Publications
399 results found
Talbot T, Lu H, Aboagye EO, 2023, Amplified therapeutic targets in high-grade serous ovarian carcinoma - a review of the literature with quantitative appraisal., Cancer Gene Ther, Pages: 1-9
High-grade serous ovarian carcinoma is a unique cancer characterised by universal TP53 mutations and widespread copy number alterations. These copy number alterations include deletion of tumour suppressors and amplification of driver oncogenes. Given their key oncogenic roles, amplified driver genes are often proposed as therapeutic targets. For example, development of anti-HER2 agents has been clinically successful in treatment of ERBB2-amplified tumours. A wide scope of preclinical work has since investigated numerous amplified genes as potential therapeutic targets in high-grade serous ovarian carcinoma. However, variable experimental procedures (e.g., choice of cell lines), ambiguous phenotypes or lack of validation hinders further clinical translation of many targets. In this review, we collate the genes proposed to be amplified therapeutic targets in high-grade serous ovarian carcinoma, and quantitatively appraise the evidence in support of each candidate gene. Forty-four genes are found to have evidence as amplified therapeutic targets; the five highest scoring genes are CCNE1, PAX8, URI1, PRKCI and FAL1. This review generates an up-to-date list of amplified therapeutic target candidates for further development and proposes comprehensive criteria to assist amplified therapeutic target discovery in the future.
Chen M, Lu H, Copley SJ, et al., 2023, A novel radiogenomics biomarker for predicting treatment response and pneumotoxicity from programmed cell death protein or ligand-1 inhibition immunotherapy in NSCLC, Journal of Thoracic Oncology, Pages: 1-13, ISSN: 1556-0864
INTRODUCTION: Patient selection for checkpoint inhibitor immunotherapy is currently guided by programmed death-ligand 1 (PD-L1) expression obtained from immunohistochemical staining of tumor tissue samples. This approach is susceptible to limitations resulting from the dynamic and heterogeneous nature of cancer cells and the invasiveness of the tissue sampling procedure. To address these challenges, we developed a novel computed tomography (CT) radiomic-based signature for predicting disease response in patients with NSCLC undergoing programmed cell death protein 1 (PD-1) or PD-L1 checkpoint inhibitor immunotherapy. METHODS: This retrospective study comprises a total of 194 patients with suitable CT scans out of 340. Using the radiomic features computed from segmented tumors on a discovery set of 85 contrast-enhanced chest CTs of patients diagnosed with having NSCLC and their CD274 count, RNA expression of the protein-encoding gene for PD-L1, as the response vector, we developed a composite radiomic signature, lung cancer immunotherapy-radiomics prediction vector (LCI-RPV). This was validated in two independent testing cohorts of 66 and 43 patients with NSCLC treated with PD-1 or PD-L1 inhibition immunotherapy, respectively. RESULTS: LCI-RPV predicted PD-L1 positivity in both NSCLC testing cohorts (area under the curve [AUC] = 0.70, 95% confidence interval [CI]: 0.57-0.84 and AUC = 0.70, 95% CI: 0.46-0.94). In one cohort, it also demonstrated good prediction of cases with high PD-L1 expression exceeding key treatment thresholds (>50%: AUC = 0.72, 95% CI: 0.59-0.85 and >90%: AUC = 0.66, 95% CI: 0.45-0.88), the tumor's objective response to treatment at 3 months (AUC = 0.68, 95% CI: 0.52-0.85), and pneumonitis occurrence (AUC = 0.64, 95% CI: 0.48-0.80). LCI-RPV achieved statistically significant stratification of the patients into a high- and low-risk survival group (hazard ratio = 2.26, 95% CI: 1.21-4.24, p = 0.011 a
Ćorović A, Wall C, Nus M, et al., 2023, Somatostatin receptor PET/MR imaging of inflammation in patients with large vessel vasculitis and atherosclerosis., Journal of the American College of Cardiology, Vol: 81, Pages: 336-354, ISSN: 0735-1097
BACKGROUND: Assessing inflammatory disease activity in large vessel vasculitis (LVV) can be challenging by conventional measures. OBJECTIVES: We aimed to investigate somatostatin receptor 2 (SST2) as a novel inflammation-specific molecular imaging target in LVV. METHODS: In a prospective, observational cohort study, in vivo arterial SST2 expression was assessed by positron emission tomography/magnetic resonance imaging (PET/MRI) using 68Ga-DOTATATE and 18F-FET-βAG-TOCA. Ex vivo mapping of the imaging target was performed using immunofluorescence microscopy; imaging mass cytometry; and bulk, single-cell, and single-nucleus RNA sequencing. RESULTS: Sixty-one participants (LVV: n = 27; recent atherosclerotic myocardial infarction of ≤2 weeks: n = 25; control subjects with an oncologic indication for imaging: n = 9) were included. Index vessel SST2 maximum tissue-to-blood ratio was 61.8% (P < 0.0001) higher in active/grumbling LVV than inactive LVV and 34.6% (P = 0.0002) higher than myocardial infarction, with good diagnostic accuracy (area under the curve: ≥0.86; P < 0.001 for both). Arterial SST2 signal was not elevated in any of the control subjects. SST2 PET/MRI was generally consistent with 18F-fluorodeoxyglucose PET/computed tomography imaging in LVV patients with contemporaneous clinical scans but with very low background signal in the brain and heart, allowing for unimpeded assessment of nearby coronary, myocardial, and intracranial artery involvement. Clinically effective treatment for LVV was associated with a 0.49 ± 0.24 (standard error of the mean [SEM]) (P = 0.04; 22.3%) reduction in the SST2 maximum tissue-to-blood ratio after 9.3 ± 3.2 months. SST2 expression was localized to macrophages, pericytes, and perivascular adipocytes in vasculitis specimens, with specific receptor binding confirmed by autoradiography. SSTR2-expressing macropha
Aboagye EO, Barwick TD, Haberkorn U, 2023, Radiotheranostics in oncology: Making precision medicine possible, CA-A CANCER JOURNAL FOR CLINICIANS, ISSN: 0007-9235
Li X, Marcus D, Russell J, et al., 2022, An integrated clinical-MR radiomics model to estimate survival time in patients with endometrial cancer, Journal of Magnetic Resonance Imaging, ISSN: 1053-1807
Background:Determination of survival time in women with endometrial cancer using clinical features remains imprecise. Features from MRI may improve the survival estimation allowing improved treatment planning.Purpose:To identify clinical features and imaging signatures on T2-weighted MRI that can be used in an integrated model to estimate survival time for endometrial cancer subjects.Study Type:Retrospective.Population:Four hundred thirteen patients with endometrial cancer as training (N = 330, 66.41 ± 11.42 years) and validation (N = 83, 67.60 ± 11.89 years) data and an independent set of 82 subjects as testing data (63.26 ± 12.38 years).Field Strength/Sequence:1.5-T and 3-T scanners with sagittal T2-weighted spin echo sequence.Assessment:Tumor regions were manually segmented on T2-weighted images. Features were extracted from segmented masks, and clinical variables including age, cancer histologic grade and risk score were included in a Cox proportional hazards (CPH) model. A group least absolute shrinkage and selection operator method was implemented to determine the model from the training and validation datasets.Statistical Tests:A likelihood-ratio test and decision curve analysis were applied to compare the models. Concordance index (CI) and area under the receiver operating characteristic curves (AUCs) were calculated to assess the model.Results:Three radiomic features (two image intensity and volume features) and two clinical variables (age and cancer grade) were selected as predictors in the integrated model. The CI was 0.797 for the clinical model (includes clinical variables only) and 0.818 for the integrated model using training and validation datasets, the associated mean AUC value was 0.805 and 0.853. Using the testing dataset, the CI was 0.792 and 0.882, significantly different and the mean AUC was 0.624 and 0.727 for the clinical model and integrated model, respective
Hunter B, Chen M, Ratnakumar P, et al., 2022, A radiomics-based decision support tool improves lung cancer diagnosis in combination with the Herder score in large lung nodules, EBioMedicine, Vol: 86, ISSN: 2352-3964
Background:Large lung nodules (≥15 mm) have the highest risk of malignancy, and may exhibit important differences in phenotypic or clinical characteristics to their smaller counterparts. Existing risk models do not stratify large nodules well. We aimed to develop and validate an integrated segmentation and classification pipeline, incorporating deep-learning and traditional radiomics, to classify large lung nodules according to cancer risk.Methods:502 patients from five U.K. centres were recruited to the large-nodule arm of the retrospective LIBRA study between July 2020 and April 2022. 838 CT scans were used for model development, split into training and test sets (70% and 30% respectively). An nnUNet model was trained to automate lung nodule segmentation. A radiomics signature was developed to classify nodules according to malignancy risk. Performance of the radiomics model, termed the large-nodule radiomics predictive vector (LN-RPV), was compared to three radiologists and the Brock and Herder scores.Findings:499 patients had technically evaluable scans (mean age 69 ± 11, 257 men, 242 women). In the test set of 252 scans, the nnUNet achieved a DICE score of 0.86, and the LN-RPV achieved an AUC of 0.83 (95% CI 0.77–0.88) for malignancy classification. Performance was higher than the median radiologist (AUC 0.75 [95% CI 0.70–0.81], DeLong p = 0.03). LN-RPV was robust to auto-segmentation (ICC 0.94). For baseline solid nodules in the test set (117 patients), LN-RPV had an AUC of 0.87 (95% CI 0.80–0.93) compared to 0.67 (95% CI 0.55–0.76, DeLong p = 0.002) for the Brock score and 0.83 (95% CI 0.75–0.90, DeLong p = 0.4) for the Herder score. In the international external test set (n = 151), LN-RPV maintained an AUC of 0.75 (95% CI 0.63–0.85). 18 out of 22 (82%) malignant nodules in the Herder 10–70% category in the test set were identified as high risk by the decision-support tool, and may have been referred for earl
Hindocha S, Charlton TG, Linton-Reid K, et al., 2022, Author Correction: Gross tumour volume radiomics for prognostication of recurrence & death following radical radiotherapy for NSCLC, NPJ PRECISION ONCOLOGY, Vol: 6
Evans JS, Beaumont J, Braga M, et al., 2022, Epigenetic potentiation of somatostatin-2 by guadecitabine in neuroendocrine neoplasias as a novel method to allow delivery of peptide receptor radiotherapy, European Journal of Cancer, Vol: 176, Pages: 110-120, ISSN: 0959-8049
BackgroundSomatostatin receptor-2 (SSTR2) is expressed on cell surface of neuroendocrine neoplasias; its presence is exploited for the delivery of peptide receptor radionuclide therapy (PRRT). Patients with no or low expression of SSTR2 are not candidates for PRRT. SSTR2 promotor undergoes epigenetic modification, known to regulate gene expression. We investigated whether the demethylation agent, guadecitabine, could enhance the expression of SSTR2 in NET models, using radioligand uptake/PET imaging as a biomarker of epigenetic modification.MethodsThe effects of guadecitabine on the transcriptional, translational, and functional regulation of SSTR2 both in vitro and in vivo using low (QGP-1) and high (BON-1) methylated neuroendocrine neoplasia models was characterised. Promotor region methylation profiling of clinical samples (n = 61) was undertaken. Safety of combination guadecitabine and PRRT was assessed in vivo.ResultsPyrosequencing of cell lines illustrated differential methylation indices – BON: 1 94%, QGP: 1 21%. Following guadecitabine treatment, a dose-dependent increase in SSTR2 in BON-1 at a transcriptional, translational, and functional levels using the SSTR2-directed radioligand, 18F-FET-βAG-TOCA ([18F]-FETO) (150% increase [18F]-FETO uptake, p < 0.05) was observed. In vivo, guadecitabine treatment resulted in a 70% increase in [18F]-FETO uptake in BON-1 tumour models compared models with low baseline percentage methylation (p < 0.05). No additive toxicity was observed with the combination treatment of PRRT and guadecitabine in vivo. Methylation index in clinical samples was 10.5% compared to 5.2% in controls (p = 0.03) and correlated with SSTR2 expression (Wilcoxon rank sign −3.75,p < 0.01).ConclusionGuadecitabine increases SSTR2 expression both in vitro and in vivo. The combination of demethylation agents with PRRT warrants further investigation.
Islam S, Inglese M, Aravind P, et al., 2022, 18F-Fluoropivalate PET/MRI: imaging of treatment naive patients and patients treated with radiosurgery, 34th EORTC-NCI-AACR Symposium on Molecular Targets and Cancer Therapeutics, Publisher: Elsevier, Pages: S49-S49, ISSN: 0959-8049
Piletsky S, Kassem S, Yesilkaya H, et al., 2022, Assessing the in vivo biocompatibility of molecularly imprinted polymer nanoparticles, Polymers, Vol: 14, Pages: 1-15, ISSN: 2073-4360
Molecularly imprinted polymer nanoparticles (nanoMIPs) are high affinity synthetic receptors which show promise as imaging and therapeutic agents. Comprehensive analysis of the in vivo behaviour of nanoMIPs must be performed before they can be considered for clinical applications. This work reports the solid-phase synthesis of nanoMIPs and an investigation of their biodistribution, clearance and cytotoxicity in a rat model following both intravenous and oral administration. These nanoMIPs were found in each harvested tissue type, including brain tissue, implying their ability to cross the blood brain barrier. The nanoMIPs were cleared from the body via both faeces and urine. Furthermore, we describe an immunogenicity study in mice, demonstrating that nanoMIPs specific for a cell surface protein showed moderate adjuvant properties, whilst those imprinted for a scrambled peptide showed no such behaviour. Given their ability to access all tissue types and their relatively low cytotoxicity, these results pave the way for in vivo applications of nanoMIPs.
Hindocha S, Charlton TG, Linton-Reid K, et al., 2022, Gross tumour volume radiomics for prognostication of recurrence & death following radical radiotherapy for NSCLC, npj Precision Oncology, Vol: 6, Pages: 1-11, ISSN: 2397-768X
Recurrence occurs in up to 36% of patients treated with curative-intent radiotherapy for NSCLC. Identifying patients at higher risk of recurrence for more intensive surveillance may facilitate the earlier introduction of the next line of treatment. We aimed to use radiotherapy planning CT scans to develop radiomic classification models that predict overall survival (OS), recurrence-free survival (RFS) and recurrence two years post-treatment for risk-stratification. A retrospective multi-centre study of >900 patients receiving curative-intent radiotherapy for stage I-III NSCLC was undertaken. Models using radiomic and/or clinical features were developed, compared with 10-fold cross-validation and an external test set, and benchmarked against TNM-stage. Respective validation and test set AUCs (with 95% confidence intervals) for the radiomic-only models were: (1) OS: 0.712 (0.592–0.832) and 0.685 (0.585–0.784), (2) RFS: 0.825 (0.733–0.916) and 0.750 (0.665–0.835), (3) Recurrence: 0.678 (0.554–0.801) and 0.673 (0.577–0.77). For the combined models: (1) OS: 0.702 (0.583–0.822) and 0.683 (0.586–0.78), (2) RFS: 0.805 (0.707–0.903) and 0·755 (0.672–0.838), (3) Recurrence: 0·637 (0.51–0.·765) and 0·738 (0.649–0.826). Kaplan-Meier analyses demonstrate OS and RFS difference of >300 and >400 days respectively between low and high-risk groups. We have developed validated and externally tested radiomic-based prediction models. Such models could be integrated into the routine radiotherapy workflow, thus informing a personalised surveillance strategy at the point of treatment. Our work lays the foundations for future prospective clinical trials for quantitative personalised risk-stratification for surveillance following curative-intent radiotherapy for NSCLC.
Amgheib A, Fu R, Aboagye EO, 2022, Positron emission tomography probes for imaging cytotoxic immune cells, Pharmaceutics, Vol: 14, Pages: 1-31, ISSN: 1999-4923
Non-invasive positron emission tomography (PET) imaging of immune cells is a powerful approach for monitoring the dynamics of immune cells in response to immunotherapy. Despite the clinical success of many immunotherapeutic agents, their clinical efficacy is limited to a subgroup of patients. Conventional imaging, as well as analysis of tissue biopsies and blood samples do not reflect the complex interaction between tumour and immune cells. Consequently, PET probes are being developed to capture the dynamics of such interactions, which may improve patient stratification and treatment evaluation. The clinical efficacy of cancer immunotherapy relies on both the infiltration and function of cytotoxic immune cells at the tumour site. Thus, various immune biomarkers have been investigated as potential targets for PET imaging of immune response. Herein, we provide an overview of the most recent developments in PET imaging of immune response, including the radiosynthesis approaches employed in their development.
Satchwell L, Wedlake L, Greenlay E, et al., 2022, Development of machine learning support for reading whole body diffusion-weighted MRI (WB-MRI) in myeloma for the detection and quantification of the extent of disease before and after treatment (MALIMAR): protocol for a cross-sectional diagnostic test accuracy study, BMJ Open, Vol: 12, Pages: 1-9, ISSN: 2044-6055
Introduction Whole-body MRI (WB-MRI) is recommended by the National Institute of Clinical Excellence as the first-line imaging tool for diagnosis of multiple myeloma. Reporting WB-MRI scans requires expertise to interpret and can be challenging for radiologists who need to meet rapid turn-around requirements. Automated computational tools based on machine learning (ML) could assist the radiologist in terms of sensitivity and reading speed and would facilitate improved accuracy, productivity and cost-effectiveness. The MALIMAR study aims to develop and validate a ML algorithm to increase the diagnostic accuracy and reading speed of radiological interpretation of WB-MRI compared with standard methods.Methods and analysis This phase II/III imaging trial will perform retrospective analysis of previously obtained clinical radiology MRI scans and scans from healthy volunteers obtained prospectively to implement training and validation of an ML algorithm. The study will comprise three project phases using approximately 633 scans to (1) train the ML algorithm to identify active disease, (2) clinically validate the ML algorithm and (3) determine change in disease status following treatment via a quantification of burden of disease in patients with myeloma. Phase 1 will primarily train the ML algorithm to detect active myeloma against an expert assessment (‘reference standard’). Phase 2 will use the ML output in the setting of radiology reader study to assess the difference in sensitivity when using ML-assisted reading or human-alone reading. Phase 3 will assess the agreement between experienced readers (with and without ML) and the reference standard in scoring both overall burden of disease before and after treatment, and response.Ethics and dissemination MALIMAR has ethical approval from South Central—Oxford C Research Ethics Committee (REC Reference: 17/SC/0630). IRAS Project ID: 233501. CPMS Portfolio adoption (CPMS ID: 36766). Participants gave informe
Aboagye E, Brickute D, Allott L, et al., 2022, Design, synthesis, and evaluation of a novel PET imaging agent targeting lipofuscin in senescent cells, RSC Advances: an international journal to further the chemical sciences, Vol: 12, Pages: 26372-26381, ISSN: 2046-2069
Promoting a senescent phenotype to supress tumour progression may present an alternative strategy for treating cancerand encourages the development of positron emission tomography (PET) imaging biomarkers for assessing response totreatment. The accumulation of lipofuscin deposits in senescent cells are visualised using the pathology stain Sudan Black B(SBB) and is an emerging biomarker of senescence. We describe the design, synthesis and evaluation of[18F]fluoroethyltriazole-SBB ([18F]FET-SBB), a fluorine-18 radiolabelled derivative of SBB. The in vitro uptake of [18F]FET-SBBin a senescent cell line corelated with lipofuscin deposits; in vivo PET imaging and metabolite analysis confirms a favourablepharmacokinetic and metabolic profile for futher studies of in vivo models of senescence.
Hubbard Cristinacce PL, Keaveney S, Aboagye EO, et al., 2022, Clinical translation of quantitative magnetic resonance imaging biomarkers - An overview and gap analysis of current practice, Physica Medica: an international journal devoted to the applications of physics to medicine and biology, Vol: 101, Pages: 165-182, ISSN: 1120-1797
PURPOSE: This overview of the current landscape of quantitative magnetic resonance imaging biomarkers (qMR IBs) aims to support the standardisation of academic IBs to assist their translation to clinical practice. METHODS: We used three complementary approaches to investigate qMR IB use and quality management practices within the UK: 1) a literature search of qMR and quality management terms during 2011-2015 and 2016-2020; 2) a database search for clinical research studies using qMR IBs during 2016-2020; and 3) a survey to ascertain the current availability and quality management practices for clinical MRI scanners and associated equipment at research institutions across the UK. RESULTS: The analysis showed increased use of all qMR methods between the periods 2011-2015 and 2016-2020 and diffusion-tensor MRI and volumetry to be popular methods. However, the "translation ratio" of journal articles to clinical research studies was higher for qMR methods that have evidence of clinical translation via a commercial route, such as fat fraction and T2 mapping. The number of journal articles citing quality management terms doubled between the periods 2011-2015 and 2016-2020; although, its proportion relative to all journal articles only increased by 3.0%. The survey suggested that quality assurance (QA) and quality control (QC) of data acquisition procedures are under-reported in the literature and that QA/QC of acquired data/data analysis are under-developed and lack consistency between institutions. CONCLUSIONS: We summarise current attempts to standardise and translate qMR IBs, and conclude by outlining the ideal quality management practices and providing a gap analysis between current practice and a metrological standard.
Boubnovski MM, Chen M, Linton-Reid K, et al., 2022, Development of a multi-task learning V-Net for pulmonary lobar segmentation on CT and application to diseased lungs, Clinical Radiology, Vol: 77, Pages: e620-e627, ISSN: 0009-9260
AIMTo develop a multi-task learning (MTL) V-Net for pulmonary lobar segmentation on computed tomography (CT) and application to diseased lungs.MATERIALS AND METHODSThe described methodology utilises tracheobronchial tree information to enhance segmentation accuracy through the algorithm's spatial familiarity to define lobar extent more accurately. The method undertakes parallel segmentation of lobes and auxiliary tissues simultaneously by employing MTL in conjunction with V-Net-attention, a popular convolutional neural network in the imaging realm. Its performance was validated by an external dataset of patients with four distinct lung conditions: severe lung cancer, COVID-19 pneumonitis, collapsed lungs, and chronic obstructive pulmonary disease (COPD), even though the training data included none of these cases.RESULTSThe following Dice scores were achieved on a per-segment basis: normal lungs 0.97, COPD 0.94, lung cancer 0.94, COVID-19 pneumonitis 0.94, and collapsed lung 0.92, all at p<0.05.CONCLUSIONDespite severe abnormalities, the model provided good performance at segmenting lobes, demonstrating the benefit of tissue learning. The proposed model is poised for adoption in the clinical setting as a robust tool for radiologists and researchers to define the lobar distribution of lung diseases and aid in disease treatment planning.
Davis C, Li C, Nie R, et al., 2022, Highly effective liquid and solid phase extraction methods to concentrate radioiodine isotopes for radioiodination chemistry, Journal of Labelled Compounds and Radiopharmaceuticals, Vol: 65, Pages: 280-287, ISSN: 0362-4803
Radioactive iodine isotopes play a pivotal role in radiopharmaceuticals. Large-scale production of multi-patient dose of radioiodinated nuclear medicines requires high concentration of radioiodine. We demonstrate that tetrabutylammonium chloride and methyltrioctylamonium chloride are effective phase transfer reagents to concentrate iodide-124, iodide-125 and iodide-131 from the corresponding commercial water solutions. The resulting concentrated radioiodide, in the presence of either phase transfer reagent, does not hamper the chemical reactivity of aqueous radioiodide in the copper (II)-mediated one-pot three-component click chemistry to produce radioiodinated iodotriazoles.
Barnes C, Nair M, Aboagye EO, et al., 2022, A practical guide to automating fluorine-18 PET radiochemistry using commercially available cassette-based platforms, Reaction Chemistry and Engineering, ISSN: 2058-9883
The automation of positron emission tomography (PET) radiochemistry using cassette-based automated radiosynthesis platforms is an essential component of clinical translation for the vast majority of 18F-based radiopharmaceuticals. The technology is widely adopted by good manufacturing practice (GMP) compliant radiopharmaceutical production facilities and research institutions developing novel tracers for clinical studies. Despite automation being fundamental to clinical translation, educational resources which introduce this branch of radiochemistry to the uninitiated are limited. Publications featuring automation assume previous experience of using these platforms and therefore, the detail they provide may not be sufficient for a novice user. In this Tutorial Account, we aim to bridge this knowledge gap and provide a resource for efficient automation for radiochemists across all levels of experience.
Komodromos M, Aboagye EO, Evangelou M, et al., 2022, Variational Bayes for high-dimensional proportional hazards models with applications within gene expression, BIOINFORMATICS, Vol: 38, Pages: 3918-3926, ISSN: 1367-4803
Motivation:Few Bayesian methods for analyzing high-dimensional sparse survival data provide scalable variable selection, effect estimation and uncertainty quantification. Such methods often either sacrifice uncertainty quantification by computing maximum a posteriori estimates, or quantify the uncertainty at high (unscalable) computational expense.Results:We bridge this gap and develop an interpretable and scalable Bayesian proportional hazards model for prediction and variable selection, referred to as SVB. Our method, based on a mean-field variational approximation, overcomes the high computational cost of MCMC whilst retaining useful features, providing a posterior distribution for the parameters and offering a natural mechanism for variable selection via posterior inclusion probabilities. The performance of our proposed method is assessed via extensive simulations and compared against other state-of-the-art Bayesian variable selection methods, demonstrating comparable or better performance. Finally, we demonstrate how the proposed method can be used for variable selection on two transcriptomic datasets with censored survival outcomes, and how the uncertainty quantification offered by our method can be used to provide an interpretable assessment of patient risk.Availability and implementation:our method has been implemented as a freely available R package survival.svb (https://github.com/mkomod/survival.svb).
Inglese M, Patel N, Linton-Reid K, et al., 2022, A predictive model using the mesoscopic architecture of the living brain to detect Alzheimer’s disease, Communications Medicine, Vol: 2, ISSN: 2730-664X
Background:Alzheimer’s disease, the most common cause of dementia, causes a progressive and irreversible deterioration of cognition that can sometimes be difficult to diagnose, leading to suboptimal patient care.Methods:We developed a predictive model that computes multi-regional statistical morpho-functional mesoscopic traits from T1-weighted MRI scans, with or without cognitive scores. For each patient, a biomarker called “Alzheimer’s Predictive Vector” (ApV) was derived using a two-stage least absolute shrinkage and selection operator (LASSO).Results:The ApV reliably discriminates between people with (ADrp) and without (nADrp) Alzheimer’s related pathologies (98% and 81% accuracy between ADrp - including the early form, mild cognitive impairment - and nADrp in internal and external hold-out test sets, respectively), without any a priori assumptions or need for neuroradiology reads. The new test is superior to standard hippocampal atrophy (26% accuracy) and cerebrospinal fluid beta amyloid measure (62% accuracy). A multiparametric analysis compared DTI-MRI derived fractional anisotropy, whose readout of neuronal loss agrees with ADrp phenotype, and SNPrs2075650 is significantly altered in patients with ADrp-like phenotype.Conclusions:This new data analytic method demonstrates potential for increasing accuracy of Alzheimer diagnosis.
Inglese M, Patel N, Linton-Reid K, et al., 2022, A predictive model using the mesoscopic architecture of the living brain to detect Alzheimer's disease., Commun Med (Lond), Vol: 2
BACKGROUND: Alzheimer's disease, the most common cause of dementia, causes a progressive and irreversible deterioration of cognition that can sometimes be difficult to diagnose, leading to suboptimal patient care. METHODS: We developed a predictive model that computes multi-regional statistical morpho-functional mesoscopic traits from T1-weighted MRI scans, with or without cognitive scores. For each patient, a biomarker called "Alzheimer's Predictive Vector" (ApV) was derived using a two-stage least absolute shrinkage and selection operator (LASSO). RESULTS: The ApV reliably discriminates between people with (ADrp) and without (nADrp) Alzheimer's related pathologies (98% and 81% accuracy between ADrp - including the early form, mild cognitive impairment - and nADrp in internal and external hold-out test sets, respectively), without any a priori assumptions or need for neuroradiology reads. The new test is superior to standard hippocampal atrophy (26% accuracy) and cerebrospinal fluid beta amyloid measure (62% accuracy). A multiparametric analysis compared DTI-MRI derived fractional anisotropy, whose readout of neuronal loss agrees with ADrp phenotype, and SNPrs2075650 is significantly altered in patients with ADrp-like phenotype. CONCLUSIONS: This new data analytic method demonstrates potential for increasing accuracy of Alzheimer diagnosis.
Kenny LM, Gilbert FJ, Gopalakrishnan G, et al., 2022, The HERPET study: Imaging HER2 expression in breast cancer with the novel PET tracer [F-18]GE-226, a first-in-patient study., Annual Meeting of the American-Society-of-Clinical-Oncology (ASCO), Publisher: LIPPINCOTT WILLIAMS & WILKINS, ISSN: 0732-183X
Aravind P, Popat S, Barwick TD, et al., 2022, [F-18]Fluorothymidine(FLT)-PET imaging of thymidine kinase 1 pharmacodynamics in non-small cell lung cancer treated with pemetrexed., ASCO, Publisher: LIPPINCOTT WILLIAMS & WILKINS, ISSN: 0732-183X
Piletsky S, Garcia Cruz A, Piletska E, et al., 2022, Iodo silanes as superior substrates for the solid phase synthesis of molecularly imprinted polymer nanoparticles, Polymers, Vol: 14, Pages: 1-8, ISSN: 2073-4360
Current state-of-the-art techniques for the solid phase synthesis of molecularly imprinted polymer (MIP) nanoparticles typically rely on amino silanes for the immobilisation of template molecules prior to polymerisation. An investigation into commonly used amino silanes identified a number of problematic side reactions which negatively affect the purity and affinity of these polymers. Iodo silanes are presented as a superior alternative in a case study describing the synthesis of MIPs against epitopes of a common cancer biomarker, epidermal growth factor receptor (EGFR). The proposed iodo silane outperformed the amino silane by all metrics tested, showing high purity and specificity, and nanomolar affinity for the target peptide.
Hindocha S, Charlton TG, Linton-Reid K, et al., 2022, A comparison of machine learning methods for predicting recurrence and death after curative-intent radiotherapy for non-small cell lung cancer: Development and validation of multivariable clinical prediction models., EBioMedicine, Vol: 77, ISSN: 2352-3964
BackgroundSurveillance is universally recommended for non-small cell lung cancer (NSCLC) patients treated with curative-intent radiotherapy. High-quality evidence to inform optimal surveillance strategies is lacking. Machine learning demonstrates promise in accurate outcome prediction for a variety of health conditions. The purpose of this study was to utilise readily available patient, tumour, and treatment data to develop, validate and externally test machine learning models for predicting recurrence, recurrence-free survival (RFS) and overall survival (OS) at 2 years from treatment.MethodsA retrospective, multicentre study of patients receiving curative-intent radiotherapy for NSCLC was undertaken. A total of 657 patients from 5 hospitals were eligible for inclusion. Data pre-processing derived 34 features for predictive modelling. Combinations of 8 feature reduction methods and 10 machine learning classification algorithms were compared, producing risk-stratification models for predicting recurrence, RFS and OS. Models were compared with 10-fold cross validation and an external test set and benchmarked against TNM-stage and performance status. Youden Index was derived from validation set ROC curves to distinguish high and low risk groups and Kaplan-Meier analyses performed.FindingsMedian follow-up time was 852 days. Parameters were well matched across training-validation and external test sets: Mean age was 73 and 71 respectively, and recurrence, RFS and OS rates at 2 years were 43% vs 34%, 54% vs 47% and 54% vs 47% respectively. The respective validation and test set AUCs were as follows: 1) RFS: 0·682 (0·575–0·788) and 0·681 (0·597–0·766), 2) Recurrence: 0·687 (0·582–0·793) and 0·722 (0·635–0·81), and 3) OS: 0·759 (0·663–0·855) and 0·717 (0·634–0·8). Our models were superior to TNM stage and performan
Doran SJ, Al Sad M, Petts JA, et al., 2022, Integrating the OHIF viewer into XNAT: achievements, challenges and prospects for quantitative imaging studies, Tomography, Vol: 8, Pages: 497-512, ISSN: 2379-139X
Purpose: XNAT is an informatics software platform to support imaging research, particularly in the context of large, multicentre studies of the type that are essential to validate quantitative imaging biomarkers. XNAT provides import, archiving, processing and secure distribution facilities for image and related study data. Until recently, however, modern data visualisation and annotation tools were lacking on the XNAT platform. We describe the background to, and implementation of, an integration of the Open Health Imaging Foundation (OHIF) Viewer into the XNAT environment. We explain the challenges overcome and discuss future prospects for quantitative imaging studies. Materials and methods: The OHIF Viewer adopts an approach based on the DICOM web protocol. To allow operation in an XNAT environment, a data-routing methodology was developed to overcome the mismatch between the DICOM and XNAT information models and a custom viewer panel created to allow navigation within the viewer between different XNAT projects, subjects and imaging sessions. Modifications to the development environment were made to allow developers to test new code more easily against a live XNAT instance. Major new developments focused on the creation and storage of regions-of-interest (ROIs) and included: ROI creation and editing tools for both contour- and mask-based regions; a “smart CT” paintbrush tool; the integration of NVIDIA’s Artificial Intelligence Assisted Annotation (AIAA); the ability to view surface meshes, fractional segmentation maps and image overlays; and a rapid image reader tool aimed at radiologists. We have incorporated the OHIF microscopy extension and, in parallel, introduced support for microscopy session types within XNAT for the first time. Results: Integration of the OHIF Viewer within XNAT has been highly successful and numerous additional and enhanced tools have been created in a programme started in 2017 that is still ongoing. The software has bee
Fotopoulou C, Rockall A, Lu H, et al., 2021, Validation analysis of the novel imaging-based prognostic radiomic signature in patients undergoing primary surgery for advanced high-grade serous ovarian cancer (HGSOC), British Journal of Cancer, Vol: 126, Pages: 1047-1054, ISSN: 0007-0920
BackgroundPredictive models based on radiomics features are novel, highly promising approaches for gynaecological oncology. Here, we wish to assess the prognostic value of the newly discovered Radiomic Prognostic Vector (RPV) in an independent cohort of high-grade serous ovarian cancer (HGSOC) patients, treated within a Centre of Excellence, thus avoiding any bias in treatment quality.MethodsRPV was calculated using standardised algorithms following segmentation of routine preoperative imaging of patients (n = 323) who underwent upfront debulking surgery (01/2011-07/2018). RPV was correlated with operability, survival and adjusted for well-established prognostic factors (age, postoperative residual disease, stage), and compared to previous validation models.ResultsThe distribution of low, medium and high RPV scores was 54.2% (n = 175), 33.4% (n = 108) and 12.4% (n = 40) across the cohort, respectively. High RPV scores independently associated with significantly worse progression-free survival (PFS) (HR = 1.69; 95% CI:1.06–2.71; P = 0.038), even after adjusting for stage, age, performance status and residual disease. Moreover, lower RPV was significantly associated with total macroscopic tumour clearance (OR = 2.02; 95% CI:1.56–2.62; P = 0.00647).ConclusionsRPV was validated to independently identify those HGSOC patients who will not be operated tumour-free in an optimal setting, and those who will relapse early despite complete tumour clearance upfront. Further prospective, multicentre trials with a translational aspect are warranted for the incorporation of this radiomics approach into clinical routine.
Rajgor AD, Patel S, Mcculloch D, et al., 2021, The application of radiomics in laryngeal cancer, British Journal of Radiology, Vol: 94, Pages: 1-13, ISSN: 0007-1285
Objectives:Radiomics is the conversion of medical images into quantitative high-dimensional data. Laryngeal cancer, one of the most common head and neck cancers, has risen globally by 58.7%. CT, MRI and PET are acquired during the diagnostic process providing potential data for radiomic analysis and correlation with outcomes.This review aims to examine the applications of this technique to laryngeal cancer and the future considerations for translation into clinical practice.Methods:A comprehensive systematic review-informed search of the MEDLINE and EMBASE databases was undertaken. Keywords “laryngeal cancer” OR “larynx“ OR “larynx cancer” OR “head and neck cancer” were combined with “radiomic” OR “signature” OR “machine learning” OR “artificial intelligence”. Additional articles were obtained from bibliographies using the “snowball method”.Results:The included studies (n = 15) demonstrated that radiomic features are significantly associated with various clinical outcomes (including stage, overall survival, treatment response, progression-free survival) and that predictive models incorporating radiomic features are superior to those that do not. Two studies demonstrated radiomics could improve laryngeal cancer staging whilst 12 studies affirmed its predictive capability for clinical outcomes.Conclusions:Radiomics has potential for improving multiple aspects of laryngeal cancer care; however, the heterogeneous cohorts and lack of data on laryngeal cancer exclusively inhibits firm conclusions. Large prospective well-designed studies in laryngeal cancer are required to progress this field. Furthermore, to implement radiomics into clinical practice, a unified research effort is required to standardise radiomics practice.Advances in knowledge:This review has highlighted the value of radiomics in enhancing laryngeal cancer care (including staging, prognosis and pred
Piletsky S, Piletska E, Poblocka M, et al., 2021, Snapshot imprinting: Rapid identification of cancer cell surface proteins and epitopes using molecularly imprinted polymers, Nano Today: an international rapid reviews journal, Vol: 41, Pages: 1-8, ISSN: 1748-0132
Proteomic mapping of cell surfaces is an invaluable tool for drug development and clinical diagnostics. This work describes a new ‘snapshot imprinting’ method designed to obtain proteomic maps of cell surfaces, with the aim of identifying cell surface markers and epitopes for diagnostic and therapeutic applications. The analysis of two cancer cell lines, HN5 and MDA-MB-468, is described herein as a proof of concept, along with the selective targeting of three identified epitopes of epidermal growth factor receptor using molecularly imprinted polymer nanoparticles. 438 proteins were identified using this technique, with 283 considered to be transmembrane or extracellular proteins. The major advantage of the molecular imprinting approach developed here is the ability to analyse cell surface proteins without tedious fractionation, affinity separation or labelling. We believe that this system of protein analysis may provide a basic molecular diagnostics toolbox for precise, personalised treatment of cancer and other diseases.
Braga M, Leow CH, Gil JH, et al., 2021, Investigating CXCR4 expression of tumor cells and the vascular compartment: A multimodal approach, PLoS One, Vol: 16, Pages: 1-21, ISSN: 1932-6203
The C-X-C chemokine receptor 4 (CXCR4) is G protein-coupled receptor that upon binding to its cognate ligand, can lead to tumor progression. Several CXCR4-targeted therapies are currently under investigation, and with it comes the need for imaging agents capable of accurate depiction of CXCR4 for therapeutic stratification and monitoring. PET agents enjoy the most success, but more cost-effective and radiation-free approaches such as ultrasound (US) imaging could represent an attractive alternative. In this work, we developed a targeted microbubble (MB) for imaging of vascular CXCR4 expression in cancer. A CXCR4-targeted MB was developed through incorporation of the T140 peptide into the MB shell. Binding properties of the T140-MB and control, non-targeted MB (NT-MB) were evaluated in MDA-MB-231 cells where CXCR4 expression was knocked-down (via shRNA) through optical imaging, and in the lymphoma tumor models U2932 and SuDHL8 (high and low CXCR4 expression, respectively) by US imaging. PET imaging of [18F]MCFB, a tumor-penetrating CXCR4-targeted small molecule, was used to provide whole-tumor CXCR4 readouts. CXCR4 expression and microvessel density were performed by immunohistochemistry analysis and western blot. T140-MB were formed with similar properties to NT-MB and accumulated sensitively and specifically in cells according to their CXCR4 expression. In NOD SCID mice, T140-MB persisted longer in tumors than NT-MB, indicative of target interaction, but showed no difference between U2932 and SuDHL8. In contrast, PET imaging with [18F]MCFB showed a marked difference in tumor uptake at 40–60 min post-injection between the two tumor models (p<0.05). Ex vivo analysis revealed that the large differences in CXCR4 expression between the two models are not reflected in the vascular compartment, where the MB are restricted; in fact, microvessel density and CXCR4 expression in the vasculature was comparable between U2932 and SuDHL8 tumors. In conclusion, we success
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