Radial volumetric imaging breath-hold examination (VIBE) with k-space weighted image contrast (KWIC) for dynamic gadoxetic acid (Gd-EOB-DTPA)-enhanced MRI of the liver: advantages over Cartesian VIBE in the arterial phase

To compare radial volumetric imaging breath-hold examination with k-space weighted image contrast reconstruction (r-VIBE-KWIC) to Cartesian VIBE (c-VIBE) in arterial phase dynamic gadoxetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (DCE-MRI) of the liver. We reviewed 53 consecutive DCE-MRI studies performed on a 3-T unit using c-VIBE and 53 consecutive cases performed using r-VIBE-KWIC with full-frame image subset (r-VIBEfull) and sub-frame image subsets (r-VIBEsub; temporal resolution, 2.5-3 s). All arterial phase images were scored by two readers on: (1) contrast-enhancement ratio (CER) in the abdominal aorta; (2) scan timing; (3) artefacts; (4) visualisation of the common, right, and left hepatic arteries. Mean abdominal aortic CERs for c-VIBE, r-VIBEfull, and r-VIBEsub were 3.2, 4.3 and 6.5, respectively. There were significant differences between each group (P < 0.0001). The mean score for c-VIBE was significantly lower than that for r-VIBEfull and r-VIBEsub in all factors except for visualisation of the common hepatic artery (P < 0.05). The mean score of all factors except for scan timing for r-VIBEsub was not significantly different from that for r-VIBEfull. Radial VIBE-KWIC provides higher image quality than c-VIBE, and r-VIBEsub features high temporal resolution without image degradation in arterial phase DCE-MRI. aEuro cent Radial VIBE-KWIC minimised artefact and produced high-quality and high-temporal-resolution images. aEuro cent Maximum abdominal aortic enhancement was observed on sub-frame images of r-VIBE-KWIC. aEuro cent Using r-VIBE-KWIC, optimal arterial phase images were obtained in over 90 %. aEuro cent Using r-VIBE-KWIC, visualisation of the hepatic arteries was improved. aEuro cent A two-reader study revealed r-VIBE-KWIC's advantages over Cartesian VIBE.ArticleEUROPEAN RADIOLOGY. 24(6):1290-1299 (2014)journal articl

Human liver flukes

Liver fluke infections occur in people worldwide. In some low-income regions, a combination of ecological, agricultural, and culinary factors leads to a very high prevalence of infection but, in higher-income regions, infections are uncommon. Infection is associated with substantial morbidity and several liver fluke species are recognised as biological carcinogens. Here, we review the epidemiology, clinical significance, and diagnostic and treatment strategies of human infection with these pathogens

Lung Cancer Detection using Supervised Machine Learning Techniques

In recent times, Lung cancer is the most common cause of mortality in both men and women around the world. Lung cancer is the second most well-known disease after heart disease. Although lung cancer prevention is impossible, early detection of lung cancer can effectively treat lung cancer at an early stage. The possibility of a patient's survival rate increasing if lung cancer is identified early. To detect and diagnose lung cancer in its early stages, a variety of data analysis and machine learning techniques have been applied. In this paper, we applied supervised machine learning algorithms like SVM (Support vector machine), ANN (Artificial neural networks), MLR (Multiple linear regression), and RF (random forest), to detect the early stages of lung tumors. The main purpose of this study is to examine the success of machine learning algorithms in detecting lung cancer at an early stage. When compared to all other supervised machine learning algorithms, the Random forest model produces a high result, with a 99.99% accuracy rat

Genetic Analysis of Prostate Cancer with Computer Science Methods

Metastatic prostate cancer is one of the most common cancers in men. In the advanced stages of prostate cancer, tumours can metastasise to other tissues in the body, which is fatal. In this thesis, we performed a genetic analysis of prostate cancer tumours at different metastatic sites using data science, machine learning and topological network analysis methods. We presented a general procedure for pre-processing gene expression datasets and pre-filtering significant genes by analytical methods. We then used machine learning models for further key gene filtering and secondary site tumour classification. Finally, we performed gene co-expression network analysis and community detection on samples from different prostate cancer secondary site types. In this work, 13 of the 14,379 genes were selected as the most metastatic prostate cancer related genes, achieving approximately 92% accuracy under cross-validation. In addition, we provide preliminary insights into the co-expression patterns of genes in gene co-expression networks. Project code is available at https://github.com/zcablii/Master_cancer_project

Clinical relevance of circulating tumour cells in the bone marrow of patients with SCCHN

Background: Clinical outcome of patients with head and neck squamous cell carcinoma (SCCHN) depends on several risk factors like the presence of locoregional lymph node or distant metastases, stage, localisation and histologic differentiation of the tumour. Circulating tumour cells in the bone marrow indicate a poor prognosis for patients with various kinds of malignoma. The present study examines the clinical relevance of occult tumour cells in patients suffering from SCCHN. Patients and Methods: Bone marrow aspirates of 176 patients suffering from SCCHN were obtained prior to surgery and stained for the presence of disseminated tumour cells. Antibodies for cytokeratin 19 were used for immunohistochemical detection with APAAP on cytospin slides. Within a clinical follow-up protocol over a period of 60 months, the prognostic relevance of several clinicopathological parameters and occult tumour cells was evaluated. Results: Single CK19-expressing tumour cells could be detected in the bone marrow of 30.7% of the patients. There is a significant correlation between occult tumour cells in the bone marrow and relapse. Uni- and multivariate analysis of all clinical data showed the metastases in the locoregional lymph system and detection of disseminated tumour cells in the bone marrow to be statistically highly significant for clinical prognosis. Conclusion: The detection of minimal residual disease underlines the understanding of SCCHN as a systemic disease. Further examination of such cells will lead to a better understanding of the tumour biology, as well as to improvement of diagnostic and therapeutic strategies

Multimodal Multispectral Optical Endoscopic Imaging for Biomedical Applications

Optical imaging is an emerging field of clinical diagnostics that can address the growing medical need for early cancer detection and diagnosis. Various human cancers are amenable to better prognosis and patient survival if found and treated during early disease onset. Besides providing wide-field, macroscopic diagnostic information similar to existing clinical imaging techniques, optical imaging modalities have the added advantage of microscopic, high resolution cellular-level imaging from in vivo tissues in real time. This comprehensive imaging approach to cancer detection and the possibility of performing an ‘optical biopsy’ without tissue removal has led to growing interest in the field with numerous techniques under investigation. Three optical techniques are discussed in this thesis, namely multispectral fluorescence imaging (MFI), hyperspectral reflectance imaging (HRI) and fluorescence confocal endomicroscopy (FCE). MFI and HRI are novel endoscopic imaging-based extensions of single point detection techniques, such as laser induced fluorescence spectroscopy and diffuse reflectance spectroscopy. This results in the acquisition of spectral data in an intuitive imaging format that allows for quantitative evaluation of tissue disease states. We demonstrate MFI and HRI on fluorophores, tissue phantoms and ex vivo tissues and present the results as an RGB colour image for more intuitive assessment. This follows dimensionality reduction of the acquired spectral data with a fixed-reference isomap diagnostic algorithm to extract only the most meaningful data parameters. FCE is a probe-based point imaging technique offering confocal detection in vivo with almost histology-grade images. We perform FCE imaging on chemotherapy-treated in vitro human ovarian cancer cells, ex vivo human cancer tissues and photosensitiser-treated in vivo murine tumours to show the enhanced detection capabilities of the technique. Finally, the three modalities are applied in combination to demonstrate an optical viewfinder approach as a possible minimally-invasive imaging method for early cancer detection and diagnosis

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Crop conditional Convolutional Neural Networks for massive multi-crop plant disease classification over cell phone acquired images taken on real field conditions

Convolutional Neural Networks (CNN) have demonstrated their capabilities on the agronomical field, especially for plant visual symptoms assessment. As these models grow both in the number of training images and in the number of supported crops and diseases, there exist the dichotomy of (1) generating smaller models for specific crop or, (2) to generate a unique multi-crop model in a much more complex task (especially at early disease stages) but with the benefit of the entire multiple crop image dataset variability to enrich image feature description learning. In this work we first introduce a challenging dataset of more than one hundred-thousand images taken by cell phone in real field wild conditions. This dataset contains almost equally distributed disease stages of seventeen diseases and five crops (wheat, barley, corn, rice and rape-seed) where several diseases can be present on the same picture. When applying existing state of the art deep neural network methods to validate the two hypothesised approaches, we obtained a balanced accuracy (BAC=0.92) when generating the smaller crop specific models and a balanced accuracy (BAC=0.93) when generating a single multi-crop model. In this work, we propose three different CNN architectures that incorporate contextual non-image meta-data such as crop information onto an image based Convolutional Neural Network. This combines the advantages of simultaneously learning from the entire multi-crop dataset while reducing the complexity of the disease classification tasks. The crop-conditional plant disease classification network that incorporates the contextual information by concatenation at the embedding vector level obtains a balanced accuracy of 0.98 improving all previous methods and removing 71% of the miss-classifications of the former methods

Development of the minimally invasive paediatric & perinatal autopsy

Introduction Perinatal autopsy contributes useful clinical information to patient management in approximately 40% of cases but remains poorly accepted due to parental concerns regarding disfigurement. Post-mortem imaging is an alternative, but 1.5 T MRI lacks resolution below 18 gestational weeks. Additionally, the Royal College of Pathologists autopsy guidelines recommend extensive tissue sampling as part of the investigation of fetal loss, which imaging alone cannot provide. Possible mitigating strategies include micro-CT for phenotyping small fetuses and laparoscopic techniques to obtain tissue samples. Interrogation of the evidence base for tissue sampling in different clinical scenarios is necessary to develop evidence-based practice and recommendations. Methods Minimally Invasive Autopsy with Laparoscopy (MinImAL) was performed in 103 cases. Micro-CT was optimised in extracted organs and the diagnostic accuracy evaluated in 20 fetuses. The Great Ormond Street Autopsy Database was retrospectively interrogated to investigate the yield of internal examination and visceral histology to the cause of death in 5,311 cases. Results MinImAL examination is reliable (97.8% successfully completed, 91/93) with good tissue sampling success rates (100% in lung, kidney, heart). Micro-CT offers an accurate method of scanning small fetuses (97.5% agreement with autopsy, 95% CI, 96.6-98.4) with fewer non-diagnostic indices than standard autopsy in < 14 weeks gestation (22/440 vs 48/348 respectively; p<0.001). Histology of macroscopically normal viscera is valuable in the investigation of infant and childhood deaths. However, it provides almost no useful information relevant to cause of death or main diagnosis (<1%) in fetal cases. Conclusions MinImAL examination offers a reliable method of internal examination and tissue sampling, which may be acceptable when standard autopsy is declined. Micro-CT provides an accurate, non-invasive method for phenotyping early gestation fetal anatomy. Histological sampling of macroscopically normal visceral organs is valuable when investigating infant or child deaths but of limited value in fetal loss and hence should not be routinely performed