Utility of sarcoma-specific fusion gene analysis in paraffin-embedded material for routine diagnosis at a specialist centre.

AIMS: Diagnosis of soft tissue sarcomas can be difficult. It can be aided by detection of specific genetic aberrations in many cases. This study assessed the utility of a molecular genetics/cytogenetics service as part of the routine diagnostic service at the Royal Marsden Hospital. METHODS: A retrospective audit was performed over a 15-month period to evaluate the diagnostic usefulness for soft tissue sarcomas with translocations of fluorescence in situ hybridisation (FISH) and reverse-transcriptase PCR (RT-PCR) in paraffin-embedded (PE) material. Results were compared with histology, and evaluated. RESULTS: Molecular investigations were performed on PE material in 158 samples (total 194 RT-PCR and 174 FISH tests), of which 85 were referral cases. Synovial sarcoma, Ewing sarcoma and low-grade fibromyxoid sarcoma were the most commonly tested tumours. Myxoid liposarcoma showed the best histological and molecular concordance, and alveolar rhabdomyosarcoma showed the best agreement between methods. FISH had a higher sensitivity for detecting tumours (73%, compared with 59% for RT-PCR) with a better success rate than RT-PCR, although the latter was specific in identifying the partner gene for each fusion. In particular, referral blocks in which methods of tissue fixation and processing were not certain resulted in higher RT-PCR failure rates. CONCLUSIONS: FISH and RT-PCR on PE tissue are practical and effective ancillary tools in the diagnosis of soft tissue sarcomas. They are useful in confirming doubtful histological diagnoses and excluding malignant diagnoses. PCR is less sensitive than FISH, and the use of both techniques is optimal for maximising the detection rate of translocation-positive sarcomas.

Refining the diagnosis and EGFR status of non-small cell lung carcinoma in biopsy and cytologic material, using a panel of mucin staining, TTF-1, cytokeratin 5/6, and P63, and EGFR mutation analysis.

INTRODUCTION: The dichotomization of non-small cell carcinoma (NSCLC) subtype into squamous (SQCC) and adenocarcinoma (ADC) has become important in recent years and is increasingly required with regard to management. The aim of this study was to determine the utility of a panel of commercially available antibodies in refining the diagnosis on small biopsies and also to determine whether cytologic material is suitable for somatic EGFR genotyping in a prospectively analyzed series of patients undergoing investigation for suspected lung cancer. METHODS: Thirty-two consecutive cases of NSCLC were first tested using a panel comprising cytokeratin 5/6, P63, thyroid transcription factor-1, 34betaE12, and a D-PAS stain for mucin, to determine their value in refining diagnosis of NSCLC. After this test phase, two further pathologists independently reviewed the cases using a refined panel that excluded 34betaE12 because of its low specificity for SQCC, and refinement of diagnosis and concordance were assessed. Ten cases of ADC, including eight derived from cytologic samples, were sent for EGFR mutation analysis. RESULTS: There was refinement of diagnosis in 65% of cases of NSCLC to either SQCC or ADC in the test phase. This included 10 of 13 cases where cell pellets had been prepared from transbronchial needle aspirates. Validation by two further pathologists with varying expertise in lung pathology confirmed increased refinement and concordance of diagnosis. All samples were adequate for analysis, and they all showed a wild-type EGFR genotype. CONCLUSION: A panel comprising cytokeratin 5/6, P63, thyroid transcription factor-1, and a D-PAS stain for mucin increases diagnostic accuracy and agreement between pathologists when faced with refining a diagnosis of NSCLC to SQCC or ADC. These small samples, even cell pellets derived from transbronchial needle aspirates, seem to be adequate for EGFR mutation analysis.

Does video feedback analysis improve CPR performance in phase 5 medical students?

Background The use of simulation in medical education is increasing, with students taught and assessed using simulated patients and manikins. Medical students at Queen’s University of Belfast are taught advanced life support cardiopulmonary resuscitation as part of the undergraduate curriculum. Teaching and feedback in these skills have been developed in Queen’s University with high-fidelity manikins. This study aimed to evaluate the effectiveness of video compared to verbal feedback in assessment of student cardiopulmonary resuscitation performance Methods Final year students participated in this study using a high-fidelity manikin, in the Clinical Skills Centre, Queen’s University Belfast. Cohort A received verbal feedback only on their performance and cohort B received video feedback only. Video analysis using ‘StudioCode’ software was distributed to students. Each group returned for a second scenario and evaluation 4 weeks later. An assessment tool was created for performance assessment, which included individual skill and global score evaluation. Results One hundred thirty eight final year medical students completed the study. 62 % were female and the mean age was 23.9 years. Students having video feedback had significantly greater improvement in overall scores compared to those receiving verbal feedback (p = 0.006, 95 % CI: 2.8–15.8). Individual skills, including ventilation quality and global score were significantly better with video feedback (p = 0.002 and p < 0.001, respectively) when compared with cohort A. There was a positive change in overall score for cohort B from session one to session two (p < 0.001, 95 % CI: 6.3–15.8) indicating video feedback significantly benefited skill retention. In addition, using video feedback showed a significant improvement in the global score (p < 0.001, 95 % CI: 3.3–7.2) and drug administration timing (p = 0.004, 95 % CI: 0.7–3.8) of cohort B participants, from session one to session two. Conclusions There is increased use of simulation in medicine but a paucity of published data comparing feedback methods in cardiopulmonary resuscitation training. Our study shows the use of video feedback when teaching cardiopulmonary resuscitation is more effective than verbal feedback, and enhances skill retention. This is one of the first studies to demonstrate the benefit of video feedback in cardiopulmonary resuscitation teaching.

Improving the Accuracy of 3-D Reconstruction in Robotic Vision Applications

Thesis (Ph.D.)--University of Washington, 2016-08

Clinical utility of amyloid PET imaging in the differential diagnosis of atypical dementias and its impact on caregivers

La maladie d’Alzheimer (MA) se caractérise pathologiquement par l’accumulation de plaques amyloïde dans le cerveau. La tomographie par émission de positrons (TEP) permet d’imager les plaques amyloïde in vivo. Le but de ce projet est d’évaluer le rôle de la TEP amyloïde dans le processus diagnostique de la MA dans des cas de démences atypiques. Le deuxième but de ce projet est de déterminer l’impact de la révélation d’un diagnostic plus certain chez les proches aidants. 28 patients sans diagnostic malgré une investigation exhaustive ont été sélectionnées et imagées avec le traceur amyloïde 18F-NAV4694 (âge 59,3 ans, é-t. 5,8; MMSE 21.4, é-t 6.0). Les neurologues référents documentaient par la suite tout changement de niveau de certitude, de diagnostic, de traitement et/ou de prise en charge. Les proches aidants consentants ont été rencontrés subséquemment, et un questionnaire avec une échelle de Likert a été utilisé afin de documenter l’impact de l’imagerie leur perception de la maladie. Notre cohorte a été également divisée entre amyloïde positifs (14/28) et négatifs (14/28). Un changement de diagnostic a lieu dans 9/28 cas (32,1% :17.8% ont changé de MA à non-MA, 14,3% de non-MA à MA). Il y avait une augmentation significative (p<0,05) de 44% dans la certitude du neurologue suite à cet examen. Un changement de prise en charge a été obtenu dans 20/28 (71,4%) des cas. Bien que non significatifs statistiquement, un impact favorable sur les proches-aidants a été noté. Cette étude suggère que l’imagerie amyloïde a un rôle bénéfique dans les cas de démences atypiques n’ayant pu être élucidés avec les techniques d’investigations actuellement recommandées. De plus, le processus a été perçu positivement par les proches aidants, notamment en encourageant du temps de qualité avec leurs personnes chères. Ceci illustre un rôle prometteur des biomarqueurs, qui sont de plus en plus explorés.

Anomaly Detection in Noisy Images

Finding rare events in multidimensional data is an important detection problem that has applications in many fields, such as risk estimation in insurance industry, finance, flood prediction, medical diagnosis, quality assurance, security, or safety in transportation. The occurrence of such anomalies is so infrequent that there is usually not enough training data to learn an accurate statistical model of the anomaly class. In some cases, such events may have never been observed, so the only information that is available is a set of normal samples and an assumed pairwise similarity function. Such metric may only be known up to a certain number of unspecified parameters, which would either need to be learned from training data, or fixed by a domain expert. Sometimes, the anomalous condition may be formulated algebraically, such as a measure exceeding a predefined threshold, but nuisance variables may complicate the estimation of such a measure. Change detection methods used in time series analysis are not easily extendable to the multidimensional case, where discontinuities are not localized to a single point. On the other hand, in higher dimensions, data exhibits more complex interdependencies, and there is redundancy that could be exploited to adaptively model the normal data. In the first part of this dissertation, we review the theoretical framework for anomaly detection in images and previous anomaly detection work done in the context of crack detection and detection of anomalous components in railway tracks. In the second part, we propose new anomaly detection algorithms. The fact that curvilinear discontinuities in images are sparse with respect to the frame of shearlets, allows us to pose this anomaly detection problem as basis pursuit optimization. Therefore, we pose the problem of detecting curvilinear anomalies in noisy textured images as a blind source separation problem under sparsity constraints, and propose an iterative shrinkage algorithm to solve it. Taking advantage of the parallel nature of this algorithm, we describe how this method can be accelerated using graphical processing units (GPU). Then, we propose a new method for finding defective components on railway tracks using cameras mounted on a train. We describe how to extract features and use a combination of classifiers to solve this problem. Then, we scale anomaly detection to bigger datasets with complex interdependencies. We show that the anomaly detection problem naturally fits in the multitask learning framework. The first task consists of learning a compact representation of the good samples, while the second task consists of learning the anomaly detector. Using deep convolutional neural networks, we show that it is possible to train a deep model with a limited number of anomalous examples. In sequential detection problems, the presence of time-variant nuisance parameters affect the detection performance. In the last part of this dissertation, we present a method for adaptively estimating the threshold of sequential detectors using Extreme Value Theory on a Bayesian framework. Finally, conclusions on the results obtained are provided, followed by a discussion of possible future work.

Comparative assessment of seller’s staining test (SST) and direct fluorescent antibody test for rapid and accurate laboratory diagnosis of rabies.

Background: Rabies causes 55, 000 annual human deaths globally and about 10,000 people are exposed annually in Nigeria. Diagnosis of animal rabies in most African countries has been by direct microscopic examination. In Nigeria, the Seller’s stain test (SST) was employed until 2009. Before then, both SST and dFAT were used concurrently until the dFAT became the only standard method. Objective: This study was designed to assess the sensitivity and specificity of the SST in relation to the ‘gold standard’ dFAT in diagnosis of rabies in Nigeria. Methods: A total of 88 animal specimens submitted to the Rabies National Reference Laboratory, Nigeria were routinely tested for rabies by SST and dFAT. Results: Overall, 65.9% of the specimens were positive for rabies by SST, while 81.8% were positive by dFAT. The sensitivity of SST in relation to the gold standard dFAT was 81.0% (95% CIs; 69.7% - 88.6%), while the specificity was 100% (95% CIs; 76% - 100%). Conclusion: The relatively low sensitivity of the SST observed in this study calls for its replacement with the dFAT for accurate diagnosis of rabies and timely decisions on administration of PEP to prevent untimely deaths of exposed humans.

Five years retrospective cohort analysis of treatment outcomes of TB-HIV patients at a PEPFAR/DOTS Centre in South Eastern Nigeria.

Background: Human immunodeficiency virus (HIV) associated tuberculosis (TB) remains a major global public health challenge, with an estimated 1.4 million patients worldwide. Co-infection with HIV leads to challenges in the diagnosis and treatment of patients. Objectives: The aim of this study was to assess treatment outcomes of a cohort of smear positive TB-HIV co-infected patients over a five-year study period. Methods: A retrospective cohort study of 600 smear-positive tuberculosis patients registered at the chest unit of the University of Nigeria Teaching Hospital, Enugu from January 2008 to December 2012 was done. The data was analyzed using SPSS Version 17. Results: One hundred and three (17.2%) of the patients were co-infected with TB/HIV, while 398 (66.3%) and 99 (16.5%) were HIV negative and unknown respectively. Among the co-infected patients, 45(43.7%) were cured as against 222(55.8%) in the TBHIV negatives (Z=4.53, p=0.000, 95%CI= 0.12-0.34). Respectively in the TB-HIV co-infected and TB-HIV negative patients, treatment completed were 21(20.4%) and 71(17.8%) (Z=9.15, p=0.000, 95%= 0.4035-0.60); defaulted 19(18.5%) vs 70 (17.6%) (Z=9.29, p=0.000, 95%CI=0.42-0.60), died 10(9.7%) vs. 6(1.5%) (Z=1.22, p=0.224, 95%CI= -0.0286-0.1086), and failures were 1(0.9%) vs. 7(1.8%) (Z=2.48, p=0.013, 95%CI=0.04-0.10). Treatment success rate was lower in TB-HIV co-infected patients, 64.1% compared to TB-HIV negative patients with 73.6%. Also those that defaulted among the TB-HIV co-infected patients (18.5%) were higher than 17.6% among TB-HIV negative patients, a difference of 0.9%. Conclusion: Findings demonstrate that HIV co-infection affects TB treatment outcomes adversely. Treatment adherence, timely and sustained access to antiretroviral therapy for TB/HIV co-infected patients are important.

The importance of EN ISO 15189 accreditation of allergen-specific IgE determination for reliable in vitro allergy diagnosis

International audience

The Impact of the Eye in Dementia: The Eye and its Role in Diagnosis and Follow‐up

Over the last few decades, the importance of ophthalmic examination in neurodegenerative diseases of the CNS has reportedly increased. The retina is an extension of the CNS and thus should not be surprising to find abnormal results in both the test exploring visual processing and those examining the retina of patients with CNS degeneration. Current in vivo imaging techniques are allowing ophthalmologists to detect and quantify data consistent with the histopathological findings described in the retinas of Alzheimer’s disease (AD) patients and may help to reveal unsuspected retinal and optic‐nerve repercussions of other CNS diseases. In this chapter, we perform an analysis of the physiological changes in ocular and cerebral ageing. We analyse the ocular manifestations in CNS disorders such as stroke, AD and Parkinson’s disease. In addition, the pathophysiology of both the eye and the visual pathway in AD are described. The value of the visual psychophysical tests in AD diagnosis is reviewed as well as the main findings of the optical coherence tomography as a contribution to the diagnosis and monitoring of the disease. Finally, we examine the association of two neurodegenerative diseases, AD and glaucoma, as mere coincidence or possible role in the progression of the neurodegeneration.

Different Kinds of Paraneoplastic Syndromes in Childhood Neuroblastoma

Background: Clinical presentations of paraneoplastic syndromes in neuroblastoma may multiply. Review of the clinical data and the literature on this syndrome may help in the diagnosis of neuroblastoma. Objectives: In order to make more accurate diagnosis, we reviewed the clinical data and the literature on this syndrome. Patients and Methods: Between April 2007 and April 2012, 68 children were diagnosed with neuroblastoma or ganglioneuroblastoma in our institution, 9 of which presented exclusively with paraneoplastic syndromes and were not treated with chemotherapy prior to diagnosis. After the diagnosis, all patients received chemotherapy and operation on NB97 protocol. Results: Among 68 pediatric patients with neuroblastoma or ganglioneuroblastoma, 4 (5.9%) patients suffered from neurological complications at diagnosis, 2 (2.9%) patients had digestive tract disorders, 2 (2.9%) patients had immune diseases, and 1 (1.5%) suffered from hematological disorder (without bone marrow involvement). All paraneoplastic syndrome patients achieved complete remission on paraneoplastic syndrome before completion of chemotherapy. Conclusions: Neuroblastoma may present with a range of non-specific neurologic symptoms in addition to the well-known opsoclonus-myoclonus syndrome and cerebellar ataxia. In any case, the presence of unexplained neurologic manifestations and other common clinical presentations such as rash, constipation, diarrhea, and especially immune disorders in an otherwise healthy child had raised the possibility of paraneoplastic syndrome due to the presence of an undiagnosed tumor.

MobiMed: Framework for Rapid Application Development of Medical Mobile Apps

In the medical field images obtained from high definition cameras and other medical imaging systems are an integral part of medical diagnosis. The analysis of these images are usually performed by the physicians who sometimes need to spend long hours reviewing the images before they are able to come up with a diagnosis and then decide on the course of action. In this dissertation we present a framework for a computer-aided analysis of medical imagery via the use of an expert system. While this problem has been discussed before, we will consider a system based on mobile devices. Since the release of the iPhone on April 2003, the popularity of mobile devices has increased rapidly and our lives have become more reliant on them. This popularity and the ease of development of mobile applications has now made it possible to perform on these devices many of the image analyses that previously required a personal computer. All of this has opened the door to a whole new set of possibilities and freed the physicians from their reliance on their desktop machines. The approach proposed in this dissertation aims to capitalize on these new found opportunities by providing a framework for analysis of medical images that physicians can utilize from their mobile devices thus remove their reliance on desktop computers. We also provide an expert system to aid in the analysis and advice on the selection of medical procedure. Finally, we also allow for other mobile applications to be developed by providing a generic mobile application development framework that allows for access of other applications into the mobile domain. In this dissertation we outline our work leading towards development of the proposed methodology and the remaining work needed to find a solution to the problem. In order to make this difficult problem tractable, we divide the problem into three parts: the development user interface modeling language and tooling, the creation of a game development modeling language and tooling, and the development of a generic mobile application framework. In order to make this problem more manageable, we will narrow down the initial scope to the hair transplant, and glaucoma domains.

Probabilistic subgroup identification using Bayesian finite mixture modelling : a case study in Parkinson’s disease phenotype identification

This article explores the use of probabilistic classification, namely finite mixture modelling, for identification of complex disease phenotypes, given cross-sectional data. In particular, if focuses on posterior probabilities of subgroup membership, a standard output of finite mixture modelling, and how the quantification of uncertainty in these probabilities can lead to more detailed analyses. Using a Bayesian approach, we describe two practical uses of this uncertainty: (i) as a means of describing a person’s membership to a single or multiple latent subgroups and (ii) as a means of describing identified subgroups by patient-centred covariates not included in model estimation. These proposed uses are demonstrated on a case study in Parkinson’s disease (PD), where latent subgroups are identified using multiple symptoms from the Unified Parkinson’s Disease Rating Scale (UPDRS).

Comparison of standard image segmentation methods for segmentation of brain tumors from 2D MR images

In the analysis of medical images for computer-aided diagnosis and therapy, segmentation is often required as a preliminary step. Medical image segmentation is a complex and challenging task due to the complex nature of the images. The brain has a particularly complicated structure and its precise segmentation is very important for detecting tumors, edema, and necrotic tissues in order to prescribe appropriate therapy. Magnetic Resonance Imaging is an important diagnostic imaging technique utilized for early detection of abnormal changes in tissues and organs. It possesses good contrast resolution for different tissues and is, thus, preferred over Computerized Tomography for brain study. Therefore, the majority of research in medical image segmentation concerns MR images. As the core juncture of this research a set of MR images have been segmented using standard image segmentation techniques to isolate a brain tumor from the other regions of the brain. Subsequently the resultant images from the different segmentation techniques were compared with each other and analyzed by professional radiologists to find the segmentation technique which is the most accurate. Experimental results show that the Otsu’s thresholding method is the most suitable image segmentation method to segment a brain tumor from a Magnetic Resonance Image.