiple tumours with different initial conditions makes it possible to perform an in silico study of the correlation of these variables with the dose for 50% tumour control ([Formula: see text]) calculated by the model. We find that the three simulated variables correlate with the calculated [Formula: see text]. In addition, we find that different variables have different levels of sensitivity to the spatial distribution of hypoxia within the tumour, as well as to the dynamics of the migration mechanism. Finally, based on our results, we observe that an adequate combination of the variables may potentially result in higher predictive power.
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15
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Kolokotroni E, Dionysiou D, Veith C, Kim YJ, Sabczynski J, Franz A, Grgic A, Palm J, Bohle RM, Stamatakos G. In Silico Oncology: Quantification of the In Vivo Antitumor Efficacy of Cisplatin-Based Doublet Therapy in Non-Small Cell Lung Cancer (NSCLC) through a Multiscale Mechanistic Model. PLoS Comput Biol 2016; 12:e1005093. [PMID: 27657742 PMCID: PMC5033576 DOI: 10.1371/journal.pcbi.1005093] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2016] [Accepted: 08/01/2016] [Indexed: 11/30/2022] Open
Abstract
The 5-year survival of non-small cell lung cancer patients can be as low as 1% in advanced stages. For patients with resectable disease, the successful choice of preoperative chemotherapy is critical to eliminate micrometastasis and improve operability. In silico experimentations can suggest the optimal treatment protocol for each patient based on their own multiscale data. A determinant for reliable predictions is the a priori estimation of the drugs’ cytotoxic efficacy on cancer cells for a given treatment. In the present work a mechanistic model of cancer response to treatment is applied for the estimation of a plausible value range of the cell killing efficacy of various cisplatin-based doublet regimens. Among others, the model incorporates the cancer related mechanism of uncontrolled proliferation, population heterogeneity, hypoxia and treatment resistance. The methodology is based on the provision of tumor volumetric data at two time points, before and after or during treatment. It takes into account the effect of tumor microenvironment and cell repopulation on treatment outcome. A thorough sensitivity analysis based on one-factor-at-a-time and latin hypercube sampling/partial rank correlation coefficient approaches has established the volume growth rate and the growth fraction at diagnosis as key features for more accurate estimates. The methodology is applied on the retrospective data of thirteen patients with non-small cell lung cancer who received cisplatin in combination with gemcitabine, vinorelbine or docetaxel in the neoadjuvant context. The selection of model input values has been guided by a comprehensive literature survey on cancer-specific proliferation kinetics. The latin hypercube sampling has been recruited to compensate for patient-specific uncertainties. Concluding, the present work provides a quantitative framework for the estimation of the in-vivo cell-killing ability of various chemotherapies. Correlation studies of such estimates with the molecular profile of patients could serve as a basis for reliable personalized predictions. Less than 14% of medically treated patients with locally advanced and metastatic non-small cell lung cancer are expected to be alive 5 years after diagnosis. Standard therapeutic strategies include the administration of two drugs in combination, aiming at shrinking the tumor before surgery and improving overall survival. Knowing the sensitivity profile of each patient to different treatment strategies at diagnosis may help choose the most appropriate ones. We develop a methodology for the quantitative estimation of the cytotoxic efficacy of cisplatin-based doublets on cancer cells by applying a simulation model of cancer progression and response. The model incorporates the proliferation cycle, quiescence, differentiation and loss of tumor cells. We evaluate the effect of in vivo microenvironment of real tumors, as expressed by measurable tumor proliferation kinetics, such as how fast the tumor grows, the percentage of cells that are actively dividing, the resistance of stem cells, etc. on treatment outcome so as to derive more accurate estimates. A literature survey guides the selection of values. The methodology is applied to a real clinical dataset of patients. Correlation studies between the derived cytotoxicities and the patients’ molecular profile could lead to predictions of treatment response at the time of diagnosis.
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Affiliation(s)
- Eleni Kolokotroni
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
| | - Dimitra Dionysiou
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
| | - Christian Veith
- Institute of Pathology, University of Saarland, Homburg (Saar), Germany
| | - Yoo-Jin Kim
- Institute of Pathology, University of Saarland, Homburg (Saar), Germany
| | | | | | - Aleksandar Grgic
- Department of Nuclear Medicine, University of Saarland, Homburg (Saar), Germany
| | - Jan Palm
- Department of Radiotherapy and Radiation Oncology, University of Saarland, Homburg (Saar), Germany
| | - Rainer M. Bohle
- Institute of Pathology, University of Saarland, Homburg (Saar), Germany
| | - Georgios Stamatakos
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
- * E-mail:
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16
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Badri H, Leder K. Optimal treatment and stochastic modeling of heterogeneous tumors. Biol Direct 2016; 11:40. [PMID: 27549860 PMCID: PMC4994177 DOI: 10.1186/s13062-016-0142-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2016] [Accepted: 07/07/2016] [Indexed: 12/24/2022] Open
Abstract
UNLABELLED In this work we review past articles that have mathematically studied cancer heterogeneity and the impact of this heterogeneity on the structure of optimal therapy. We look at past works on modeling how heterogeneous tumors respond to radiotherapy, and take a particularly close look at how the optimal radiotherapy schedule is modified by the presence of heterogeneity. In addition, we review past works on the study of optimal chemotherapy when dealing with heterogeneous tumors. REVIEWERS This article was reviewed by Thomas McDonald, David Axelrod, and Leonid Hanin.
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Affiliation(s)
- Hamidreza Badri
- Department of Industrial and Systems Engineering, University of Minnesota, Minneapolis, MN 55455 USA
| | - Kevin Leder
- Department of Industrial and Systems Engineering, University of Minnesota, Minneapolis, MN 55455 USA
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17
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Hathout L, Ellingson B, Pope W. Modeling the efficacy of the extent of surgical resection in the setting of radiation therapy for glioblastoma. Cancer Sci 2016; 107:1110-6. [PMID: 27240229 PMCID: PMC4982585 DOI: 10.1111/cas.12979] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2016] [Revised: 05/24/2016] [Accepted: 05/27/2016] [Indexed: 01/22/2023] Open
Abstract
Standard therapy for glioblastoma (GBM) includes maximal surgical resection and radiation therapy. While it is established that radiation therapy provides the greatest survival benefit of standard treatment modalities, the impact of the extent of surgical resection (EOR) on patient outcome remains highly controversial. While some studies describe no correlation between EOR and patient survival even up to total resection, others propose either qualitative (partial versus subtotal versus complete resection) or quantitative EOR thresholds, below which there is no correlation with survival. This work uses a mathematical model in the form of a reaction–diffusion partial differential equation to simulate tumor growth and treatment with radiation therapy and surgical resection based on tumor‐specific rates of diffusion and proliferation. Simulation of 36 tumors across a wide spectrum of diffusion and proliferation rates suggests that while partial or subtotal resections generally do not provide a survival advantage, complete resection significantly improves patient outcomes. Furthermore, our model predicts a tumor‐specific quantitative threshold below which EOR has no effect on patient survival and demonstrates that this threshold increases with tumor aggressiveness, particularly with the rate of proliferation. Thus, this model may serve as an aid for determining both when surgical resection is indicated as well as the surgical margins necessary to provide clinically significant improvements in patient survival. In addition, by assigning relative benefits to radiation and surgical resection based on tumor invasiveness and proliferation, this model confirms that (with the exception of the least aggressive tumors) the survival benefit of radiation therapy exceeds that of surgical resection.
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Affiliation(s)
| | - Benjamin Ellingson
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA.,UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California, Los Angeles, California, USA.,Department of Biomedical Physics, David Geffen School of Medicine, University of California, Los Angeles, California, USA.,Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California Los Angeles, Los Angeles, California, USA
| | - Whitney Pope
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA
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18
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Yamamoto M, Murakami Y, Iwatsuki K, Kurimoto S, Hirata H. Feasibility of four-dimensional preoperative simulation for elbow debridement arthroplasty. BMC Musculoskelet Disord 2016; 17:144. [PMID: 27039373 PMCID: PMC4818897 DOI: 10.1186/s12891-016-0996-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2015] [Accepted: 03/24/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Recent advances in imaging modalities have enabled three-dimensional preoperative simulation. A four-dimensional preoperative simulation system would be useful for debridement arthroplasty of primary degenerative elbow osteoarthritis because it would be able to detect the impingement lesions. METHODS We developed a four-dimensional simulation system by adding the anatomical axis to the three-dimensional computed tomography scan data of the affected arm in one position. Eleven patients with primary degenerative elbow osteoarthritis were included. A "two rings" method was used to calculate the flexion-extension axis of the elbow by converting the surface of the trochlea and capitellum into two rings. A four-dimensional simulation movie was created and showed the optimal range of motion and the impingement area requiring excision. To evaluate the reliability of the flexion-extension axis, interobserver and intraobserver reliabilities regarding the assessment of bony overlap volumes were calculated twice for each patient by two authors. Patients were treated by open or arthroscopic debridement arthroplasties. Pre- and postoperative examinations included elbow range of motion measurement, and completion of the patient-rated questionnaire Hand20, Japanese Orthopaedic Association-Japan Elbow Society Elbow Function Score, and the Mayo Elbow Performance Score. RESULTS Measurement of the bony overlap volume showed an intraobserver intraclass correlation coefficient of 0.93 and 0.90, and an interobserver intraclass correlation coefficient of 0.94. The mean elbow flexion-extension arc significantly improved from 101° to 125°. The mean Hand20 score significantly improved from 52 to 22. The mean Japanese Orthopaedic Association-Japan Elbow Society Elbow Function Score significantly improved from 67 to 88. The mean Mayo Elbow Performance Score significantly improved from 71 to 91 at the final follow-up evaluation. CONCLUSION We showed that four-dimensional, preoperative simulation can be generated by adding the rotation axis to the one-position, three-dimensional computed tomography image of the affected arm. This method is feasible for elbow debridement arthroplasty.
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Affiliation(s)
- Michiro Yamamoto
- Department of Hand Surgery, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan.
| | - Yukimi Murakami
- Department of Hand Surgery, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan
| | - Katsuyuki Iwatsuki
- Department of Hand Surgery, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan
| | - Shigeru Kurimoto
- Department of Hand Surgery, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan
| | - Hitoshi Hirata
- Department of Hand Surgery, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan
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19
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Argyri KD, Dionysiou DD, Misichroni FD, Stamatakos GS. Numerical simulation of vascular tumour growth under antiangiogenic treatment: addressing the paradigm of single-agent bevacizumab therapy with the use of experimental data. Biol Direct 2016; 11:12. [PMID: 27005569 PMCID: PMC4804544 DOI: 10.1186/s13062-016-0114-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2016] [Accepted: 03/14/2016] [Indexed: 11/30/2022] Open
Abstract
Background Antiangiogenic agents have been recently added to the oncological armamentarium with bevacizumab probably being the most popular representative in current clinical practice. The elucidation of the mode of action of these agents is a prerequisite for personalized prediction of antiangiogenic treatment response and selection of patients who may benefit from this kind of therapy. To this end, having used as a basis a preexisting continuous vascular tumour growth model which addresses the targeted nature of antiangiogenic treatment, we present a paper characterized by the following three features. First, the integration of a two-compartmental bevacizumab specific pharmacokinetic module into the core of the aforementioned preexisting model. Second, its mathematical modification in order to reproduce the asymptotic behaviour of tumour volume in the theoretical case of a total destruction of tumour neovasculature. Third, the exploitation of a range of published animal datasets pertaining to antitumour efficacy of bevacizumab on various tumour types (breast, lung, head and neck, colon). Results Results for both the unperturbed growth and the treatment module reveal qualitative similarities with experimental observations establishing the biologically acceptable behaviour of the model. The dynamics of the untreated tumour has been studied via a parameter analysis, revealing the role of each relevant input parameter to tumour evolution. The combined effect of endogenous proangiogenic and antiangiogenic factors on the angiogenic potential of a tumour is also studied, in order to capture the dynamics of molecular competition between the two key-players of tumoural angiogenesis. The adopted methodology also allows accounting for the newly recognized direct antitumour effect of the specific agent. Conclusions Interesting observations have been made, suggesting a potential size-dependent tumour response to different treatment modalities and determining the relative timing of cytotoxic versus antiangiogenic agents administration. Insight into the comparative effectiveness of different antiangiogenic treatment strategies is revealed. The results of a series of in vivo experiments in mice bearing diverse types of tumours (breast, lung, head and neck, colon) and treated with bevacizumab are successfully reproduced, supporting thus the validity of the underlying model. Reviewers This article was reviewed by L. Hanin, T. Radivoyevitch and L. Edler.
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Affiliation(s)
- Katerina D Argyri
- In Silico Oncology and In Silico Medicine Group, Laboratory of Microwaves and Fiber Optics, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou, Zografos, GR 157 80, Athens, Greece
| | - Dimitra D Dionysiou
- In Silico Oncology and In Silico Medicine Group, Laboratory of Microwaves and Fiber Optics, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou, Zografos, GR 157 80, Athens, Greece
| | - Fay D Misichroni
- In Silico Oncology and In Silico Medicine Group, Laboratory of Microwaves and Fiber Optics, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou, Zografos, GR 157 80, Athens, Greece
| | - Georgios S Stamatakos
- In Silico Oncology and In Silico Medicine Group, Laboratory of Microwaves and Fiber Optics, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou, Zografos, GR 157 80, Athens, Greece.
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20
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Li XL, Oduola WO, Qian L, Dougherty ER. Integrating Multiscale Modeling with Drug Effects for Cancer Treatment. Cancer Inform 2016; 14:21-31. [PMID: 26792977 PMCID: PMC4712979 DOI: 10.4137/cin.s30797] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2015] [Revised: 11/08/2015] [Accepted: 11/15/2015] [Indexed: 12/12/2022] Open
Abstract
In this paper, we review multiscale modeling for cancer treatment with the incorporation of drug effects from an applied system's pharmacology perspective. Both the classical pharmacology and systems biology are inherently quantitative; however, systems biology focuses more on networks and multi factorial controls over biological processes rather than on drugs and targets in isolation, whereas systems pharmacology has a strong focus on studying drugs with regard to the pharmacokinetic (PK) and pharmacodynamic (PD) relations accompanying drug interactions with multiscale physiology as well as the prediction of dosage-exposure responses and economic potentials of drugs. Thus, it requires multiscale methods to address the need for integrating models from the molecular levels to the cellular, tissue, and organism levels. It is a common belief that tumorigenesis and tumor growth can be best understood and tackled by employing and integrating a multifaceted approach that includes in vivo and in vitro experiments, in silico models, multiscale tumor modeling, continuous/discrete modeling, agent-based modeling, and multiscale modeling with PK/PD drug effect inputs. We provide an example application of multiscale modeling employing stochastic hybrid system for a colon cancer cell line HCT-116 with the application of Lapatinib drug. It is observed that the simulation results are similar to those observed from the setup of the wet-lab experiments at the Translational Genomics Research Institute.
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Affiliation(s)
- Xiangfang L. Li
- Department of Electrical and Computer Engineering, Prairie View A&M University, Prairie View, TX, USA
| | - Wasiu O. Oduola
- Department of Electrical and Computer Engineering, Prairie View A&M University, Prairie View, TX, USA
| | - Lijun Qian
- Department of Electrical and Computer Engineering, Prairie View A&M University, Prairie View, TX, USA
| | - Edward R. Dougherty
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA
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21
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Kempf H, Bleicher M, Meyer-Hermann M. Spatio-Temporal Dynamics of Hypoxia during Radiotherapy. PLoS One 2015; 10:e0133357. [PMID: 26273841 PMCID: PMC4537194 DOI: 10.1371/journal.pone.0133357] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2015] [Accepted: 06/26/2015] [Indexed: 12/27/2022] Open
Abstract
Tumour hypoxia plays a pivotal role in cancer therapy for most therapeutic approaches from radiotherapy to immunotherapy. The detailed and accurate knowledge of the oxygen distribution in a tumour is necessary in order to determine the right treatment strategy. Still, due to the limited spatial and temporal resolution of imaging methods as well as lacking fundamental understanding of internal oxygenation dynamics in tumours, the precise oxygen distribution map is rarely available for treatment planing. We employ an agent-based in silico tumour spheroid model in order to study the complex, localized and fast oxygen dynamics in tumour micro-regions which are induced by radiotherapy. A lattice-free, 3D, agent-based approach for cell representation is coupled with a high-resolution diffusion solver that includes a tissue density-dependent diffusion coefficient. This allows us to assess the space- and time-resolved reoxygenation response of a small subvolume of tumour tissue in response to radiotherapy. In response to irradiation the tumour nodule exhibits characteristic reoxygenation and re-depletion dynamics which we resolve with high spatio-temporal resolution. The reoxygenation follows specific timings, which should be respected in treatment in order to maximise the use of the oxygen enhancement effects. Oxygen dynamics within the tumour create windows of opportunity for the use of adjuvant chemotherapeutica and hypoxia-activated drugs. Overall, we show that by using modelling it is possible to follow the oxygenation dynamics beyond common resolution limits and predict beneficial strategies for therapy and in vitro verification. Models of cell cycle and oxygen dynamics in tumours should in the future be combined with imaging techniques, to allow for a systematic experimental study of possible improved schedules and to ultimately extend the reach of oxygenation monitoring available in clinical treatment.
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Affiliation(s)
- Harald Kempf
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, Braunschweig, Germany
- Frankfurt Institute for Advanced Studies, Frankfurt, Germany
| | - Marcus Bleicher
- Frankfurt Institute for Advanced Studies, Frankfurt, Germany
| | - Michael Meyer-Hermann
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, Braunschweig, Germany
- Institute for Biochemistry, Biotechnology and Bioinformatics, Technische Universität Braunschweig, Braunschweig, Germany
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22
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Espinoza I, Peschke P, Karger CP. A voxel-based multiscale model to simulate the radiation response of hypoxic tumors. Med Phys 2015; 42:90-102. [PMID: 25563250 DOI: 10.1118/1.4903298] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE In radiotherapy, it is important to predict the response of tumors to irradiation prior to the treatment. This is especially important for hypoxic tumors, which are known to be highly radioresistant. Mathematical modeling based on the dose distribution, biological parameters, and medical images may help to improve this prediction and to optimize the treatment plan. METHODS A voxel-based multiscale tumor response model for simulating the radiation response of hypoxic tumors was developed. It considers viable and dead tumor cells, capillary and normal cells, as well as the most relevant biological processes such as (i) proliferation of tumor cells, (ii) hypoxia-induced angiogenesis, (iii) spatial exchange of cells leading to tumor growth, (iv) oxygen-dependent cell survival after irradiation, (v) resorption of dead cells, and (vi) spatial exchange of cells leading to tumor shrinkage. Oxygenation is described on a microscopic scale using a previously published tumor oxygenation model, which calculates the oxygen distribution for each voxel using the vascular fraction as the most important input parameter. To demonstrate the capabilities of the model, the dependence of the oxygen distribution on tumor growth and radiation-induced shrinkage is investigated. In addition, the impact of three different reoxygenation processes is compared and tumor control probability (TCP) curves for a squamous cells carcinoma of the head and neck (HNSSC) are simulated under normoxic and hypoxic conditions. RESULTS The model describes the spatiotemporal behavior of the tumor on three different scales: (i) on the macroscopic scale, it describes tumor growth and shrinkage during radiation treatment, (ii) on a mesoscopic scale, it provides the cell density and vascular fraction for each voxel, and (iii) on the microscopic scale, the oxygen distribution may be obtained in terms of oxygen histograms. With increasing tumor size, the simulated tumors develop a hypoxic core. Within the model, tumor shrinkage was found to be significantly more important for reoxygenation than angiogenesis or decreased oxygen consumption due to an increased fraction of dead cells. In the studied HNSSC-case, the TCD50 values (dose at 50% TCP) decreased from 71.0 Gy under hypoxic to 53.6 Gy under the oxic condition. CONCLUSIONS The results obtained with the developed multiscale model are in accordance with expectations based on radiobiological principles and clinical experience. As the model is voxel-based, radiological imaging methods may help to provide the required 3D-characterization of the tumor prior to irradiation. For clinical application, the model has to be further validated with experimental and clinical data. If this is achieved, the model may be used to optimize fractionation schedules and dose distributions for the treatment of hypoxic tumors.
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Affiliation(s)
- I Espinoza
- Institute of Physics, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile and Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - P Peschke
- Clinical Cooperation Unit Molecular Radiooncology, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - C P Karger
- Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
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23
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Stamatakos GS, Kolokotroni E, Dionysiou D, Veith C, Kim YJ, Franz A, Marias K, Sabczynski J, Bohle R, Graf N. In silico oncology: exploiting clinical studies to clinically adapt and validate multiscale oncosimulators. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2013:5545-9. [PMID: 24110993 DOI: 10.1109/embc.2013.6610806] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
This paper presents a brief outline of the notion and the system of oncosimulator in conjunction with a high level description of the basics of its core multiscale model simulating clinical tumor response to treatment. The exemplary case of lung cancer preoperatively treated with a combination of chemotherapeutic agents is considered. The core oncosimulator model is based on a primarily top-down, discrete entity - discrete event multiscale simulation approach. The critical process of clinical adaptation of the model by exploiting sets of multiscale data originating from clinical studies/trials is also outlined. Concrete clinical adaptation results are presented. The adaptation process also conveys important aspects of the planned clinical validation procedure since the same type of multiscale data - although not the same data itself- is to be used for clinical validation. By having exploited actual clinical data in conjunction with plausible literature-based values of certain model parameters, a realistic tumor dynamics behavior has been demonstrated. The latter supports the potential of the specific oncosimulator to serve as a personalized treatment optimizer following an eventually successful completion of the clinical adaptation and validation process.
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Badri H, Pitter K, Holland EC, Michor F, Leder K. Optimization of radiation dosing schedules for proneural glioblastoma. J Math Biol 2015; 72:1301-36. [PMID: 26094055 DOI: 10.1007/s00285-015-0908-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2014] [Revised: 06/04/2015] [Indexed: 12/19/2022]
Abstract
Glioblastomas are the most aggressive primary brain tumor. Despite treatment with surgery, radiation and chemotherapy, these tumors remain uncurable and few significant increases in survival have been observed over the last half-century. We recently employed a combined theoretical and experimental approach to predict the effectiveness of radiation administration schedules, identifying two schedules that led to superior survival in a mouse model of the disease (Leder et al., Cell 156(3):603-616, 2014). Here we extended this approach to consider fractionated schedules to best minimize toxicity arising in early- and late-responding tissues. To this end, we decomposed the problem into two separate solvable optimization tasks: (i) optimization of the amount of radiation per dose, and (ii) optimization of the amount of time that passes between radiation doses. To ensure clinical applicability, we then considered the impact of clinical operating hours by incorporating time constraints consistent with operational schedules of the radiology clinic. We found that there was no significant loss incurred by restricting dosage to an 8:00 a.m. to 5:00 p.m. window. Our flexible approach is also applicable to other tumor types treated with radiotherapy.
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Affiliation(s)
- H Badri
- Department of Industrial and Systems Engineering, University of Minnesota, Minneapolis, MN, 55455, USA.
| | - K Pitter
- Weill Cornell Medical College, New York, NY, 10065, USA.
| | - E C Holland
- Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA, 98195, USA.
| | - F Michor
- Department of Computational Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA. .,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
| | - K Leder
- Department of Industrial and Systems Engineering, University of Minnesota, Minneapolis, MN, 55455, USA.
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Belfatto A, Riboldi M, Ciardo D, Cattani F, Cecconi A, Lazzari R, Jereczek-Fossa BA, Orecchia R, Baroni G, Cerveri P. Kinetic Models for Predicting Cervical Cancer Response to Radiation Therapy on Individual Basis Using Tumor Regression Measured In Vivo With Volumetric Imaging. Technol Cancer Res Treat 2015; 15:146-58. [PMID: 25759423 DOI: 10.1177/1533034615573796] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2014] [Accepted: 01/27/2015] [Indexed: 11/15/2022] Open
Abstract
This article describes a macroscopic mathematical modeling approach to capture the interplay between solid tumor evolution and cell damage during radiotherapy. Volume regression profiles of 15 patients with uterine cervical cancer were reconstructed from serial cone-beam computed tomography data sets, acquired for image-guided radiotherapy, and used for model parameter learning by means of a genetic-based optimization. Patients, diagnosed with either squamous cell carcinoma or adenocarcinoma, underwent different treatment modalities (image-guided radiotherapy and image-guided chemo-radiotherapy). The mean volume at the beginning of radiotherapy and the end of radiotherapy was on average 23.7 cm(3) (range: 12.7-44.4 cm(3)) and 8.6 cm(3) (range: 3.6-17.1 cm(3)), respectively. Two different tumor dynamics were taken into account in the model: the viable (active) and the necrotic cancer cells. However, according to the results of a preliminary volume regression analysis, we assumed a short dead cell resolving time and the model was simplified to the active tumor volume. Model learning was performed both on the complete patient cohort (cohort-based model learning) and on each single patient (patient-specific model learning). The fitting results (mean error: ∼ 16% and ∼ 6% for the cohort-based model and patient-specific model, respectively) highlighted the model ability to quantitatively reproduce tumor regression. Volume prediction errors of about 18% on average were obtained using cohort-based model computed on all but 1 patient at a time (leave-one-out technique). Finally, a sensitivity analysis was performed and the data uncertainty effects evaluated by simulating an average volume perturbation of about 1.5 cm(3) obtaining an error increase within 0.2%. In conclusion, we showed that simple time-continuous models can represent tumor regression curves both on a patient cohort and patient-specific basis; this discloses the opportunity in the future to exploit such models to predict how changes in the treatment schedule (number of fractions, doses, intervals among fractions) might affect the tumor regression on an individual basis.
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Affiliation(s)
- Antonella Belfatto
- Department of Electronics, Information and Bioengineering, Politecnico di Milano University, Milan, Italy
| | - Marco Riboldi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano University, Milan, Italy Bioengineering Unit, Centro Nazionale di Adroterapia Oncologica, Pave, Italy
| | - Delia Ciardo
- Division of Radiotherapy, European Institute of Oncology, Milan, Italy
| | - Federica Cattani
- Division of Radiotherapy, European Institute of Oncology, Milan, Italy
| | - Agnese Cecconi
- Division of Radiotherapy, European Institute of Oncology, Milan, Italy
| | - Roberta Lazzari
- Division of Radiotherapy, European Institute of Oncology, Milan, Italy
| | - Barbara Alicja Jereczek-Fossa
- Division of Radiotherapy, European Institute of Oncology, Milan, Italy Department of Health Sciences, University of Milan, Milan, Italy
| | - Roberto Orecchia
- Bioengineering Unit, Centro Nazionale di Adroterapia Oncologica, Pave, Italy Division of Radiotherapy, European Institute of Oncology, Milan, Italy Department of Health Sciences, University of Milan, Milan, Italy
| | - Guido Baroni
- Department of Electronics, Information and Bioengineering, Politecnico di Milano University, Milan, Italy Bioengineering Unit, Centro Nazionale di Adroterapia Oncologica, Pave, Italy
| | - Pietro Cerveri
- Department of Electronics, Information and Bioengineering, Politecnico di Milano University, Milan, Italy Bioengineering Unit, Centro Nazionale di Adroterapia Oncologica, Pave, Italy
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Optimal weekly scheduling in fractionated radiotherapy: effect of an upper bound on the dose fraction size. J Math Biol 2014; 71:361-98. [PMID: 25168624 DOI: 10.1007/s00285-014-0805-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2013] [Revised: 06/09/2014] [Indexed: 10/24/2022]
Abstract
This work concerns the optimization of the dose fractionation for cancer radiotherapy schedules of the kind one fraction/day, five fractions/week, assuming a fixed overall treatment time. Constraints are set to limit the radiation damages to surrounding normal tissues, as well as the daily fraction size. The response to radiation of tumour and normal tissues is represented by the classical LQ model, including the exponential repopulation term. We provide a framework to analytically determine the optimal weekly scheme of radiation doses as a function of the tumour type, the fraction upper bound and the normal tissue parameters. For a comparison with the literature, we present some numerical examples of optimal treatment schedules for specific tumour types.
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Identification of crucial parameters in a mathematical multiscale model of glioblastoma growth. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2014; 2014:437094. [PMID: 24899919 PMCID: PMC4034489 DOI: 10.1155/2014/437094] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/16/2014] [Accepted: 03/22/2014] [Indexed: 02/08/2023]
Abstract
Glioblastomas are highly malignant brain tumours. Mathematical models and their analysis
provide a tool to support the understanding of the development of these tumours as well as
the design of more effective treatment strategies. We have previously developed a multiscale
model of glioblastoma progression that covers processes on the cellular and molecular scale.
Here, we present a novel nutrient-dependent multiscale sensitivity analysis of this model
that helps to identify those reaction parameters of the molecular interaction network that influence
the tumour progression on the cellular scale the most. In particular, those parameters
are identified that essentially determine tumour expansion and could be therefore used as potential
therapy targets. As indicators for the success of a potential therapy target, a deceleration
of the tumour expansion and a reduction of the tumour volume are employed.
From the results, it can be concluded that no single parameter variation results in a less
aggressive tumour. However, it can be shown that a few combined perturbations of two
systematically selected parameters cause a slow-down of the tumour expansion velocity accompanied
with a decrease of the tumour volume. Those parameters are primarily linked to
the reactions that involve the microRNA-451 and the thereof regulated protein MO25.
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Stamatakos G, Dionysiou D, Lunzer A, Belleman R, Kolokotroni E, Georgiadi E, Erdt M, Pukacki J, Rueping S, Giatili S, d'Onofrio A, Sfakianakis S, Marias K, Desmedt C, Tsiknakis M, Graf N. The Technologically Integrated Oncosimulator: Combining Multiscale Cancer Modeling With Information Technology in the In Silico Oncology Context. IEEE J Biomed Health Inform 2014; 18:840-54. [DOI: 10.1109/jbhi.2013.2284276] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Georgios Stamatakos
- Institute of Communication and Computer Systems, National Technical University of Athens, In Silico Oncology Group, 9 Iroon Polytechniou, Zografos, Greece
| | - Dimitra Dionysiou
- Oncology Group, Institute of Communication and Computer Systems, National Technical University of Athens, GR , Greece
| | | | | | - Eleni Kolokotroni
- Oncology Group, Institute of Communication and Computer Systems, National Technical University of Athens, GR , Greece
| | - Eleni Georgiadi
- Oncology Group, Institute of Communication and Computer Systems, National Technical University of Athens, GR , Greece
| | | | - Juliusz Pukacki
- Poznan Supercomputing and Networking Center (PSNC), Poznan, Poland
| | - Stefan Rueping
- Fraunhofer IAIS, Schloss Birlinghoven, St. Augustin, Germany
| | - Stavroula Giatili
- Oncology Group, Institute of Communication and Computer Systems, National Technical University of Athens, GR , Greece
| | | | | | - Kostas Marias
- Foundation for Research and Technology Hellas, Heraklion, Greece
| | | | - Manolis Tsiknakis
- Department of Informatics Engineering, TEI Crete and the Computational Medicine Laboratory, Institute of Computer Science, FORTH , Heraklion, Greece
| | - Norbert Graf
- University Hospital of the Saarland, Pediatric Haematology and Oncology, Homburg, Germany
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29
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Mathematical modeling of PDGF-driven glioblastoma reveals optimized radiation dosing schedules. Cell 2014; 156:603-616. [PMID: 24485463 DOI: 10.1016/j.cell.2013.12.029] [Citation(s) in RCA: 181] [Impact Index Per Article: 18.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2012] [Revised: 09/18/2013] [Accepted: 12/24/2013] [Indexed: 12/19/2022]
Abstract
Glioblastomas (GBMs) are the most common and malignant primary brain tumors and are aggressively treated with surgery, chemotherapy, and radiotherapy. Despite this treatment, recurrence is inevitable and survival has improved minimally over the last 50 years. Recent studies have suggested that GBMs exhibit both heterogeneity and instability of differentiation states and varying sensitivities of these states to radiation. Here, we employed an iterative combined theoretical and experimental strategy that takes into account tumor cellular heterogeneity and dynamically acquired radioresistance to predict the effectiveness of different radiation schedules. Using this model, we identified two delivery schedules predicted to significantly improve efficacy by taking advantage of the dynamic instability of radioresistance. These schedules led to superior survival in mice. Our interdisciplinary approach may also be applicable to other human cancer types treated with radiotherapy and, hence, may lay the foundation for significantly increasing the effectiveness of a mainstay of oncologic therapy. PAPERCLIP:
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A mathematical model of tumor volume changes during radiotherapy. ScientificWorldJournal 2013; 2013:181070. [PMID: 24222726 PMCID: PMC3814055 DOI: 10.1155/2013/181070] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2013] [Accepted: 09/03/2013] [Indexed: 11/18/2022] Open
Abstract
PURPOSE To develop a clinically viable mathematical model that quantitatively predicts tumor volume change during radiotherapy in order to provide treatment response assessment for prognosis, treatment plan optimization, and adaptation. METHOD AND MATERIALS The correction factors containing hypoxia, DNA single strand breaks, potentially lethal damage, and other factors were used to develop an improved cell survival model based on the popular linear-quadratic model of cell survival in radiotherapy. The four-level cell population model proposed by Chvetsov et al. was further simplified by removing the initial hypoxic fraction and reoxygenation parameter, which are hard to obtain in routine clinics, such that an easy-to-use model can be developed for clinical applications. The new model was validated with data of nine lung and cervical cancer patients. RESULTS Out of the nine cases, the new model can predict tumor volume change in six cases with a correlation index R (2) greater than 0.9 and the rest of three with R (2) greater than 0.85. CONCLUSION Based on a four-level cell population model, a more practical and simplified cell survival curve was proposed to model the tumor volume changes during radiotherapy. Validation study with patient data demonstrated feasibility and clinical usefulness of the new model in predicting tumor volume change in radiotherapy.
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Sfakianakis S, Sakkalis V, Marias K, Stamatakos G, McKeever S, Deisboeck TS, Graf N. An architecture for integrating cancer model repositories. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2012:6628-31. [PMID: 23367449 DOI: 10.1109/embc.2012.6347514] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The TUMOR project aims at developing a European clinically oriented semantic-layered cancer digital model repository from existing EU projects that will be interoperable with the US grid-enabled semantic-layered digital model repository platform at CViT.org (Center for the Development of a Virtual Tumor, Massachusetts General Hospital (MGH), Boston, USA) which is NIH/NCI-caGRID compatible. In this paper we describe the modular and federated architecture of TUMOR that effectively addresses model integration, interoperability, and security related issues.
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Affiliation(s)
- Stelios Sfakianakis
- Institute of Computer Science at FORTH, Vassilika Vouton, GR-70013 Heraklion, Crete, Greece.
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Jeong J, Shoghi KI, Deasy JO. Modelling the interplay between hypoxia and proliferation in radiotherapy tumour response. Phys Med Biol 2013; 58:4897-919. [PMID: 23787766 DOI: 10.1088/0031-9155/58/14/4897] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
A tumour control probability computational model for fractionated radiotherapy was developed, with the goal of incorporating the fundamental interplay between hypoxia and proliferation, including reoxygenation over a course of radiotherapy. The fundamental idea is that the local delivery of oxygen and glucose limits the amount of proliferation and metabolically-supported cell survival a tumour sub-volume can support. The model has three compartments: a proliferating compartment of cells receiving oxygen and glucose; an intermediate, metabolically-active compartment receiving glucose; and a highly hypoxic compartment of starving cells. Following the post-mitotic cell death of proliferating cells, intermediate cells move into the proliferative compartment and hypoxic cells move into the intermediate compartment. A key advantage of the proposed model is that the initial compartmental cell distribution is uniquely determined from the assumed local growth fraction (GF) and volume doubling time (TD) values. Varying initial cell state distributions, based on the local (voxel) GF and TD, were simulated. Tumour response was simulated for head and neck squamous cell carcinoma using relevant parameter values based on published sources. The tumour dose required to achieve a 50% local control rate (TCD50) was found for various GFs and TD's, and the effect of fraction size on TCD50 was also evaluated. Due to the advantage of reoxygenation over a course of radiotherapy, conventional fraction sizes (2-2.4 Gy fx(-1)) were predicted to result in smaller TCD50's than larger fraction sizes (4-5 Gy fx(-1)) for a 10 cc tumour with GFs of around 0.15. The time to eliminate hypoxic cells (the reoxygenation time) was estimated for a given GF and decreased as GF increased. The extra dose required to overcome accelerated stem cell accumulation in longer treatment schedules was estimated to be 0.68 Gy/day (in EQD26.6), similar to published values derived from clinical data. The model predicts, for a 2 Gy/weekday fractionation, that increased initial proliferation (high GF) should, surprisingly, lead to moderately higher local control values. Tumour hypoxia is predicted to increase the required dose for local control by approximately 30%. Predicted tumour regression patterns are consistent with clinical observations. This simple yet flexible model shows how the local competition for chemical resources might impact local control rates under varying fractionation conditions.
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Affiliation(s)
- J Jeong
- Memorial Sloan-Kettering Cancer Center, New York, NY, USA
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Johnson D, McKeever S, Stamatakos G, Dionysiou D, Graf N, Sakkalis V, Marias K, Wang Z, Deisboeck TS. Dealing with diversity in computational cancer modeling. Cancer Inform 2013; 12:115-24. [PMID: 23700360 PMCID: PMC3653811 DOI: 10.4137/cin.s11583] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
This paper discusses the need for interconnecting computational cancer models from different sources and scales within clinically relevant scenarios to increase the accuracy of the models and speed up their clinical adaptation, validation, and eventual translation. We briefly review current interoperability efforts drawing upon our experiences with the development of in silico models for predictive oncology within a number of European Commission Virtual Physiological Human initiative projects on cancer. A clinically relevant scenario, addressing brain tumor modeling that illustrates the need for coupling models from different sources and levels of complexity, is described. General approaches to enabling interoperability using XML-based markup languages for biological modeling are reviewed, concluding with a discussion on efforts towards developing cancer-specific XML markup to couple multiple component models for predictive in silico oncology.
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Affiliation(s)
- David Johnson
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Steve McKeever
- Department of Informatics and Media, Uppsala University, Uppsala, Sweden
| | - Georgios Stamatakos
- Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
| | - Dimitra Dionysiou
- Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
| | - Norbert Graf
- Department of Paediatric Haematology and Oncology, Saarland University Hospital, Homburg, Germany
| | - Vangelis Sakkalis
- Institute of Computer Science at the Foundation for Research and Technology—Hellas, Heraklion, Crete, Greece
| | - Konstantinos Marias
- Institute of Computer Science at the Foundation for Research and Technology—Hellas, Heraklion, Crete, Greece
| | - Zhihui Wang
- Department of Pathology, University of New Mexico, Albuquerque, NM, USA
| | - Thomas S. Deisboeck
- Harvard-MIT (HST), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
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Chvetsov AV. Tumor response parameters for head and neck cancer derived from tumor-volume variation during radiation therapy. Med Phys 2013; 40:034101. [DOI: 10.1118/1.4789632] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
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In silico modelling of tumour margin diffusion and infiltration: review of current status. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2012; 2012:672895. [PMID: 22919432 PMCID: PMC3418724 DOI: 10.1155/2012/672895] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/13/2012] [Accepted: 04/11/2012] [Indexed: 11/17/2022]
Abstract
As a result of advanced treatment techniques, requiring precise target definitions, a need for more accurate delineation of the Clinical Target Volume (CTV) has arisen. Mathematical modelling is found to be a powerful tool to provide fairly accurate predictions for the Microscopic Extension (ME) of a tumour to be incorporated in a CTV. In general terms, biomathematical models based on a sequence of observations or development of a hypothesis assume some links between biological mechanisms involved in cancer development and progression to provide quantitative or qualitative measures of tumour behaviour as well as tumour response to treatment. Generally, two approaches are taken: deterministic and stochastic modelling. In this paper, recent mathematical models, including deterministic and stochastic methods, are reviewed and critically compared. It is concluded that stochastic models are more promising to provide a realistic description of cancer tumour behaviour due to being intrinsically probabilistic as well as discrete, which enables incorporation of patient-specific biomedical data such as tumour heterogeneity and anatomical boundaries.
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The HYP-RT hypoxic tumour radiotherapy algorithm and accelerated repopulation dose per fraction study. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2012; 2012:363564. [PMID: 22778783 PMCID: PMC3385694 DOI: 10.1155/2012/363564] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2012] [Accepted: 04/11/2012] [Indexed: 11/23/2022]
Abstract
The HYP-RT model simulates hypoxic tumour growth for head and neck cancer as well as radiotherapy and the effects of accelerated repopulation and reoxygenation. This report outlines algorithm design, parameterisation and the impact of accelerated repopulation on the increase in dose/fraction needed to control the extra cell propagation during accelerated repopulation. Cell kill probabilities are based on Linear Quadratic theory, with oxygenation levels and proliferative capacity influencing cell death. Hypoxia is modelled through oxygen level allocation based on pO2 histograms. Accelerated repopulation is modelled by increasing the stem cell symmetrical division probability, while the process of reoxygenation utilises randomised pO2 increments to the cell population after each treatment fraction. Propagation of 108 tumour cells requires 5–30 minutes. Controlling the extra cell growth induced by accelerated repopulation requires a dose/fraction increase of 0.5–1.0 Gy, in agreement with published reports. The average reoxygenation pO2 increment of 3 mmHg per fraction results in full tumour reoxygenation after shrinkage to approximately 1 mm. HYP-RT is a computationally efficient model simulating tumour growth and radiotherapy, incorporating accelerated repopulation and reoxygenation. It may be used to explore cell kill outcomes during radiotherapy while varying key radiobiological and tumour specific parameters, such as the degree of hypoxia.
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Optimal solution for a cancer radiotherapy problem. J Math Biol 2012; 66:311-49. [DOI: 10.1007/s00285-012-0512-2] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2011] [Revised: 11/08/2011] [Indexed: 11/25/2022]
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Roniotis A, Sakkalis V, Karatzanis I, Zervakis ME, Marias K. In-depth analysis and evaluation of diffusive glioma models. ACTA ACUST UNITED AC 2012; 16:299-307. [PMID: 22287245 DOI: 10.1109/titb.2012.2185704] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Glioma is one of the most aggressive types of brain tumor. Several mathematical models have been developed during the past two decades, toward simulating the mechanisms that govern the development of glioma. The most common models use the diffusion-reaction equation (DRE) for simulating the spatiotemporal variation of tumor cell concentration. Nevertheless, despite the applications presented, there has been little work on studying the details of the mathematical solution and implementation of the 3-D diffusion model and presenting a qualitative analysis of the algorithmic results. This paper presents a complete mathematical framework on the solution of the DRE using different numerical schemes. This framework takes into account all characteristics of the latest models, such as brain tissue heterogeneity, anisotropic tumor cell migration, chemotherapy, and resection modeling. The different numerical schemes presented have been evaluated based upon the degree to which the DRE exact solution is approximated. Experiments have been conducted both on real datasets and a test case for which there is a known algebraic expression of the solution. Thus, it is possible to calculate the accuracy of the different models.
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Affiliation(s)
- Alexandros Roniotis
- Institute of Computer Science, Foundation for Research and Technology, Heraklion, Greece.
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40
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Harriss-Phillips WM, Bezak E, Yeoh EK. Monte Carlo radiotherapy simulations of accelerated repopulation and reoxygenation for hypoxic head and neck cancer. Br J Radiol 2011; 84:903-18. [PMID: 21933980 DOI: 10.1259/bjr/25012212] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE A temporal Monte Carlo tumour growth and radiotherapy effect model (HYP-RT) simulating hypoxia in head and neck cancer has been developed and used to analyse parameters influencing cell kill during conventionally fractionated radiotherapy. The model was designed to simulate individual cell division up to 10(8) cells, while incorporating radiobiological effects, including accelerated repopulation and reoxygenation during treatment. METHOD Reoxygenation of hypoxic tumours has been modelled using randomised increments of oxygen to tumour cells after each treatment fraction. The process of accelerated repopulation has been modelled by increasing the symmetrical stem cell division probability. Both phenomena were onset immediately or after a number of weeks of simulated treatment. RESULTS The extra dose required to control (total cell kill) hypoxic vs oxic tumours was 15-25% (8-20 Gy for 5 × 2 Gy per week) depending on the timing of accelerated repopulation onset. Reoxygenation of hypoxic tumours resulted in resensitisation and reduction in total dose required by approximately 10%, depending on the time of onset. When modelled simultaneously, accelerated repopulation and reoxygenation affected cell kill in hypoxic tumours in a similar manner to when the phenomena were modelled individually; however, the degree was altered, with non-additive results. Simulation results were in good agreement with standard linear quadratic theory; however, differed for more complex comparisons where hypoxia, reoxygenation as well as accelerated repopulation effects were considered. CONCLUSION Simulations have quantitatively confirmed the need for patient individualisation in radiotherapy for hypoxic head and neck tumours, and have shown the benefits of modelling complex and dynamic processes using Monte Carlo methods.
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Affiliation(s)
- W M Harriss-Phillips
- Department of Medical Physics, Royal Adelaide Hospital Cancer Centre, Adelaide, SA, Australia.
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41
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Studying the growth kinetics of untreated clinical tumors by using an advanced discrete simulation model. ACTA ACUST UNITED AC 2011. [DOI: 10.1016/j.mcm.2011.05.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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Coupling biomechanics to a cellular level model: An approach to patient-specific image driven multi-scale and multi-physics tumor simulation. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2011; 107:193-9. [DOI: 10.1016/j.pbiomolbio.2011.06.007] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2011] [Accepted: 06/20/2011] [Indexed: 01/06/2023]
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Marias K, Dionysiou D, Sakkalis V, Graf N, Bohle RM, Coveney PV, Wan S, Folarin A, Büchler P, Reyes M, Clapworthy G, Liu E, Sabczynski J, Bily T, Roniotis A, Tsiknakis M, Kolokotroni E, Giatili S, Veith C, Messe E, Stenzhorn H, Kim YJ, Zasada S, Haidar AN, May C, Bauer S, Wang T, Zhao Y, Karasek M, Grewer R, Franz A, Stamatakos G. Clinically driven design of multi-scale cancer models: the ContraCancrum project paradigm. Interface Focus 2011; 1:450-61. [PMID: 22670213 PMCID: PMC3262443 DOI: 10.1098/rsfs.2010.0037] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2010] [Accepted: 03/07/2011] [Indexed: 12/13/2022] Open
Abstract
The challenge of modelling cancer presents a major opportunity to improve our ability to reduce mortality from malignant neoplasms, improve treatments and meet the demands associated with the individualization of care needs. This is the central motivation behind the ContraCancrum project. By developing integrated multi-scale cancer models, ContraCancrum is expected to contribute to the advancement of in silico oncology through the optimization of cancer treatment in the patient-individualized context by simulating the response to various therapeutic regimens. The aim of the present paper is to describe a novel paradigm for designing clinically driven multi-scale cancer modelling by bringing together basic science and information technology modules. In addition, the integration of the multi-scale tumour modelling components has led to novel concepts of personalized clinical decision support in the context of predictive oncology, as is also discussed in the paper. Since clinical adaptation is an inelastic prerequisite, a long-term clinical adaptation procedure of the models has been initiated for two tumour types, namely non-small cell lung cancer and glioblastoma multiforme; its current status is briefly summarized.
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Affiliation(s)
- K. Marias
- Institute of Computer Science at FORTH, Heraklion, Greece
| | - D. Dionysiou
- In Silico Oncology Group, Institute of Communications and Computer Systems, National Technical University of Athens, Athens, Greece
| | - V. Sakkalis
- Institute of Computer Science at FORTH, Heraklion, Greece
| | - N. Graf
- Departments of Paediatric Oncology and Haematology, Pathology, Genetics, Universität des Saarlandes, Homburg, Germany
| | - R. M. Bohle
- Departments of Paediatric Oncology and Haematology, Pathology, Genetics, Universität des Saarlandes, Homburg, Germany
| | - P. V. Coveney
- Centre for Computational Science, Department of Chemistry, University College London, 20 Gordon Street, London WC1H 0AJ, UK
| | - S. Wan
- Centre for Computational Science, Department of Chemistry, University College London, 20 Gordon Street, London WC1H 0AJ, UK
| | - A. Folarin
- Cancer Research Institute, University College London, 72 Huntley Street, London WC1E 6BT, UK
| | - P. Büchler
- Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland
| | - M. Reyes
- Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland
| | - G. Clapworthy
- Department of Computer Science & Technology, University of Bedfordshire, Luton, UK
| | - E. Liu
- Department of Computer Science & Technology, University of Bedfordshire, Luton, UK
| | - J. Sabczynski
- Philips Technologie GmbH, Innovative Technologies, Hamburg, Germany
| | - T. Bily
- Faculty of Mathematics and Physics, Department of Applied Mathematics, Charles University in Prague, Prague, Czech Republic
| | - A. Roniotis
- Institute of Computer Science at FORTH, Heraklion, Greece
| | - M. Tsiknakis
- Institute of Computer Science at FORTH, Heraklion, Greece
| | - E. Kolokotroni
- In Silico Oncology Group, Institute of Communications and Computer Systems, National Technical University of Athens, Athens, Greece
| | - S. Giatili
- In Silico Oncology Group, Institute of Communications and Computer Systems, National Technical University of Athens, Athens, Greece
| | - C. Veith
- Departments of Paediatric Oncology and Haematology, Pathology, Genetics, Universität des Saarlandes, Homburg, Germany
| | - E. Messe
- Departments of Paediatric Oncology and Haematology, Pathology, Genetics, Universität des Saarlandes, Homburg, Germany
| | - H. Stenzhorn
- Departments of Paediatric Oncology and Haematology, Pathology, Genetics, Universität des Saarlandes, Homburg, Germany
| | - Yoo-Jin Kim
- Departments of Paediatric Oncology and Haematology, Pathology, Genetics, Universität des Saarlandes, Homburg, Germany
| | - S. Zasada
- Centre for Computational Science, Department of Chemistry, University College London, 20 Gordon Street, London WC1H 0AJ, UK
| | - A. N. Haidar
- Centre for Computational Science, Department of Chemistry, University College London, 20 Gordon Street, London WC1H 0AJ, UK
| | - C. May
- Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland
| | - S. Bauer
- Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland
| | - T. Wang
- Department of Computer Science & Technology, University of Bedfordshire, Luton, UK
| | - Y. Zhao
- Department of Computer Science & Technology, University of Bedfordshire, Luton, UK
| | - M. Karasek
- Faculty of Mathematics and Physics, Department of Applied Mathematics, Charles University in Prague, Prague, Czech Republic
| | - R. Grewer
- Philips Technologie GmbH, Innovative Technologies, Hamburg, Germany
| | - A. Franz
- Philips Technologie GmbH, Innovative Technologies, Hamburg, Germany
| | - G. Stamatakos
- In Silico Oncology Group, Institute of Communications and Computer Systems, National Technical University of Athens, Athens, Greece
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Sakkalis V, Roniotis A, Farmaki C, Karatzanis I, Marias K. Evaluation framework for the multilevel macroscopic models of solid tumor growth in the glioma case. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2011; 2010:6809-12. [PMID: 21095846 DOI: 10.1109/iembs.2010.5625961] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
This paper investigates the applicability of multilevel macroscopic models for simulating solid tumor growth in the invasive glioblastoma multiforme (GBM) case. The continuum case approach tumor model based on the diffusion reaction equation is evaluated on a pre-segmented tomographic atlas where all tissue properties are known a priori. The atlas is further registered on a real clinical case where the tumor invasion status is gauged in two successive points in time. Based on the latter, the model attempts to fully replicate tumor growth taking into account tissue based properties as identified from the atlas template. The whole process is performed on a clinical platform specially designed to facilitate precise identification and delineation of tumors of large number of 3D tomographic datasets by an expert clinician. The promising results presented encourage the potential clinical applicability of the proposed model in the glioma case and identify crucial points and direction of further model refinement and research.
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Affiliation(s)
- Vangelis Sakkalis
- Institute of Computer Science at FORTH, Vassilika Vouton, GR-70013 Heraklion, Crete, Greece.
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Stamatakos GS, Georgiadi EC, Graf N, Kolokotroni EA, Dionysiou DD. Exploiting clinical trial data drastically narrows the window of possible solutions to the problem of clinical adaptation of a multiscale cancer model. PLoS One 2011; 6:e17594. [PMID: 21407827 PMCID: PMC3048172 DOI: 10.1371/journal.pone.0017594] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2010] [Accepted: 01/28/2011] [Indexed: 11/30/2022] Open
Abstract
The development of computational models for simulating tumor growth and response to treatment has gained significant momentum during the last few decades. At the dawn of the era of personalized medicine, providing insight into complex mechanisms involved in cancer and contributing to patient-specific therapy optimization constitute particularly inspiring pursuits. The in silico oncology community is facing the great challenge of effectively translating simulation models into clinical practice, which presupposes a thorough sensitivity analysis, adaptation and validation process based on real clinical data. In this paper, the behavior of a clinically-oriented, multiscale model of solid tumor response to chemotherapy is investigated, using the paradigm of nephroblastoma response to preoperative chemotherapy in the context of the SIOP/GPOH clinical trial. A sorting of the model's parameters according to the magnitude of their effect on the output has unveiled the relative importance of the corresponding biological mechanisms; major impact on the result of therapy is credited to the oxygenation and nutrient availability status of the tumor and the balance between the symmetric and asymmetric modes of stem cell division. The effect of a number of parameter combinations on the extent of chemotherapy-induced tumor shrinkage and on the tumor's growth rate are discussed. A real clinical case of nephroblastoma has served as a proof of principle study case, demonstrating the basics of an ongoing clinical adaptation and validation process. By using clinical data in conjunction with plausible values of model parameters, an excellent fit of the model to the available medical data of the selected nephroblastoma case has been achieved, in terms of both volume reduction and histological constitution of the tumor. In this context, the exploitation of multiscale clinical data drastically narrows the window of possible solutions to the clinical adaptation problem.
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Affiliation(s)
- Georgios S Stamatakos
- In Silico Oncology Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece.
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Modellbildung & Simulation (2). BIOMED ENG-BIOMED TE 2011. [DOI: 10.1515/bmt.2011.836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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47
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Roniotis A, Marias K, Sakkalis V, Stamatakos G, Zervakis M. Comparing finite elements and finite differences for developing diffusive models of glioma growth. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2010; 2010:6797-800. [PMID: 21095843 DOI: 10.1109/iembs.2010.5625973] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Glioma is the most aggressive type of brain tumor. Several mathematical models have been developed during the last two decades, towards simulating the mechanisms that govern the development of glioma. The most common models use the diffusion-reaction equation (DRE) for simulating the spatiotemporal variation of tumor cell concentration. The proposed diffusive models have mainly used finite differences (FDs) or finite elements (FEs) for the approximation of the solution of the partial differential DRE. This paper presents experimental results on the comparison of the FEs and FDs, especially focused on the glioma model case. It is studied how the different meshes of brain can affect computational consistency, simulation time and efficiency of the model. The experiments have been studied on a test case, for which there is a known algebraic expression of the solution. Thus, it is possible to calculate the error that the different models yield.
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Affiliation(s)
- Alexandros Roniotis
- Institute of Computer Science, Foundation for Research and Technology (FORTH), Heraklion 71110, Greece.
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Harting C, Peschke P, Karger CP. Computer simulation of tumour control probabilities after irradiation for varying intrinsic radio-sensitivity using a single cell based model. Acta Oncol 2010; 49:1354-62. [PMID: 20843178 DOI: 10.3109/0284186x.2010.485208] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
BACKGROUND Currently, optimisation of the dose distribution and clinical acceptance are almost entirely based on the physical dose distribution and tumour control probability modelling is far from being routinely used as objective in treatment planning. For future individualised radiotherapeutic strategies, a reliable patient specific simulation model, taking into account customised tumour features, is needed to predict and improve treatment outcome. MATERIALS AND METHODS To approach these demands, a single cell and Monte-Carlo based model was developed, which enables three-dimensional tumour growth and radiation response simulation. Tumour cells were characterised by cell-associated features such as age, intrinsic radio-sensitivity, proliferation ability, and oxygenation status, while capillary cells were considered as sources of a radial-dependent oxygen profile. Response to radiation was simulated by the linear-quadratic model, taking into account the lower radio-sensitivity of poorly oxygenated tumour cells. RESULTS The present study shows the influence of the model components and demonstrates the impact of the intra- and inter-tumoural radio-sensitivity heterogeneity on the treatment response. CONCLUSION The simulation model adequately delineates the importance of the above described selected parameters on tumour control probability, providing an insight into the interplay of different physical and biological parameters, and its relevance for an individual tumour response.
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Affiliation(s)
- Christine Harting
- German Cancer Research Center, Department of Medical Physics in Radiation Oncology, Heidelberg, Germany
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49
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Stamatakos G, Kolokotroni E, Dionysiou D, Georgiadi E, Desmedt C. An advanced discrete state–discrete event multiscale simulation model of the response of a solid tumor to chemotherapy: Mimicking a clinical study. J Theor Biol 2010; 266:124-39. [DOI: 10.1016/j.jtbi.2010.05.019] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2009] [Revised: 03/29/2010] [Accepted: 05/14/2010] [Indexed: 12/24/2022]
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50
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Naumov L, Hoekstra A, Sloot P. The influence of mitoses rate on growth dynamics of a cellular automata model of tumour growth. ACTA ACUST UNITED AC 2010. [DOI: 10.1016/j.procs.2010.04.107] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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