410 results found
Welgemoed C, Spezi E, Riddle P, et al., 2023, Clinical evaluation of atlas-based auto-segmentation in breast and nodal radiotherapy., Br J Radiol, Vol: 96
OBJECTIVES: Accurate contouring of anatomical structures allows for high-precision radiotherapy planning, targeting the dose at treatment volumes and avoiding organs at risk. Manual contouring is time-consuming with significant user variability, whereas auto-segmentation (AS) has proven efficiency benefits but requires editing before treatment planning. This study investigated whether atlas-based AS (ABAS) accuracy improves with template atlas group size and character-specific atlas and test case selection. METHODS AND MATERIALS: One clinician retrospectively contoured the breast, nodes, lung, heart, and brachial plexus on 100 CT scans, adhering to peer-reviewed guidelines. Atlases were clustered in group sizes, treatment positions, chest wall separations, and ASs created with Mirada software. The similarity of ASs compared to reference contours was described by the Jaccard similarity coefficient (JSC) and centroid distance variance (CDV). RESULTS: Across group sizes, for all structures combined, the mean JSC was 0.6 (SD 0.3, p = .999). Across atlas-specific groups, 0.6 (SD 0.3, p = 1.000). The correlation between JSC and structure volume was weak in both scenarios (adjusted R2-0.007 and 0.185).Mean CDV was similar across groups but varied up to 1.2 cm for specific structures. CONCLUSIONS: Character-specific atlas groups and test case selection did not improve accuracy outcomes. High-quality ASs were obtained from groups containing as few as ten atlases, subsequently simplifying the application of ABAS. CDV measures indicating auto-segmentation variations on the x, y, and z axes can be utilised to decide on the clinical relevance of variations and reduce AS editing. ADVANCES IN KNOWLEDGE: High-quality ABASs can be obtained from as few as ten template atlases.Atlas and test case selection do not improve AS accuracy.Unlike well-known quantitative similarity indices, volume displacement metrics provide information on the location of segmentation variations, helping ass
Hunter B, Bunce C, Blackledge M, et al., 2023, Response to A. Eleuteri regarding "A radiomics-based decision support tool improves lung cancer diagnosis in combination with the Herder score in large lung nodules"., EBioMedicine, Vol: 94
Chen M, Copley SJ, Viola P, et al., 2023, Radiomics and artificial intelligence for precision medicine in lung cancer treatment., Semin Cancer Biol, Vol: 93, Pages: 97-113
Lung cancer is the leading cause of cancer-related deaths worldwide. It exhibits, at the mesoscopic scale, phenotypic characteristics that are generally indiscernible to the human eye but can be captured non-invasively on medical imaging as radiomic features, which can form a high dimensional data space amenable to machine learning. Radiomic features can be harnessed and used in an artificial intelligence paradigm to risk stratify patients, and predict for histological and molecular findings, and clinical outcome measures, thereby facilitating precision medicine for improving patient care. Compared to tissue sampling-driven approaches, radiomics-based methods are superior for being non-invasive, reproducible, cheaper, and less susceptible to intra-tumoral heterogeneity. This review focuses on the application of radiomics, combined with artificial intelligence, for delivering precision medicine in lung cancer treatment, with discussion centered on pioneering and groundbreaking works, and future research directions in the area.
Aboagye E, Islam S, Inglese M, et al., 2023, Feasibility of [18F]fluoropivalate hybrid PET/MRI for imaging lower and higher grade glioma: a prospective first-in-patient pilot study, European Journal of Nuclear Medicine and Molecular Imaging, ISSN: 0340-6997
Aboagye E, Aravind P, Popat S, et al., 2023, A subset of non-small cell lung cancer patients treated with pemetrexed show 18f-fluorothymidine ‘flare’ on positron emission tomography, Cancers, Vol: 15, Pages: 1-14, ISSN: 2072-6694
Thymidylate synthase (TS) remains a major target for cancer therapy. TS inhibition elicits increases in DNA salvage pathway activity, detected as a transient compensatory “flare” in 3′-deoxy-3′-[18F]fluorothymidine positron emission tomography (18F-FLT PET). We determined the magnitude of the 18F-FLT flare in non-small cell lung cancer (NSCLC) patients treated with the antifolate pemetrexed in relation to clinical outcome. Method: Twenty-one patients with advanced/metastatic non-small cell lung cancer (NSCLC) scheduled to receive palliative pemetrexed ± platinum-based chemotherapy underwent 18F-FLT PET at baseline and 4 h after initiating single-agent pemetrexed. Plasma deoxyuridine (dUrd) levels and thymidine kinase 1 (TK1) activity were measured before each scan. Patients were then treated with the combination therapy. The 18F-FLT PET variables were compared to RECIST 1.1 and overall survival (OS). Results: Nineteen patients had evaluable PET scans at both time points. A total of 32% (6/19) of patients showed 18F-FLT flares (>20% change in SUVmax-wsum). At the lesion level, only one patient had an FLT flare in all the lesions above (test–retest borders). The remaining had varied uptake. An 18F-FLT flare occurred in all lesions in 1 patient, while another patient had an 18F-FLT reduction in all lesions; 17 patients showed varied lesion uptake. All patients showed global TS inhibition reflected in plasma dUrd levels (p < 0.001) and 18F-FLT flares of TS-responsive normal tissues including small bowel and bone marrow (p = 0.004 each). Notably, 83% (5/6) of patients who exhibited 18F-FLT flares were also RECIST responders with a median OS of 31 m, unlike patients who did not exhibit 18F-FLT flares (15 m). Baseline plasma TK1 was prognostic of survival but its activity remained unchanged following treatment. Conclusions: The better radiological response and longer survival observed in patients with an 18F-FLT flare suggest the
Aboagye E, Lu H, Lou H, et al., 2023, Tumour and local lymphoid tissue interaction determines prognosis in high grade serous ovarian cancer, Cell Reports Medicine, Vol: 4, Pages: 1-24, ISSN: 2666-3791
Tertiary lymphoid structure (TLS) is associated with prognosis in copy number-driven tumours,including high grade serous ovarian cancer (HGSOC), although the function of TLS and its interactionwith copy-number alterations in HGSOC is not fully understood. In the current study, we confirmthat TLS-high HGSOC patients show significantly better progression free survival. We show thatpresence of TLS in HGSOC tumours is associated with B-cell maturation and cytotoxic tumourspecific T-cells activation and proliferation. Additionally, the copy number loss of IL15 and CXCL10may limit TLS formation in HGSOC; a list of genes that may dysregulate TLS function is also proposed.Manuscript Click here to view linked ReferencesLastly, a radiomics-based signature is developed to predict presence of TLS, which independentlypredicts PFS in both HGSOC patients and ICI-treated NSCLC patients. Overall, we reveal that TLScoordinates intratumoural B-cell and T-cell response against HGSOC tumour, while cancer genomeevolves to counteract TLS formation and function.
Aboagye E, Teh JH, Amgheib A, et al., 2023, Evaluation of [18F]AlF-EMP-105 for molecular imaging of 2 C-Met, Pharmaceutics, Vol: 15, Pages: 1-13, ISSN: 1999-4923
C-Met is a receptor tyrosine kinase that is overexpressed in a range of different cancer types, and has been identified as a potential biomarker for cancer imaging and therapy. Previously, a 68Ga-labelled peptide, [68Ga]Ga-EMP-100, has shown promise for imaging c-Met in renal cell carcinoma in humans. Herein, we report the synthesis and preliminary biological evaluation of an [18F]AlF-labelled analogue, [18F]AlF-EMP-105, for c-Met imaging by positron emission tomography. EMP-105 was radiolabelled using the aluminium-[18F]fluoride method with 46 ± 2% RCY and >95% RCP in 35–40 min. In vitro evaluation showed that [18F]AlF-EMP-105 has a high specificity for c-Met-expressing cells. Radioactive metabolite analysis at 5 and 30 min post-injection revealed that [18F]AlF-EMP-105 has good blood stability, but undergoes transformation—transchelation, defluorination or demetallation—in the liver and kidneys. PET imaging in non-tumour-bearing mice showed high radioactive accumulation in the kidneys, bladder and urine, demonstrating that the tracer is cleared predominantly as [18F]fluoride by the renal system. With its high specificity for c-Met expressing cells, [18F]AlF-EMP-105 shows promise as a potential diagnostic tool for imaging cancer.
Kalantar R, Hindocha S, Hunter B, et al., 2023, Non-contrast CT synthesis using patch-based cycle-consistent generative adversarial network (Cycle-GAN) for radiomics and deep learning in the era of COVID-19., Sci Rep, Vol: 13
Handcrafted and deep learning (DL) radiomics are popular techniques used to develop computed tomography (CT) imaging-based artificial intelligence models for COVID-19 research. However, contrast heterogeneity from real-world datasets may impair model performance. Contrast-homogenous datasets present a potential solution. We developed a 3D patch-based cycle-consistent generative adversarial network (cycle-GAN) to synthesize non-contrast images from contrast CTs, as a data homogenization tool. We used a multi-centre dataset of 2078 scans from 1,650 patients with COVID-19. Few studies have previously evaluated GAN-generated images with handcrafted radiomics, DL and human assessment tasks. We evaluated the performance of our cycle-GAN with these three approaches. In a modified Turing-test, human experts identified synthetic vs acquired images, with a false positive rate of 67% and Fleiss' Kappa 0.06, attesting to the photorealism of the synthetic images. However, on testing performance of machine learning classifiers with radiomic features, performance decreased with use of synthetic images. Marked percentage difference was noted in feature values between pre- and post-GAN non-contrast images. With DL classification, deterioration in performance was observed with synthetic images. Our results show that whilst GANs can produce images sufficient to pass human assessment, caution is advised before GAN-synthesized images are used in medical imaging applications.
Rockall AG, Li X, Johnson N, et al., 2023, Development and evaluation of machine learning in whole-body magnetic resonance imaging for detecting metastases in patients with lung or colon cancer: a diagnostic test accuracy study., Investigative Radiology, ISSN: 0020-9996
OBJECTIVES: Whole-body magnetic resonance imaging (WB-MRI) has been demonstrated to be efficient and cost-effective for cancer staging. The study aim was to develop a machine learning (ML) algorithm to improve radiologists' sensitivity and specificity for metastasis detection and reduce reading times. MATERIALS AND METHODS: A retrospective analysis of 438 prospectively collected WB-MRI scans from multicenter Streamline studies (February 2013-September 2016) was undertaken. Disease sites were manually labeled using Streamline reference standard. Whole-body MRI scans were randomly allocated to training and testing sets. A model for malignant lesion detection was developed based on convolutional neural networks and a 2-stage training strategy. The final algorithm generated lesion probability heat maps. Using a concurrent reader paradigm, 25 radiologists (18 experienced, 7 inexperienced in WB-/MRI) were randomly allocated WB-MRI scans with or without ML support to detect malignant lesions over 2 or 3 reading rounds. Reads were undertaken in the setting of a diagnostic radiology reading room between November 2019 and March 2020. Reading times were recorded by a scribe. Prespecified analysis included sensitivity, specificity, interobserver agreement, and reading time of radiology readers to detect metastases with or without ML support. Reader performance for detection of the primary tumor was also evaluated. RESULTS: Four hundred thirty-three evaluable WB-MRI scans were allocated to algorithm training (245) or radiology testing (50 patients with metastases, from primary 117 colon [n = 117] or lung [n = 71] cancer). Among a total 562 reads by experienced radiologists over 2 reading rounds, per-patient specificity was 86.2% (ML) and 87.7% (non-ML) (-1.5% difference; 95% confidence interval [CI], -6.4%, 3.5%; P = 0.39). Sensitivity was 66.0% (ML) and 70.0% (non-ML) (-4.0% difference; 95% CI, -13.5%, 5.5%; P = 0.344). Among 161 reads by inexperienced readers, per-patient spec
Li X, Marcus D, Russell J, et al., 2023, An integrated clinical-MR radiomics model to estimate survival time in patients with endometrial cancer, Journal of Magnetic Resonance Imaging, Vol: 57, Pages: 1922-1933, 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
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, Vol: 18, Pages: 718-730, 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
Li X, Aboagye E, Michele D, et al., 2023, Prediction of deep myometrial infiltration, clinical risk category, histological type, and lymphovascular space invasion in women with endometrial cancer based on clinical and T2-weighted MRI radiomic features, Cancers, Vol: 15, ISSN: 2072-6694
Deep myometrial infiltration, clinical risk score, histological type, and lymphovascular space invasion are important clinical variables that have significant management implications for endometrial cancer patients. Determination of these factors using pure T2-weighted MRI is time-consuming, and the accuracy of this relies on the experience of the clinicians. Combining clinical information and radiomic features from MRI, we developed machine learning classification models to predict these clinical variables. Based on a training dataset, an automatic selection classification model with an optimized hyperparameters method was adopted to find the optimal classifiers. The accuracy of the model predictions was evaluated using an independent external testing dataset. The results suggest that an integrated model (combining clinical and radiomic features) achieved a reasonable accuracy for endometrial cancer clinical variable prediction. The application of these models in clinical practice could potentially lead to cost reductions and personalized treatment.
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 THERAPY, ISSN: 0929-1903
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
Yang Z, Barnes C, Domarkas J, et al., 2023, Automated sulfur-[<sup>18</sup>F]fluoride exchange radiolabelling of a prostate specific membrane antigen (PSMA) targeted ligand using the GE FASTlab™ cassette-based platform, Reaction Chemistry & Engineering
<jats:p>The sulfur-[<jats:sup>18</jats:sup>F]fluoride exchange reaction is a facile <jats:sup>19</jats:sup>F/<jats:sup>18</jats:sup>F isotopic exchange labelling chemistry which is simple to automated for the preparation of positron emission tomography (PET) radiopharmaceuticals.</jats:p>
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.
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
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.
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).
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