Diagnosis and Treatment of Small Bowel Disorders

Over the last few decades, remarkable progress has been made in understanding the aetiology and pathophysiology of diseases and many new theories emphasize the importance of the small bowel ‘ecosystem’ in the pathogenesis of acute and chronic illness. Emerging factors such as microbiome, stem cells, innate intestinal immunity and the enteric nervous system along with mucosal and endothelial barriers have key role in the development of gastrointestinal and extra-intestinal diseases. Therefore, the small intestine is considered key player in metabolic disease development, including diabetes mellitus, and other diet-related disorders such as celiac and non-celiac enteropathies. Another major field is drug metabolism and its interaction with microbiota. Moreover, the emergence of gut-brain, gut-liver and gut-blood barriers points toward the important role of small intestine in the pathogenesis of common disorders, such as liver disease, hypertension and neurodegenerative disease. However, the small bowel remains an organ that is difficult to fully access and assess and accurate diagnosis often poses a clinical challenge. Eventually, the therapeutic potential remains untapped. Therefore, it is due time to direct our interest towards the small intestine and unravel the interplay between small-bowel and other gastrointestinal (GI) and non-GI related maladies

A Proposed Machine Learning Based Collective Disease Model to Enable Predictive Diagnostics in Necrotising Enterocolitis

© 2018 IEEE. Despite 60 years of research into necrotising enterocolitis (NEC), our understanding of the disease has not improved enough to achieve better outcomes. Even though NEC has remained the leading cause of death and poor outcomes in preterm infants, there remain vital questions on how to define, differentiate and detect the condition. Numerous international groups have recently highlighted NEC as a research priority and called for broader engagement of the scientific community to move the field forward. The three foremost barriers at present are lack of suitable definition(s), lack of clean datasets and consequently a lack of scope to gain sufficient insights from data. This research paper proposes a new direction of travel to advance neonatal gastro-intestinal monitoring and strengthen our efforts to gain better insights from global databases. An integrated machine learning based model is recommended to produce a comprehensive disease model to manage the complexity of this multi-variate disease. This intelligent disease model would be used in the daily neonatal settings to help aggregate data to support clinical decision making, better capture the complexity of each patient to enrich global datasets to create bigger and better data. This paper reviews current machine learning and CAD technologies in neonatology and suggests an innovative approach for an NEC disease model

Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world

Optical Diagnostics in Human Diseases

Optical technologies provide unique opportunities for the diagnosis of various pathological disorders. The range of biophotonics applications in clinical practice is considerably wide given that the optical properties of biological tissues are subject to significant changes during disease progression. Due to the small size of studied objects (from μm to mm) and despite some minimum restrictions (low-intensity light is used), these technologies have great diagnostic potential both as an additional tool and in cases of separate use, for example, to assess conditions affecting microcirculatory bed and tissue viability. This Special Issue presents topical articles by researchers engaged in the development of new methods and devices for optical non-invasive diagnostics in various fields of medicine. Several studies in this Special Issue demonstrate new information relevant to surgical procedures, especially in oncology and gynecology. Two articles are dedicated to the topical problem of breast cancer early detection, including during surgery. One of the articles is devoted to urology, namely to the problem of chronic or recurrent episodic urethral pain. Several works describe the studies in otolaryngology and dentistry. One of the studies is devoted to diagnosing liver diseases. A number of articles contribute to the studying of the alterations caused by diabetes mellitus and cardiovascular diseases. The results of all the presented articles reflect novel innovative research and emerging ideas in optical non-invasive diagnostics aimed at their wider translation into clinical practice

Image texture analysis of transvaginal ultrasound in the diagnosis of ovarian lesions

Ovarian cancer has the highest mortality rate of all gynaecological cancers and is the fifth most common cancer to occur in women in the UK. Amongst various imaging modalities, ultrasound is considered the main modality for ovarian cancer triage. As with other imaging modalities, the main issue is that the interpretation of ultrasound images is subjective and observer dependent. Texture analysis has been shown to have potential in the objective assessment of ovarian cancer in a preliminary study. Another form of texture analysis is Acoustic Structure Quantification (ASQ), which has been documented to have a number of successful uses in liver diseases. However, it has not been applied to ovarian lesions. Therefore, the aim of this study was to assess prospectively the diagnostic performance of texture analysis methods such as GLCM, Wavelet, and ASQ in discriminating between benign and malignant adnexal masses and between different types of benign masses and compare it to widely used scoring models. Prior to applying ASQ to ovarian images, its reliability and repeatability were first evaluated. This includes random variation caused by the ultrasound system and the operator during image acquisition. A tissue-equivalent phantom was used in these tests. It was found that the ASQ feature demonstrated excellent repeatability for ASQ software, with all transducers showing less than 0.4% variance from the mean: thus, ASQ software is able to produce reliable ASQ output measures. When testing the factors that may influence the performance of the ASQ analysis, the results revealed that three factors do not influence the mean of the output curve: the ROI size, depth and gain setting. However, focal position has a significant effect on the mean of the output curve. Transducer frequency does not affect the output curve except when using high frequencies such as 8 MHz. Other tests were done to determine the appropriate parameters in the software to be used on images of ovarian masses. Firstly, ASQ was applied to 45 pelvic masses. The preliminary results showed no significant difference between benign and malignant masses using the ASQ technique: therefore, the study was terminated due to failure to discriminate the benign from the malignant masses using ASQ. iv Secondly, two types of textural features were investigated in this study: grey-level co-occurrence matrix (GLCM) and wavelet, as recommended by a preliminary study. A sample of 169 masses was collected from participants, of which 140 were benign and 29 were malignant by histology. In addition to texture features, other widely used scoring models were applied on the same images for comparison, namely RMI, PMI and ADNEX. The results revealed excellent discriminatory ability in both GLCM and wavelet between malignant and cystic masses and between benign and cystic masses, with AUC of .994 and .895 for GLCM and .894 and .814 for wavelet respectively, as well as between normal and malignant tissue, with p >.05 and p=.004 in both GLCM and wavelet respectively. Results also showed that GLCM outperformed RMI and ADNEX in distinguishing between benign and malignant masses, even when dividing the study population into pre- and postmenopausal groups. In addition, GLCM has the advantage of being objective and not operator dependent. Receiver operating characteristic (ROC) curve analysis was carried out to determine the discriminatory ability of textural features, which was found to be satisfactory. The principal conclusion was that GLCM and wavelet features can potentially be used as computer aided diagnosis (CAD) tools to help clinicians in the diagnosis of ovarian cancer

Cancer Biomarker Research and Personalized Medicine

Biomarkers are measures of a biological state. The treatment of individual patients based on particular factors, such as biomarkers, distinguishes standard, generalized treatment plans from personalized medicine. Even though personalized medicine is applicable to most branches of medicine, the field of oncology is perhaps where it is most easily employed. Cancer is a heterogeneous disease; although patients may be diagnosed histologically with the same cancer type, their tumors can comprise varying tumor microenvironments and molecular characteristics that can impact treatment response and prognosis. There has been a major drive over the past decade to try and realize personalized cancer medicine through the discovery and use of disease-specific biomarkers. This book, entitled “Cancer Biomarker Research and Personalized Medicine”, encompasses 22 publications from colleagues working on a diverse range of cancers, including prostate, breast, ovarian, head and neck, liver, gastric, bladder, colorectal, and kidney. The biomarkers assessed in these studies include genes, intracellular or secreted proteins, exosomes, DNA, RNA, miRNA, circulating tumor cells, circulating immune cells, in addition to radiomic features