Publications by authors named "William H Nailon"

20 Publications

  • Page 1 of 1

Peppermint protocol: first results for gas chromatography-ion mobility spectrometry.

J Breath Res 2022 05 26;16(3). Epub 2022 May 26.

Centre for Analytical Science, Department of Chemistry, Loughborough University, Loughborough, United Kingdom.

Theseeks to inform the standardisation of breath analysis methods. Fivewith gas chromatography-ion mobility spectrometry (GC-IMS), operating in the positive mode with a tritiumH 5.68 keV, 370 MBq ionisation source, were undertaken to provide benchmarkdata for this technique, to support its use in breath-testing, analysis, and research. Headspace analysis of a peppermint-oil capsule by GC-IMS with on-column injection (0.5 cm) identified 12 IMS responsive compounds, of which the four most abundant were: eucalyptol;-pinene;-pinene; and limonene. Elevated concentrations of these four compounds were identified in exhaled-breath following ingestion of a peppermint-oil capsule. An unidentified compound attributed as a volatile catabolite of peppermint-oil was also observed. The most intense exhaled peppermint-oil component was eucalyptol, which was selected as a peppermint marker for benchmarking GC-IMS. Twenty-five washout experiments monitored levels of exhaled eucalyptol, by GC-IMS with on-column injection (0.5 cm), at= 0 min, and then at+ 60,+ 90,+ 165,+ 285 and+ 360 min from ingestion of a peppermint capsule resulting in 148 peppermint breath analyses. Additionally, thedata was used to evaluate clinical deployments with a further five washout tests run in clinical settings generating an additional 35 breath samples. Regression analysis yielded an average extrapolated time taken for exhaled eucalyptol levels to return to baseline values to be 429 ± 62 min (±95% confidence-interval). The benchmark value was assigned to the lower 95% confidence-interval, 367 min. Further evaluation of the data indicated that the maximum number of volatile organic compounds discernible from a 0.5 cmbreath sample was 69, while the use of an in-line biofilter appeared to reduce this to 34.
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http://dx.doi.org/10.1088/1752-7163/ac6ca0DOI Listing
May 2022

Fast and automated biomarker detection in breath samples with machine learning.

PLoS One 2022 12;17(4):e0265399. Epub 2022 Apr 12.

Computer Science Department, School of Science, Loughborough University, Loughborough, United Kingdom.

Volatile organic compounds (VOCs) in human breath can reveal a large spectrum of health conditions and can be used for fast, accurate and non-invasive diagnostics. Gas chromatography-mass spectrometry (GC-MS) is used to measure VOCs, but its application is limited by expert-driven data analysis that is time-consuming, subjective and may introduce errors. We propose a machine learning-based system to perform GC-MS data analysis that exploits deep learning pattern recognition ability to learn and automatically detect VOCs directly from raw data, thus bypassing expert-led processing. We evaluate this new approach on clinical samples and with four types of convolutional neural networks (CNNs): VGG16, VGG-like, densely connected and residual CNNs. The proposed machine learning methods showed to outperform the expert-led analysis by detecting a significantly higher number of VOCs in just a fraction of time while maintaining high specificity. These results suggest that the proposed novel approach can help the large-scale deployment of breath-based diagnosis by reducing time and cost, and increasing accuracy and consistency.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0265399PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9004778PMC
April 2022

Response to Letter to the Editor of Radiotherapy and Oncology regarding the paper entitled "50 years of radiotherapy research: Evolution, trends and lessons for the future" by Berger et al. (December 2021, volume 165).

Radiother Oncol 2022 07 7;172:151-152. Epub 2022 Apr 7.

Department of Oncology Physics, Edinburgh Cancer Centre, Western General Hospital, United Kingdom; School of Engineering, the University of Edinburgh, the King's Buildings, United Kingdom.

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http://dx.doi.org/10.1016/j.radonc.2022.04.001DOI Listing
July 2022

50 years of radiotherapy research: Evolution, trends and lessons for the future.

Radiother Oncol 2021 12 4;165:75-86. Epub 2021 Oct 4.

Department of Oncology Physics, Edinburgh Cancer Centre, Western General Hospital, Edinburgh, United Kingdom; School of Engineering, The University of Edinburgh, Edinburgh, United Kingdom.

Rapid and relentless technological advances in an ever-more globalized world have shaped the field of radiation oncology in which we practise today. These developments have drastically modified the habitus of health professionals and researchers at an individual and organisational level. In this article we present an analysis of trends in radiation oncology research over the last half a century. To do so, the data from >350,000 scientific publications pertaining to a yearly search of the PubMed database with the keywords cancer radiotherapy was analysed. This analysis revealed that, over the years, radiotherapy research output has declined relative to alternative cancer therapies, representing 64% in 1970 it decreased to 31% in 2019. Also, the pace of research has significantly accelerated with, in the last 15 years, a doubling in the number of articles published by the 10% most productive researchers. Researchers are also facing stronger competition today with a proportion of first authors that will never get to publish as a last author increasing steadily from 58% in 1970 to 84% in 2000. Additionally, radiotherapy research output is extremely unequally distributed in the world, with Africa and South America contributing to ∼3% of radiotherapy articles in 2019 while representing 23% of the world's population. This disparity, reflecting economic situations and radiotherapy capabilities, has a knock-on effect for the provision of routine clinical treatment. Since research activity is inherent to delivery of high quality clinical care, this contributes to the global inequity of radiotherapy services. Learning from these trends is crucial for the future not only of radiation oncology research but also for effective and equitable cancer care.
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http://dx.doi.org/10.1016/j.radonc.2021.09.026DOI Listing
December 2021

Dual Convolutional Neural Networks for Breast Mass Segmentation and Diagnosis in Mammography.

IEEE Trans Med Imaging 2022 01 30;41(1):3-13. Epub 2021 Dec 30.

Deep convolutional neural networks (CNNs) have emerged as a new paradigm for Mammogram diagnosis. Contemporary CNN-based computer-aided-diagnosis systems (CADs) for breast cancer directly extract latent features from input mammogram image and ignore the importance of morphological features. In this paper, we introduce a novel end-to-end deep learning framework for mammogram image processing, which computes mass segmentation and simultaneously predicts diagnosis results. Specifically, our method is constructed in a dual-path architecture that solves the mapping in a dual-problem manner, with an additional consideration of important shape and boundary knowledge. One path, called the Locality Preserving Learner (LPL), is devoted to hierarchically extracting and exploiting intrinsic features of the input. Whereas the other path, called the Conditional Graph Learner (CGL), focuses on generating geometrical features via modeling pixel-wise image to mask correlations. By integrating the two learners, both the cancer semantics and cancer representations are well learned, and the component learning paths in return complement each other, contributing an improvement to the mass segmentation and cancer classification problem at the same time. In addition, by integrating an automatic detection set-up, the DualCoreNet achieves fully automatic breast cancer diagnosis practically. Experimental results show that in benchmark DDSM dataset, DualCoreNet has outperformed other related works in both segmentation and classification tasks, achieving 92.27% DI coefficient and 0.85 AUC score. In another benchmark INbreast dataset, DualCoreNet achieves the best mammography segmentation (93.69% DI coefficient) and competitive classification performance (0.93 AUC score).
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http://dx.doi.org/10.1109/TMI.2021.3102622DOI Listing
January 2022

Breath markers for therapeutic radiation.

J Breath Res 2020 10 24;15(1):016004. Epub 2020 Oct 24.

Centre for Analytical Science, Chemistry, Loughborough University, Loughborough, United Kingdom.

Radiation dose is important in radiotherapy. Too little, and the treatment is not effective, too much causes radiation toxicity. A biochemical measurement of the effect of radiotherapy would be useful in personalisation of this treatment. This study evaluated changes in exhaled breath volatile organic compounds (VOC) associated with radiotherapy with thermal desorption gas chromatography mass-spectrometry followed by data processing and multivariate statistical analysis. Further the feasibility of adopting gas chromatography ion mobility spectrometry for radiotherapy point-of-care breath was assessed. A total of 62 participants provided 240 end-tidal 1 dm breath samples before radiotherapy and at 1, 3, and 6 h post-exposure, that were analysed by thermal-desorption/gas-chromatography/quadrupole mass-spectrometry. Data were registered by retention-index and mass-spectra before multivariate statistical analyses identified candidate markers. A panel of sulfur containing compounds (thio-VOC) were observed to increase in concentration over the 6 h following irradiation. 3-methylthiophene (80 ng.m to 790 ng.m) had the lowest abundance while 2-thiophenecarbaldehyde(380 ng.m to 3.85 μg.m) the highest; note, exhaled 2-thiophenecarbaldehyde has not been observed previously. The putative tumour metabolite 2,4-dimethyl-1-heptene concentration reduced by an average of 73% over the same time. Statistical scoring based on the signal intensities thio-VOC and 3-methylthiophene appears to reflect individuals' responses to radiation exposure from radiotherapy. The thio-VOC are hypothesised to derive from glutathione and Maillard-based reactions and these are of interest as they are associated with radio-sensitivity. Further studies with continuous monitoring are needed to define the development of the breath biochemistry response to irradiation and to determine the optimum time to monitor breath for radiotherapy markers. Consequently, a single 0.5 cm breath-sample gas chromatography-ion mobility approach was evaluated. The calibrated limit of detection for 3-methylthiophene was 10 μg.m with a lower limit of the detector's response estimated to be 210 fg.s; the potential for a point-of-care radiation exposure study exists.
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http://dx.doi.org/10.1088/1752-7163/aba816DOI Listing
October 2020

From multisource data to clinical decision aids in radiation oncology: The need for a clinical data science community.

Radiother Oncol 2020 12 13;153:43-54. Epub 2020 Oct 13.

Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, The Netherlands. Electronic address:

Big data are no longer an obstacle; now, by using artificial intelligence (AI), previously undiscovered knowledge can be found in massive data collections. The radiation oncology clinic daily produces a large amount of multisource data and metadata during its routine clinical and research activities. These data involve multiple stakeholders and users. Because of a lack of interoperability, most of these data remain unused, and powerful insights that could improve patient care are lost. Changing the paradigm by introducing powerful AI analytics and a common vision for empowering big data in radiation oncology is imperative. However, this can only be achieved by creating a clinical data science community in radiation oncology. In this work, we present why such a community is needed to translate multisource data into clinical decision aids.
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http://dx.doi.org/10.1016/j.radonc.2020.09.054DOI Listing
December 2020

VOCCluster: Untargeted Metabolomics Feature Clustering Approach for Clinical Breath Gas Chromatography/Mass Spectrometry Data.

Anal Chem 2020 02 5;92(4):2937-2945. Epub 2020 Feb 5.

Pharmacology, Toxicology and Therapeutics Unit , University of Edinburgh , Edinburgh EH8 9YL , U.K.

Metabolic profiling of breath analysis involves processing, alignment, scaling, and clustering of thousands of features extracted from gas chromatography/mass spectrometry (GC/MS) data from hundreds of participants. The multistep data processing is complicated, operator error-prone, and time-consuming. Automated algorithmic clustering methods that are able to cluster features in a fast and reliable way are necessary. These accelerate metabolic profiling and discovery platforms for next-generation medical diagnostic tools. Our unsupervised clustering technique, VOCCluster, prototyped in Python, handles features of deconvolved GC/MS breath data. VOCCluster was created from a heuristic ontology based on the observation of experts undertaking data processing with a suite of software packages. VOCCluster identifies and clusters groups of volatile organic compounds (VOCs) from deconvolved GC/MS breath with similar mass spectra and retention index profiles. VOCCluster was used to cluster more than 15 000 features extracted from 74 GC/MS clinical breath samples obtained from participants with cancer before and after a radiation therapy. Results were evaluated against a panel of ground truth compounds and compared to other clustering methods (DBSCAN and OPTICS) that were used in previous metabolomics studies. VOCCluster was able to cluster those features into 1081 groups (including endogenous and exogenous compounds and instrumental artifacts) with an accuracy rate of 96% (±0.04 at 95% confidence interval).
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http://dx.doi.org/10.1021/acs.analchem.9b03084DOI Listing
February 2020

EPID-based in vivo dosimetry using Dosimetry Check™: Overview and clinical experience in a 5-yr study including breast, lung, prostate, and head and neck cancer patients.

J Appl Clin Med Phys 2019 Jan 7;20(1):6-16. Epub 2018 Dec 7.

Department of Oncology Physics, Edinburgh Cancer Centre, Western General Hospital, Edinburgh, UK.

Background: Independent verification of the dose delivered by complex radiotherapy can be performed by electronic portal imaging device (EPID) dosimetry. This paper presents 5-yr EPID in vivo dosimetry (IVD) data obtained using the Dosimetry Check (DC) software on a large cohort including breast, lung, prostate, and head and neck (H&N) cancer patients.

Material And Methods: The difference between in vivo dose measurements obtained by DC and point doses calculated by the Eclipse treatment planning system was obtained on 3795 radiotherapy patients treated with volumetric modulated arc therapy (VMAT) (n = 842) and three-dimensional conformal radiotherapy (3DCRT) (n = 2953) at 6, 10, and 15 MV. In cases where the dose difference exceeded ±10% further inspection and additional phantom measurements were performed.

Results: The mean and standard deviation of the percentage difference in dose obtained by DC and calculated by Eclipse in VMAT was: in brain, in H&N, and in prostate cancer. In 3DCRT, this was in brain, in breast, in bladder, in H&N, 2.60 ± 5.35% in lung and in prostate cancer. A total of 153 plans exceeded the ±10% alert criteria, which included: 88 breast plans accounting for 7.9% of all breast treatments; 28 H&N plans accounting for 4.4% of all H&N treatments; and 12 prostate plans accounting for 3.5% of all prostate treatments. All deviations were found to be as a result of patient-related anatomical deviations and not from procedural errors.

Conclusions: This preliminary data shows that EPID-based IVD with DC may not only be useful in detecting errors but has the potential to be used to establish site-specific dose action levels. The approach is straightforward and has been implemented as a radiographer-led service with no disruption to the patient and no impact on treatment time.
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http://dx.doi.org/10.1002/acm2.12441DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6333145PMC
January 2019

Quantitative cone-beam CT reconstruction with polyenergetic scatter model fusion.

Phys Med Biol 2018 11 7;63(22):225001. Epub 2018 Nov 7.

School of Engineering, Institute for Digital Communications, University of Edinburgh, Edinburgh, EH9 3JL, United Kingdom. Author to whom any correspondence should be addressed.

Scatter can account for large errors in cone-beam CT (CBCT) due to its wide field of view, and its complicated nature makes its compensation difficult. Iterative polyenergetic reconstruction algorithms offer the potential to provide quantitative imaging in CT, but they are usually incompatible with scatter contaminated measurements. In this work, we introduce a polyenergetic convolutional scatter model that is directly fused into the reconstruction process, and exploits information readily available at each iteration for a fraction of additional computational cost. We evaluate this method with numerical and real CBCT measurements, and show significantly enhanced electron density estimation and artifact mitigation over pre-calculated fast adaptive scatter kernel superposition (fASKS). We demonstrate our approach has two levels of benefit: reducing the bias introduced by estimating scatter prior to reconstruction; and adapting to the spectral and spatial properties of the specimen.
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http://dx.doi.org/10.1088/1361-6560/aae794DOI Listing
November 2018

Polyquant CT: direct electron and mass density reconstruction from a single polyenergetic source.

Phys Med Biol 2017 Nov 2;62(22):8739-8762. Epub 2017 Nov 2.

School of Engineering, Institute for Digital Communications, The University of Edinburgh, Edinburgh, EH9 3JL, United Kingdom.

Quantifying material mass and electron density from computed tomography (CT) reconstructions can be highly valuable in certain medical practices, such as radiation therapy planning. However, uniquely parameterising the x-ray attenuation in terms of mass or electron density is an ill-posed problem when a single polyenergetic source is used with a spectrally indiscriminate detector. Existing approaches to single source polyenergetic modelling often impose consistency with a physical model, such as water-bone or photoelectric-Compton decompositions, which will either require detailed prior segmentation or restrictive energy dependencies, and may require further calibration to the quantity of interest. In this work, we introduce a data centric approach to fitting the attenuation with piecewise-linear functions directly to mass or electron density, and present a segmentation-free statistical reconstruction algorithm for exploiting it, with the same order of complexity as other iterative methods. We show how this allows both higher accuracy in attenuation modelling, and demonstrate its superior quantitative imaging, with numerical chest and metal implant data, and validate it with real cone-beam CT measurements.
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http://dx.doi.org/10.1088/1361-6560/aa9162DOI Listing
November 2017

Application of Texture Analysis to Study Small Vessel Disease and Blood-Brain Barrier Integrity.

Front Neurol 2017 19;8:327. Epub 2017 Jul 19.

Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.

Objectives: We evaluate the alternative use of texture analysis for evaluating the role of blood-brain barrier (BBB) in small vessel disease (SVD).

Methods: We used brain magnetic resonance imaging from 204 stroke patients, acquired before and 20 min after intravenous gadolinium administration. We segmented tissues, white matter hyperintensities (WMH) and applied validated visual scores. We measured textural features in all tissues pre- and post-contrast and used ANCOVA to evaluate the effect of SVD indicators on the pre-/post-contrast change, Kruskal-Wallis for significance between patient groups and linear mixed models for pre-/post-contrast variations in cerebrospinal fluid (CSF) with Fazekas scores.

Results: Textural "homogeneity" increase in normal tissues with higher presence of SVD indicators was consistently more overt than in abnormal tissues. Textural "homogeneity" increased with age, basal ganglia perivascular spaces scores ( < 0.01) and SVD scores ( < 0.05) and was significantly higher in hypertensive patients ( < 0.002) and lacunar stroke ( = 0.04). Hypertension (74% patients), WMH load (median = 1.5 ± 1.6% of intracranial volume), and age (mean = 65.6 years, SD = 11.3) predicted the pre/post-contrast change in normal white matter, WMH, and index stroke lesion. CSF signal increased with increasing SVD post-contrast.

Conclusion: A consistent general pattern of increasing textural "homogeneity" with increasing SVD and post-contrast change in CSF with increasing WMH suggest that texture analysis may be useful for the study of BBB integrity.
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http://dx.doi.org/10.3389/fneur.2017.00327DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5515862PMC
July 2017

Field correction factors for a PTW-31016 Pinpoint ionization chamber for both flattened and unflattened beams. Study of the main sources of uncertainties.

Med Phys 2017 May 13;44(5):1930-1938. Epub 2017 Apr 13.

Institut de Tècniques Energètiques (INTE), Universitat Politècnica de Catalunya, Barcelona, Spain.

Purpose: The primary aim of this study was to determine correction factors, kQclin,Qmsrfclin,fmsr for a PTW-31016 ionization chamber on field sizes from 0.5 cm × 0.5 cm to 2 cm × 2 cm for both flattened (FF) and flattened filter-free (FFF) beams produced in a TrueBeam clinical accelerator. The secondary objective was the determination of field output factors, ΩQclin,Qmsrfclin,fmsr over this range of field sizes using both Monte Carlo (MC) simulation and measurements.

Methods: kQclin,Qmsrfclin,fmsr for the PTW-31016 chamber were calculated by MC simulation for field sizes of 0.5 cm × 0.5 cm, 1 cm × 1 cm, and 2 cm × 2 cm. MC simulations were performed with the PENELOPE code system for the 10 MV FFF Particle Space File from a TrueBeam linear accelerator (LINAC) provided by the manufacturer (Varian Medical Systems, Inc. Palo Alto, CA, USA). Simulations were repeated taking into account chamber manufacturing tolerances and accelerator jaw positioning in order to assess the uncertainty of the calculated correction factors. Output ratios were measured on square fields ranging from 0.5 cm × 0.5 cm to 10 cm × 10 cm for 6 MV and 10 MV FF and FFF beams produced by a TrueBeam using a PTW-31016 ionization chamber; a Sun Nuclear Edge detector (SunNuclear Corp., Melbourne, FL, USA) and TLD-700R (Harshaw, Thermo Scientific, Waltham, MA, USA). The validity of the proposed correction factors was verified using the calculated correction factors for the determination of ΩQclin,Qmsrfclin,fmsr using a PTW-31016 at the four TrueBeam energies and comparing the results with both TLD-700R measurements and MC simulations. Finally, the proposed correction factors were used to assess the correction factors of the SunNuclear Edge detector.

Results: The present work provides a set of MC calculated correction factors for a PTW-31016 chamber used on a TrueBeam FF and FFF mode. For the 0.5 cm × 0.5 cm square field size, kQclin,Qmsrfclin,fmsr is equal to 1.17 with a combined uncertainty of 2% (k = 1). A detailed analysis of the most influential parameters is presented in this work. PTW-31016 corrected measurements were used for the determination of ΩQclin,Qmsrfclin,fmsr for 6 MV and 10 MV FF and FFF and the results were in agreement with values obtained using a TLD-700R detector (differences < 3% for a 0.5 cm square field) for the four energies studied. Uncertainty in field collimation was found to be the main source of influence of ΩQclin,Qmsrfclin,fmsr and caused differences of up to 15% between calculations and measurements for the 0.5 cm × 0.5 cm field. This was also confirmed by repeating the same measurements at two different institutions.

Conclusions: This study confirms the need to introduce correction factors when using a PTW-31016 chamber and the hypothesis of their low energy dependence. MC simulation has been shown to be a useful methodology to determine detector correction factors for small fields and to analyze the main sources of uncertainty. However, due to the influence of the LINAC jaw setup for field sizes below or equal to 1 cm, MC methods are not recommended in this range for field output factor calculations.
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http://dx.doi.org/10.1002/mp.12189DOI Listing
May 2017

Targeted SERS nanosensors measure physicochemical gradients and free energy changes in live 3D tumor spheroids.

Nanoscale 2016 Sep;8(37):16710-16718

EaStCHEM, School of Chemistry, University of Edinburgh, Edinburgh, EH9 3FJ, UK.

Use of multicellular tumor spheroids (MTS) to investigate therapies has gained impetus because they have potential to mimic factors including zonation, hypoxia and drug-resistance. However, analysis remains difficult and often destroys 3D integrity. Here we report an optical technique using targeted nanosensors that allows in situ 3D mapping of redox potential gradients whilst retaining MTS morphology and function. The magnitude of the redox potential gradient can be quantified as a free energy difference (ΔG) and used as a measurement of MTS viability. We found that by delivering different doses of radiotherapy to MTS we could correlate loss of ΔG with increasing therapeutic dose. In addition, we found that resistance to drug therapy was indicated by an increase in ΔG. This robust and reproducible technique allows interrogation of an in vitro tumor-model's bioenergetic response to therapy, indicating its potential as a tool for therapy development.
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http://dx.doi.org/10.1039/c6nr06031eDOI Listing
September 2016

Correction: Measuring the effects of fractionated radiation therapy in a 3D prostate cancer model system using SERS nanosensors.

Analyst 2016 10;141(20):5900

School of Chemistry, University of Edinburgh, Edinburgh, EH9 3FJ, UK.

Correction for 'Measuring the effects of fractionated radiation therapy in a 3D prostate cancer model system using SERS nanosensors' by Victoria L. Camus, et al., Analyst, 2016, 141, 5056-5061.
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http://dx.doi.org/10.1039/c6an90079hDOI Listing
October 2016

Measuring the effects of fractionated radiation therapy in a 3D prostate cancer model system using SERS nanosensors.

Analyst 2016 08;141(17):5056-61

School of Chemistry, University of Edinburgh, Edinburgh, EH9 3FJ, UK.

Multicellular tumour spheroids (MTS) are three-dimensional cell cultures that possess their own microenvironments and provide a more meaningful model of tumour biology than monolayer cultures. As a result, MTS are becoming increasingly used as tumor models when measuring the efficiency of therapies. Monitoring the viability of live MTS is complicated by their 3D nature and conventional approaches such as fluorescence often require fixation and sectioning. In this paper we detail the use of Surface Enhanced Raman Spectroscopy (SERS) to measure the viability of MTS grown from prostate cancer (PC3) cells. Our results show that we can monitor loss of viability by measuring pH and redox potential in MTS and furthermore we demonstrate that SERS can be used to measure the effects of fractionation of a dose of radiotherapy in a way that has potential to inform treatment planning.
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http://dx.doi.org/10.1039/c6an01032fDOI Listing
August 2016

INVITED REVIEW--IMAGE REGISTRATION IN VETERINARY RADIATION ONCOLOGY: INDICATIONS, IMPLICATIONS, AND FUTURE ADVANCES.

Vet Radiol Ultrasound 2016 Mar-Apr;57(2):113-23. Epub 2016 Jan 18.

Department of Oncology Physics, Edinburgh Cancer Centre, Western General Hospital, The University of Edinburgh, Edinburgh, UK.

The field of veterinary radiation therapy (RT) has gained substantial momentum in recent decades with significant advances in conformal treatment planning, image-guided radiation therapy (IGRT), and intensity-modulated (IMRT) techniques. At the root of these advancements lie improvements in tumor imaging, image alignment (registration), target volume delineation, and identification of critical structures. Image registration has been widely used to combine information from multimodality images such as computerized tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) to improve the accuracy of radiation delivery and reliably identify tumor-bearing areas. Many different techniques have been applied in image registration. This review provides an overview of medical image registration in RT and its applications in veterinary oncology. A summary of the most commonly used approaches in human and veterinary medicine is presented along with their current use in IGRT and adaptive radiation therapy (ART). It is important to realize that registration does not guarantee that target volumes, such as the gross tumor volume (GTV), are correctly identified on the image being registered, as limitations unique to registration algorithms exist. Research involving novel registration frameworks for automatic segmentation of tumor volumes is ongoing and comparative oncology programs offer a unique opportunity to test the efficacy of proposed algorithms.
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http://dx.doi.org/10.1111/vru.12342DOI Listing
November 2016

Identifying radiotherapy target volumes in brain cancer by image analysis.

Healthc Technol Lett 2015 Oct 2;2(5):123-8. Epub 2015 Oct 2.

Department of Oncology Physics , Edinburgh Cancer Centre, Western General Hospital , Crewe Road South, Edinburgh EH4 2XU , UK ; School of Engineering , University of Edinburgh , King's Buildings, Mayfield Road, Edinburgh EH9 3JL , UK.

To establish the optimal radiotherapy fields for treating brain cancer patients, the tumour volume is often outlined on magnetic resonance (MR) images, where the tumour is clearly visible, and mapped onto computerised tomography images used for radiotherapy planning. This process requires considerable clinical experience and is time consuming, which will continue to increase as more complex image sequences are used in this process. Here, the potential of image analysis techniques for automatically identifying the radiation target volume on MR images, and thereby assisting clinicians with this difficult task, was investigated. A gradient-based level set approach was applied on the MR images of five patients with grades II, III and IV malignant cerebral glioma. The relationship between the target volumes produced by image analysis and those produced by a radiation oncologist was also investigated. The contours produced by image analysis were compared with the contours produced by an oncologist and used for treatment. In 93% of cases, the Dice similarity coefficient was found to be between 60 and 80%. This feasibility study demonstrates that image analysis has the potential for automatic outlining in the management of brain cancer patients, however, more testing and validation on a much larger patient cohort is required.
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http://dx.doi.org/10.1049/htl.2015.0014DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4625830PMC
October 2015

Identifying the dominant prostate cancer focal lesion using image analysis and planning of a simultaneous integrated stereotactic boost.

Acta Oncol 2015 23;54(9):1543-50. Epub 2015 Sep 23.

a Department of Oncology Physics , Edinburgh Cancer Centre, Western General Hospital , Crewe Road South, Edinburgh , UK.

Background: Prostate cancer is now the only solid organ cancer in which therapy is commonly applied to the whole gland. One of the main challenges in adopting focal boost or true focal therapy is in the accurate mapping of cancer foci defined on magnetic resonance (MR) images onto the computerised tomography (CT) images used for radiotherapy planning.

Material And Methods: Prostate cancer patients (n = 14) previously treated at the Edinburgh Cancer Centre (ECC) were selected for this study. All patients underwent MR scanning for the purpose of diagnosis and staging. Patients received three months of androgen deprivation hormone therapy followed by a radiotherapy planning CT scan. The dominant focal prostate lesions were identified on MR scans by a radiologist and a novel image analysis approach was used to map the location of the dominant focal lesion from MR to CT. An offline planning study was undertaken on suitable patients (n = 7) to investigate boosting of the radiation dose to the tumour using a stereotactic ablative body radiotherapy (SABR) technique.

Results: The non-rigid registration algorithm showed clinically acceptable estimates of the location of the dominant focal disease on all CT image data of patients suitable for a boost treatment. Standard rigid registration was found to produce unacceptable estimates of the dominant focal lesion on CT. A SABR boost dose of 47.5 Gy was delivered to the dominant focal lesion of all patients whilst meeting all dose-volume histogram (DVH) constraints. Normal tissue complication probability (NTCP) for the rectum decreased from 1.28% to 0.73% with this method.

Conclusions: These preliminary results demonstrate the potential of this image analysis method for reliably mapping dominant focal disease within the prostate from MR images onto planning CT images. Significant dose escalation using a simultaneous integrated SABR boost was achieved in all patients.
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http://dx.doi.org/10.3109/0284186X.2015.1063782DOI Listing
July 2016

Characterisation of radiotherapy planning volumes using textural analysis.

Acta Oncol 2008 ;47(7):1303-8

Department of Oncology Physics, Edinburgh Cancer Centre, Western General Hospital, Edinburgh, UK.

Computer-based artificial intelligence methods for classification and delineation of the gross tumour volume (GTV) on computerised tomography (CT) and magnetic resonance (MR) images do not, at present, provide the accuracy required for radiotherapy applications. This paper describes an image analysis method for classification of distinct regions within the GTV, and other clinically relevant regions, on CT images acquired on eight bladder cancer patients at the radiotherapy planning stage and thereafter at regular intervals during treatment. Statistical and fractal textural features (N=27) were calculated on the bladder, rectum and a control region identified on axial, coronal and sagittal CT images. Unsupervised classification results demonstrate that with a reduced feature set (N=3) the approach offers significant classification accuracy on axial, coronal and sagittal CT image planes and has the potential to be developed further for radiotherapy applications, particularly towards an automatic outlining approach.
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http://dx.doi.org/10.1080/02841860802256467DOI Listing
October 2008
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