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Kenneth A Miles - One of the best experts on this subject based on the ideXlab platform.
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Can CT measures of Tumour Heterogeneity stratify risk for nodal metastasis in patients with non-small cell lung cancer?
Clinical radiology, 2017Co-Authors: M. Craigie, J. Squires, Kenneth A MilesAbstract:Aim To undertake a preliminary assessment of the potential for computed tomography (CT) measurement of Tumour Heterogeneity to stratify risk of nodal metastasis in patients with non-small cell lung cancer (NSCLC). Materials and methods Tumour Heterogeneity in CT images from combined positron-emission tomography (PET)/CT examinations in 150 consecutive patients with NSCLC was assessed using CT texture analysis (CTTA). The short axis diameter of the largest mediastinal node was also measured. Forty-two patients without distant metastases subsequently had Tumour nodal status confirmed at surgery ( n= 26) or endobronchial ultrasound (EBUS; n= 16). CTTA parameters and largest nodal diameter were related to nodal status using the rank correlation and the risk ratio for each nodal stage (>N0, >N1, >N2) was compared between patients categorised as high and low risk by CTTA or nodal size. The most significant predictor of nodal status was related to overall survival using Kaplan–Meier analysis. Results N-stage was more significantly correlated with CTTA than nodal diameter (Rs = -0.39, p= 0.011, Rs = -0.45, p= 0.0025, Rs = -0.40, p= 0.0091 for normalised standard deviation (SD), normalised entropy and kurtosis respectively; Rs = -0.39, p= 0.042 for nodal diameter). The presence of two or more high-risk CTTA values was the greatest risk factor for mediastinal metastasis (risk ratio: 11.0, 95% confidence interval: 1.56–77.8, p= 0.0014) and was associated with significantly poorer overall survival ( p= 0.016). Conclusion CTTA in NSCLC is related to nodal status in patients without distant metastases and has the potential to inform selection of investigative strategies for the assessment of mediastinal malignancy.
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The use of molecular imaging combined with genomic techniques to understand the Heterogeneity in cancer metastasis
The British journal of radiology, 2014Co-Authors: R Chowdhury, Balaji Ganeshan, Kenneth A Miles, Sheeba Irshad, Katherine Lawler, M Eisenblätter, Hanna Milewicz, Manuel Rodriguez-justo, Peter R. Ellis, Ashley M. GrovesAbstract:Tumour Heterogeneity has, in recent times, come to play a vital role in how we understand and treat cancers; however, the clinical translation of this has lagged behind advances in research. Although significant advancements in oncological management have been made, personalized care remains an elusive goal. Inter- and intraTumour Heterogeneity, particularly in the clinical setting, has been difficult to quantify and therefore to treat. The histological quantification of Heterogeneity of Tumours can be a logistical and clinical challenge. The ability to examine not just the whole Tumour but also all the molecular variations of metastatic disease in a patient is obviously difficult with current histological techniques. Advances in imaging techniques and novel applications, alongside our understanding of Tumour Heterogeneity, have opened up a plethora of non-invasive biomarker potential to examine Tumours, their Heterogeneity and the clinical translation. This review will focus on how various imaging methods that allow for quantification of metastatic Tumour Heterogeneity, along with the potential of developing imaging, integrated with other in vitro diagnostic approaches such as genomics and exosome analyses, have the potential role as a non-invasive biomarker for guiding the treatment algorithm.
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Quantifying Tumour Heterogeneity with CT.
Cancer imaging : the official publication of the International Cancer Imaging Society, 2013Co-Authors: Balaji Ganeshan, Kenneth A MilesAbstract:Heterogeneity is a key feature of malignancy associated with adverse Tumour biology. Quantifying Heterogeneity could provide a useful non-invasive imaging biomarker. Heterogeneity on computed tomography (CT) can be quantified using texture analysis which extracts spatial information from CT images (unenhanced, contrast-enhanced and derived images such as CT perfusion) that may not be perceptible to the naked eye. The main components of texture analysis can be categorized into image transformation and quantification. Image transformation filters the conventional image into its basic components (spatial, frequency, etc.) to produce derived subimages. Texture quantification techniques include structural-, model- (fractal dimensions), statistical- and frequency-based methods. The underlying Tumour biology that CT texture analysis may reflect includes (but is not limited to) Tumour hypoxia and angiogenesis. Emerging studies show that CT texture analysis has the potential to be a useful adjunct in clinical oncologic imaging, providing important information about Tumour characterization, prognosis and treatment prediction and response.
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Measurements of Heterogeneity in gliomas on computed tomography relationship to Tumour grade
Journal of neuro-oncology, 2012Co-Authors: Karoline Skogen, Balaji Ganeshan, Catriona Good, Giles Critchley, Kenneth A MilesAbstract:To undertake a preliminary study that uses CT texture analysis (CTTA) to quantify Heterogeneity in gliomas on contrast-enhanced CT and to assess the relationship between Tumour Heterogeneity and grade. Retrospective analysis of contrast enhanced CT images was performed in 44 patients with histologically proven cerebral glioma between 2007 and 2010. 11 Tumours were low grade (Grade I = 3; Grade II, = 8) and 33 high grade (Grade III = 10, Grade IV = 23). CTTA assessment of Tumour Heterogeneity was performed using a proprietary software algorithm (TexRAD) that selectively filters and extracts textures at different anatomical scales between filter values 1.0 (fine detail) and 2.5 (coarse features). Heterogeneity was quantified as standard deviation (SD) with or without filtration. Tumour Heterogeneity, size and attenuation were correlated with Tumour grade. For each parameter, receiver operating characteristics characterised the diagnostic performance for discrimination of high grade from low grade glioma and of grade III Tumours from grade IV. Further the CTTA was compared to the radiological diagnosis. Tumour Heterogeneity correlated significantly with grade (SD without filtration rs = 0.664, p < 0.001, SD with coarse filtration (rs = 0.714, p < 0.001). Tumour size and attenuation showed only moderate correlations with Tumour grade (rs = 0.426, p = 0.004 and rs = 0.447, p = 0.002 respectively). Coarse texture was the best discriminator between high and low grade Tumours (AUC 0.832, p < 0.0001) and between grade III and grade IV gliomas (AUC = 0.878 p = 0.0001). Compared to the radiological diagnosis, CTTA further characterised the indetermined cases. By quantifying Tumour Heterogeneity, CTTA has the potential to provide a marker of Tumour grade for patients with cerebral glioma. By differentiating between high and low grade Tumours, CTTA could possibly assist clinical management.
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Tumour Heterogeneity in non small cell lung carcinoma assessed by ct texture analysis a potential marker of survival
European Radiology, 2012Co-Authors: Balaji Ganeshan, Elleny Panayiotou, Kate Burnand, Sabina Dizdarevic, Kenneth A MilesAbstract:Purpose To establish the potential for Tumour Heterogeneity in non-small cell lung cancer (NSCLC) as assessed by CT texture analysis (CTTA) to provide an independent marker of survival for patients with NSCLC.
Balaji Ganeshan - One of the best experts on this subject based on the ideXlab platform.
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CT texture analysis as a prognostic marker in metastatic colorectal cancer patients treated with bevacizumab
Cancer Imaging, 2015Co-Authors: Shih-hsin Chen, Julien Edeline, Kai-keen Shiu, Sarah Benafif, Sofia Wong, Ashley M. Groves, John Bridgewater, Balaji GaneshanAbstract:Aim Following anti-angiogenic treatment, response might be represented by changes in Tumour Heterogeneity and may not be reflected on traditional size-based criteria. CT texture analysis (CTTA) is one emerging tool to quantify Tumour Heterogeneity, and has been shown to be prognostic in different Tumour applications. We aimed to assess the association of CTTA with overall survival (OS) in a series of metastatic colorectal cancer (mCRC) patients treated with bevacizumab.
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The use of molecular imaging combined with genomic techniques to understand the Heterogeneity in cancer metastasis
The British journal of radiology, 2014Co-Authors: R Chowdhury, Balaji Ganeshan, Kenneth A Miles, Sheeba Irshad, Katherine Lawler, M Eisenblätter, Hanna Milewicz, Manuel Rodriguez-justo, Peter R. Ellis, Ashley M. GrovesAbstract:Tumour Heterogeneity has, in recent times, come to play a vital role in how we understand and treat cancers; however, the clinical translation of this has lagged behind advances in research. Although significant advancements in oncological management have been made, personalized care remains an elusive goal. Inter- and intraTumour Heterogeneity, particularly in the clinical setting, has been difficult to quantify and therefore to treat. The histological quantification of Heterogeneity of Tumours can be a logistical and clinical challenge. The ability to examine not just the whole Tumour but also all the molecular variations of metastatic disease in a patient is obviously difficult with current histological techniques. Advances in imaging techniques and novel applications, alongside our understanding of Tumour Heterogeneity, have opened up a plethora of non-invasive biomarker potential to examine Tumours, their Heterogeneity and the clinical translation. This review will focus on how various imaging methods that allow for quantification of metastatic Tumour Heterogeneity, along with the potential of developing imaging, integrated with other in vitro diagnostic approaches such as genomics and exosome analyses, have the potential role as a non-invasive biomarker for guiding the treatment algorithm.
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Quantifying Tumour Heterogeneity with CT.
Cancer imaging : the official publication of the International Cancer Imaging Society, 2013Co-Authors: Balaji Ganeshan, Kenneth A MilesAbstract:Heterogeneity is a key feature of malignancy associated with adverse Tumour biology. Quantifying Heterogeneity could provide a useful non-invasive imaging biomarker. Heterogeneity on computed tomography (CT) can be quantified using texture analysis which extracts spatial information from CT images (unenhanced, contrast-enhanced and derived images such as CT perfusion) that may not be perceptible to the naked eye. The main components of texture analysis can be categorized into image transformation and quantification. Image transformation filters the conventional image into its basic components (spatial, frequency, etc.) to produce derived subimages. Texture quantification techniques include structural-, model- (fractal dimensions), statistical- and frequency-based methods. The underlying Tumour biology that CT texture analysis may reflect includes (but is not limited to) Tumour hypoxia and angiogenesis. Emerging studies show that CT texture analysis has the potential to be a useful adjunct in clinical oncologic imaging, providing important information about Tumour characterization, prognosis and treatment prediction and response.
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Measurements of Heterogeneity in gliomas on computed tomography relationship to Tumour grade
Journal of neuro-oncology, 2012Co-Authors: Karoline Skogen, Balaji Ganeshan, Catriona Good, Giles Critchley, Kenneth A MilesAbstract:To undertake a preliminary study that uses CT texture analysis (CTTA) to quantify Heterogeneity in gliomas on contrast-enhanced CT and to assess the relationship between Tumour Heterogeneity and grade. Retrospective analysis of contrast enhanced CT images was performed in 44 patients with histologically proven cerebral glioma between 2007 and 2010. 11 Tumours were low grade (Grade I = 3; Grade II, = 8) and 33 high grade (Grade III = 10, Grade IV = 23). CTTA assessment of Tumour Heterogeneity was performed using a proprietary software algorithm (TexRAD) that selectively filters and extracts textures at different anatomical scales between filter values 1.0 (fine detail) and 2.5 (coarse features). Heterogeneity was quantified as standard deviation (SD) with or without filtration. Tumour Heterogeneity, size and attenuation were correlated with Tumour grade. For each parameter, receiver operating characteristics characterised the diagnostic performance for discrimination of high grade from low grade glioma and of grade III Tumours from grade IV. Further the CTTA was compared to the radiological diagnosis. Tumour Heterogeneity correlated significantly with grade (SD without filtration rs = 0.664, p < 0.001, SD with coarse filtration (rs = 0.714, p < 0.001). Tumour size and attenuation showed only moderate correlations with Tumour grade (rs = 0.426, p = 0.004 and rs = 0.447, p = 0.002 respectively). Coarse texture was the best discriminator between high and low grade Tumours (AUC 0.832, p < 0.0001) and between grade III and grade IV gliomas (AUC = 0.878 p = 0.0001). Compared to the radiological diagnosis, CTTA further characterised the indetermined cases. By quantifying Tumour Heterogeneity, CTTA has the potential to provide a marker of Tumour grade for patients with cerebral glioma. By differentiating between high and low grade Tumours, CTTA could possibly assist clinical management.
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Tumour Heterogeneity in non small cell lung carcinoma assessed by ct texture analysis a potential marker of survival
European Radiology, 2012Co-Authors: Balaji Ganeshan, Elleny Panayiotou, Kate Burnand, Sabina Dizdarevic, Kenneth A MilesAbstract:Purpose To establish the potential for Tumour Heterogeneity in non-small cell lung cancer (NSCLC) as assessed by CT texture analysis (CTTA) to provide an independent marker of survival for patients with NSCLC.
Zena Werb - One of the best experts on this subject based on the ideXlab platform.
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Tumour Heterogeneity and metastasis at single-cell resolution
Nature Cell Biology, 2018Co-Authors: Devon A. Lawson, Kai Kessenbrock, Ryan T. Davis, Nicholas Pervolarakis, Zena WerbAbstract:Lawson et al. review recent advances in single-cell technologies and discuss in detail how they can be leveraged to understand Tumour Heterogeneity and metastasis. Tumours comprise a heterogeneous collection of cells with distinct genetic and phenotypic properties that can differentially promote progression, metastasis and drug resistance. Emerging single-cell technologies provide a new opportunity to profile individual cells within Tumours and investigate what roles they play in these processes. This Review discusses key technological considerations for single-cell studies in cancer, new findings using single-cell technologies and critical open questions for future applications.
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Tumour Heterogeneity and metastasis at single cell resolution
Nature Cell Biology, 2018Co-Authors: Devon A. Lawson, Kai Kessenbrock, Ryan T. Davis, Nicholas Pervolarakis, Zena WerbAbstract:Tumours comprise a heterogeneous collection of cells with distinct genetic and phenotypic properties that can differentially promote progression, metastasis and drug resistance. Emerging single-cell technologies provide a new opportunity to profile individual cells within Tumours and investigate what roles they play in these processes. This Review discusses key technological considerations for single-cell studies in cancer, new findings using single-cell technologies and critical open questions for future applications.
Hyun Hoon Chung - One of the best experts on this subject based on the ideXlab platform.
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Preoperative [18F]FDG PET/CT Tumour Heterogeneity index in patients with uterine leiomyosarcoma: a multicentre retrospective study
European journal of nuclear medicine and molecular imaging, 2018Co-Authors: Jeong-won Lee, Jeong-yeol Park, Hyun-ju Lee, Jong Jin Lee, Seung Hwan Moon, Seo Young Kang, Gi Jeong Cheon, Hyun Hoon ChungAbstract:Purpose We investigated the prognostic value of the Tumour Heterogeneity index determined on preoperative [18F]FDG PET/CT in patients with uterine leiomyosarcoma (LMS).
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preoperative 18f fdg pet ct Tumour Heterogeneity index in patients with uterine leiomyosarcoma a multicentre retrospective study
European Journal of Nuclear Medicine and Molecular Imaging, 2018Co-Authors: Jeong-won Lee, Jeong-yeol Park, Hyun-ju Lee, Jong Jin Lee, Seung Hwan Moon, Seo Young Kang, Gi Jeong Cheon, Hyun Hoon ChungAbstract:Purpose We investigated the prognostic value of the Tumour Heterogeneity index determined on preoperative [18F]FDG PET/CT in patients with uterine leiomyosarcoma (LMS).
Charles Swanton - One of the best experts on this subject based on the ideXlab platform.
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The role of Tumour Heterogeneity and clonal cooperativity in metastasis, immune evasion and clinical outcome
BMC medicine, 2017Co-Authors: Deborah R. Caswell, Charles SwantonAbstract:The advent of rapid and inexpensive sequencing technology allows scientists to decipher Heterogeneity within primary Tumours, between primary and metastatic sites, and between metastases. Charting the evolutionary history of individual Tumours has revealed drivers of Tumour Heterogeneity and highlighted its impact on therapeutic outcomes. Scientists are using improved sequencing technologies to characterise and address the challenge of Tumour Heterogeneity, which is a major cause of resistance to therapy and relapse. Heterogeneity may fuel metastasis through the selection of rare, aggressive, somatically altered cells. However, extreme levels of chromosomal instability, which contribute to intraTumour Heterogeneity, are associated with improved patient outcomes, suggesting a delicate balance between high and low levels of genome instability. We review evidence that intraTumour Heterogeneity influences Tumour evolution, including metastasis, drug resistance, and the immune response. We discuss the prevalence of Tumour Heterogeneity, and how it can be initiated and sustained by external and internal forces. Understanding Tumour evolution and metastasis could yield novel therapies that leverage the immune system to control emerging Tumour neo-antigens.
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Tumour Heterogeneity and the evolution of polyclonal drug resistance.
Molecular oncology, 2014Co-Authors: Rebecca A. Burrell, Charles SwantonAbstract:Cancer drug resistance is a major problem, with the majority of patients with metastatic disease ultimately developing multidrug resistance and succumbing to their disease. Our understanding of molecular events underpinning treatment failure has been enhanced by new genomic technologies and pre-clinical studies. IntraTumour genetic Heterogeneity (ITH) is a prominent contributor to therapeutic failure, and it is becoming increasingly apparent that individual Tumours may achieve resistance via multiple routes simultaneously – termed polyclonal resistance. Efforts to target single resistance mechanisms to overcome therapeutic failure may therefore yield only limited success. Clinical studies with sequential analysis of Tumour material are needed to enhance our understanding of inter-clonal functional relationships and Tumour evolution during therapy, and to improve drug development strategies in cancer medicine.
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Tumour Heterogeneity and immune-modulation
Current opinion in pharmacology, 2013Co-Authors: Mariam Jamal-hanjani, Eirini Thanopoulou, Karl S. Peggs, Sergio A. Quezada, Charles SwantonAbstract:Recent advances in sequencing technologies have revealed extensive intraTumour Heterogeneity (ITH) both within individual Tumours and between primary and metastatic Tumours for different cancer types. Such genetic diversity may have clinical implications for both cancer diagnosis and treatment with increasing evidence linking ITH and therapeutic resistance. Nonetheless, whilst limiting the activity of targeted agents, Tumour genetic Heterogeneity may provide a new therapeutic opportunity through generation of neo-antigens that could be recognised and targeted by the patient's own immune system in response to immune-modulatory therapies. Longitudinal genomic studies assessing Tumour clonal architecture and its correlation with the underlying immune response to cancer in each particular patient are needed to follow Tumour evolutionary dynamics over time and through therapy, in order to further understand the mechanisms behind drug resistance and to inform the development of new combinatorial therapeutic strategies.
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Cancer chromosomal instability: therapeutic and diagnostic challenges.
EMBO reports, 2012Co-Authors: Nicholas Mcgranahan, Rebecca A. Burrell, David Endesfelder, Marco Novelli, Charles SwantonAbstract:Chromosomal instability (CIN)—which is a high rate of loss or gain of whole or parts of chromosomes—is a characteristic of most human cancers and a cause of Tumour aneuploidy and intra-Tumour Heterogeneity. CIN is associated with poor patient outcome and drug resistance, which could be mediated by evolutionary adaptation fostered by intra-Tumour Heterogeneity. In this review, we discuss the clinical consequences of CIN and the challenges inherent to its measurement in Tumour specimens. The relationship between CIN and prognosis supports assessment of CIN status in the clinical setting and suggests that stratifying Tumours according to levels of CIN could facilitate clinical risk assessment.
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Tumour Heterogeneity and drug resistance: personalising cancer medicine through functional genomics.
Biochemical pharmacology, 2011Co-Authors: Alvin J. X. Lee, Charles SwantonAbstract:Intrinsic and acquired drug resistance leads to the eventual failure of cancer treatment regimens in the majority of advanced solid Tumours. Understanding drug resistance mechanisms will prove vital in the future development of personalised therapeutic approaches. Functional genomics technologies may permit the discovery of predictive biomarkers by unravelling pathways involved in drug resistance and allow the systematic identification of novel therapeutic targets. Such technologies offer the opportunity to develop personalised treatments and diagnostic tools that may improve the survival and quality of life of patients with cancer. However, despite progress in biomarker and drug target discovery, inter-Tumour and intra-Tumour molecular Heterogeneity will limit the effective treatment of this disease. Combining an improved understanding of cancer cell survival mechanisms associated with intra-Tumour Heterogeneity and drug resistance may allow the selection of patients for specific treatment regimens that will maximise benefit, limit the acquisition of drug resistance and lessen the impact of deleterious side effects.
