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Prezja F, Annala L, Kiiskinen S, Lahtinen S, Ojala T, Ruusuvuori P, Kuopio T. Improving performance in colorectal cancer histology decomposition using deep and ensemble machine learning. Heliyon 2024; 10:e37561. [PMID: 39309850 PMCID: PMC11415691 DOI: 10.1016/j.heliyon.2024.e37561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2024] [Accepted: 09/05/2024] [Indexed: 09/25/2024] Open
Abstract
In routine colorectal cancer management, histologic samples stained with hematoxylin and eosin are commonly used. Nonetheless, their potential for defining objective biomarkers for patient stratification and treatment selection is still being explored. The current gold standard relies on expensive and time-consuming genetic tests. However, recent research highlights the potential of convolutional neural networks (CNNs) to facilitate the extraction of clinically relevant biomarkers from these readily available images. These CNN-based biomarkers can predict patient outcomes comparably to golden standards, with the added advantages of speed, automation, and minimal cost. The predictive potential of CNN-based biomarkers fundamentally relies on the ability of CNNs to accurately classify diverse tissue types from whole slide microscope images. Consequently, enhancing the accuracy of tissue class decomposition is critical to amplifying the prognostic potential of imaging-based biomarkers. This study introduces a hybrid deep transfer learning and ensemble machine learning model that improves upon previous approaches, including a transformer and neural architecture search baseline for this task. We employed a pairing of the EfficientNetV2 architecture with a random forest classification head. Our model achieved 96.74% accuracy (95% CI: 96.3%-97.1%) on the external test set and 99.89% on the internal test set. Recognizing the potential of these models in the task, we have made them publicly available.
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Affiliation(s)
- Fabi Prezja
- University of Jyväskylä, Faculty of Information Technology, Jyväskylä, 40014, Finland
| | - Leevi Annala
- University of Helsinki, Faculty of Science, Department of Computer Science, Helsinki, Finland
- University of Helsinki, Faculty of Agriculture and Forestry, Department of Food and Nutrition, Helsinki, Finland
| | - Sampsa Kiiskinen
- University of Jyväskylä, Faculty of Information Technology, Jyväskylä, 40014, Finland
| | - Suvi Lahtinen
- University of Jyväskylä, Faculty of Information Technology, Jyväskylä, 40014, Finland
- University of Jyväskylä, Faculty of Mathematics and Science, Department of Biological and Environmental Science, Jyväskylä, 40014, Finland
| | - Timo Ojala
- University of Jyväskylä, Faculty of Information Technology, Jyväskylä, 40014, Finland
| | - Pekka Ruusuvuori
- University of Turku, Institute of Biomedicine, Cancer Research Unit, Turku, 20014, Finland
- Turku University Hospital, FICAN West Cancer Centre, Turku, 20521, Finland
| | - Teijo Kuopio
- University of Jyväskylä, Department of Biological and Environmental Science, Jyväskylä, 40014, Finland
- Hospital Nova of Central Finland, Department of Pathology, Jyväskylä, 40620, Finland
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2
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Hu X, Ye K, Bo S, Xiao Z, Ma M, Pan J, Zhong X, Zhang D, Mo X, Yu X, Chen M, Luo L, Shi C. Monitoring imatinib decreasing pericyte coverage and HIF-1α level in a colorectal cancer model by an ultrahigh-field multiparametric MRI approach. J Transl Med 2024; 22:712. [PMID: 39085929 PMCID: PMC11293104 DOI: 10.1186/s12967-024-05497-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Accepted: 07/10/2024] [Indexed: 08/02/2024] Open
Abstract
BACKGROUND Excessive pericyte coverage promotes tumor growth, and a downregulation may solve this dilemma. Due to the double-edged sword role of vascular pericytes in tumor microenvironment (TME), indiscriminately decreasing pericyte coverage by imatinib causes poor treatment outcomes. Here, we optimized the use of imatinib in a colorectal cancer (CRC) model in high pericyte-coverage status, and revealed the value of multiparametric magnetic resonance imaging (mpMRI) at 9.4T in monitoring treatment-related changes in pericyte coverage and the TME. METHODS CRC xenograft models were evaluated by histological vascular characterizations and mpMRI. Mice with the highest pericyte coverage were treated with imatinib or saline; then, vascular characterizations, tumor apoptosis and HIF-1α level were analyzed histologically, and alterations in the expression of Bcl-2/bax pathway were assessed through qPCR. The effects of imatinib were monitored by dynamic contrast-enhanced (DCE)-, diffusion-weighted imaging (DWI)- and amide proton transfer chemical exchange saturation transfer (APT CEST)-MRI at 9.4T. RESULTS The DCE- parameters provided a good histologic match the tumor vascular characterizations. In the high pericyte coverage status, imatinib exhibited significant tumor growth inhibition, necrosis increase and pericyte coverage downregulation, and these changes were accompanied by increased vessel permeability, decreased microvessel density (MVD), increased tumor apoptosis and altered gene expression of apoptosis-related Bcl-2/bax pathway. Strategically, a 4-day imatinib effectively decreased pericyte coverage and HIF-1α level, and continuous treatment led to a less marked decrease in pericyte coverage and re-elevated HIF-1α level. Correlation analysis confirmed the feasibility of using mpMRI parameters to monitor imatinib treatment, with DCE-derived Ve and Ktrans being most correlated with pericyte coverage, Ve with vessel permeability, AUC with microvessel density (MVD), DWI-derived ADC with tumor apoptosis, and APT CEST-derived MTRasym at 1 µT with HIF-1α. CONCLUSIONS These results provided an optimized imatinib regimen to achieve decreasing pericyte coverage and HIF-1α level in the high pericyte-coverage CRC model, and offered an ultrahigh-field multiparametric MRI approach for monitoring pericyte coverage and dynamics response of the TME to treatment.
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Affiliation(s)
- Xinpeng Hu
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, West Huangpu Avenue No. 613, Guangzhou, 510630, China
| | - Kunlin Ye
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, West Huangpu Avenue No. 613, Guangzhou, 510630, China
| | - Shaowei Bo
- Department of Medical Imaging, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Xingang Middle Road No. 466, Guangzhou, 510317, China
| | - Zeyu Xiao
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, West Huangpu Avenue No. 613, Guangzhou, 510630, China
- Engineering Research Center of Medical Imaging Artificial Intelligence for Precision Diagnosis and Treatment, West Huangpu Avenue No. 613, Guangzhou, 510630, China
| | - Mengjie Ma
- Department of Radiology, Guangzhou First People's Hospital, Panfu Road No. 1, Guangzhou, 510080, China
| | - Jinghua Pan
- Department of General Surgery, The First Affiliated Hospital of Jinan University, West Huangpu Avenue No. 613, Guangzhou, 510630, China
| | - Xing Zhong
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, West Huangpu Avenue No. 613, Guangzhou, 510630, China
| | - Dong Zhang
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, West Huangpu Avenue No. 613, Guangzhou, 510630, China
| | - Xukai Mo
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, West Huangpu Avenue No. 613, Guangzhou, 510630, China
| | - Xiaojun Yu
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, West Huangpu Avenue No. 613, Guangzhou, 510630, China
| | - Minfeng Chen
- College of Pharmacy, Jinan University, West Huangpu Avenue No.601, Guangzhou, 510632, China.
| | - Liangping Luo
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, West Huangpu Avenue No. 613, Guangzhou, 510630, China.
- Engineering Research Center of Medical Imaging Artificial Intelligence for Precision Diagnosis and Treatment, West Huangpu Avenue No. 613, Guangzhou, 510630, China.
| | - Changzheng Shi
- Medical Imaging Center, The First Affiliated Hospital of Jinan University, West Huangpu Avenue No. 613, Guangzhou, 510630, China.
- Engineering Research Center of Medical Imaging Artificial Intelligence for Precision Diagnosis and Treatment, West Huangpu Avenue No. 613, Guangzhou, 510630, China.
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Andronache I, Peptenatu D, Ahammer H, Radulovic M, Djuričić GJ, Jelinek HF, Russo C, Di Ieva A. Fractals in the Neurosciences: A Translational Geographical Approach. ADVANCES IN NEUROBIOLOGY 2024; 36:953-981. [PMID: 38468071 DOI: 10.1007/978-3-031-47606-8_47] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
The chapter presents three new fractal indices (fractal fragmentation index, fractal tentacularity index, and fractal anisotropy index) and normalized Kolmogorov complexity with proven applicability in geographic research, developed by the authors, and the possibility of their future use in neuroscience. The research demonstrates the relevance of fractal analysis in different fields and the basic concepts and principles of fractal geometry being sufficient for the development of models relevant to the studied reality. Also, the research highlighted the need to continue interdisciplinary research based on known fractal indicators, as well as the development of new analysis methods with the translational potential between fields.
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Affiliation(s)
- Ion Andronache
- Research Center for Integrated Analysis and Territorial Management, Faculty of Geography, University of Bucharest, Bucharest, Romania.
| | - Daniel Peptenatu
- Research Center for Integrated Analysis and Territorial Management, Faculty of Geography, University of Bucharest, Bucharest, Romania
| | - Helmut Ahammer
- GSRC, Division of Medical Physics and Biophysics, Medical University of Graz, Graz, Austria
| | - Marko Radulovic
- Department of Experimental Oncology, Institute of Oncology and Radiology of Serbia, Belgrade, Serbia
| | - Goran J Djuričić
- Department of Radiology, Faculty of Medicine, University of Belgrade, University Children's Hospital, Belgrade, Serbia
| | - Herbert F Jelinek
- Department of Medical Sciences and Biotechnology Center, Khalifa University, Abu Dhabi, UAE
| | - Carlo Russo
- Computational NeuroSurgery (CNS) Lab, Faculty of Medicine, Health and Human Sciences, Macquarie Medical School, Macquarie University, Sydney, NSW, Australia
| | - Antonio Di Ieva
- Computational NeuroSurgery (CNS) Lab, Faculty of Medicine, Health and Human Sciences, Macquarie Medical School, Macquarie University, Sydney, NSW, Australia
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Prezja F, Äyrämö S, Pölönen I, Ojala T, Lahtinen S, Ruusuvuori P, Kuopio T. Improved accuracy in colorectal cancer tissue decomposition through refinement of established deep learning solutions. Sci Rep 2023; 13:15879. [PMID: 37741820 PMCID: PMC10517936 DOI: 10.1038/s41598-023-42357-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 09/08/2023] [Indexed: 09/25/2023] Open
Abstract
Hematoxylin and eosin-stained biopsy slides are regularly available for colorectal cancer patients. These slides are often not used to define objective biomarkers for patient stratification and treatment selection. Standard biomarkers often pertain to costly and slow genetic tests. However, recent work has shown that relevant biomarkers can be extracted from these images using convolutional neural networks (CNNs). The CNN-based biomarkers predicted colorectal cancer patient outcomes comparably to gold standards. Extracting CNN-biomarkers is fast, automatic, and of minimal cost. CNN-based biomarkers rely on the ability of CNNs to recognize distinct tissue types from microscope whole slide images. The quality of these biomarkers (coined 'Deep Stroma') depends on the accuracy of CNNs in decomposing all relevant tissue classes. Improving tissue decomposition accuracy is essential for improving the prognostic potential of CNN-biomarkers. In this study, we implemented a novel training strategy to refine an established CNN model, which then surpassed all previous solutions . We obtained a 95.6% average accuracy in the external test set and 99.5% in the internal test set. Our approach reduced errors in biomarker-relevant classes, such as Lymphocytes, and was the first to include interpretability methods. These methods were used to better apprehend our model's limitations and capabilities.
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Affiliation(s)
- Fabi Prezja
- Faculty of Information Technology, University of Jyväskylä, Jyväskylä, 40014, Finland.
- Digital Health Intelligence Laboratory, University of Jyväskylä, Jyväskylä, 40014, Finland.
| | - Sami Äyrämö
- Faculty of Information Technology, University of Jyväskylä, Jyväskylä, 40014, Finland
- Digital Health Intelligence Laboratory, University of Jyväskylä, Jyväskylä, 40014, Finland
| | - Ilkka Pölönen
- Faculty of Information Technology, University of Jyväskylä, Jyväskylä, 40014, Finland
- Spectral Imaging Laboratory, University of Jyväskylä, Jyväskylä, 40014, Finland
| | - Timo Ojala
- Faculty of Information Technology, University of Jyväskylä, Jyväskylä, 40014, Finland
- Digital Health Intelligence Laboratory, University of Jyväskylä, Jyväskylä, 40014, Finland
| | - Suvi Lahtinen
- Faculty of Information Technology, University of Jyväskylä, Jyväskylä, 40014, Finland
- Department of Biological and Environmental Science, Faculty of Mathematics and Science, University of Jyväskylä, Jyväskylä, 40014, Finland
| | - Pekka Ruusuvuori
- Institute of Biomedicine, Cancer Research Unit, University of Turku, Turku, 20014, Finland
- FICAN West Cancer Centre, Turku University Hospital, Turku, 20521, Finland
| | - Teijo Kuopio
- Department of Education and Research, Hospital Nova of Central Finland, Jyväskylä, 40620, Finland
- Department of Biological and Environmental Science, University of Jyväskylä, Jyväskylä, 40014, Finland
- Department of Pathology, Hospital Nova of Central Finland, Jyväskylä, 40620, Finland
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Vestal BE, Ghosh D, Estépar RSJ, Kechris K, Fingerlin T, Carlson NE. Quantifying the spatial clustering characteristics of radiographic emphysema explains variability in pulmonary function. Sci Rep 2023; 13:13862. [PMID: 37620507 PMCID: PMC10449810 DOI: 10.1038/s41598-023-40950-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 08/18/2023] [Indexed: 08/26/2023] Open
Abstract
Quantitative assessment of emphysema in CT scans has mostly focused on calculating the percentage of lung tissue that is deemed abnormal based on a density thresholding strategy. However, this overall measure of disease burden discards virtually all the spatial information encoded in the scan that is implicitly utilized in a visual assessment. This simplification is likely grouping heterogenous disease patterns and is potentially obscuring clinical phenotypes and variable disease outcomes. To overcome this, several methods that attempt to quantify heterogeneity in emphysema distribution have been proposed. Here, we compare three of those: one based on estimating a power law for the size distribution of contiguous emphysema clusters, a second that looks at the number of emphysema-to-emphysema voxel adjacencies, and a third that applies a parametric spatial point process model to the emphysema voxel locations. This was done using data from 587 individuals from Phase 1 of COPDGene that had an inspiratory CT scan and plasma protein abundance measurements. The associations between these imaging metrics and visual assessment with clinical measures (FEV[Formula: see text], FEV[Formula: see text]-FVC ratio, etc.) and plasma protein biomarker levels were evaluated using a variety of regression models. Our results showed that a selection of spatial measures had the ability to discern heterogeneous patterns among CTs that had similar emphysema burdens. The most informative quantitative measure, average cluster size from the point process model, showed much stronger associations with nearly every clinical outcome examined than existing CT-derived emphysema metrics and visual assessment. Moreover, approximately 75% more plasma biomarkers were found to be associated with an emphysema heterogeneity phenotype when accounting for spatial clustering measures than when they were excluded.
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Affiliation(s)
- Brian E Vestal
- Center for Genes, Environment and Health, National Jewish Health, Denver, CO, USA.
| | - Debashis Ghosh
- Department of Biostatistics and Informatics, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA
| | - Raúl San José Estépar
- Applied Chest Imaging Laboratory (ACIL), Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Katerina Kechris
- Department of Biostatistics and Informatics, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA
| | - Tasha Fingerlin
- Center for Genes, Environment and Health, National Jewish Health, Denver, CO, USA
| | - Nichole E Carlson
- Department of Biostatistics and Informatics, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA
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Shi L, Zhang Y, Wang H. Prognostic prediction based on histopathologic features of tumor microenvironment in colorectal cancer. Front Med (Lausanne) 2023; 10:1154077. [PMID: 37089601 PMCID: PMC10117979 DOI: 10.3389/fmed.2023.1154077] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 03/20/2023] [Indexed: 04/09/2023] Open
Abstract
PurposeTo automatically quantify colorectal tumor microenvironment (TME) in hematoxylin and eosin stained whole slide images (WSIs), and to develop a TME signature for prognostic prediction in colorectal cancer (CRC).MethodsA deep learning model based on VGG19 architecture and transfer learning strategy was trained to recognize nine different tissue types in whole slide images of patients with CRC. Seven of the nine tissue types were defined as TME components besides background and debris. Then 13 TME features were calculated based on the areas of TME components. A total of 562 patients with gene expression data, survival information and WSIs were collected from The Cancer Genome Atlas project for further analysis. A TME signature for prognostic prediction was developed and validated using Cox regression method. A prognostic prediction model combined the TME signature and clinical variables was also established. At last, gene-set enrichment analysis was performed to identify the significant TME signature associated pathways by querying Gene Ontology database and Kyoto Encyclopedia of Genes and Genomes database.ResultsThe deep learning model achieved an accuracy of 94.2% for tissue type recognition. The developed TME signature was found significantly associated to progression-free survival. The clinical combined model achieved a concordance index of 0.714. Gene-set enrichment analysis revealed the TME signature associated genes were enriched in neuroactive ligand-receptor interaction pathway.ConclusionThe TME signature was proved to be a prognostic factor and the associated biologic pathways would be beneficial to a better understanding of TME in CRC patients.
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Affiliation(s)
- Liang Shi
- School of Clinical Medicine, Hebei University, Baoding, Hebei, China
- The First Department of General Surgery, Cangzhou Central Hospital of Hebei Province, Cangzhou, Hebei, China
| | - Yuhao Zhang
- Department of Neurosurgery, Zhejiang Provincial People's Hospital, Affiliated to Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Hong Wang
- School of Clinical Medicine, Hebei University, Baoding, Hebei, China
- *Correspondence: Hong Wang,
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7
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Djuričić GJ, Ahammer H, Rajković S, Kovač JD, Milošević Z, Sopta JP, Radulovic M. Directionally Sensitive Fractal Radiomics Compatible With Irregularly Shaped Magnetic Resonance Tumor Regions of Interest: Association With Osteosarcoma Chemoresistance. J Magn Reson Imaging 2023; 57:248-258. [PMID: 35561019 DOI: 10.1002/jmri.28232] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 05/03/2022] [Accepted: 05/04/2022] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Computational analysis of routinely acquired MRI has potential to improve the tumor chemoresistance prediction and to provide decision support in precision medicine, which may extend patient survival. Most radiomic analytical methods are compatible only with rectangular regions of interest (ROIs) and irregular tumor shape is therefore an important limitation. Furthermore, the currently used analytical methods are not directionally sensitive. PURPOSE To implement a tumor analysis that is directionally sensitive and compatible with irregularly shaped ROIs. STUDY TYPE Retrospective. SUBJECTS A total of 54 patients with histopathologic diagnosis of primary osteosarcoma on tubular long bones and with prechemotherapy MRI. FIELD STRENGTH/SEQUENCE A 1.5 T, T2-weighted-short-tau-inversion-recovery-fast-spin-echo. ASSESSMENT A model to explore associations with osteosarcoma chemo-responsiveness included MRI data obtained before OsteoSa MAP neoadjuvant cytotoxic chemotherapy. Osteosarcoma morphology was analyzed in the MRI data by calculation of the nondirectional two-dimensional (2D) and directional and nondirectional one-dimensional (1D) Higuchi dimensions (Dh). MAP chemotherapy response was assessed by histopathological necrosis. STATISTICAL TESTS The area under the receiver operating characteristic (ROC) curve (AUC) evaluated the association of the calculated features with the actual chemoresponsiveness, using tumor histopathological necrosis (95%) as the endpoint. Least absolute shrinkage and selection operator (LASSO) machine learning and multivariable regression were used for feature selection. Significance was set at <0.05. RESULTS The nondirectional 1D Dh reached an AUC of 0.88 in association with the 95% tumor necrosis, while the directional 1D analysis along 180 radial lines significantly improved this association according to the Hanley/McNeil test, reaching an AUC of 0.95. The model defined by variable selection using LASSO reached an AUC of 0.98. The directional analysis showed an optimal predictive range between 90° and 97° and revealed structural osteosarcoma anisotropy manifested by its directionally dependent textural properties. DATA CONCLUSION Directionally sensitive radiomics had superior predictive performance in comparison to the standard nondirectional image analysis algorithms with AUCs reaching 0.95 and full compatibility with irregularly shaped ROIs. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Goran J Djuričić
- Faculty of Medicine, Department of Radiology, University of Belgrade, University Children's Hospital, Belgrade, Serbia
| | - Helmut Ahammer
- Division of Biophysics, GSRC, Medical University of Graz, Graz, Austria
| | - Stanislav Rajković
- Faculty of Medicine, University of Belgrade, Institute for Orthopaedics "Banjica", Belgrade, Serbia
| | - Jelena Djokić Kovač
- University of Belgrade, Faculty of Medicine, Center for Radiology, Clinical Center of Serbia, Belgrade, Serbia
| | - Zorica Milošević
- University of Belgrade, Faculty of Medicine, Institute for Oncology & Radiology of Serbia, Clinic for Radiation Oncology and Radiology, Belgrade, Serbia
| | - Jelena P Sopta
- University of Belgrade, Faculty of Medicine, Institute of Pathology, Belgrade, Serbia
| | - Marko Radulovic
- Department of Experimental Oncology, Institute for Oncology & Radiology of Serbia, Belgrade, Serbia
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Civale J, Parasaram V, Bamber JC, Harris EJ. High frequency ultrasound vibrational shear wave elastography for preclinical research. Phys Med Biol 2022; 67:245005. [PMID: 36410042 PMCID: PMC9728510 DOI: 10.1088/1361-6560/aca4b8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 11/21/2022] [Indexed: 11/22/2022]
Abstract
Preclinical evaluation of novel therapies using models of cancer is an important tool in cancer research, where imaging can provide non-invasive tools to characterise the internal structure and function of tumours. The short propagation paths when imaging tumours and organs in small animals allow the use of high frequencies for both ultrasound and shear waves, providing the opportunity for high-resolution shear wave elastography and hence its use for studying the heterogeneity of tissue elasticity, where heterogeneity may be a predictor of tissue response. Here we demonstrate vibrational shear wave elastography (VSWE) using a mechanical actuator to produce high frequency (up to 1000 Hz) shear waves in preclinical tumours, an alternative to the majority of preclinical ultrasound SWE studies where an acoustic radiation force impulse is required to create a relatively low-frequency broad-band shear-wave pulse. We implement VSWE with a high frequency (17.8 MHz) probe running a focused line-by-line ultrasound imaging sequence which as expected was found to offer improved detection of 1000 Hz shear waves over an ultrafast planar wave imaging sequence in a homogenous tissue-mimicking phantom. We test the VSWE in anex vivotumour xenograft, demonstrating the ability to detect shear waves up to 10 mm from the contactor position at 1000 Hz. By reducing the kernel size used for shear wave speed estimation to 1 mm we are able to produce shear wave speed images with spatial resolution of this order. Finally, we present VSWE data from xenograft tumoursin vivo, demonstrating the feasibility of the technique in mice under isoflurane sedation. Mean shear wave speeds in the tumours are in good agreements with those reported by previous authors. Characterising the frequency dependence of shear wave speed demonstrates the potential to quantify the viscoelastic properties of tumoursin vivo.
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Affiliation(s)
- J Civale
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, Sutton, United Kingdom
| | - V Parasaram
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, Sutton, United Kingdom
| | - JC Bamber
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, Sutton, United Kingdom
| | - EJ Harris
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, Sutton, United Kingdom
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Rodrigues-Ferreira S, Nahmias C. Predictive biomarkers for personalized medicine in breast cancer. Cancer Lett 2022; 545:215828. [PMID: 35853538 DOI: 10.1016/j.canlet.2022.215828] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 07/04/2022] [Accepted: 07/10/2022] [Indexed: 12/14/2022]
Abstract
Breast cancer is one of the most frequent malignancies among women worldwide. Based on clinical and molecular features of breast tumors, patients are treated with chemotherapy, hormonal therapy and/or radiotherapy and more recently with immunotherapy or targeted therapy. These different therapeutic options have markedly improved patient outcomes. However, further improvement is needed to fight against resistance to treatment. In the rapidly growing area of research for personalized medicine, predictive biomarkers - which predict patient response to therapy - are essential tools to select the patients who are most likely to benefit from the treatment, with the aim to give the right therapy to the right patient and avoid unnecessary overtreatment. The search for predictive biomarkers is an active field of research that includes genomic, proteomic and/or machine learning approaches. In this review, we describe current strategies and innovative tools to identify, evaluate and validate new biomarkers. We also summarize current predictive biomarkers in breast cancer and discuss companion biomarkers of targeted therapy in the context of precision medicine.
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Affiliation(s)
- Sylvie Rodrigues-Ferreira
- Gustave Roussy Institute, INSERM U981, Prédicteurs moléculaires et nouvelles cibles en oncologie, Villejuif, France; LabEx LERMIT, Université Paris-Saclay, 92296 Châtenay-Malabry, France; Inovarion, 75005, Paris, France
| | - Clara Nahmias
- Gustave Roussy Institute, INSERM U981, Prédicteurs moléculaires et nouvelles cibles en oncologie, Villejuif, France; LabEx LERMIT, Université Paris-Saclay, 92296 Châtenay-Malabry, France.
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10
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Schick F. Automatic segmentation and volumetric assessment of internal organs and fatty tissue: what are the benefits? MAGNETIC RESONANCE MATERIALS IN PHYSICS, BIOLOGY AND MEDICINE 2022; 35:187-192. [PMID: 34919193 PMCID: PMC8995273 DOI: 10.1007/s10334-021-00986-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 12/03/2021] [Accepted: 12/05/2021] [Indexed: 02/07/2023]
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Xie P, Zuo K, Liu J, Chen M, Zhao S, Kang W, Li F. Interpretable Diagnosis for Whole-Slide Melanoma Histology Images Using Convolutional Neural Network. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:8396438. [PMID: 34760142 PMCID: PMC8575613 DOI: 10.1155/2021/8396438] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 10/13/2021] [Indexed: 02/08/2023]
Abstract
At present, deep learning-based medical image diagnosis had achieved high performance in several diseases. However, the black-box nature of the convolutional neural network (CNN) limits their role in diagnosis. In this study, a novel interpretable diagnosis pipeline using the CNN model was proposed. Furthermore, a sizeable melanoma database that contains 841 digital whole-slide images (WSIs) was built to train and evaluate the model. The model achieved strong melanoma classification ability (0.962 areas under the receiver operating characteristic, 0.887 sensitivity, and 0.925 specificity). Moreover, the proposed model outperformed the existing schemes in terms of accuracy that is 20 pathologists (0.933 vs 0.732 accuracy). Finally, the gradient-weighted class activation mapping (Grad-CAM) method was used to show the inner logic of the proposed model and its feasibility to improve diagnosis process in healthcare. The mechanism of feature heat maps which is visualized through a saliency mapping has demonstrated that features learned or extracted by the proposed model are compatible with the accepted pathological features. Conclusively, the proposed model provides a rapid and accurate diagnosis by locating the distinctive features of melanoma to build doctors' trust in the CNNs' diagnosis results.
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Affiliation(s)
- Peizhen Xie
- National University of Defense Technology, Changsha 410073, China
| | - Ke Zuo
- National University of Defense Technology, Changsha 410073, China
| | - Jie Liu
- National University of Defense Technology, Changsha 410073, China
| | - Mingliang Chen
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Shuang Zhao
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha 410008, China
- Hunan Key Laboratory of Skin Cancer and Psoriasis, Changsha 410005, China
- Hunan Engineering Research Center of Skin Health and Disease, Changsha 410005, China
| | - Wenjie Kang
- National University of Defense Technology, Changsha 410073, China
- Hunan Provincial Key Laboratory of Network Investigational Technology, Hunan Police Academy, Changsha 410138, China
- Key Laboratory of Police Internet of Things Application,Ministry of Public Security, Changsha 410138, China
| | - Fangfang Li
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha 410008, China
- Hunan Key Laboratory of Skin Cancer and Psoriasis, Changsha 410005, China
- Hunan Engineering Research Center of Skin Health and Disease, Changsha 410005, China
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12
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Karami G, Giuseppe Orlando M, Delli Pizzi A, Caulo M, Del Gratta C. Predicting Overall Survival Time in Glioblastoma Patients Using Gradient Boosting Machines Algorithm and Recursive Feature Elimination Technique. Cancers (Basel) 2021; 13:4976. [PMID: 34638460 PMCID: PMC8507924 DOI: 10.3390/cancers13194976] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 09/20/2021] [Accepted: 09/29/2021] [Indexed: 11/16/2022] Open
Abstract
Despite advances in tumor treatment, the inconsistent response is a major challenge among glioblastoma multiform (GBM) that lead to different survival time. Our aim was to integrate multimodal MRI with non-supervised and supervised machine learning methods to predict GBM patients' survival time. To this end, we identified different compartments of the tumor and extracted their features. Next, we applied Random Forest-Recursive Feature Elimination (RF-RFE) to identify the most relevant features to feed into a GBoost machine. This study included 29 GBM patients with known survival time. RF-RFE GBoost model was evaluated to assess the survival prediction performance using optimal features. Furthermore, overall survival (OS) was analyzed using univariate and multivariate Cox regression analyses, to evaluate the effect of ROIs and their features on survival. The results showed that a RF-RFE Gboost machine was able to predict survival time with 75% accuracy. The results also revealed that the rCBV in the low perfusion area was significantly different between groups and had the greatest effect size in terms of the rate of change of the response variable (survival time). In conclusion, not only integration of multi-modality MRI but also feature selection method can enhance the classifier performance.
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Affiliation(s)
- Golestan Karami
- Department of Neuroscience, Imaging and Clinical Sciences, Gabriele D'Annunzio University, 66100 Chieti, Italy
- Institute for Advanced Biomedical Technologies, Gabriele D'Annunzio University, 66100 Chieti, Italy
| | - Marco Giuseppe Orlando
- Department of Neuroscience, Imaging and Clinical Sciences, Gabriele D'Annunzio University, 66100 Chieti, Italy
| | - Andrea Delli Pizzi
- Department of Neuroscience, Imaging and Clinical Sciences, Gabriele D'Annunzio University, 66100 Chieti, Italy
- Institute for Advanced Biomedical Technologies, Gabriele D'Annunzio University, 66100 Chieti, Italy
| | - Massimo Caulo
- Department of Neuroscience, Imaging and Clinical Sciences, Gabriele D'Annunzio University, 66100 Chieti, Italy
- Institute for Advanced Biomedical Technologies, Gabriele D'Annunzio University, 66100 Chieti, Italy
| | - Cosimo Del Gratta
- Department of Neuroscience, Imaging and Clinical Sciences, Gabriele D'Annunzio University, 66100 Chieti, Italy
- Institute for Advanced Biomedical Technologies, Gabriele D'Annunzio University, 66100 Chieti, Italy
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13
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Sun H, Hu H, Liu C, Sun N, Duan C. Methods used for the measurement of blood-brain barrier integrity. Metab Brain Dis 2021; 36:723-735. [PMID: 33635479 DOI: 10.1007/s11011-021-00694-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 02/11/2021] [Indexed: 01/12/2023]
Abstract
The blood-brain barrier (BBB) comprises the interface between blood, brain and cerebrospinal fluid. Its primary function, which is mainly carried out by tight junctions, is to stabilize the tightly controlled microenvironment of the brain. To study the development and maintenance of the BBB, as well as various roles their intrinsic mechanisms that play in neurological disorders, suitable measurements are required to demonstrate integrity and functional changes at the interfaces between the blood and brain tissue. Markers and plasma proteins with different molecular weight (MW) are used to measure the permeability of BBB. In addition, the expression changes of tight-junction proteins form the basic structure of BBB, and imaging modalities are available to study the disruption of BBB. In the present review, above mentioned methods are depicted in details, together with the pros and cons as well as the differences between these methods, which maybe benefit research studies focused on the detection of BBB breakdown.
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Affiliation(s)
- Huixin Sun
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
- Department of Clinical Laboratory, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong Province, China
| | - Huiling Hu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
- Department of Clinical Laboratory, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong Province, China
| | - Chuanjie Liu
- Weihai City Key Laboratory of Autoimmunity, Weihai Central Hospital, Weihai, 264400, Shandong Province, China
| | - Nannan Sun
- State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, 100005, China.
| | - Chaohui Duan
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China.
- Department of Clinical Laboratory, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong Province, China.
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Coolens C, Gwilliam MN, Alcaide-Leon P, de Freitas Faria IM, Ynoe de Moraes F. Transformational Role of Medical Imaging in (Radiation) Oncology. Cancers (Basel) 2021; 13:cancers13112557. [PMID: 34070984 PMCID: PMC8197089 DOI: 10.3390/cancers13112557] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 05/18/2021] [Accepted: 05/19/2021] [Indexed: 12/30/2022] Open
Abstract
Simple Summary Onboard, imaging techniques have brought about a huge transformation in the ability to deliver targeted radiation therapies. Each generation of these technologies enables us to better visualize where to deliver lethal doses of radiation and thus allows the shrinking of necessary geometric margins leading to reduced toxicities. Alongside improvements in treatment delivery, advances in medical imaging have also allowed us to better define the volumes we wish to target. The development of imaging techniques that can capture aspects of the tumor’s biology before, during and after therapy is transforming how treatment can be delivered. Technological changes have further made these biological imaging techniques available in real-time providing the opportunity to monitor a patient’s response to treatment closely and often before any volume changes are visible on conventional radiological images. Here we discuss the development of robust quantitative imaging biomarkers and how they can personalize therapy towards meaningful clinical endpoints. Abstract Onboard, real-time, imaging techniques, from the original megavoltage planar imaging devices, to the emerging combined MRI-Linear Accelerators, have brought a huge transformation in the ability to deliver targeted radiation therapies. Each generation of these technologies enables lethal doses of radiation to be delivered to target volumes with progressively more accuracy and thus allows shrinking of necessary geometric margins, leading to reduced toxicities. Alongside these improvements in treatment delivery, advances in medical imaging, e.g., PET, and MRI, have also allowed target volumes themselves to be better defined. The development of functional and molecular imaging is now driving a conceptually larger step transformation to both better understand the cancer target and disease to be treated, as well as how tumors respond to treatment. A biological description of the tumor microenvironment is now accepted as an essential component of how to personalize and adapt treatment. This applies not only to radiation oncology but extends widely in cancer management from surgical oncology planning and interventional radiology, to evaluation of targeted drug delivery efficacy in medical oncology/immunotherapy. Here, we will discuss the role and requirements of functional and metabolic imaging techniques in the context of brain tumors and metastases to reliably provide multi-parametric imaging biomarkers of the tumor microenvironment.
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Affiliation(s)
- Catherine Coolens
- Department of Medical Physics, Princess Margaret Cancer Centre & University Health Network, Toronto, ON M5G 1Z5, Canada;
- Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
- Department of Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada
- TECHNA Institute, University Health Network, Toronto, ON M5G 1Z5, Canada
- Correspondence:
| | - Matt N. Gwilliam
- Department of Medical Physics, Princess Margaret Cancer Centre & University Health Network, Toronto, ON M5G 1Z5, Canada;
| | - Paula Alcaide-Leon
- Joint Department of Medical Imaging, University Health Network, Toronto, ON M5G 1Z5, Canada;
| | | | - Fabio Ynoe de Moraes
- Department of Oncology, Division of Radiation Oncology, Queen’s University, Kingston, ON K7L 5P9, Canada;
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Könik A, O'Donoghue JA, Wahl RL, Graham MM, Van den Abbeele AD. Theranostics: The Role of Quantitative Nuclear Medicine Imaging. Semin Radiat Oncol 2021; 31:28-36. [PMID: 33246633 DOI: 10.1016/j.semradonc.2020.07.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Theranostics is a precision medicine discipline that integrates diagnostic nuclear medicine imaging with radionuclide therapy in a manner that provides both a tumor phenotype and personalized therapy to patients with cancer using radiopharmaceuticals aimed at the same target-specific biological pathway or receptor. The aim of quantitative nuclear medicine imaging is to plan the alpha or beta-emitting therapy based on an accurate 3-dimensional representation of the in-vivo distribution of radioactivity concentration within the tumor and normal organs/tissues in a noninvasive manner. In general, imaging may be either based on positron emission tomography (PET) or single photon emission computed tomography (SPECT) invariably in combination with X-ray CT (PET/CT; SPECT/CT) or, to a much lesser extent, MRI. PET and SPECT differ in terms of the radionuclides and physical processes that give rise to the emission of high energy photons, as well as the sets of technologies involved in their detection. Using a variety of standardized quantitative parameters, system calibration, patient preparation, imaging acquisition and reconstruction protocols, and image analysis protocols, an accurate quantification of the tracer distribution can be obtained, which helps prescribe the therapeutic dose for each patient.
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Affiliation(s)
- Arda Könik
- Department of Imaging, Dana-Farber Cancer Institute, Boston, MA.
| | - Joseph A O'Donoghue
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Richard L Wahl
- Department of Radiology, Mallinckrodt Institute of Radiology, Washington University in St Louis School of Medicine, St. Louis, MO
| | - Michael M Graham
- Past Director of Nuclear Medicine, Roy J and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, IA
| | - Annick D Van den Abbeele
- Department of Imaging, Dana-Farber Cancer Institute, Boston, MA; Division of Cancer Imaging, Mass General Brigham, Boston, MA; Dana-Farber Cancer Institute and Mass General Brigham, Boston, MA; Center for Biomedical Imaging in Oncology, Dana-Farber Cancer Institute, Boston, MA; Tumor Imaging Metrics Core, Dana-Farber/Harvard Cancer Center, Boston, MA
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16
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Song J, Wang L, Ng NN, Zhao M, Shi J, Wu N, Li W, Liu Z, Yeom KW, Tian J. Development and Validation of a Machine Learning Model to Explore Tyrosine Kinase Inhibitor Response in Patients With Stage IV EGFR Variant-Positive Non-Small Cell Lung Cancer. JAMA Netw Open 2020; 3:e2030442. [PMID: 33331920 PMCID: PMC7747022 DOI: 10.1001/jamanetworkopen.2020.30442] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
IMPORTANCE An end-to-end efficacy evaluation approach for identifying progression risk after epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) therapy in patients with stage IV EGFR variant-positive non-small cell lung cancer (NSCLC) is lacking. OBJECTIVE To propose a clinically applicable large-scale bidirectional generative adversarial network for predicting the efficacy of EGFR-TKI therapy in patients with NSCLC. DESIGN, SETTING, AND PARTICIPANTS This diagnostic/prognostic study enrolled 465 patients from January 1, 2010, to August 1, 2017, with follow-up from February 1, 2010, to June 1, 2020. A deep learning (DL) semantic signature to predict progression-free survival (PFS) was constructed in the training cohort, validated in 2 external validation and 2 control cohorts, and compared with the radiomics signature. EXPOSURES An end-to-end bidirectional generative adversarial network framework was designed to predict the progression risk in patients with NSCLC. MAIN OUTCOMES AND MEASURES The primary end point was PFS, considering the time from the initiation of therapy to the date of recurrence, confirmed disease progression, or death. RESULTS A total of 342 patients with stage IV EGFR variant-positive NSCLC receiving EGFR-TKI therapy met the inclusion criteria. Of these, 145 patients from 2 of the hospitals (n = 117 and 28) formed a training cohort (mean [SD] age, 61 [11] years; 87 [60.0%] female), and the patients from 2 other hospitals comprised 2 external validation cohorts (validation cohort 1: n = 101; mean [SD] age, 57 [12] years; 60 [59.4%] female; and validation cohort 2: n = 96, mean [SD] age, 58 [9] years; 55 [57.3%] female). Fifty-six patients with advanced-stage EGFR variant-positive NSCLC (mean [SD] age, 52 [11] years; 26 [46.4%] female) and 67 patients with advanced-stage EGFR wild-type NSCLC (mean [SD] age, 54 [10] years; 10 [15.0%] female) who received first-line chemotherapy were included. A total of 90 (26%) receiving EGFR-TKI therapy with a high risk of rapid disease progression were identified (median [range] PFS, 7.3 [1.4-32.0] months in the training cohort, 5.0 [0.6-34.6] months in validation cohort 1, and 6.4 [1.8-20.1] months, in validation cohort 2) using the DL semantic signature.The PFS decreased by 36% (hazard ratio, 2.13; 95% CI, 1.30-3.49; P < .001) compared with that in other patients (median [range] PFS, 11.5 [1.5-64.2] months in the training cohort, 10.9 [1.1-50.5] in validation cohort 1, and 8.9 [0.8-40.6] months in validation cohort 2. No significant differences were observed when comparing the PFS of high-risk patients receiving EGFR-TKI therapy with the chemotherapy cohorts (median PFS, 6.9 vs 4.4 months; P = .08). In terms of predicting the tumor progression risk after EGFR-TKI therapy, clinical decisions based on the DL semantic signature led to better survival outcomes than those based on radiomics signature across all risk probabilities by the decision curve analysis. CONCLUSIONS AND RELEVANCE This diagnostic/prognostic study provides a clinically applicable approach for identifying patients with stage IV EGFR variant-positive NSCLC who are not likely to benefit from EGFR-TKI therapy. The end-to-end DL-derived semantic features eliminated all manual interventions required while using previous radiomics methods and have a better prognostic performance.
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Affiliation(s)
- Jiangdian Song
- Department of Biomedical Engineering, College of Medicine and Biological Information Engineering, Northeastern University. Shenyang, Liaoning, China
- Department of Radiology, School of Medicine Stanford University, Stanford, California
| | - Lu Wang
- Department of Medical Informatics, China Medical University, Shenyang, Liaoning, China
| | - Nathan Norton Ng
- Department of Radiology, School of Medicine Stanford University, Stanford, California
| | - Mingfang Zhao
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Jingyun Shi
- Department of Radiology, Shanghai Pulmonary Hospital, Shanghai, China
| | - Ning Wu
- National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Weimin Li
- Department of Respiratory and Critical Care Medicine, West China Hospital, Chengdu, Sichuan, China
| | - Zaiyi Liu
- Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Kristen W. Yeom
- Department of Radiology, School of Medicine Stanford University, Stanford, California
| | - Jie Tian
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, China
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Zeng H, Chen L, Huang Y, Luo Y, Ma X. Integrative Models of Histopathological Image Features and Omics Data Predict Survival in Head and Neck Squamous Cell Carcinoma. Front Cell Dev Biol 2020; 8:553099. [PMID: 33195188 PMCID: PMC7658095 DOI: 10.3389/fcell.2020.553099] [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: 04/17/2020] [Accepted: 10/08/2020] [Indexed: 02/05/2023] Open
Abstract
Background Both histopathological image features and genomics data were associated with survival outcome of cancer patients. However, integrating features of histopathological images, genomics and other omics for improving prognosis prediction has not been reported in head and neck squamous cell carcinoma (HNSCC). Methods A dataset of 216 HNSCC patients was derived from the Cancer Genome Atlas (TCGA) with information of clinical characteristics, genetic mutation, RNA sequencing, protein expression and histopathological images. Patients were randomly assigned into training (n = 108) or validation (n = 108) sets. We extracted 593 quantitative image features, and used random forest algorithm with 10-fold cross-validation to build prognostic models for overall survival (OS) in training set, then compared the area under the time-dependent receiver operating characteristic curve (AUC) in validation set. Results In validation set, histopathological image features had significant predictive value for OS (5-year AUC = 0.784). The histopathology + omics models showed better predictive performance than genomics, transcriptomics or proteomics alone. Moreover, the multi-omics model incorporating image features, genomics, transcriptomics and proteomics reached the maximal 1-, 3-, and 5-year AUC of 0.871, 0.908, and 0.929, with most significant survival difference (HR = 10.66, 95%CI: 5.06–26.8, p < 0.001). Decision curve analysis also revealed a better net benefit of multi-omics model. Conclusion The histopathological images could provide complementary features to improve prognostic performance for HNSCC patients. The integrative model of histopathological image features and omics data might serve as an effective tool for survival prediction and risk stratification in clinical practice.
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Affiliation(s)
- Hao Zeng
- State Key Laboratory of Biotherapy, Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University Collaborative Innovation Center, Chengdu, China
| | - Linyan Chen
- State Key Laboratory of Biotherapy, Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University Collaborative Innovation Center, Chengdu, China
| | - Yeqian Huang
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Yuling Luo
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Xuelei Ma
- State Key Laboratory of Biotherapy, Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University Collaborative Innovation Center, Chengdu, China
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Shaikh F, Dupont-Roettger D, Dehmeshki J, Awan O, Kubassova O, Bisdas S. The Role of Imaging Biomarkers Derived From Advanced Imaging and Radiomics in the Management of Brain Tumors. Front Oncol 2020; 10:559946. [PMID: 33072586 PMCID: PMC7539039 DOI: 10.3389/fonc.2020.559946] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 08/13/2020] [Indexed: 01/22/2023] Open
Affiliation(s)
- Faiq Shaikh
- Image Analysis Group, Philadelphia, PA, United States
| | | | - Jamshid Dehmeshki
- Image Analysis Group, Philadelphia, PA, United States.,Department of Computer Science, Kingston University, Kingston-upon-Thames, United Kingdom
| | - Omer Awan
- Department of Radiology, University of Maryland Medical Center, Baltimore, MD, United States
| | | | - Sotirios Bisdas
- Department of Neuroradiology, University College London, London, United Kingdom
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19
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Besson FL, Fernandez B, Faure S, Mercier O, Seferian A, Mignard X, Mussot S, le Pechoux C, Caramella C, Botticella A, Levy A, Parent F, Bulifon S, Montani D, Mitilian D, Fadel E, Planchard D, Besse B, Ghigna-Bellinzoni MR, Comtat C, Lebon V, Durand E. 18F-FDG PET and DCE kinetic modeling and their correlations in primary NSCLC: first voxel-wise correlative analysis of human simultaneous [18F]FDG PET-MRI data. EJNMMI Res 2020; 10:88. [PMID: 32734484 PMCID: PMC7392998 DOI: 10.1186/s13550-020-00671-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 07/14/2020] [Indexed: 02/07/2023] Open
Abstract
OBJECTIVES To decipher the correlations between PET and DCE kinetic parameters in non-small-cell lung cancer (NSCLC), by using voxel-wise analysis of dynamic simultaneous [18F]FDG PET-MRI. MATERIAL AND METHODS Fourteen treatment-naïve patients with biopsy-proven NSCLC prospectively underwent a 1-h dynamic [18F]FDG thoracic PET-MRI scan including DCE. The PET and DCE data were normalized to their corresponding T1-weighted MR morphological space, and tumors were masked semi-automatically. Voxel-wise parametric maps of PET and DCE kinetic parameters were computed by fitting the dynamic PET and DCE tumor data to the Sokoloff and Extended Tofts models respectively, by using in-house developed procedures. Curve-fitting errors were assessed by computing the relative root mean square error (rRMSE) of the estimated PET and DCE signals at the voxel level. For each tumor, Spearman correlation coefficients (rs) between all the pairs of PET and DCE kinetic parameters were estimated on a voxel-wise basis, along with their respective bootstrapped 95% confidence intervals (n = 1000 iterations). RESULTS Curve-fitting metrics provided fit errors under 20% for almost 90% of the PET voxels (median rRMSE = 10.3, interquartile ranges IQR = 8.1; 14.3), whereas 73.3% of the DCE voxels showed fit errors under 45% (median rRMSE = 31.8%, IQR = 22.4; 46.6). The PET-PET, DCE-DCE, and PET-DCE voxel-wise correlations varied according to individual tumor behaviors. Beyond this wide variability, the PET-PET and DCE-DCE correlations were mainly high (absolute rs values > 0.7), whereas the PET-DCE correlations were mainly low to moderate (absolute rs values < 0.7). Half the tumors showed a hypometabolism with low perfused/vascularized profile, a hallmark of hypoxia, and tumor aggressiveness. CONCLUSION A dynamic "one-stop shop" procedure applied to NSCLC is technically feasible in clinical practice. PET and DCE kinetic parameters assessed simultaneously are not highly correlated in NSCLC, and these correlations showed a wide variability among tumors and patients. These results tend to suggest that PET and DCE kinetic parameters might provide complementary information. In the future, this might make PET-MRI a unique tool to characterize the individual tumor biological behavior in NSCLC.
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Affiliation(s)
- Florent L Besson
- Université Paris-Saclay, CEA, CNRS, Inserm, BioMAPs, 91401, Orsay, France.
- Department of Biophysics and Nuclear Medicine-Molecular Imaging, Hôpitaux Universitaires Paris-Saclay, Assistance Publique-Hôpitaux de Paris, CHU Bicêtre, 94270, Le Kremlin-Bicêtre, France.
- School of Medicine, Université Paris-Saclay, Le Kremlin-Bicêtre, France.
| | | | - Sylvain Faure
- Laboratoire de Mathématiques d'Orsay, CNRS, Université Paris-Saclay, 91405, Orsay, France
| | - Olaf Mercier
- Department of Thoracic and Vascular Surgery and Heart-Lung Transplantation, Marie Lannelongue Hospital, 92350, Le Plessis Robinson, France
| | - Andrei Seferian
- Service de Pneumologie, Centre de Référence de l'Hypertension Pulmonaire, Hôpitaux Universitaires Paris-Saclay, Assistance Publique-Hôpitaux de Paris, 94270, Le Kremlin-Bicêtre, France
- Inserm UMR_S999, Marie Lannelongue Hospital, 92350, Le Plessis Robinson, France
| | - Xavier Mignard
- Service de Pneumologie, Centre de Référence de l'Hypertension Pulmonaire, Hôpitaux Universitaires Paris-Saclay, Assistance Publique-Hôpitaux de Paris, 94270, Le Kremlin-Bicêtre, France
| | - Sacha Mussot
- Department of Thoracic and Vascular Surgery and Heart-Lung Transplantation, Marie Lannelongue Hospital, 92350, Le Plessis Robinson, France
| | - Cecile le Pechoux
- Department of Radiation Oncology, Institut d'Oncologie Thoracique (IOT), Gustave Roussy, Université Paris Saclay, Villejuif, France
| | - Caroline Caramella
- Department of Radiology, Institut d'Oncologie Thoracique (IOT), Gustave Roussy, Université Paris Saclay, Villejuif, France
| | - Angela Botticella
- Department of Radiation Oncology, Institut d'Oncologie Thoracique (IOT), Gustave Roussy, Université Paris Saclay, Villejuif, France
| | - Antonin Levy
- Department of Radiation Oncology, Institut d'Oncologie Thoracique (IOT), Gustave Roussy, Université Paris Saclay, Villejuif, France
| | - Florence Parent
- Service de Pneumologie, Centre de Référence de l'Hypertension Pulmonaire, Hôpitaux Universitaires Paris-Saclay, Assistance Publique-Hôpitaux de Paris, 94270, Le Kremlin-Bicêtre, France
- Inserm UMR_S999, Marie Lannelongue Hospital, 92350, Le Plessis Robinson, France
| | - Sophie Bulifon
- Service de Pneumologie, Centre de Référence de l'Hypertension Pulmonaire, Hôpitaux Universitaires Paris-Saclay, Assistance Publique-Hôpitaux de Paris, 94270, Le Kremlin-Bicêtre, France
- Inserm UMR_S999, Marie Lannelongue Hospital, 92350, Le Plessis Robinson, France
| | - David Montani
- Service de Pneumologie, Centre de Référence de l'Hypertension Pulmonaire, Hôpitaux Universitaires Paris-Saclay, Assistance Publique-Hôpitaux de Paris, 94270, Le Kremlin-Bicêtre, France
- Inserm UMR_S999, Marie Lannelongue Hospital, 92350, Le Plessis Robinson, France
| | - Delphine Mitilian
- Department of Thoracic and Vascular Surgery and Heart-Lung Transplantation, Marie Lannelongue Hospital, 92350, Le Plessis Robinson, France
| | - Elie Fadel
- Department of Thoracic and Vascular Surgery and Heart-Lung Transplantation, Marie Lannelongue Hospital, 92350, Le Plessis Robinson, France
| | - David Planchard
- Department of Oncology, Institut d'Oncologie Thoracique (IOT), Gustave Roussy, Université Paris Saclay, Villejuif, France
| | - Benjamin Besse
- Department of Oncology, Institut d'Oncologie Thoracique (IOT), Gustave Roussy, Université Paris Saclay, Villejuif, France
| | | | - Claude Comtat
- Université Paris-Saclay, CEA, CNRS, Inserm, BioMAPs, 91401, Orsay, France
- School of Medicine, Université Paris-Saclay, Le Kremlin-Bicêtre, France
| | - Vincent Lebon
- Université Paris-Saclay, CEA, CNRS, Inserm, BioMAPs, 91401, Orsay, France
- School of Medicine, Université Paris-Saclay, Le Kremlin-Bicêtre, France
| | - Emmanuel Durand
- Université Paris-Saclay, CEA, CNRS, Inserm, BioMAPs, 91401, Orsay, France
- Department of Biophysics and Nuclear Medicine-Molecular Imaging, Hôpitaux Universitaires Paris-Saclay, Assistance Publique-Hôpitaux de Paris, CHU Bicêtre, 94270, Le Kremlin-Bicêtre, France
- School of Medicine, Université Paris-Saclay, Le Kremlin-Bicêtre, France
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Rubin DL, Ugur Akdogan M, Altindag C, Alkim E. ePAD: An Image Annotation and Analysis Platform for Quantitative Imaging. ACTA ACUST UNITED AC 2020; 5:170-183. [PMID: 30854455 PMCID: PMC6403025 DOI: 10.18383/j.tom.2018.00055] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Medical imaging is critical for assessing the response of patients to new cancer therapies. Quantitative lesion assessment on images is time-consuming, and adopting new promising quantitative imaging biomarkers of response in clinical trials is challenging. The electronic Physician Annotation Device (ePAD) is a freely available web-based zero-footprint software application for viewing, annotation, and quantitative analysis of radiology images designed to meet the challenges of quantitative evaluation of cancer lesions. For imaging researchers, ePAD calculates a variety of quantitative imaging biomarkers that they can analyze and compare in ePAD to identify potential candidates as surrogate endpoints in clinical trials. For clinicians, ePAD provides clinical decision support tools for evaluating cancer response through reports summarizing changes in tumor burden based on different imaging biomarkers. As a workflow management and study oversight tool, ePAD lets clinical trial project administrators create worklists for users and oversee the progress of annotations created by research groups. To support interoperability of image annotations, ePAD writes all image annotations and results of quantitative imaging analyses in standardized file formats, and it supports migration of annotations from various propriety formats. ePAD also provides a plugin architecture supporting MATLAB server-side modules in addition to client-side plugins, permitting the community to extend the ePAD platform in various ways for new cancer use cases. We present an overview of ePAD as a platform for medical image annotation and quantitative analysis. We also discuss use cases and collaborations with different groups in the Quantitative Imaging Network and future directions.
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Affiliation(s)
- Daniel L Rubin
- Department of Biomedical Data Science, Radiology, and Medicine (Biomedical Informatics Research), Stanford University, Stanford, CA
| | - Mete Ugur Akdogan
- Department of Biomedical Data Science, Radiology, and Medicine (Biomedical Informatics Research), Stanford University, Stanford, CA
| | - Cavit Altindag
- Department of Biomedical Data Science, Radiology, and Medicine (Biomedical Informatics Research), Stanford University, Stanford, CA
| | - Emel Alkim
- Department of Biomedical Data Science, Radiology, and Medicine (Biomedical Informatics Research), Stanford University, Stanford, CA
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21
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Liang ZJ. Editorial: medical imaging modeling. Vis Comput Ind Biomed Art 2019; 2:26. [PMID: 32240419 PMCID: PMC7099554 DOI: 10.1186/s42492-019-0037-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 11/06/2019] [Indexed: 11/18/2022] Open
Affiliation(s)
- Zhengrong Jerome Liang
- Laboratory for Imaging Research and Informatics (IRIS), State University of New York, Stony Brook, New York, 11794, USA.
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22
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Jagannathan NR. Application of in vivo MR methods in the study of breast cancer metabolism. NMR IN BIOMEDICINE 2019; 32:e4032. [PMID: 30456917 DOI: 10.1002/nbm.4032] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2018] [Revised: 08/25/2018] [Accepted: 09/26/2018] [Indexed: 06/09/2023]
Abstract
In the last two decades, various in vivo MR methodologies have been evaluated for their potential in the study of cancer metabolism. During malignant transformation, metabolic alterations occur, leading to morphological and functional changes. Among various MR methods, in vivo MRS has been extensively used in breast cancer to study the metabolism of cells, tissues or whole organs. It provides biochemical information at the metabolite level. Altered choline, phospholipid and energy metabolism has been documented using proton (1 H), phosphorus (31 P) and carbon (13 C) isotopes. Increased levels of choline-containing compounds, phosphomonoesters and phosphodiesters in breast cancer, which are indicative of altered choline and phospholipid metabolism, have been reported using in vivo, in vitro and ex vivo NMR studies. These changes are reversed on successful therapy, which depends on the treatment regimen given. Monitoring the various tumor intermediary metabolic pathways using nuclear spin hyperpolarization of 13 C-labeled substrates by dynamic nuclear polarization has also been recently reported. Furthermore, the utility of various methods such as diffusion, dynamic contrast and perfusion MRI have also been evaluated to study breast tumor metabolism. Parameters such as tumor volume, apparent diffusion coefficient, volume transfer coefficient and extracellular volume ratio are estimated. These parameters provide information on the changes in tumor microstructure, microenvironment, abnormal vasculature, permeability and grade of the tumor. Such changes seen during cancer progression are due to alterations in the tumor metabolism, leading to changes in cell architecture. Due to architectural changes, the tissue mechanical properties are altered; this can be studied using magnetic resonance elastography, which measures the elastic properties of tissues. Moreover, these structural MRI methods can be used to investigate the effect of therapy-induced changes in tumor characteristics. This review discusses the potential of various in vivo MR methodologies in the study of breast cancer metabolism.
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Li C, Yan JL, Torheim T, McLean MA, Boonzaier NR, Zou J, Huang Y, Yuan J, van Dijken BRJ, Matys T, Markowetz F, Price SJ. Low perfusion compartments in glioblastoma quantified by advanced magnetic resonance imaging and correlated with patient survival. Radiother Oncol 2019; 134:17-24. [PMID: 31005212 PMCID: PMC6486398 DOI: 10.1016/j.radonc.2019.01.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Revised: 12/10/2018] [Accepted: 01/09/2019] [Indexed: 12/02/2022]
Abstract
BACKGROUND AND PURPOSE Glioblastoma exhibits profound intratumoral heterogeneity in perfusion. Particularly, low perfusion may induce treatment resistance. Thus, imaging approaches that define low perfusion compartments are crucial for clinical management. MATERIALS AND METHODS A total of 112 newly diagnosed glioblastoma patients were prospectively recruited for maximal safe resection. The apparent diffusion coefficient (ADC) and relative cerebral blood volume (rCBV) were calculated from diffusion and perfusion imaging, respectively. Based on the overlapping regions of lowest rCBV quartile (rCBVL) with the lowest ADC quartile (ADCL) and highest ADC quartile (ADCH) in each tumor, two low perfusion compartments (ADCH-rCBVL and ADCL-rCBVL) were identified for volumetric analysis. Lactate and macromolecule/lipid levels were determined from multivoxel MR spectroscopic imaging. Progression-free survival (PFS) and overall survival (OS) were analyzed using Kaplan-Meier's and multivariate Cox regression analyses, to evaluate the effects of compartment volume and lactate level on survival. RESULTS Two compartments displayed higher lactate and macromolecule/lipid levels compared to contralateral normal-appearing white matter (each P < 0.001). The proportion of the ADCL-rCBVL compartment in the contrast-enhancing tumor was associated with a larger infiltration on FLAIR (P < 0.001, rho = 0.42). The minimally invasive phenotype displayed a lower proportion of the ADCL-rCBVL compartment than the localized (P = 0.031) and diffuse phenotypes (not significant). Multivariate Cox regression showed higher lactate level in the ADCL-rCBVL compartment was associated with worsened survival (PFS: HR 2.995, P = 0.047; OS: HR 4.974, P = 0.005). CONCLUSIONS Our results suggest that the ADCL-rCBVL compartment may potentially indicate a clinically measurable resistant compartment.
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Affiliation(s)
- Chao Li
- Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neuroscience, University of Cambridge, UK; Department of Neurosurgery, Shanghai General Hospital (originally named "Shanghai First People's Hospital"), Shanghai Jiao Tong University School of Medicine, China; EPSRC Centre for Mathematical Imaging in Healthcare, University of Cambridge, UK.
| | - Jiun-Lin Yan
- Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neuroscience, University of Cambridge, UK; Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, Taiwan; Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Turid Torheim
- Cancer Research UK Cambridge Institute, University of Cambridge, UK; CRUK & EPSRC Cancer Imaging Centre in Cambridge and Manchester, Cambridge, UK
| | - Mary A McLean
- Cancer Research UK Cambridge Institute, University of Cambridge, UK
| | - Natalie R Boonzaier
- Developmental Imaging and Biophysics Section, Great Ormond Street Institute of Child Health, University College London, UK
| | - Jingjing Zou
- Statistical Laboratory, Centre for Mathematical Sciences, University of Cambridge, UK
| | - Yuan Huang
- EPSRC Centre for Mathematical Imaging in Healthcare, University of Cambridge, UK; Department of Radiology, University of Cambridge, UK
| | - Jianmin Yuan
- Department of Radiology, University of Cambridge, UK
| | - Bart R J van Dijken
- Department of Radiology, University Medical Center Groningen, University of Groningen, the Netherlands
| | - Tomasz Matys
- Statistical Laboratory, Centre for Mathematical Sciences, University of Cambridge, UK; Cancer Trials Unit Department of Oncology, Addenbrooke's Hospital, Cambridge, UK
| | - Florian Markowetz
- Cancer Research UK Cambridge Institute, University of Cambridge, UK; CRUK & EPSRC Cancer Imaging Centre in Cambridge and Manchester, Cambridge, UK
| | - Stephen J Price
- Cambridge Brain Tumour Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neuroscience, University of Cambridge, UK; Wolfson Brain Imaging Centre, Department of Clinical Neuroscience, University of Cambridge, UK
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24
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O'Connor JPB, Robinson SP, Waterton JC. Imaging tumour hypoxia with oxygen-enhanced MRI and BOLD MRI. Br J Radiol 2019; 92:20180642. [PMID: 30272998 PMCID: PMC6540855 DOI: 10.1259/bjr.20180642] [Citation(s) in RCA: 112] [Impact Index Per Article: 22.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Revised: 09/21/2018] [Accepted: 09/25/2018] [Indexed: 01/06/2023] Open
Abstract
Hypoxia is known to be a poor prognostic indicator for nearly all solid tumours and also is predictive of treatment failure for radiotherapy, chemotherapy, surgery and targeted therapies. Imaging has potential to identify, spatially map and quantify tumour hypoxia prior to therapy, as well as track changes in hypoxia on treatment. At present no hypoxia imaging methods are available for routine clinical use. Research has largely focused on positron emission tomography (PET)-based techniques, but there is gathering evidence that MRI techniques may provide a practical and more readily translational alternative. In this review we focus on the potential for imaging hypoxia by measuring changes in longitudinal relaxation [R1; termed oxygen-enhanced MRI or tumour oxygenation level dependent (TOLD) MRI] and effective transverse relaxation [R2*; termed blood oxygenation level dependent (BOLD) MRI], induced by inhalation of either 100% oxygen or the radiosensitising hyperoxic gas carbogen. We explain the scientific principles behind oxygen-enhanced MRI and BOLD and discuss significant studies and their limitations. All imaging biomarkers require rigorous validation in order to translate into clinical use and the steps required to further develop oxygen-enhanced MRI and BOLD MRI into decision-making tools are discussed.
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Affiliation(s)
| | - Simon P Robinson
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, UK
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Mass Spectrometry-Based Biomarkers in Drug Development. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2019; 1140:435-449. [PMID: 31347063 DOI: 10.1007/978-3-030-15950-4_25] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Advances in mass spectrometry, proteomics, protein bioanalytical approaches, and biochemistry have led to a rapid evolution and expansion in the area of mass spectrometry-based biomarker discovery and development. The last decade has also seen significant progress in establishing accepted definitions, guidelines, and criteria for the analytical validation, acceptance and qualification of biomarkers. These advances have coincided with a decreased return on investment for pharmaceutical research and development and an increasing need for better early decision making tools. Empowering development teams with tools to measure a therapeutic interventions impact on disease state and progression, measure target engagement and to confirm predicted pharmacodynamic effects is critical to efficient data-driven decision making. Appropriate implementation of a biomarker or a combination of biomarkers can enhance understanding of a drugs mechanism, facilitate effective translation from the preclinical to clinical space, enable early proof of concept and dose selection, and increases the efficiency of drug development. Here we will provide descriptions of the different classes of biomarkers that have utility in the drug development process as well as review specific, protein-centric, mass spectrometry-based approaches for the discovery of biomarkers and development of targeted assays to measure these markers in a selective and analytically precise manner.
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26
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Kather JN, Krisam J, Charoentong P, Luedde T, Herpel E, Weis CA, Gaiser T, Marx A, Valous NA, Ferber D, Jansen L, Reyes-Aldasoro CC, Zörnig I, Jäger D, Brenner H, Chang-Claude J, Hoffmeister M, Halama N. Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study. PLoS Med 2019; 16:e1002730. [PMID: 30677016 PMCID: PMC6345440 DOI: 10.1371/journal.pmed.1002730] [Citation(s) in RCA: 404] [Impact Index Per Article: 80.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Accepted: 12/17/2018] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND For virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers. In the present study, we investigated whether deep convolutional neural networks (CNNs) can extract prognosticators directly from these widely available images. METHODS AND FINDINGS We hand-delineated single-tissue regions in 86 CRC tissue slides, yielding more than 100,000 HE image patches, and used these to train a CNN by transfer learning, reaching a nine-class accuracy of >94% in an independent data set of 7,180 images from 25 CRC patients. With this tool, we performed automated tissue decomposition of representative multitissue HE images from 862 HE slides in 500 stage I-IV CRC patients in the The Cancer Genome Atlas (TCGA) cohort, a large international multicenter collection of CRC tissue. Based on the output neuron activations in the CNN, we calculated a "deep stroma score," which was an independent prognostic factor for overall survival (OS) in a multivariable Cox proportional hazard model (hazard ratio [HR] with 95% confidence interval [CI]: 1.99 [1.27-3.12], p = 0.0028), while in the same cohort, manual quantification of stromal areas and a gene expression signature of cancer-associated fibroblasts (CAFs) were only prognostic in specific tumor stages. We validated these findings in an independent cohort of 409 stage I-IV CRC patients from the "Darmkrebs: Chancen der Verhütung durch Screening" (DACHS) study who were recruited between 2003 and 2007 in multiple institutions in Germany. Again, the score was an independent prognostic factor for OS (HR 1.63 [1.14-2.33], p = 0.008), CRC-specific OS (HR 2.29 [1.5-3.48], p = 0.0004), and relapse-free survival (RFS; HR 1.92 [1.34-2.76], p = 0.0004). A prospective validation is required before this biomarker can be implemented in clinical workflows. CONCLUSIONS In our retrospective study, we show that a CNN can assess the human tumor microenvironment and predict prognosis directly from histopathological images.
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Affiliation(s)
- Jakob Nikolas Kather
- Department of Medical Oncology and Internal Medicine VI, National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany
- German Cancer Consortium (DKTK), Heidelberg, Germany
- Applied Tumor Immunity, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Gastroenterology, Hepatology and Hepatobiliary Oncology, University Hospital RWTH Aachen, Aachen, Germany
| | - Johannes Krisam
- Institute of Medical Biometry and Informatics, University Hospital Heidelberg, Heidelberg, Germany
| | - Pornpimol Charoentong
- Department of Medical Oncology and Internal Medicine VI, National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany
- Applied Tumor Immunity, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Tom Luedde
- Division of Gastroenterology, Hepatology and Hepatobiliary Oncology, University Hospital RWTH Aachen, Aachen, Germany
| | - Esther Herpel
- Institute of Pathology, Heidelberg University, Heidelberg, Germany
- Tissue Bank of the National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Cleo-Aron Weis
- Institute of Pathology, University Medical Center Mannheim, Mannheim, Germany
| | - Timo Gaiser
- Institute of Pathology, University Medical Center Mannheim, Mannheim, Germany
| | - Alexander Marx
- Institute of Pathology, University Medical Center Mannheim, Mannheim, Germany
| | - Nektarios A Valous
- Department of Medical Oncology and Internal Medicine VI, National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany
- Applied Tumor Immunity, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Dyke Ferber
- Department of Medical Oncology and Internal Medicine VI, National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany
- Applied Tumor Immunity, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Lina Jansen
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Inka Zörnig
- Department of Medical Oncology and Internal Medicine VI, National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany
- Applied Tumor Immunity, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Dirk Jäger
- Department of Medical Oncology and Internal Medicine VI, National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany
- German Cancer Consortium (DKTK), Heidelberg, Germany
- Applied Tumor Immunity, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Hermann Brenner
- German Cancer Consortium (DKTK), Heidelberg, Germany
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Jenny Chang-Claude
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Niels Halama
- Department of Medical Oncology and Internal Medicine VI, National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany
- German Cancer Consortium (DKTK), Heidelberg, Germany
- Applied Tumor Immunity, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Translational Immunotherapy, German Cancer Research Center (DKFZ), Heidelberg, Germany
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27
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Jayson GC, Zhou C, Backen A, Horsley L, Marti-Marti K, Shaw D, Mescallado N, Clamp A, Saunders MP, Valle JW, Mullamitha S, Braun M, Hasan J, McEntee D, Simpson K, Little RA, Watson Y, Cheung S, Roberts C, Ashcroft L, Manoharan P, Scherer SJ, Del Puerto O, Jackson A, O'Connor JPB, Parker GJM, Dive C. Plasma Tie2 is a tumor vascular response biomarker for VEGF inhibitors in metastatic colorectal cancer. Nat Commun 2018; 9:4672. [PMID: 30405103 PMCID: PMC6220185 DOI: 10.1038/s41467-018-07174-1] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Accepted: 10/04/2018] [Indexed: 12/22/2022] Open
Abstract
Oncological use of anti-angiogenic VEGF inhibitors has been limited by the lack of informative biomarkers. Previously we reported circulating Tie2 as a vascular response biomarker for bevacizumab-treated ovarian cancer patients. Using advanced MRI and circulating biomarkers we have extended these findings in metastatic colorectal cancer (n = 70). Bevacizumab (10 mg/kg) was administered to elicit a biomarker response, followed by FOLFOX6-bevacizumab until disease progression. Bevacizumab induced a correlation between Tie2 and the tumor vascular imaging biomarker, Ktrans (R:-0.21 to 0.47) implying that Tie2 originated from the tumor vasculature. Tie2 trajectories were independently associated with pre-treatment tumor vascular characteristics, tumor response, progression free survival (HR for progression = 3.01, p = 0.00014; median PFS 248 vs. 348 days p = 0.0008) and the modeling of progressive disease (p < 0.0001), suggesting that Tie2 should be monitored clinically to optimize VEGF inhibitor use. A vascular response is defined as a 30% reduction in Tie2; vascular progression as a 40% increase in Tie2 above the nadir. Tie2 is the first, validated, tumor vascular response biomarker for VEGFi.
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Affiliation(s)
- Gordon C Jayson
- The Christie NHS Foundation Trust and Division of Cancer Sciences, University of Manchester, Manchester, M20 4BX, UK.
| | - Cong Zhou
- Division of Cancer Sciences, Manchester Cancer Research Centre, University of Manchester, Manchester, M20 4GJ, UK
| | - Alison Backen
- Clinical and Experimental Pharmacology Group, Cancer Research UK Manchester Institute & Manchester Centre for Cancer Biomarker Sciences, Manchester, M20 4BX, UK
| | - Laura Horsley
- The Christie NHS Foundation Trust and Division of Cancer Sciences, University of Manchester, Manchester, M20 4BX, UK
| | - Kalena Marti-Marti
- The Christie NHS Foundation Trust and Division of Cancer Sciences, University of Manchester, Manchester, M20 4BX, UK
| | - Danielle Shaw
- Clatterbridge Cancer Centre, Liverpool, CH63 4JY, UK
| | - Nerissa Mescallado
- The Christie NHS Foundation Trust and Division of Cancer Sciences, University of Manchester, Manchester, M20 4BX, UK
| | - Andrew Clamp
- Manchester Academic Health Science Centre, Trials Co-ordination Unit, The Christie NHS Foundation Trust, Withington Hall Block C, Wilmslow Road, Manchester, M20 4BX, UK
| | - Mark P Saunders
- Manchester Academic Health Science Centre, Trials Co-ordination Unit, The Christie NHS Foundation Trust, Withington Hall Block C, Wilmslow Road, Manchester, M20 4BX, UK
| | - Juan W Valle
- The Christie NHS Foundation Trust and Division of Cancer Sciences, University of Manchester, Manchester, M20 4BX, UK
| | - Saifee Mullamitha
- Manchester Academic Health Science Centre, Trials Co-ordination Unit, The Christie NHS Foundation Trust, Withington Hall Block C, Wilmslow Road, Manchester, M20 4BX, UK
| | - Mike Braun
- Manchester Academic Health Science Centre, Trials Co-ordination Unit, The Christie NHS Foundation Trust, Withington Hall Block C, Wilmslow Road, Manchester, M20 4BX, UK
| | - Jurjees Hasan
- Manchester Academic Health Science Centre, Trials Co-ordination Unit, The Christie NHS Foundation Trust, Withington Hall Block C, Wilmslow Road, Manchester, M20 4BX, UK
| | - Delyth McEntee
- Manchester Academic Health Science Centre, Trials Co-ordination Unit, The Christie NHS Foundation Trust, Withington Hall Block C, Wilmslow Road, Manchester, M20 4BX, UK
| | - Kathryn Simpson
- Clinical and Experimental Pharmacology Group, Cancer Research UK Manchester Institute & Manchester Centre for Cancer Biomarker Sciences, Manchester, M20 4BX, UK
| | - Ross A Little
- Imaging Sciences, University of Manchester, Manchester, M13 9PT, UK
| | - Yvonne Watson
- Imaging Sciences, University of Manchester, Manchester, M13 9PT, UK
| | - Susan Cheung
- Imaging Sciences, University of Manchester, Manchester, M13 9PT, UK
| | - Caleb Roberts
- Imaging Sciences, University of Manchester, Manchester, M13 9PT, UK
| | - Linda Ashcroft
- Manchester Academic Health Science Centre, Trials Co-ordination Unit, The Christie NHS Foundation Trust, Withington Hall Block C, Wilmslow Road, Manchester, M20 4BX, UK
| | - Prakash Manoharan
- The Christie NHS Foundation Trust and Division of Cancer Sciences, University of Manchester, Manchester, M20 4BX, UK
| | - Stefan J Scherer
- Novartis Pharmaceuticals Corporation, One Health Plaza, 337, East Hanover, NJ, 07936-1080, USA
| | - Olivia Del Puerto
- Del Puerto Limited, 23 Porters Wood; Saint Albans, Hertfordshire, AL3 6PQ, UK
| | - Alan Jackson
- Imaging Sciences, University of Manchester, Manchester, M13 9PT, UK
| | - James P B O'Connor
- Division of Cancer Sciences, Manchester Cancer Research Centre, University of Manchester, Manchester, M20 4GJ, UK
| | - Geoff J M Parker
- Imaging Sciences, University of Manchester, Manchester, M13 9PT, UK
- Bioxydyn Ltd, Manchester, M15 6SZ, UK
| | - Caroline Dive
- Clinical and Experimental Pharmacology Group, Cancer Research UK Manchester Institute & Manchester Centre for Cancer Biomarker Sciences, Manchester, M20 4BX, UK
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Chitalia RD, Kontos D. Role of texture analysis in breast MRI as a cancer biomarker: A review. J Magn Reson Imaging 2018; 49:927-938. [PMID: 30390383 DOI: 10.1002/jmri.26556] [Citation(s) in RCA: 74] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 10/11/2018] [Accepted: 10/12/2018] [Indexed: 12/26/2022] Open
Abstract
Breast cancer is a known heterogeneous disease. Current clinically utilized histopathologic biomarkers may undersample tumor heterogeneity, resulting in higher rates of misdiagnosis for breast cancer. MRI can provide a whole-tumor sampling of disease burden and is widely utilized in clinical care. Texture analysis can provide a localized description of breast cancer, with particular emphasis on quantifying breast lesion heterogeneity. The object of this review is to provide an overview of texture analysis applications towards breast cancer diagnosis, prognosis, and treatment response evaluation and review the role of image-based texture features as noninvasive prognostic and predictive biomarkers. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:927-938.
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Affiliation(s)
- Rhea D Chitalia
- Department of Radiology, University of Pennsylvania, Perelman School of Medicine & Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Despina Kontos
- Department of Radiology, University of Pennsylvania, Perelman School of Medicine & Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
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29
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Datta A, Aznar MC, Dubec M, Parker GJM, O'Connor JPB. Delivering Functional Imaging on the MRI-Linac: Current Challenges and Potential Solutions. Clin Oncol (R Coll Radiol) 2018; 30:702-710. [PMID: 30224203 DOI: 10.1016/j.clon.2018.08.005] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Revised: 08/09/2018] [Accepted: 08/20/2018] [Indexed: 12/30/2022]
Abstract
Magnetic resonance imaging (MRI) is a highly versatile imaging modality that can be used to measure features of the tumour microenvironment including cell death, proliferation, metabolism, angiogenesis, and hypoxia. Mapping and quantifying these pathophysiological features has the potential to alter the use of adaptive radiotherapy planning. Although these methods are available for use on diagnostic machines, several challenges exist for implementing these functional MRI methods on the MRI-linear accelerators (linacs). This review considers these challenges and potential solutions.
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Affiliation(s)
- A Datta
- Department of Radiology, The Christie Hospital NHS Trust, Manchester, UK
| | - M C Aznar
- Division of Cancer Sciences, University of Manchester, Manchester, UK
| | - M Dubec
- Division of Cancer Sciences, University of Manchester, Manchester, UK; Christie Medical Physics and Engineering, The Christie Hospital NHS Trust, Manchester, UK
| | - G J M Parker
- Bioxydyn Ltd, Manchester, UK; Centre for Imaging Sciences, University of Manchester, Manchester, UK
| | - J P B O'Connor
- Department of Radiology, The Christie Hospital NHS Trust, Manchester, UK; Division of Cancer Sciences, University of Manchester, Manchester, UK.
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30
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Imaging Biomarker Ontology (IBO): A Biomedical Ontology to Annotate and Share Imaging Biomarker Data. JOURNAL ON DATA SEMANTICS 2018. [DOI: 10.1007/s13740-018-0093-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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31
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Chand M, Keller DS, Mirnezami R, Bullock M, Bhangu A, Moran B, Tekkis PP, Brown G, Mirnezami A, Berho M. Novel biomarkers for patient stratification in colorectal cancer: A review of definitions, emerging concepts, and data. World J Gastrointest Oncol 2018; 10:145-158. [PMID: 30079141 PMCID: PMC6068858 DOI: 10.4251/wjgo.v10.i7.145] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Revised: 04/22/2018] [Accepted: 06/08/2018] [Indexed: 02/05/2023] Open
Abstract
Colorectal cancer (CRC) treatment has become more personalised, incorporating a combination of the individual patient risk assessment, gene testing, and chemotherapy with surgery for optimal care. The improvement of staging with high-resolution imaging has allowed more selective treatments, optimising survival outcomes. The next step is to identify biomarkers that can inform clinicians of expected prognosis and offer the most beneficial treatment, while reducing unnecessary morbidity for the patient. The search for biomarkers in CRC has been of significant interest, with questions remaining on their impact and applicability. The study of biomarkers can be broadly divided into metabolic, molecular, microRNA, epithelial-to-mesenchymal-transition (EMT), and imaging classes. Although numerous molecules have claimed to impact prognosis and treatment, their clinical application has been limited. Furthermore, routine testing of prognostic markers with no demonstrable influence on response to treatment is a questionable practice, as it increases cost and can adversely affect expectations of treatment. In this review we focus on recent developments and emerging biomarkers with potential utility for clinical translation in CRC. We examine and critically appraise novel imaging and molecular-based approaches; evaluate the promising array of microRNAs, analyze metabolic profiles, and highlight key findings for biomarker potential in the EMT pathway.
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Affiliation(s)
- Manish Chand
- GENIE Centre, University College London, London W1W 7TS, United Kingdom
| | - Deborah S Keller
- Department of Surgery, Columbia University Medical Centre, New York, NY 10032, United States
| | - Reza Mirnezami
- Department of Surgery, Imperial College London, London SW7 2AZ, United Kingdom
| | - Marc Bullock
- Department of Surgery, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - Aneel Bhangu
- Department of Surgery, University of Birmingham, Birmingham B15 2QU, United Kingdom
| | - Brendan Moran
- Department of Colorectal Surgery, North Hampshire Hospital, Basingstoke RG24 7AL, United Kingdom
| | - Paris P Tekkis
- Department of Colorectal Surgery, Royal Marsden Hospital and Imperial College London, London SW3 6JJ, United Kingdom
| | - Gina Brown
- Department of Radiology, Royal Marsden Hospital and Imperial College London, London SW3 6JJ, United Kingdom
| | - Alexander Mirnezami
- Department of Surgical Oncology, University of Southampton and NIHR, Southampton SO17 1BJ, United Kingdom
| | - Mariana Berho
- Department of Pathology, Cleveland Clinic Florida, Weston, FL 33331, United States
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Mannheim JG, Schmid AM, Schwenck J, Katiyar P, Herfert K, Pichler BJ, Disselhorst JA. PET/MRI Hybrid Systems. Semin Nucl Med 2018; 48:332-347. [PMID: 29852943 DOI: 10.1053/j.semnuclmed.2018.02.011] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Over the last decade, the combination of PET and MRI in one system has proven to be highly successful in basic preclinical research, as well as in clinical research. Nowadays, PET/MRI systems are well established in preclinical imaging and are progressing into clinical applications to provide further insights into specific diseases, therapeutic assessments, and biological pathways. Certain challenges in terms of hardware had to be resolved concurrently with the development of new techniques to be able to reach the full potential of both combined techniques. This review provides an overview of these challenges and describes the opportunities that simultaneous PET/MRI systems can exploit in comparison with stand-alone or other combined hybrid systems. New approaches were developed for simultaneous PET/MRI systems to correct for attenuation of 511 keV photons because MRI does not provide direct information on gamma photon attenuation properties. Furthermore, new algorithms to correct for motion were developed, because MRI can accurately detect motion with high temporal resolution. The additional information gained by the MRI can be employed to correct for partial volume effects as well. The development of new detector designs in combination with fast-decaying scintillator crystal materials enabled time-of-flight detection and incorporation in the reconstruction algorithms. Furthermore, this review lists the currently commercially available systems both for preclinical and clinical imaging and provides an overview of applications in both fields. In this regard, special emphasis has been placed on data analysis and the potential for both modalities to evolve with advanced image analysis tools, such as cluster analysis and machine learning.
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Affiliation(s)
- Julia G Mannheim
- Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Andreas M Schmid
- Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Johannes Schwenck
- Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Eberhard Karls University Tuebingen, Tuebingen, Germany; Department of Nuclear Medicine and Clinical Molecular Imaging, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Prateek Katiyar
- Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Kristina Herfert
- Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Bernd J Pichler
- Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Eberhard Karls University Tuebingen, Tuebingen, Germany.
| | - Jonathan A Disselhorst
- Department of Preclinical Imaging and Radiopharmacy, Werner Siemens Imaging Center, Eberhard Karls University Tuebingen, Tuebingen, Germany
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Song J, Shi J, Dong D, Fang M, Zhong W, Wang K, Wu N, Huang Y, Liu Z, Cheng Y, Gan Y, Zhou Y, Zhou P, Chen B, Liang C, Liu Z, Li W, Tian J. A New Approach to Predict Progression-free Survival in Stage IV EGFR-mutant NSCLC Patients with EGFR-TKI Therapy. Clin Cancer Res 2018; 24:3583-3592. [PMID: 29563137 DOI: 10.1158/1078-0432.ccr-17-2507] [Citation(s) in RCA: 121] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Revised: 12/16/2017] [Accepted: 03/16/2018] [Indexed: 02/05/2023]
Abstract
Purpose: We established a CT-derived approach to achieve accurate progression-free survival (PFS) prediction to EGFR tyrosine kinase inhibitors (TKI) therapy in multicenter, stage IV EGFR-mutated non-small cell lung cancer (NSCLC) patients.Experimental Design: A total of 1,032 CT-based phenotypic characteristics were extracted according to the intensity, shape, and texture of NSCLC pretherapy images. On the basis of these CT features extracted from 117 stage IV EGFR-mutant NSCLC patients, a CT-based phenotypic signature was proposed using a Cox regression model with LASSO penalty for the survival risk stratification of EGFR-TKI therapy. The signature was validated using two independent cohorts (101 and 96 patients, respectively). The benefit of EGFR-TKIs in stratified patients was then compared with another stage-IV EGFR-mutant NSCLC cohort only treated with standard chemotherapy (56 patients). Furthermore, an individualized prediction model incorporating the phenotypic signature and clinicopathologic risk characteristics was proposed for PFS prediction, and also validated by multicenter cohorts.Results: The signature consisted of 12 CT features demonstrated good accuracy for discriminating patients with rapid and slow progression to EGFR-TKI therapy in three cohorts (HR: 3.61, 3.77, and 3.67, respectively). Rapid progression patients received EGFR TKIs did not show significant difference with patients underwent chemotherapy for progression-free survival benefit (P = 0.682). Decision curve analysis revealed that the proposed model significantly improved the clinical benefit compared with the clinicopathologic-based characteristics model (P < 0.0001).Conclusions: The proposed CT-based predictive strategy can achieve individualized prediction of PFS probability to EGFR-TKI therapy in NSCLCs, which holds promise of improving the pretherapy personalized management of TKIs. Clin Cancer Res; 24(15); 3583-92. ©2018 AACR.
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Affiliation(s)
- Jiangdian Song
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,School of Medical Informatics, China Medical University, Shenyang, Liaoning, China.,Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, Liaoning, China
| | - Jingyun Shi
- Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Mengjie Fang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Wenzhao Zhong
- Guangdong Lung Cancer Institute, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Kun Wang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Ning Wu
- PET-CT center, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yanqi Huang
- Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Yue Cheng
- Department of Respiratory and Critical Care Medicine, West China Hospital, Chengdu, China
| | - Yuncui Gan
- Department of Respiratory and Critical Care Medicine, West China Hospital, Chengdu, China
| | - Yongzhao Zhou
- Department of Respiratory and Critical Care Medicine, West China Hospital, Chengdu, China
| | - Ping Zhou
- Department of Respiratory and Critical Care Medicine, West China Hospital, Chengdu, China
| | - Bojiang Chen
- Department of Respiratory and Critical Care Medicine, West China Hospital, Chengdu, China
| | - Changhong Liang
- Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Zaiyi Liu
- Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
| | - Weimin Li
- Department of Respiratory and Critical Care Medicine, West China Hospital, Chengdu, China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China. .,University of Chinese Academy of Sciences, Beijing, China.,Beijing Key Laboratory of Molecular Imaging, Beijing, China
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Katiyar P, Divine MR, Kohlhofer U, Quintanilla-Martinez L, Schölkopf B, Pichler BJ, Disselhorst JA. A Novel Unsupervised Segmentation Approach Quantifies Tumor Tissue Populations Using Multiparametric MRI: First Results with Histological Validation. Mol Imaging Biol 2018; 19:391-397. [PMID: 27734253 PMCID: PMC5332060 DOI: 10.1007/s11307-016-1009-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Purpose We aimed to precisely estimate intra-tumoral heterogeneity using spatially regularized spectral clustering (SRSC) on multiparametric MRI data and compare the efficacy of SRSC with the previously reported segmentation techniques in MRI studies. Procedures Six NMRI nu/nu mice bearing subcutaneous human glioblastoma U87 MG tumors were scanned using a dedicated small animal 7T magnetic resonance imaging (MRI) scanner. The data consisted of T2 weighted images, apparent diffusion coefficient maps, and pre- and post-contrast T2 and T2* maps. Following each scan, the tumors were excised into 2–3-mm thin slices parallel to the axial field of view and processed for histological staining. The MRI data were segmented using SRSC, K-means, fuzzy C-means, and Gaussian mixture modeling to estimate the fractional population of necrotic, peri-necrotic, and viable regions and validated with the fractional population obtained from histology. Results While the aforementioned methods overestimated peri-necrotic and underestimated viable fractions, SRSC accurately predicted the fractional population of all three tumor tissue types and exhibited strong correlations (rnecrotic = 0.92, rperi-necrotic = 0.82 and rviable = 0.98) with the histology. Conclusions The precise identification of necrotic, peri-necrotic and viable areas using SRSC may greatly assist in cancer treatment planning and add a new dimension to MRI-guided tumor biopsy procedures. Electronic supplementary material The online version of this article (doi:10.1007/s11307-016-1009-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Prateek Katiyar
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Roentgenweg 13, 72076, Tuebingen, Germany.
- Max Planck Institute for Intelligent Systems, Tuebingen, Germany.
| | - Mathew R Divine
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Roentgenweg 13, 72076, Tuebingen, Germany
| | - Ursula Kohlhofer
- Institute of Pathology and Neuropathology, Eberhard Karls University Tuebingen and Comprehensive Cancer Center, University Hospital Tuebingen, Tuebingen, Germany
| | - Leticia Quintanilla-Martinez
- Institute of Pathology and Neuropathology, Eberhard Karls University Tuebingen and Comprehensive Cancer Center, University Hospital Tuebingen, Tuebingen, Germany
| | | | - Bernd J Pichler
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Roentgenweg 13, 72076, Tuebingen, Germany
| | - Jonathan A Disselhorst
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Roentgenweg 13, 72076, Tuebingen, Germany
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Quantitative radiomic profiling of glioblastoma represents transcriptomic expression. Oncotarget 2018; 9:6336-6345. [PMID: 29464076 PMCID: PMC5814216 DOI: 10.18632/oncotarget.23975] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2017] [Accepted: 09/06/2017] [Indexed: 01/29/2023] Open
Abstract
Quantitative imaging biomarkers have increasingly emerged in the field of research utilizing available imaging modalities. We aimed to identify good surrogate radiomic features that can represent genetic changes of tumors, thereby establishing noninvasive means for predicting treatment outcome. From May 2012 to June 2014, we retrospectively identified 65 patients with treatment-naïve glioblastoma with available clinical information from the Samsung Medical Center data registry. Preoperative MR imaging data were obtained for all 65 patients with primary glioblastoma. A total of 82 imaging features including first-order statistics, volume, and size features, were semi-automatically extracted from structural and physiologic images such as apparent diffusion coefficient and perfusion images. Using commercially available software, NordicICE, we performed quantitative imaging analysis and collected the dataset composed of radiophenotypic parameters. Unsupervised clustering methods revealed that the radiophenotypic dataset was composed of three clusters. Each cluster represented a distinct molecular classification of glioblastoma; classical type, proneural and neural types, and mesenchymal type. These clusters also reflected differential clinical outcomes. We found that extracted imaging signatures does not represent copy number variation and somatic mutation. Quantitative radiomic features provide a potential evidence to predict molecular phenotype and treatment outcome. Radiomic profiles represents transcriptomic phenotypes more well.
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Latifoltojar A, Hall-Craggs M, Bainbridge A, Rabin N, Popat R, Rismani A, D'Sa S, Dikaios N, Sokolska M, Antonelli M, Ourselin S, Yong K, Taylor SA, Halligan S, Punwani S. Whole-body MRI quantitative biomarkers are associated significantly with treatment response in patients with newly diagnosed symptomatic multiple myeloma following bortezomib induction. Eur Radiol 2017; 27:5325-5336. [PMID: 28656463 PMCID: PMC5674123 DOI: 10.1007/s00330-017-4907-8] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2016] [Revised: 03/13/2017] [Accepted: 05/23/2017] [Indexed: 11/29/2022]
Abstract
OBJECTIVES To evaluate whole-body MRI (WB-MRI) parameters significantly associated with treatment response in multiple myeloma (MM). METHODS Twenty-one MM patients underwent WB-MRI at diagnosis and after two cycles of chemotherapy. Scans acquired at 3.0 T included T2, diffusion-weighted-imaging (DWI) and mDixon pre- and post-contrast. Twenty focal lesions (FLs) matched on DWI and post-contrast mDixon were selected for each time point. Estimated tumour volume (eTV), apparent diffusion coefficient (ADC), enhancement ratio (ER) and signal fat fraction (sFF) were derived. Clinical treatment response to chemotherapy was assessed using conventional criteria. Significance of temporal parameter change was assessed by the paired t test and receiver operating characteristics/area under the curve (AUC) analysis was performed. Parameter repeatability was assessed by interclass correlation (ICC) and Bland-Altman analysis of 10 healthy volunteers scanned at two time points. RESULTS Fifteen of 21 patients responded to treatment. Of 254 FLs analysed, sFF (p < 0.0001) and ADC (p = 0.001) significantly increased in responders but not non-responders. eTV significantly decreased in 19/21 cases. Focal lesion sFF was the best discriminator of treatment response (AUC 1.0). Bone sFF repeatability was excellent (ICC 0.98) and better than bone ADC (ICC 0.47). CONCLUSION WB-MRI derived focal lesion sFF shows promise as an imaging biomarker of treatment response in newly diagnosed MM. KEY POINTS • Bone signal fat fraction using mDixon is a robust quantifiable parameter • Fat fraction and ADC significantly increase in myeloma lesions responding to treatment • Bone lesion fat fraction is the best discriminator of myeloma treatment response.
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Affiliation(s)
- Arash Latifoltojar
- Centre for Medical Imaging, University College London, 3rd Floor, Wolfson House, 4 Stephenson Way, London, UK, NW1 2HE
| | - Margaret Hall-Craggs
- Centre for Medical Imaging, University College London, 3rd Floor, Wolfson House, 4 Stephenson Way, London, UK, NW1 2HE
- Department of Radiology, University College London Hospital, London, UK
| | - Alan Bainbridge
- Department of Medical Physics and Bioengineering, University College London Hospital, London, UK
| | - Neil Rabin
- Department of Haematology, University College London Hospital, London, UK
| | - Rakesh Popat
- Department of Haematology, University College London Hospital, London, UK
| | - Ali Rismani
- Department of Haematology, University College London Hospital, London, UK
| | - Shirley D'Sa
- Department of Haematology, University College London Hospital, London, UK
| | - Nikolaos Dikaios
- Centre for Medical Imaging, University College London, 3rd Floor, Wolfson House, 4 Stephenson Way, London, UK, NW1 2HE
| | - Magdalena Sokolska
- Department of Medical Physics and Bioengineering, University College London Hospital, London, UK
| | - Michela Antonelli
- Translational Imaging Group, Centre for Medical Imaging Computing, University College London, London, UK
| | - Sebastien Ourselin
- Translational Imaging Group, Centre for Medical Imaging Computing, University College London, London, UK
| | - Kwee Yong
- Department of Haematology, University College London Hospital, London, UK
| | - Stuart A Taylor
- Centre for Medical Imaging, University College London, 3rd Floor, Wolfson House, 4 Stephenson Way, London, UK, NW1 2HE
- Department of Radiology, University College London Hospital, London, UK
| | - Steve Halligan
- Centre for Medical Imaging, University College London, 3rd Floor, Wolfson House, 4 Stephenson Way, London, UK, NW1 2HE
- Department of Radiology, University College London Hospital, London, UK
| | - Shonit Punwani
- Centre for Medical Imaging, University College London, 3rd Floor, Wolfson House, 4 Stephenson Way, London, UK, NW1 2HE.
- Department of Radiology, University College London Hospital, London, UK.
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37
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Iversen AB, Busk M, Bertelsen LB, Laustsen C, Munk OL, Nielsen T, Wittenborn TR, Bussink J, Lok J, Stødkilde-Jørgensen H, Horsman MR. The potential of hyperpolarized 13C magnetic resonance spectroscopy to monitor the effect of combretastatin based vascular disrupting agents. Acta Oncol 2017; 56:1626-1633. [PMID: 28840759 DOI: 10.1080/0284186x.2017.1351622] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
BACKGROUND Targeting tumor vasculature with vascular disrupting agents (VDAs) results in substantial cell death that precede tumor shrinkage. Here, we investigate the potential of hyperpolarized magnetic resonance spectroscopy (HPMRS) to monitor early metabolic changes associated with VDA treatment. METHODS Mice bearing C3H mammary carcinomas were treated with the VDAs combretastatin-A4-phosphate (CA4P) or the analog OXi4503, and HPMRS was performed following [1-13C]pyruvate administration. Similarly, treated mice were positron emission tomography (PET) scanned following administration of the glucose analog FDG. Finally, metabolic imaging parameters were compared to tumor regrowth delay and measures of vascular damage, derived from dynamic contrast-agent enhanced magnetic resonance imaging (DCE-MRI) and histology. RESULTS VDA-treatment impaired tumor perfusion (histology and DCE-MRI), reduced FDG uptake, increased necrosis, and slowed tumor growth. HPMRS, revealed that the [1-13C]pyruvate-to-[1-13C]lactate conversion remained unaltered, whereas [1-13C]lactate-to-[13C]bicarbonate (originating from respiratory CO2) ratios increased significantly following treatment. CONCLUSIONS DCE-MRI and FDG-PET revealed loss of vessel functionality, impaired glucose delivery and reduced metabolic activity prior to cell death. [1-13C]lactate-to-[13C]bicarbonate ratios increased significantly during treatment, indicating a decline in respiratory activity driven by the onset of hypoxia. HPMRS is promising for early detection of metabolic stress inflicted by VDAs, which cannot easily be inferred based on blood flow measurements.
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Affiliation(s)
- Ane B. Iversen
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - Morten Busk
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark
| | | | | | - Ole L. Munk
- PET Centre, Aarhus University Hospital, Aarhus, Denmark
| | - Thomas Nielsen
- Center of Functionally Integrative Neuroscience, Aarhus University Hospital, Aarhus, Denmark
| | - Thomas R. Wittenborn
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - Johan Bussink
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jasper Lok
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands
| | | | - Michael R. Horsman
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark
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Cancer Metabolism and Tumor Heterogeneity: Imaging Perspectives Using MR Imaging and Spectroscopy. CONTRAST MEDIA & MOLECULAR IMAGING 2017; 2017:6053879. [PMID: 29114178 PMCID: PMC5654284 DOI: 10.1155/2017/6053879] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2017] [Revised: 07/31/2017] [Accepted: 08/27/2017] [Indexed: 12/26/2022]
Abstract
Cancer cells reprogram their metabolism to maintain viability via genetic mutations and epigenetic alterations, expressing overall dynamic heterogeneity. The complex relaxation mechanisms of nuclear spins provide unique and convertible tissue contrasts, making magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) pertinent imaging tools in both clinics and research. In this review, we summarized MR methods that visualize tumor characteristics and its metabolic phenotypes on an anatomical, microvascular, microstructural, microenvironmental, and metabolomics scale. The review will progress from the utilities of basic spin-relaxation contrasts in cancer imaging to more advanced imaging methods that measure tumor-distinctive parameters such as perfusion, water diffusion, magnetic susceptibility, oxygenation, acidosis, redox state, and cell death. Analytical methods to assess tumor heterogeneity are also reviewed in brief. Although the clinical utility of tumor heterogeneity from imaging is debatable, the quantification of tumor heterogeneity using functional and metabolic MR images with development of robust analytical methods and improved MR methods may offer more critical roles of tumor heterogeneity data in clinics. MRI/MRS can also provide insightful information on pharmacometabolomics, biomarker discovery, disease diagnosis and prognosis, and treatment response. With these future directions in mind, we anticipate the widespread utilization of these MR-based techniques in studying in vivo cancer biology to better address significant clinical needs.
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Abstract
Radiomics, the high-throughput mining of quantitative image features from standard-of-care medical imaging that enables data to be extracted and applied within clinical-decision support systems to improve diagnostic, prognostic, and predictive accuracy, is gaining importance in cancer research. Radiomic analysis exploits sophisticated image analysis tools and the rapid development and validation of medical imaging data that uses image-based signatures for precision diagnosis and treatment, providing a powerful tool in modern medicine. Herein, we describe the process of radiomics, its pitfalls, challenges, opportunities, and its capacity to improve clinical decision making, emphasizing the utility for patients with cancer. Currently, the field of radiomics lacks standardized evaluation of both the scientific integrity and the clinical relevance of the numerous published radiomics investigations resulting from the rapid growth of this area. Rigorous evaluation criteria and reporting guidelines need to be established in order for radiomics to mature as a discipline. Herein, we provide guidance for investigations to meet this urgent need in the field of radiomics.
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40
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Kumar S, Rai R, Moses D, Choong C, Holloway L, Vinod SK, Liney G. MRI in radiotherapy for lung cancer: A free-breathing protocol at 3T. Pract Radiat Oncol 2017; 7:e175-e183. [DOI: 10.1016/j.prro.2016.10.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2016] [Revised: 09/12/2016] [Accepted: 10/14/2016] [Indexed: 01/22/2023]
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Abstract
There is interest in identifying and quantifying tumor heterogeneity at the genomic, tissue pathology and clinical imaging scales, as this may help better understand tumor biology and may yield useful biomarkers for guiding therapy-based decision making. This review focuses on the role and value of using x-ray, CT, MRI and PET based imaging methods that identify, measure and map tumor heterogeneity. In particular we highlight the potential value of these techniques and the key challenges required to validate and qualify these biomarkers for clinical use.
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Affiliation(s)
- James P B O'Connor
- Institute of Cancer Sciences, University of Manchester, Manchester, UK; Department of Radiology, The Christie Hospital NHS Trust, Manchester, UK.
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O'Connor JPB, Aboagye EO, Adams JE, Aerts HJWL, Barrington SF, Beer AJ, Boellaard R, Bohndiek SE, Brady M, Brown G, Buckley DL, Chenevert TL, Clarke LP, Collette S, Cook GJ, deSouza NM, Dickson JC, Dive C, Evelhoch JL, Faivre-Finn C, Gallagher FA, Gilbert FJ, Gillies RJ, Goh V, Griffiths JR, Groves AM, Halligan S, Harris AL, Hawkes DJ, Hoekstra OS, Huang EP, Hutton BF, Jackson EF, Jayson GC, Jones A, Koh DM, Lacombe D, Lambin P, Lassau N, Leach MO, Lee TY, Leen EL, Lewis JS, Liu Y, Lythgoe MF, Manoharan P, Maxwell RJ, Miles KA, Morgan B, Morris S, Ng T, Padhani AR, Parker GJM, Partridge M, Pathak AP, Peet AC, Punwani S, Reynolds AR, Robinson SP, Shankar LK, Sharma RA, Soloviev D, Stroobants S, Sullivan DC, Taylor SA, Tofts PS, Tozer GM, van Herk M, Walker-Samuel S, Wason J, Williams KJ, Workman P, Yankeelov TE, Brindle KM, McShane LM, Jackson A, Waterton JC. Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol 2017; 14:169-186. [PMID: 27725679 PMCID: PMC5378302 DOI: 10.1038/nrclinonc.2016.162] [Citation(s) in RCA: 688] [Impact Index Per Article: 98.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Imaging biomarkers (IBs) are integral to the routine management of patients with cancer. IBs used daily in oncology include clinical TNM stage, objective response and left ventricular ejection fraction. Other CT, MRI, PET and ultrasonography biomarkers are used extensively in cancer research and drug development. New IBs need to be established either as useful tools for testing research hypotheses in clinical trials and research studies, or as clinical decision-making tools for use in healthcare, by crossing 'translational gaps' through validation and qualification. Important differences exist between IBs and biospecimen-derived biomarkers and, therefore, the development of IBs requires a tailored 'roadmap'. Recognizing this need, Cancer Research UK (CRUK) and the European Organisation for Research and Treatment of Cancer (EORTC) assembled experts to review, debate and summarize the challenges of IB validation and qualification. This consensus group has produced 14 key recommendations for accelerating the clinical translation of IBs, which highlight the role of parallel (rather than sequential) tracks of technical (assay) validation, biological/clinical validation and assessment of cost-effectiveness; the need for IB standardization and accreditation systems; the need to continually revisit IB precision; an alternative framework for biological/clinical validation of IBs; and the essential requirements for multicentre studies to qualify IBs for clinical use.
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Affiliation(s)
- James P B O'Connor
- CRUK and EPSRC Cancer Imaging Centre in Cambridge and Manchester, University of Manchester, Manchester, UK
| | - Eric O Aboagye
- Department of Surgery and Cancer, Imperial College, London, UK
| | - Judith E Adams
- Department of Clinical Radiology, Central Manchester University Hospitals NHS Foundation Trust, Manchester, UK
| | - Hugo J W L Aerts
- Department of Radiation Oncology, Harvard Medical School, Boston, MA
| | - Sally F Barrington
- CRUK and EPSRC Comprehensive Imaging Centre at KCL and UCL, Kings College London, London, UK
| | - Ambros J Beer
- Department of Nuclear Medicine, University Hospital Ulm, Ulm, Germany
| | - Ronald Boellaard
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, Groningen, The Netherlands
| | - Sarah E Bohndiek
- CRUK and EPSRC Cancer Imaging Centre in Cambridge and Manchester, University of Cambridge, Cambridge, UK
| | - Michael Brady
- CRUK and EPSRC Cancer Imaging Centre, University of Oxford, Oxford, UK
| | - Gina Brown
- Radiology Department, Royal Marsden Hospital, London, UK
| | - David L Buckley
- Division of Biomedical Imaging, University of Leeds, Leeds, UK
| | | | | | | | - Gary J Cook
- CRUK and EPSRC Comprehensive Imaging Centre at KCL and UCL, Kings College London, London, UK
| | - Nandita M deSouza
- CRUK Cancer Imaging Centre, The Institute of Cancer Research, London, UK
| | - John C Dickson
- CRUK and EPSRC Cancer Imaging Centre at KCL and UCL, University College London, London, UK
| | - Caroline Dive
- Clinical and Experimental Pharmacology, CRUK Manchester Institute, Manchester, UK
| | | | - Corinne Faivre-Finn
- Radiotherapy Related Research Group, University of Manchester, Manchester, UK
| | - Ferdia A Gallagher
- CRUK and EPSRC Cancer Imaging Centre in Cambridge and Manchester, University of Cambridge, Cambridge, UK
| | - Fiona J Gilbert
- CRUK and EPSRC Cancer Imaging Centre in Cambridge and Manchester, University of Cambridge, Cambridge, UK
| | | | - Vicky Goh
- CRUK and EPSRC Comprehensive Imaging Centre at KCL and UCL, Kings College London, London, UK
| | - John R Griffiths
- CRUK and EPSRC Cancer Imaging Centre in Cambridge and Manchester, University of Cambridge, Cambridge, UK
| | - Ashley M Groves
- CRUK and EPSRC Cancer Imaging Centre at KCL and UCL, University College London, London, UK
| | - Steve Halligan
- CRUK and EPSRC Cancer Imaging Centre at KCL and UCL, University College London, London, UK
| | - Adrian L Harris
- CRUK and EPSRC Cancer Imaging Centre, University of Oxford, Oxford, UK
| | - David J Hawkes
- CRUK and EPSRC Cancer Imaging Centre at KCL and UCL, University College London, London, UK
| | - Otto S Hoekstra
- Department of Radiology and Nuclear Medicine, VU University Medical Centre, Amsterdam, The Netherlands
| | - Erich P Huang
- Biometric Research Program, National Cancer Institute, Bethesda, MD
| | - Brian F Hutton
- CRUK and EPSRC Cancer Imaging Centre at KCL and UCL, University College London, London, UK
| | - Edward F Jackson
- Department of Medical Physics, University of Wisconsin, Madison, WI
| | - Gordon C Jayson
- Institute of Cancer Sciences, University of Manchester, Manchester, UK
| | - Andrew Jones
- Medical Physics, The Christie Hospital NHS Foundation Trust, Manchester, UK
| | - Dow-Mu Koh
- CRUK Cancer Imaging Centre, The Institute of Cancer Research, London, UK
| | | | - Philippe Lambin
- Department of Radiation Oncology, University of Maastricht, Maastricht, Netherlands
| | - Nathalie Lassau
- Department of Imaging, Gustave Roussy Cancer Campus, Villejuif, France
| | - Martin O Leach
- CRUK Cancer Imaging Centre, The Institute of Cancer Research, London, UK
| | - Ting-Yim Lee
- Imaging Research Labs, Robarts Research Institute, London, Ontario, Canada
| | - Edward L Leen
- Department of Surgery and Cancer, Imperial College, London, UK
| | - Jason S Lewis
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Yan Liu
- EORTC Headquarters, EORTC, Brussels, Belgium
| | - Mark F Lythgoe
- Centre for Advanced Biomedical Imaging, University College London, London, UK
| | - Prakash Manoharan
- CRUK and EPSRC Cancer Imaging Centre in Cambridge and Manchester, University of Manchester, Manchester, UK
| | - Ross J Maxwell
- Northern Institute for Cancer Research, Newcastle University, Newcastle, UK
| | - Kenneth A Miles
- CRUK and EPSRC Cancer Imaging Centre at KCL and UCL, University College London, London, UK
| | - Bruno Morgan
- Cancer Studies and Molecular Medicine, University of Leicester, Leicester, UK
| | - Steve Morris
- Institute of Epidemiology and Health, University College London, London, UK
| | - Tony Ng
- CRUK and EPSRC Comprehensive Imaging Centre at KCL and UCL, Kings College London, London, UK
| | - Anwar R Padhani
- Paul Strickland Scanner Centre, Mount Vernon Hospital, London, UK
| | - Geoff J M Parker
- CRUK and EPSRC Cancer Imaging Centre in Cambridge and Manchester, University of Manchester, Manchester, UK
| | - Mike Partridge
- CRUK and EPSRC Cancer Imaging Centre, University of Oxford, Oxford, UK
| | - Arvind P Pathak
- Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, MD
| | - Andrew C Peet
- Institute of Cancer and Genomics, University of Birmingham, Birmingham, UK
| | - Shonit Punwani
- CRUK and EPSRC Cancer Imaging Centre at KCL and UCL, University College London, London, UK
| | - Andrew R Reynolds
- Breakthrough Breast Cancer Research Centre, The Institute of Cancer Research, London, UK
| | - Simon P Robinson
- CRUK Cancer Imaging Centre, The Institute of Cancer Research, London, UK
| | | | - Ricky A Sharma
- CRUK and EPSRC Cancer Imaging Centre at KCL and UCL, University College London, London, UK
| | - Dmitry Soloviev
- CRUK and EPSRC Cancer Imaging Centre in Cambridge and Manchester, University of Cambridge, Cambridge, UK
| | - Sigrid Stroobants
- Molecular Imaging Center Antwerp, University of Antwerp, Antwerp, Belgium
| | - Daniel C Sullivan
- Department of Radiology, Duke University School of Medicine, Durham, NC
| | - Stuart A Taylor
- CRUK and EPSRC Cancer Imaging Centre at KCL and UCL, University College London, London, UK
| | - Paul S Tofts
- Brighton and Sussex Medical School, University of Sussex, Brighton, UK
| | - Gillian M Tozer
- Department of Oncology and Metabolism, University of Sheffield, Sheffield, UK
| | - Marcel van Herk
- Radiotherapy Related Research Group, University of Manchester, Manchester, UK
| | - Simon Walker-Samuel
- Centre for Advanced Biomedical Imaging, University College London, London, UK
| | | | - Kaye J Williams
- CRUK and EPSRC Cancer Imaging Centre in Cambridge and Manchester, University of Manchester, Manchester, UK
| | - Paul Workman
- CRUK Cancer Therapeutics Unit, The Institute of Cancer Research, London, UK
| | - Thomas E Yankeelov
- Institute of Computational Engineering and Sciences, The University of Texas, Austin, TX
| | - Kevin M Brindle
- CRUK and EPSRC Cancer Imaging Centre in Cambridge and Manchester, University of Cambridge, Cambridge, UK
| | - Lisa M McShane
- Biometric Research Program, National Cancer Institute, Bethesda, MD
| | - Alan Jackson
- CRUK and EPSRC Cancer Imaging Centre in Cambridge and Manchester, University of Manchester, Manchester, UK
| | - John C Waterton
- CRUK and EPSRC Cancer Imaging Centre in Cambridge and Manchester, University of Manchester, Manchester, UK
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Lambin P, Zindler J, Vanneste BGL, De Voorde LV, Eekers D, Compter I, Panth KM, Peerlings J, Larue RTHM, Deist TM, Jochems A, Lustberg T, van Soest J, de Jong EEC, Even AJG, Reymen B, Rekers N, van Gisbergen M, Roelofs E, Carvalho S, Leijenaar RTH, Zegers CML, Jacobs M, van Timmeren J, Brouwers P, Lal JA, Dubois L, Yaromina A, Van Limbergen EJ, Berbee M, van Elmpt W, Oberije C, Ramaekers B, Dekker A, Boersma LJ, Hoebers F, Smits KM, Berlanga AJ, Walsh S. Decision support systems for personalized and participative radiation oncology. Adv Drug Deliv Rev 2017; 109:131-153. [PMID: 26774327 DOI: 10.1016/j.addr.2016.01.006] [Citation(s) in RCA: 81] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2015] [Revised: 12/08/2015] [Accepted: 01/06/2016] [Indexed: 12/12/2022]
Abstract
A paradigm shift from current population based medicine to personalized and participative medicine is underway. This transition is being supported by the development of clinical decision support systems based on prediction models of treatment outcome. In radiation oncology, these models 'learn' using advanced and innovative information technologies (ideally in a distributed fashion - please watch the animation: http://youtu.be/ZDJFOxpwqEA) from all available/appropriate medical data (clinical, treatment, imaging, biological/genetic, etc.) to achieve the highest possible accuracy with respect to prediction of tumor response and normal tissue toxicity. In this position paper, we deliver an overview of the factors that are associated with outcome in radiation oncology and discuss the methodology behind the development of accurate prediction models, which is a multi-faceted process. Subsequent to initial development/validation and clinical introduction, decision support systems should be constantly re-evaluated (through quality assurance procedures) in different patient datasets in order to refine and re-optimize the models, ensuring the continuous utility of the models. In the reasonably near future, decision support systems will be fully integrated within the clinic, with data and knowledge being shared in a standardized, dynamic, and potentially global manner enabling truly personalized and participative medicine.
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Affiliation(s)
- Philippe Lambin
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands.
| | - Jaap Zindler
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Ben G L Vanneste
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Lien Van De Voorde
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Daniëlle Eekers
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Inge Compter
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Kranthi Marella Panth
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Jurgen Peerlings
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Ruben T H M Larue
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Timo M Deist
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Arthur Jochems
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Tim Lustberg
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Johan van Soest
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Evelyn E C de Jong
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Aniek J G Even
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Bart Reymen
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Nicolle Rekers
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Marike van Gisbergen
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Erik Roelofs
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Sara Carvalho
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Ralph T H Leijenaar
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Catharina M L Zegers
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Maria Jacobs
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Janita van Timmeren
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Patricia Brouwers
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Jonathan A Lal
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Ludwig Dubois
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Ala Yaromina
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Evert Jan Van Limbergen
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Maaike Berbee
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Wouter van Elmpt
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Cary Oberije
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Bram Ramaekers
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Liesbeth J Boersma
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Frank Hoebers
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Kim M Smits
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Adriana J Berlanga
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Sean Walsh
- Department of Radiation Oncology (MAASTRO), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
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Zöllner FG, Gaa T, Zimmer F, Ong MM, Riffel P, Hausmann D, Schoenberg SO, Weis M. [Quantitative perfusion imaging in magnetic resonance imaging]. Radiologe 2016; 56:113-23. [PMID: 26796337 DOI: 10.1007/s00117-015-0068-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
CLINICAL/METHODICAL ISSUE Magnetic resonance imaging (MRI) is recognized for its superior tissue contrast while being non-invasive and free of ionizing radiation. Due to the development of new scanner hardware and fast imaging techniques during the last decades, access to tissue and organ functions became possible. One of these functional imaging techniques is perfusion imaging with which tissue perfusion and capillary permeability can be determined from dynamic imaging data. STANDARD RADIOLOGICAL METHODS Perfusion imaging by MRI can be performed by two approaches, arterial spin labeling (ASL) and dynamic contrast-enhanced (DCE) MRI. While the first method uses magnetically labelled water protons in arterial blood as an endogenous tracer, the latter involves the injection of a contrast agent, usually gadolinium (Gd), as a tracer for calculating hemodynamic parameters. PERFORMANCE Studies have demonstrated the potential of perfusion MRI for diagnostics and also for therapy monitoring. ACHIEVEMENTS The utilization and application of perfusion MRI are still restricted to specialized centers, such as university hospitals. A broad application of the technique has not yet been implemented. PRACTICAL RECOMMENDATIONS The MRI perfusion technique is a valuable tool that might come broadly available after implementation of standards on European and international levels. Such efforts are being promoted by the respective professional bodies.
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Affiliation(s)
- F G Zöllner
- Computerunterstützte Klinische Medizin, Medizinische Fakultät Mannheim, Universität Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Deutschland.
| | - T Gaa
- Computerunterstützte Klinische Medizin, Medizinische Fakultät Mannheim, Universität Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Deutschland
| | - F Zimmer
- Computerunterstützte Klinische Medizin, Medizinische Fakultät Mannheim, Universität Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Deutschland
| | - M M Ong
- Institut für Klinische Radiologie und Nuklearmedizin, Universitätsmedizin Mannheim, Medizinische Fakultät Mannheim, Universität Heidelberg, Mannheim, Deutschland
| | - P Riffel
- Institut für Klinische Radiologie und Nuklearmedizin, Universitätsmedizin Mannheim, Medizinische Fakultät Mannheim, Universität Heidelberg, Mannheim, Deutschland
| | - D Hausmann
- Institut für Klinische Radiologie und Nuklearmedizin, Universitätsmedizin Mannheim, Medizinische Fakultät Mannheim, Universität Heidelberg, Mannheim, Deutschland
| | - S O Schoenberg
- Institut für Klinische Radiologie und Nuklearmedizin, Universitätsmedizin Mannheim, Medizinische Fakultät Mannheim, Universität Heidelberg, Mannheim, Deutschland
| | - M Weis
- Institut für Klinische Radiologie und Nuklearmedizin, Universitätsmedizin Mannheim, Medizinische Fakultät Mannheim, Universität Heidelberg, Mannheim, Deutschland
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Baker LCJ, Boult JKR, Thomas M, Koehler A, Nayak T, Tessier J, Ooi CH, Birzele F, Belousov A, Zajac M, Horn C, LeFave C, Robinson SP. Acute tumour response to a bispecific Ang-2-VEGF-A antibody: insights from multiparametric MRI and gene expression profiling. Br J Cancer 2016; 115:691-702. [PMID: 27529514 PMCID: PMC5023775 DOI: 10.1038/bjc.2016.236] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Revised: 06/03/2016] [Accepted: 07/06/2016] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND To assess antivascular effects, and evaluate clinically translatable magnetic resonance imaging (MRI) biomarkers of tumour response in vivo, following treatment with vanucizumab, a bispecific human antibody against angiopoietin-2 (Ang-2) and vascular endothelial growth factor-A (VEGF-A). METHODS Colo205 colon cancer xenografts were imaged before and 5 days after treatment with a single 10 mg kg(-1) dose of either vanucizumab, bevacizumab (anti-human VEGF-A), LC06 (anti-murine/human Ang-2) or omalizumab (anti-human IgE control). Volumetric response was assessed using T2-weighted MRI, and diffusion-weighted, dynamic contrast-enhanced (DCE) and susceptibility contrast MRI used to quantify tumour water diffusivity (apparent diffusion coefficient (ADC), × 10(6) mm(2) s(-1)), vascular perfusion/permeability (K(trans), min(-1)) and fractional blood volume (fBV, %) respectively. Pathological correlates were sought, and preliminary gene expression profiling performed. RESULTS Treatment with vanucizumab, bevacizumab or LC06 induced a significant (P<0.01) cytolentic response compared with control. There was no significant change in tumour ADC in any treatment group. Uptake of Gd-DTPA was restricted to the tumour periphery in all post-treatment groups. A significant reduction in tumour K(trans) (P<0.05) and fBV (P<0.01) was determined 5 days after treatment with vanucizumab only. This was associated with a significant (P<0.05) reduction in Hoechst 33342 uptake compared with control. Gene expression profiling identified 20 human genes exclusively regulated by vanucizumab, 6 of which are known to be involved in vasculogenesis and angiogenesis. CONCLUSIONS Vanucizumab is a promising antitumour and antiangiogenic treatment, whose antivascular activity can be monitored using DCE and susceptibility contrast MRI. Differential gene expression in vanucizumab-treated tumours is regulated by the combined effect of Ang-2 and VEGF-A inhibition.
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MESH Headings
- Adenocarcinoma/blood supply
- Adenocarcinoma/diagnostic imaging
- Adenocarcinoma/drug therapy
- Adenocarcinoma/pathology
- Angiogenesis Inhibitors/immunology
- Angiogenesis Inhibitors/therapeutic use
- Angiopoietin-2/antagonists & inhibitors
- Angiopoietin-2/immunology
- Animals
- Antibodies, Bispecific/therapeutic use
- Antibodies, Monoclonal/immunology
- Antibodies, Monoclonal/therapeutic use
- Antibodies, Monoclonal, Humanized
- Bevacizumab/therapeutic use
- Cell Line, Tumor
- Colonic Neoplasms/blood supply
- Colonic Neoplasms/diagnostic imaging
- Colonic Neoplasms/drug therapy
- Colonic Neoplasms/pathology
- DNA Replication/drug effects
- Gene Expression Profiling
- Gene Expression Regulation, Neoplastic/drug effects
- Humans
- Immunoglobulin E/immunology
- Magnetic Resonance Imaging/methods
- Mice
- Molecular Targeted Therapy
- Neovascularization, Pathologic/diagnostic imaging
- Neovascularization, Pathologic/drug therapy
- Neovascularization, Pathologic/pathology
- Omalizumab/therapeutic use
- Tumor Burden
- Vascular Endothelial Growth Factor A/antagonists & inhibitors
- Vascular Endothelial Growth Factor A/immunology
- Xenograft Model Antitumor Assays
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Affiliation(s)
- Lauren CJ Baker
- Cancer Research UK Cancer Imaging Centre, Division of Radiotherapy and Imaging, The Institute of Cancer Research, London SM2 5NG, UK
| | - Jessica KR Boult
- Cancer Research UK Cancer Imaging Centre, Division of Radiotherapy and Imaging, The Institute of Cancer Research, London SM2 5NG, UK
| | - Markus Thomas
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center, Penzberg DE-82377, Germany
| | - Astrid Koehler
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center, Penzberg DE-82377, Germany
| | - Tapan Nayak
- Roche pRED, Roche Innovation Center, Basel CH-4070, Switzerland
| | - Jean Tessier
- Roche pRED, Roche Innovation Center, Basel CH-4070, Switzerland
| | - Chia-Huey Ooi
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center, Penzberg DE-82377, Germany
| | - Fabian Birzele
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center, Penzberg DE-82377, Germany
| | - Anton Belousov
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center, Penzberg DE-82377, Germany
| | | | - Carsten Horn
- Roche pRED, Roche Innovation Center, Basel CH-4070, Switzerland
| | - Clare LeFave
- Roche pRED, Roche Innovation Center, New York, NY 10016, USA
| | - Simon P Robinson
- Cancer Research UK Cancer Imaging Centre, Division of Radiotherapy and Imaging, The Institute of Cancer Research, London SM2 5NG, UK
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Molina D, Pérez-Beteta J, Martínez-González A, Sepúlveda JM, Peralta S, Gil-Gil MJ, Reynes G, Herrero A, De Las Peñas R, Luque R, Capellades J, Balaña C, Pérez-García VM. Geometrical Measures Obtained from Pretreatment Postcontrast T1 Weighted MRIs Predict Survival Benefits from Bevacizumab in Glioblastoma Patients. PLoS One 2016; 11:e0161484. [PMID: 27557121 PMCID: PMC4996463 DOI: 10.1371/journal.pone.0161484] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2016] [Accepted: 08/06/2016] [Indexed: 11/18/2022] Open
Abstract
Background Antiangiogenic therapies for glioblastoma (GBM) such as bevacizumab (BVZ), have been unable to extend survival in large patient cohorts. However, a subset of patients having angiogenesis-dependent tumors might benefit from these therapies. Currently, there are no biomarkers allowing to discriminate responders from non-responders before the start of the therapy. Methods 40 patients from the randomized GENOM009 study complied the inclusion criteria (quality of images, clinical data available). Of those, 23 patients received first line temozolomide (TMZ) for eight weeks and then concomitant radiotherapy and TMZ. 17 patients received BVZ+TMZ for seven weeks and then added radiotherapy to the treatment. Clinical variables were collected, tumors segmented and several geometrical measures computed including: Contrast enhancing (CE), necrotic, and total volumes; equivalent spherical CE width; several geometric measures of the CE ‘rim’ geometry and a set of image texture measures. The significance of the results was studied using Kaplan-Meier and Cox proportional hazards analysis. Correlations were assessed using Spearman correlation coefficients. Results Kaplan-Meier and Cox proportional hazards analysis showed that total, CE and inner volume (p = 0.019, HR = 4.258) and geometric heterogeneity of the CE areas (p = 0.011, HR = 3.931) were significant parameters identifying response to BVZ. The group of patients with either regular CE areas (small geometric heterogeneity, median difference survival 15.88 months, p = 0.011) or those with small necrotic volume (median survival difference 14.50 months, p = 0.047) benefited substantially from BVZ. Conclusion Imaging biomarkers related to the irregularity of contrast enhancing areas and the necrotic volume were able to discriminate GBM patients with a substantial survival benefit from BVZ. A prospective study is needed to validate our results.
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Affiliation(s)
- David Molina
- Laboratory of Mathematical Oncology (MôLAB), Instituto de Matemática Aplicada a la Ciencia y la Ingeniería, Edificio Politécnico, Avda. Camilo José Cela 3, Universidad de Castilla-La Mancha, 13071 Ciudad Real, Spain
- * E-mail:
| | - Julián Pérez-Beteta
- Laboratory of Mathematical Oncology (MôLAB), Instituto de Matemática Aplicada a la Ciencia y la Ingeniería, Edificio Politécnico, Avda. Camilo José Cela 3, Universidad de Castilla-La Mancha, 13071 Ciudad Real, Spain
| | - Alicia Martínez-González
- Laboratory of Mathematical Oncology (MôLAB), Instituto de Matemática Aplicada a la Ciencia y la Ingeniería, Edificio Politécnico, Avda. Camilo José Cela 3, Universidad de Castilla-La Mancha, 13071 Ciudad Real, Spain
| | - Juan M. Sepúlveda
- Medical Oncology Service, Hospital Universitario, 12 de Octubre, Madrid, Spain
| | - Sergi Peralta
- Medical Oncology Service, Hospital Sant Joan de Reus, Reus, Spain
| | - Miguel J. Gil-Gil
- Medical Oncology Service, Institut Catalá d’Oncologia IDIBELL, Hospitalet de Llobregat, Barcelona, Spain
| | - Gaspar Reynes
- Medical Oncology Service, Hospital Universitario La Fe, Valencia, Spain
| | - Ana Herrero
- Medical Oncology Service, Hospital Miguel Servet, Zaragoza, Spain
| | - Ramón De Las Peñas
- Medical Oncology Service, Hospital Provincial de Castellón, Castellón, Spain
| | - Raquel Luque
- Medical Oncology Service, Hospital Universitario Virgen de las Nieves, Granada, Spain
| | - Jaume Capellades
- Neuroradiology Section. Radiology Service. Hospital del Mar, Barcelona, Spain
| | - Carmen Balaña
- Medical Oncology Service, Institut Català d’Oncologia, IGTP, Hospital Universitari Germans Trias i Pujol, Badalona, Spain
| | - Víctor M. Pérez-García
- Laboratory of Mathematical Oncology (MôLAB), Instituto de Matemática Aplicada a la Ciencia y la Ingeniería, Edificio Politécnico, Avda. Camilo José Cela 3, Universidad de Castilla-La Mancha, 13071 Ciudad Real, Spain
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Desmond KL, Al-Ebraheem A, Janik R, Oakden W, Kwiecien JM, Dabrowski W, Rola R, Geraki K, Farquharson MJ, Stanisz GJ, Bock NA. Differences in iron and manganese concentration may confound the measurement of myelin from R1 and R2 relaxation rates in studies of dysmyelination. NMR IN BIOMEDICINE 2016; 29:985-998. [PMID: 27226282 DOI: 10.1002/nbm.3549] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2016] [Revised: 03/20/2016] [Accepted: 04/10/2016] [Indexed: 06/05/2023]
Abstract
A model of dysmyelination, the Long Evans Shaker (les) rat, was used to study the contribution of myelin to MR tissue properties in white matter. A large region of white matter was identified in the deep cerebellum and was used for measurements of the MR relaxation rate constants, R1 = 1/T1 and R2 = 1/T2 , at 7 T. In this study, R1 of the les deep cerebellar white matter was found to be 0.55 ± 0.08 s (-1) and R2 was found to be 15 ± 1 s(-1) , revealing significantly lower R1 and R2 in les white matter relative to wild-type (wt: R1 = 0.69 ± 0.05 s(-1) and R2 = 18 ± 1 s(-1) ). These deviated from the expected ΔR1 and ΔR2 values, given a complete lack of myelin in the les white matter, derived from the literature using values of myelin relaxivity, and we suspect that metals could play a significant role. The absolute concentrations of the paramagnetic transition metals iron (Fe) and manganese (Mn) were measured by a micro-synchrotron radiation X-ray fluorescence (μSRXRF) technique, with significantly greater Fe and Mn in les white matter than in wt (in units of μg [metal]/g [wet weight tissue]: les: Fe concentration,19 ± 1; Mn concentration, 0.71 ± 0.04; wt: Fe concentration,10 ± 1; Mn concentration, 0.47 ± 0.04). These changes in Fe and Mn could explain the deviations in R1 and R2 from the expected values in white matter. Although it was found that the influence of myelin still dominates R1 and R2 in wt rats, there were non-negligible changes in the contribution of the metals to relaxation. Although there are already problems with the estimation of myelin from R1 and R2 changes in disease models with pathology that also affects the relaxation rate constants, this study points to a specific pitfall in the estimation of changes in myelin in diseases or models with disrupted concentrations of paramagnetic transition metals. Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
- Kimberly L Desmond
- Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, ON, Canada
- Imaging Research, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Alia Al-Ebraheem
- School of Interdisciplinary Science, Medical Radiation Sciences program, McMaster University, Hamilton, ON, Canada
| | - Rafal Janik
- Imaging Research, Sunnybrook Research Institute, Toronto, ON, Canada
- Medical Biophysics, University of Toronto, ON, Canada
| | - Wendy Oakden
- Imaging Research, Sunnybrook Research Institute, Toronto, ON, Canada
- Medical Biophysics, University of Toronto, ON, Canada
| | - Jacek M Kwiecien
- Pathology & Molecular Medicine, McMaster University, Hamilton, ON, Canada
- Department of Clinical Pathomorphology, Lublin Medical University, Lublin, Poland
| | - Wojciech Dabrowski
- Anaesthesiology and Intensive Therapy, Lublin Medical University, Lublin, Poland
| | - Radoslaw Rola
- Neurosurgery & Pediatric Neurosurgery, Lublin Medical University, Lublin, Poland
| | - Kalotina Geraki
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot, Oxfordshire, UK
| | - Michael J Farquharson
- School of Interdisciplinary Science, Medical Radiation Sciences program, McMaster University, Hamilton, ON, Canada
| | - Greg J Stanisz
- Imaging Research, Sunnybrook Research Institute, Toronto, ON, Canada
- Medical Biophysics, University of Toronto, ON, Canada
- Neurosurgery & Pediatric Neurosurgery, Lublin Medical University, Lublin, Poland
| | - Nicholas A Bock
- Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, ON, Canada
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Lo GM, Al Zahrani H, Jang HJ, Menezes R, Hudson J, Burns P, McNamara MG, Kandel S, Khalili K, Knox J, Rogalla P, Kim TK. Detection of Early Tumor Response to Axitinib in Advanced Hepatocellular Carcinoma by Dynamic Contrast Enhanced Ultrasound. ULTRASOUND IN MEDICINE & BIOLOGY 2016; 42:1303-1311. [PMID: 27033332 DOI: 10.1016/j.ultrasmedbio.2016.01.025] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2015] [Revised: 01/27/2016] [Accepted: 01/30/2016] [Indexed: 06/05/2023]
Abstract
This study aimed to evaluate the utility of dynamic contrast-enhanced ultrasound (DCE-US) in measuring early tumor response of advanced hepatocellular carcinoma to axitinib. Twenty patients were enrolled (aged 18-78 y; median 65). DCE-US was performed with bolus injection and infusion/disruption replenishment. Median overall survival was 7.1 mo (1.8-27.3) and progression free survival was 3.6 mo (1.8-17.4). Fifteen patients completed infusion scans and 12 completed bolus scans at 2 wk. Among the perfusion parameters, fractional blood volume at infusion (INFBV) decreased at 2 wk in 10/15 (16%-81% of baseline, mean 47%) and increased in 5/15 (116%-535%, mean 220%). This was not significantly associated with progression free survival (p = 0.310) or progression at 16 wk (p = 0.849), but was borderline statistically significant (p = 0.050) with overall survival, limited by a small sample size. DCE-US is potentially useful in measuring early tumor response of advanced hepatocellular carcinoma to axitinib, but a larger trial is needed.
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Affiliation(s)
- Glen M Lo
- Medical Imaging, University of Toronto, Toronto, ON, Canada; Department of Radiology, Sir Charles Gairdner Hospital, QEII Medical Centre, Perth, Western Australia
| | | | - Hyun Jung Jang
- Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Ravi Menezes
- Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - John Hudson
- Department of Medical Biophysics, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Peter Burns
- Department of Medical Biophysics, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Mairéad G McNamara
- Division of Medical Oncology, Princess Margaret Cancer Centre, University of Toronto, Toronto, ON, Canada; Department of Medical Oncology, The Christie NHS Foundation Trust/University of Manchester, Institute of Cancer Sciences, Manchester, UK
| | - Sonja Kandel
- Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Korosh Khalili
- Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Jennifer Knox
- Division of Medical Oncology, Princess Margaret Cancer Centre, University of Toronto, Toronto, ON, Canada
| | - Patrik Rogalla
- Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Tae Kyoung Kim
- Medical Imaging, University of Toronto, Toronto, ON, Canada.
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50
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Quantitative Computed Tomography Imaging Biomarkers in the Diagnosis and Management of Lung Cancer. Invest Radiol 2016; 50:571-83. [PMID: 25811833 DOI: 10.1097/rli.0000000000000152] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Tumor diameter has traditionally been used as a standard metric in terms of diagnosis and prognosis prediction of lung cancer. However, recent advances in imaging techniques and data analyses have enabled novel quantitative imaging biomarkers that can characterize disease status more comprehensively and/or predict tumor behavior more precisely. The most widely used imaging modality for lung tumor assessment is computed tomography. Therefore, we focused on computed tomography imaging biomarkers such as tumor volume and mass, ground-glass opacities, perfusion parameters, as well as texture features in this review. Herein, we first appraised the conventional 1- or 2-dimensional measurement with brief discussion on their limits and then introduced the potential imaging biomarkers with emphasis on the current understanding of their clinical usefulness with respect to the malignancy differentiation, treatment response monitoring, and patient outcome prediction.
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