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Su L, Chen L, Tang W, Gao H, Chen Y, Gao C, Yi H, Cao X. Dictionary Learning Method Based on K-Sparse Approximation and Orthogonal Procrustes Analysis for Reconstruction in Bioluminescence Tomography. JOURNAL OF BIOPHOTONICS 2024; 17:e202400308. [PMID: 39375540 DOI: 10.1002/jbio.202400308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2024] [Revised: 09/08/2024] [Accepted: 09/11/2024] [Indexed: 10/09/2024]
Abstract
Bioluminescence tomography (BLT) is one kind of noninvasive optical molecular imaging technology, widely used to study molecular activities and disease progression inside live animals. By combining the optical propagation model and inversion algorithm, BLT enables three-dimensional imaging and quantitative analysis of light sources within organisms. However, challenges like light scattering and absorption in tissues, and the complexity of biological structures, significantly impact the accuracy of BLT reconstructions. Here, we propose a dictionary learning method based on K-sparse approximation and Orthogonal Procrustes analysis (KSAOPA). KSAOPA uses an iterative alternating optimization strategy, enhancing solution sparsity with k-coefficients Lipschitzian mappings for sparsity(K-LIMAPS) in the sparse coding stage, and reducing errors with Orthogonal Procrustes analysis in the dictionary update stage, leading to stable and precise reconstructions. We assessed the method performance through simulations and in vivo experiments, which showed that KSAOPA excels in localization accuracy, morphological recovery, and in vivo applicability compared to other methods.
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Affiliation(s)
- Linzhi Su
- School of Information Science and Technology, Northwest University, Xi'an, China
| | - Limin Chen
- School of Information Science and Technology, Northwest University, Xi'an, China
| | - Wenlong Tang
- School of Information Science and Technology, Northwest University, Xi'an, China
| | - Huimin Gao
- School of Information Science and Technology, Northwest University, Xi'an, China
| | - Yi Chen
- School of Electrical and Mechanical Engineering, The University of Adelaide, Adelaide, Australia
| | - Chengyi Gao
- Department of Oncology, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, China
| | - Huangjian Yi
- School of Information Science and Technology, Northwest University, Xi'an, China
| | - Xin Cao
- School of Information Science and Technology, Northwest University, Xi'an, China
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2
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Zhang G, Zhang J, Chen Y, Du M, Li K, Su L, Yi H, Zhao F, Cao X. Logarithmic total variation regularization via preconditioned conjugate gradient method for sparse reconstruction of bioluminescence tomography. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 243:107863. [PMID: 37871449 DOI: 10.1016/j.cmpb.2023.107863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 10/09/2023] [Accepted: 10/11/2023] [Indexed: 10/25/2023]
Abstract
BACKGROUND AND OBJECTIVE Bioluminescence Tomography (BLT) is a powerful optical molecular imaging technique that enables the noninvasive investigation of dynamic biological phenomena. It aims to reconstruct the three-dimensional spatial distribution of bioluminescent sources from optical measurements collected on the surface of the imaged object. However, BLT reconstruction is a challenging ill-posed problem due to the scattering effect of light and the limitations in detecting surface photons, which makes it difficult for existing methods to achieve satisfactory reconstruction results. In this study, we propose a novel method for sparse reconstruction of BLT based on a preconditioned conjugate gradient with logarithmic total variation regularization (PCG-logTV). METHOD This PCG-logTV method incorporates the sparsity of overlapping groups and enhances the sparse structure of these groups using logarithmic functions, which can preserve edge features and achieve more stable reconstruction results in BLT. To accelerate the convergence of the algorithm solution, we use the preconditioned conjugate gradient iteration method on the objective function and obtain the reconstruction results. We demonstrate the performance of our proposed method through numerical simulations and in vivo experiment. RESULTS AND CONCLUSIONS The results show that the PCG-logTV method obtains the most accurate reconstruction results, and the minimum position error (LE) is 0.254mm, which is 26%, 31% and 34% of the FISTA (0.961), IVTCG (0.81) and L1-TV (0.739) methods, and the root mean square error (RMSE) and relative intensity error (RIE) are the smallest, indicating that it is closest to the real light source. In addition, compared with the other three methods, the PCG-logTV method also has the highest DICE similarity coefficient, which is 0.928, which means that this method can effectively reconstruct the three-dimensional spatial distribution of bioluminescent light sources, has higher resolution and robustness, and is beneficial to the preclinical and clinical studies of BLT.
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Affiliation(s)
- Gege Zhang
- School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, China; National and Local Joint Engineering Research Center for Cultural Heritage Digitization, Xi'an, Shaanxi 710127, China
| | - Jun Zhang
- School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, China; National and Local Joint Engineering Research Center for Cultural Heritage Digitization, Xi'an, Shaanxi 710127, China
| | - Yi Chen
- School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, China; National and Local Joint Engineering Research Center for Cultural Heritage Digitization, Xi'an, Shaanxi 710127, China
| | - Mengfei Du
- School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, China; National and Local Joint Engineering Research Center for Cultural Heritage Digitization, Xi'an, Shaanxi 710127, China
| | - Kang Li
- School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, China; National and Local Joint Engineering Research Center for Cultural Heritage Digitization, Xi'an, Shaanxi 710127, China
| | - Linzhi Su
- School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, China; National and Local Joint Engineering Research Center for Cultural Heritage Digitization, Xi'an, Shaanxi 710127, China
| | - Huangjian Yi
- School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, China
| | - Fengjun Zhao
- School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, China
| | - Xin Cao
- School of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, China; National and Local Joint Engineering Research Center for Cultural Heritage Digitization, Xi'an, Shaanxi 710127, China.
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3
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Abramson A, Chan CT, Khan Y, Mermin-Bunnell A, Matsuhisa N, Fong R, Shad R, Hiesinger W, Mallick P, Gambhir SS, Bao Z. A flexible electronic strain sensor for the real-time monitoring of tumor regression. SCIENCE ADVANCES 2022; 8:eabn6550. [PMID: 36112679 PMCID: PMC9481124 DOI: 10.1126/sciadv.abn6550] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 07/12/2022] [Indexed: 05/02/2023]
Abstract
Assessing the efficacy of cancer therapeutics in mouse models is a critical step in treatment development. However, low-resolution measurement tools and small sample sizes make determining drug efficacy in vivo a difficult and time-intensive task. Here, we present a commercially scalable wearable electronic strain sensor that automates the in vivo testing of cancer therapeutics by continuously monitoring the micrometer-scale progression or regression of subcutaneously implanted tumors at the minute time scale. In two in vivo cancer mouse models, our sensor discerned differences in tumor volume dynamics between drug- and vehicle-treated tumors within 5 hours following therapy initiation. These short-term regression measurements were validated through histology, and caliper and bioluminescence measurements taken over weeklong treatment periods demonstrated the correlation with longer-term treatment response. We anticipate that real-time tumor regression datasets could help expedite and automate the process of screening cancer therapies in vivo.
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Affiliation(s)
- Alex Abramson
- Department of Chemical Engineering, Stanford University, Stanford, CA 94305, USA
| | - Carmel T. Chan
- Department of Radiology, Stanford University, Stanford, CA 94305, USA
- Molecular Imaging Program at Stanford (MIPS) and Bio-X Program, Stanford University, Stanford CA, 94305, USA
| | - Yasser Khan
- Department of Chemical Engineering, Stanford University, Stanford, CA 94305, USA
| | - Alana Mermin-Bunnell
- Department of Chemical Engineering, Stanford University, Stanford, CA 94305, USA
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Naoji Matsuhisa
- Department of Chemical Engineering, Stanford University, Stanford, CA 94305, USA
| | - Robyn Fong
- Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Rohan Shad
- Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - William Hiesinger
- Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Parag Mallick
- Department of Radiology, Stanford University, Stanford, CA 94305, USA
- Canary Center at Stanford for Cancer Early Detection, Stanford University, Stanford, CA 94305, USA
| | - Sanjiv Sam Gambhir
- Department of Radiology, Stanford University, Stanford, CA 94305, USA
- Molecular Imaging Program at Stanford (MIPS) and Bio-X Program, Stanford University, Stanford CA, 94305, USA
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Canary Center at Stanford for Cancer Early Detection, Stanford University, Stanford, CA 94305, USA
- Department of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Zhenan Bao
- Department of Chemical Engineering, Stanford University, Stanford, CA 94305, USA
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Pfeifer R, Henze J, Wittich K, Gosselink A, Kinkhabwala A, Gremse F, Bleilevens C, Bigott K, Jungblut M, Hardt O, Alves F, Al Rawashdeh W. A multimodal imaging workflow for monitoring CAR T cell therapy against solid tumor from whole-body to single-cell level. Am J Cancer Res 2022; 12:4834-4850. [PMID: 35836798 PMCID: PMC9274742 DOI: 10.7150/thno.68966] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 05/19/2022] [Indexed: 01/12/2023] Open
Abstract
CAR T cell research in solid tumors often lacks spatiotemporal information and therefore, there is a need for a molecular tomography to facilitate high-throughput preclinical monitoring of CAR T cells. Furthermore, a gap exists between macro- and microlevel imaging data to better assess intratumor infiltration of therapeutic cells. We addressed this challenge by combining 3D µComputer tomography bioluminescence tomography (µCT/BLT), light-sheet fluorescence microscopy (LSFM) and cyclic immunofluorescence (IF) staining. Methods: NSG mice with subcutaneous AsPC1 xenograft tumors were treated with EGFR CAR T cell (± IL-2) or control BDCA-2 CAR T cell (± IL-2) (n = 7 each). Therapeutic T cells were genetically modified to co-express the CAR of interest and the luciferase CBR2opt. IL-2 was administered s.c. under the xenograft tumor on days 1, 3, 5 and 7 post-therapy-initiation at a dose of 25,000 IU/mouse. CAR T cell distribution was measured in 2D BLI and 3D µCT/BLT every 3-4 days. On day 6, 4 tumors were excised for cyclic IF where tumor sections were stained with a panel of 25 antibodies. On day 6 and 13, 8 tumors were excised from rhodamine lectin-preinjected mice, permeabilized, stained for CD3 and imaged by LSFM. Results: 3D µCT/BLT revealed that CAR T cells pharmacokinetics is affected by antigen recognition, where CAR T cell tumor accumulation based on target-dependent infiltration was significantly increased in comparison to target-independent infiltration, and spleen accumulation was delayed. LSFM supported these findings and revealed higher T cell accumulation in target-positive groups at day 6, which also infiltrated the tumor deeper. Interestingly, LSFM showed that most CAR T cells accumulate at the tumor periphery and around vessels. Surprisingly, LSFM and cyclic IF revealed that local IL-2 application resulted in early-phase increased proliferation, but long-term overstimulation of CAR T cells, which halted the early added therapeutic effect. Conclusion: Overall, we demonstrated that 3D µCT/BLT is a valuable non-isotope-based technology for whole-body cell therapy monitoring and investigating CAR T cell pharmacokinetics. We also presented combining LSFM and MICS for ex vivo 3D- and 2D-microscopy tissue analysis to assess intratumoral therapeutic cell distribution and status.
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Affiliation(s)
- Rita Pfeifer
- Miltenyi Biotec B.V. & Co. KG, R&D Reagents, Bergisch Gladbach, North Rhine-Westphalia, Germany
| | - Janina Henze
- Miltenyi Biotec B.V. & Co. KG, R&D Reagents, Bergisch Gladbach, North Rhine-Westphalia, Germany.,University Medical Center Göttingen, Translational Molecular Imaging, Institute for Diagnostic and Interventional Radiology & Clinic for Haematology and Medical Oncology, Göttingen, Lower Saxony, Germany
| | - Katharina Wittich
- Miltenyi Biotec B.V. & Co. KG, R&D Reagents, Bergisch Gladbach, North Rhine-Westphalia, Germany
| | - Andre Gosselink
- Miltenyi Biotec B.V. & Co. KG, R&D Reagents, Bergisch Gladbach, North Rhine-Westphalia, Germany.,Institute of Medical Statistics and Computational Biology, University of Cologne, Cologne, North Rhine-Westphalia, Germany
| | - Ali Kinkhabwala
- Miltenyi Biotec B.V. & Co. KG, R&D Reagents, Bergisch Gladbach, North Rhine-Westphalia, Germany
| | - Felix Gremse
- Gremse-IT GmbH, Aachen, North Rhine-Westphalia, Germany
| | - Cathrin Bleilevens
- Miltenyi Biotec B.V. & Co. KG, R&D Reagents, Bergisch Gladbach, North Rhine-Westphalia, Germany
| | - Kevin Bigott
- Miltenyi Biotec B.V. & Co. KG, R&D Reagents, Bergisch Gladbach, North Rhine-Westphalia, Germany
| | - Melanie Jungblut
- Miltenyi Biotec B.V. & Co. KG, R&D Reagents, Bergisch Gladbach, North Rhine-Westphalia, Germany
| | - Olaf Hardt
- Miltenyi Biotec B.V. & Co. KG, R&D Reagents, Bergisch Gladbach, North Rhine-Westphalia, Germany
| | - Frauke Alves
- University Medical Center Göttingen, Translational Molecular Imaging, Institute for Diagnostic and Interventional Radiology & Clinic for Haematology and Medical Oncology, Göttingen, Lower Saxony, Germany.,Max-Planck-Institute for Multidisciplinary Science, Translational Molecular Imaging, Göttingen, Lower Saxony, Germany
| | - Wa'el Al Rawashdeh
- Miltenyi Biotec B.V. & Co. KG, R&D Reagents, Bergisch Gladbach, North Rhine-Westphalia, Germany.,Ossium Health Inc, Indianapolis, Indiana, United States of America.,✉ Corresponding author: E-mail: (W.A.)
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5
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Nakhjavani M, Smith E, Palethorpe HM, Tomita Y, Yeo K, Price TJ, Townsend AR, Hardingham JE. Anti-Cancer Effects of an Optimised Combination of Ginsenoside Rg3 Epimers on Triple Negative Breast Cancer Models. Pharmaceuticals (Basel) 2021; 14:ph14070633. [PMID: 34208799 PMCID: PMC8308773 DOI: 10.3390/ph14070633] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 06/23/2021] [Accepted: 06/23/2021] [Indexed: 12/26/2022] Open
Abstract
Key problems of chemotherapies, as the mainstay of treatment for triple-negative breast cancer (TNBC), are toxicity and development of tumour resistance. Using response surface methodology, we previously optimised the combination of epimers of ginsenoside Rg3 (Rg3) for anti-angiogenic action. Here, we show that the optimised combination of 50 µM SRg3 and 25 µM RRg3 (C3), derived from an RSM model of migration of TNBC cell line MDA-MB-231, inhibited migration of MDA-MB-231 and HCC1143, in 2D and 3D migration assays (p < 0.0001). C3 inhibited mammosphere formation efficiency in both cell lines and decreased the CD44+ stem cell marker in the mammospheres. Molecular docking predicted that Rg3 epimers had a better binding score with IGF-1R than with EGFR, HER-2 or PDGFR, and predicted an mTOR inhibitory function of Rg3. C3 affected the signalling of AKT in MDA-MB-231 and HCC1143 mammospheres. In a mouse model of metastatic TNBC, an equivalent dose of C3 (23 mg/kg SRg3 + 11 mg/kg RRg3) or an escalated dose of 46 mg/kg SRg3 + 23 mg/kg RRg3 was administered to NSG mice bearing MDA-MB-231-Luc cells. Calliper and IVIS spectrum measurement of the primary and secondary tumour showed that the treatment shrunk the primary tumour and decreased the load of metastasis in mice. In conclusion, this combination of Rg3 epimers showed promising results as a potential treatment option for TNBC patients.
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Affiliation(s)
- Maryam Nakhjavani
- Molecular Oncology, Basil Hetzel Institute, The Queen Elizabeth Hospital, Woodville South, SA 5011, Australia; (M.N.); (Y.T.); (K.Y.); (J.E.H.)
- Adelaide Medical School, University of Adelaide, Adelaide, SA 5005, Australia; (T.J.P.); (A.R.T.)
| | - Eric Smith
- Molecular Oncology, Basil Hetzel Institute, The Queen Elizabeth Hospital, Woodville South, SA 5011, Australia; (M.N.); (Y.T.); (K.Y.); (J.E.H.)
- Adelaide Medical School, University of Adelaide, Adelaide, SA 5005, Australia; (T.J.P.); (A.R.T.)
- Correspondence: ; Tel.: +61-8-8222-6142
| | - Helen M. Palethorpe
- Centre for Cancer Biology, University of South Australia and SA Pathology, Adelaide, SA 5000, Australia;
| | - Yoko Tomita
- Molecular Oncology, Basil Hetzel Institute, The Queen Elizabeth Hospital, Woodville South, SA 5011, Australia; (M.N.); (Y.T.); (K.Y.); (J.E.H.)
- Adelaide Medical School, University of Adelaide, Adelaide, SA 5005, Australia; (T.J.P.); (A.R.T.)
- Oncology Unit, The Queen Elizabeth Hospital, Woodville South, SA 5011, Australia
| | - Kenny Yeo
- Molecular Oncology, Basil Hetzel Institute, The Queen Elizabeth Hospital, Woodville South, SA 5011, Australia; (M.N.); (Y.T.); (K.Y.); (J.E.H.)
- Adelaide Medical School, University of Adelaide, Adelaide, SA 5005, Australia; (T.J.P.); (A.R.T.)
| | - Tim J. Price
- Adelaide Medical School, University of Adelaide, Adelaide, SA 5005, Australia; (T.J.P.); (A.R.T.)
- Oncology Unit, The Queen Elizabeth Hospital, Woodville South, SA 5011, Australia
| | - Amanda R. Townsend
- Adelaide Medical School, University of Adelaide, Adelaide, SA 5005, Australia; (T.J.P.); (A.R.T.)
- Oncology Unit, The Queen Elizabeth Hospital, Woodville South, SA 5011, Australia
| | - Jennifer E. Hardingham
- Molecular Oncology, Basil Hetzel Institute, The Queen Elizabeth Hospital, Woodville South, SA 5011, Australia; (M.N.); (Y.T.); (K.Y.); (J.E.H.)
- Adelaide Medical School, University of Adelaide, Adelaide, SA 5005, Australia; (T.J.P.); (A.R.T.)
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6
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Yin L, Wang K, Tong T, Wang Q, An Y, Yang X, Tian J. Adaptive Grouping Block Sparse Bayesian Learning Method for Accurate and Robust Reconstruction in Bioluminescence Tomography. IEEE Trans Biomed Eng 2021; 68:3388-3398. [PMID: 33830917 DOI: 10.1109/tbme.2021.3071823] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVE Bioluminescence tomography (BLT) is a promising modality that is designed to provide non-invasive quantitative three-dimensional information regarding the tumor distribution in living animals. However, BLT suffers from inferior reconstructions due to its ill-posedness. This study aims to improve the reconstruction performance of BLT. METHODS We propose an adaptive grouping block sparse Bayesian learning (AGBSBL) method, which incorporates the sparsity prior, correlation of neighboring mesh nodes, and anatomical structure prior to balance the sparsity and morphology in BLT. Specifically, an adaptive grouping prior model is proposed to adjust the grouping according to the intensity of the mesh nodes during the optimization process. RESULTS Numerical simulations and in vivo experiments demonstrate that AGBSBL yields a high position and morphology recovery accuracy, stability, and practicality. CONCLUSION The proposed method is a robust and effective reconstruction algorithm for BLT. Moreover, the proposed adaptive grouping strategy can further increase the practicality of BLT in biomedical applications.
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7
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Alsawaftah N, Farooq A, Dhou S, Majdalawieh AF. Bioluminescence Imaging Applications in Cancer: A Comprehensive Review. IEEE Rev Biomed Eng 2021; 14:307-326. [PMID: 32746363 DOI: 10.1109/rbme.2020.2995124] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
Abstract
Bioluminescence imaging (BLI), an optical preclinical imaging modality, is an invaluable imaging modality due to its low-cost, high throughput, fast acquisition times, and functional imaging capabilities. BLI is being extensively used in the field of cancer imaging, especially with the recent developments in genetic-engineering, stem cell, and gene therapy treatments. The purpose of this paper is to provide a comprehensive review of the principles, developments, and current status of BLI in cancer research. This paper covers the fundamental BLI concepts including BLI reporters and enzyme-substrate systems, data acquisition, and image characteristics. It reviews the studies discussing the use of BLI in cancer research such as imaging tumor-characteristic phenomena including tumorigenesis, metastasis, cancer metabolism, apoptosis, hypoxia, and angiogenesis, and response to cancer therapy treatments including chemotherapy, radiotherapy, immunotherapy, gene therapy, and stem cell therapy. The key advantages and disadvantages of BLI compared to other common imaging modalities are also discussed.
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Yu J, Tang Q, Li Q, Guo H, He X. Hybrid reconstruction method for multispectral bioluminescence tomography with log-sum regularization. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA. A, OPTICS, IMAGE SCIENCE, AND VISION 2020; 37:1060-1066. [PMID: 32543609 DOI: 10.1364/josaa.386961] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Accepted: 05/07/2020] [Indexed: 06/11/2023]
Abstract
Bioluminescence tomography (BLT) has important applications in the in vivo visualization of a pathological process for preclinical studies. However, the reconstruction of BLT is severely ill-posed. To recover the bioluminescence source stably and efficiently, we use a log-sum regularization term in the objective function and utilize a hybrid optimization algorithm for solving the nonconvex regularized problems (HONOR). The hybrid optimization scheme of HONOR merges second-order information and first-order information to reconstruction by choosing either the quasi-Newton (QN) or gradient descent step at each iteration. The QN step uses the limited-memory Broyden-Fletcher-Goldfarb-Shanno algorithm (L-BFGS) to acquire second-order information. Simulations and in vivo experiments based on multispectral measurements demonstrated the remarkable performance of the proposed hybrid method in the sparse reconstruction of BLT.
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Guo H, Gao L, Yu J, He X, Wang H, Zheng J, Yang X. Sparse-graph manifold learning method for bioluminescence tomography. JOURNAL OF BIOPHOTONICS 2020; 13:e201960218. [PMID: 31990430 DOI: 10.1002/jbio.201960218] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 01/09/2020] [Accepted: 01/15/2020] [Indexed: 06/10/2023]
Abstract
In preclinical researches, bioluminescence tomography (BLT) has widely been used for tumor imaging and monitoring, imaged-guided tumor therapy, and so forth. For these biological applications, both tumor spatial location and morphology analysis are the leading problems needed to be taken into account. However, most existing BLT reconstruction methods were proposed for some specific applications with a focus on sparse representation or morphology recovery, respectively. How to design a versatile algorithm that can simultaneously deal with both aspects remains an impending problem. In this study, a Sparse-Graph Manifold Learning (SGML) method was proposed to balance the source sparseness and morphology, by integrating non-convex sparsity constraint and dynamic Laplacian graph model. Furthermore, based on the nonconvex optimization theory and some iterative approximation, we proposed a novel iteratively reweighted soft thresholding algorithm (IRSTA) to solve the SGML model. Numerical simulations and in vivo experiments result demonstrated that the proposed SGML method performed much superior to the comparative methods in spatial localization and tumor morphology recovery for various source settings. It is believed that the SGML method can be applied to the related optical tomography and facilitate the development of optical molecular tomography.
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Affiliation(s)
- Hongbo Guo
- School of Information Sciences and Technology, Northwest University, Xi'an, China
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
- The State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services, Northwest University, Xi'an, China
| | - Ling Gao
- School of Information Sciences and Technology, Northwest University, Xi'an, China
- The State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services, Northwest University, Xi'an, China
- The State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services, Xi'an Polytechnic University, Xi'an, China
| | - Jingjing Yu
- The School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
| | - Xiaowei He
- School of Information Sciences and Technology, Northwest University, Xi'an, China
- The Xi'an Key Laboratory of Radiomics and Intelligent Perception, Xi'an, China
| | - Hai Wang
- School of Information Sciences and Technology, Northwest University, Xi'an, China
- The State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services, Northwest University, Xi'an, China
| | - Jie Zheng
- School of Information Sciences and Technology, Northwest University, Xi'an, China
- The State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services, Northwest University, Xi'an, China
| | - Xudong Yang
- School of Information Sciences and Technology, Northwest University, Xi'an, China
- The State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services, Northwest University, Xi'an, China
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10
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Wu Q, Arnheim AD, Finley SD. In silico mouse study identifies tumour growth kinetics as biomarkers for the outcome of anti-angiogenic treatment. J R Soc Interface 2019; 15:rsif.2018.0243. [PMID: 30135261 PMCID: PMC6127173 DOI: 10.1098/rsif.2018.0243] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Accepted: 07/27/2018] [Indexed: 12/11/2022] Open
Abstract
Angiogenesis is a crucial step in tumour progression, as this process allows tumours to recruit new blood vessels and obtain oxygen and nutrients to sustain growth. Therefore, inhibiting angiogenesis remains a viable strategy for cancer therapy. However, anti-angiogenic therapy has not proved to be effective in reducing tumour growth across a wide range of tumours, and no reliable predictive biomarkers have been found to determine the efficacy of anti-angiogenic treatment. Using our previously established computational model of tumour-bearing mice, we sought to determine whether tumour growth kinetic parameters could be used to predict the outcome of anti-angiogenic treatment. A model trained with datasets from six in vivo mice studies was used to generate a randomized in silico tumour-bearing mouse population. We analysed tumour growth in untreated mice (control) and mice treated with an anti-angiogenic agent and determined the Kaplan–Meier survival estimates based on simulated tumour volume data. We found that the ratio between two kinetic parameters, k0 and k1, which characterize the tumour's exponential and linear growth rates, as well as k1 alone, can be used as prognostic biomarkers of the population survival outcome. Our work demonstrates a robust, quantitative approach for identifying tumour growth kinetic parameters as prognostic biomarkers and serves as a template that can be used to identify other biomarkers for anti-angiogenic treatment.
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Affiliation(s)
- Qianhui Wu
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
| | - Alyssa D Arnheim
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Stacey D Finley
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA .,Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, USA
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11
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Advances in the strategies for designing receptor-targeted molecular imaging probes for cancer research. J Control Release 2019; 305:1-17. [DOI: 10.1016/j.jconrel.2019.04.030] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2018] [Revised: 04/09/2019] [Accepted: 04/21/2019] [Indexed: 12/24/2022]
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Souza TKF, Nucci MP, Mamani JB, da Silva HR, Fantacini DMC, de Souza LEB, Picanço-Castro V, Covas DT, Vidoto EL, Tannús A, Gamarra LF. Image and motor behavior for monitoring tumor growth in C6 glioma model. PLoS One 2018; 13:e0201453. [PMID: 30048545 PMCID: PMC6062126 DOI: 10.1371/journal.pone.0201453] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2017] [Accepted: 07/15/2018] [Indexed: 12/17/2022] Open
Abstract
The primary objective of this study is to monitor tumor growth by using image techniques and behavioral testing through general and specific motor activities (spontaneous movements and gait). Our sample includes male Wistar rats, 2 months old and weighing 250-300 g, that is categorized into three groups: control, sham, and experimental. The experimental group was anesthetized; the C6 cells with luciferase expression that were suspended in a culture medium were implanted into the right frontoparietal cortex of the rats. The sham group received implant only with culture medium without cells. Images and behavioral tests were evaluated at base time and at 7, 14, 21, and 28 days after induced tumor growth analysis. The tumor volume measured by magnetic resonance imaging (MRI) and quantitative bioluminescence imaging (BLI) signal showed a correlation coefficient of r = 0.96. The MRI showed that the mean tumor volume increased by approximately 10, 26, and 49 times according to a comparison of tumor volume on the seventh day with 14, 21, and 28 days, respectively. The quantification of the BLI signal was (4.12 ± 2.01) x 10(8), (8.33 ± 3.12) x 10(8), (28.43 ± 6.32) x 10(8), and (63.02 ± 10.53) x 10(8) photons/s at the seventh, fourteenth, twenty-first, and twenty-eighth day, respectively. After 14 days of tumor induction, both behavioral tests showed significant differences between tumor and sham or control groups. Our study showed a high correlation between MRI and BLI for tumor growth monitoring with complement aspects analysis in tumor volume. In addition, functional behavioral analysis displayed sensitivity to monitor tumor growth, as well as to detect early significant changes between groups, primarily in the tumor group. The results of gait analysis were more sensitive than general motor analysis.
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Affiliation(s)
| | | | | | | | | | | | - Virginia Picanço-Castro
- Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, SP, Brazil
| | - Dimas Tadeu Covas
- Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, SP, Brazil
| | - Edson Luis Vidoto
- CIERMag-Instituto de Física de São Carlos, Universidade de São Paulo, São Paulo, Brazil
| | - Alberto Tannús
- CIERMag-Instituto de Física de São Carlos, Universidade de São Paulo, São Paulo, Brazil
| | - Lionel Fernel Gamarra
- Hospital Israelita Albert Einstein, São Paulo, SP, Brazil
- Santa Casa Misericórdia de São Paulo, São Paulo, Brazil
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Pinkert MA, Salkowski LR, Keely PJ, Hall TJ, Block WF, Eliceiri KW. Review of quantitative multiscale imaging of breast cancer. J Med Imaging (Bellingham) 2018; 5:010901. [PMID: 29392158 PMCID: PMC5777512 DOI: 10.1117/1.jmi.5.1.010901] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2017] [Accepted: 12/19/2017] [Indexed: 12/12/2022] Open
Abstract
Breast cancer is the most common cancer among women worldwide and ranks second in terms of overall cancer deaths. One of the difficulties associated with treating breast cancer is that it is a heterogeneous disease with variations in benign and pathologic tissue composition, which contributes to disease development, progression, and treatment response. Many of these phenotypes are uncharacterized and their presence is difficult to detect, in part due to the sparsity of methods to correlate information between the cellular microscale and the whole-breast macroscale. Quantitative multiscale imaging of the breast is an emerging field concerned with the development of imaging technology that can characterize anatomic, functional, and molecular information across different resolutions and fields of view. It involves a diverse collection of imaging modalities, which touch large sections of the breast imaging research community. Prospective studies have shown promising results, but there are several challenges, ranging from basic physics and engineering to data processing and quantification, that must be met to bring the field to maturity. This paper presents some of the challenges that investigators face, reviews currently used multiscale imaging methods for preclinical imaging, and discusses the potential of these methods for clinical breast imaging.
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Affiliation(s)
- Michael A. Pinkert
- Morgridge Institute for Research, Madison, Wisconsin, United States
- University of Wisconsin–Madison, Laboratory for Optical and Computational Instrumentation, Madison, Wisconsin, United States
- University of Wisconsin–Madison, Department of Medical Physics, Madison, Wisconsin, United States
| | - Lonie R. Salkowski
- University of Wisconsin–Madison, Department of Medical Physics, Madison, Wisconsin, United States
- University of Wisconsin–Madison, Department of Radiology, Madison, Wisconsin, United States
| | - Patricia J. Keely
- University of Wisconsin–Madison, Department of Cell and Regenerative Biology, Madison, Wisconsin, United States
- University of Wisconsin–Madison, Department of Biomedical Engineering, Madison, Wisconsin, United States
| | - Timothy J. Hall
- University of Wisconsin–Madison, Department of Medical Physics, Madison, Wisconsin, United States
- University of Wisconsin–Madison, Department of Biomedical Engineering, Madison, Wisconsin, United States
| | - Walter F. Block
- University of Wisconsin–Madison, Department of Medical Physics, Madison, Wisconsin, United States
- University of Wisconsin–Madison, Department of Radiology, Madison, Wisconsin, United States
- University of Wisconsin–Madison, Department of Biomedical Engineering, Madison, Wisconsin, United States
| | - Kevin W. Eliceiri
- Morgridge Institute for Research, Madison, Wisconsin, United States
- University of Wisconsin–Madison, Laboratory for Optical and Computational Instrumentation, Madison, Wisconsin, United States
- University of Wisconsin–Madison, Department of Medical Physics, Madison, Wisconsin, United States
- University of Wisconsin–Madison, Department of Biomedical Engineering, Madison, Wisconsin, United States
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14
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Imbs DC, El Cheikh R, Boyer A, Ciccolini J, Mascaux C, Lacarelle B, Barlesi F, Barbolosi D, Benzekry S. Revisiting Bevacizumab + Cytotoxics Scheduling Using Mathematical Modeling: Proof of Concept Study in Experimental Non-Small Cell Lung Carcinoma. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2017; 7:42-50. [PMID: 29218795 PMCID: PMC5784740 DOI: 10.1002/psp4.12265] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2017] [Revised: 10/25/2017] [Accepted: 10/30/2017] [Indexed: 12/20/2022]
Abstract
Concomitant administration of bevacizumab and pemetrexed‐cisplatin is a common treatment for advanced nonsquamous non‐small cell lung cancer (NSCLC). Vascular normalization following bevacizumab administration may transiently enhance drug delivery, suggesting improved efficacy with sequential administration. To investigate optimal scheduling, we conducted a study in NSCLC‐bearing mice. First, experiments demonstrated improved efficacy when using sequential vs. concomitant scheduling of bevacizumab and chemotherapy. Combining this data with a mathematical model of tumor growth under therapy accounting for the normalization effect, we predicted an optimal delay of 2.8 days between bevacizumab and chemotherapy. This prediction was confirmed experimentally, with reduced tumor growth of 38% as compared to concomitant scheduling, and prolonged survival (74 vs. 70 days). Alternate sequencing of 8 days failed in achieving a similar increase in efficacy, thus emphasizing the utility of modeling support to identify optimal scheduling. The model could also be a useful tool in the clinic to personally tailor regimen sequences.
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Affiliation(s)
| | - Raouf El Cheikh
- SMARTc Unit, Inserm S_911 CRO2, Aix-Marseille University, Marseille, France
| | - Arnaud Boyer
- SMARTc Unit, Inserm S_911 CRO2, Aix-Marseille University, Marseille, France.,Multidisciplinary Oncology and Therapeutic Innovations Department, Assistance Publique Hôpitaux de Marseille, Marseille, France
| | - Joseph Ciccolini
- SMARTc Unit, Inserm S_911 CRO2, Aix-Marseille University, Marseille, France
| | - Céline Mascaux
- SMARTc Unit, Inserm S_911 CRO2, Aix-Marseille University, Marseille, France.,Multidisciplinary Oncology and Therapeutic Innovations Department, Assistance Publique Hôpitaux de Marseille, Marseille, France
| | - Bruno Lacarelle
- SMARTc Unit, Inserm S_911 CRO2, Aix-Marseille University, Marseille, France
| | - Fabrice Barlesi
- SMARTc Unit, Inserm S_911 CRO2, Aix-Marseille University, Marseille, France.,Multidisciplinary Oncology and Therapeutic Innovations Department, Assistance Publique Hôpitaux de Marseille, Marseille, France
| | | | - Sébastien Benzekry
- MONC Team, Inria Bordeaux Sud-Ouest, Talence, France.,Institut de Mathématiques de Bordeaux, UMR 5251, University of Bordeaux and CNRS, Talence, France
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Wen L, Tan Y, Dai S, Zhu Y, Meng T, Yang X, Liu Y, Liu X, Yuan H, Hu F. VEGF-mediated tight junctions pathological fenestration enhances doxorubicin-loaded glycolipid-like nanoparticles traversing BBB for glioblastoma-targeting therapy. Drug Deliv 2017; 24:1843-1855. [PMID: 29182025 PMCID: PMC8241127 DOI: 10.1080/10717544.2017.1386731] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Revised: 09/25/2017] [Accepted: 09/27/2017] [Indexed: 01/03/2023] Open
Abstract
The existence of blood-brain barrier (BBB) greatly hindered the penetration and accumulation of chemotherapeutics into glioblastoma (GBM), accompany with poor therapeutic effects. The growth of GBM supervene the impairment of tight junctions (TJs); however, the pathogenesis of BBB breakdown in GBM is essentially poorly understood. This study found that vascular endothelial growth factor (VEGF) secreted by GBM cells plays an important role in increasing the permeability of BBB by disrupting endothelial tight junction proteins claudin-5 and thus gave doxorubicin (DOX)-loaded glycolipid-like nanoparticles (Ap-CSSA/DOX), an effective entrance to brain tumor region for GBM-targeting therapy. In addition, VEGF downregulates the expression of claudin-5 with a dose-dependent mode, and interfering with the VEGF/VEGFR pathway using its inhibitor axitinib could reduce the permeability of BBB and enhance the integrity of the barrier. Ap-CSSA/DOX nanoparticles showed high affinity to expressed low-density lipoprotein receptor-related proteins 1 (LRP1) in both BBB and GBM. And BBB pathological fenestration in GBM further exposed more LRP1 binding sites for Ap-CSSA/DOX nanoparticles targeting to brain tumor, resulting in a higher transmembrane transport ratio in vitro and a stronger brain tumor biodistribution in vivo, and finally realizing a considerable antitumor effect. Overall, taking advantage of BBB pathological features to design an appropriate nanodrug delivery system (NDDS) might provide new insights into other central nervous system (CNS) diseases treatment.
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Affiliation(s)
- Lijuan Wen
- College of Pharmaceutical Science, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China
| | - Yanan Tan
- Ocean College, Zhejiang University, Zhoushan, Zhejiang, People’s Republic of China
| | - Suhuan Dai
- College of Pharmaceutical Science, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China
| | - Yun Zhu
- Ocean College, Zhejiang University, Zhoushan, Zhejiang, People’s Republic of China
| | - Tingting Meng
- College of Pharmaceutical Science, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China
| | - Xiqin Yang
- College of Pharmaceutical Science, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China
| | - Yupeng Liu
- College of Pharmaceutical Science, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China
| | - Xuan Liu
- College of Pharmaceutical Science, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China
| | - Hong Yuan
- College of Pharmaceutical Science, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China
| | - Fuqiang Hu
- College of Pharmaceutical Science, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China
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