Search results for: disease diagnosis

Authors: Shraddha Gopal, Jayam Lazarus

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Aims: To study the referral practice and impact of DAT-scan in the diagnosis or exclusion of Parkinson’s disease. Settings and Designs: A retrospective study Materials and methods: A retrospective study of the results of 60 patients who were referred for a DAT scan over a period of 2 years from the Department of Neurology at Northern Lincolnshire and Goole NHS trust. The reason for DAT scan referral was noted under 5 categories against Parkinson’s disease; drug-induced Parkinson’s, essential tremors, diagnostic dilemma, not responding to Parkinson’s treatment, and others. We assessed the number of patients who were diagnosed with Parkinson’s disease against the number of patients in whom Parkinson’s disease was excluded or an alternative diagnosis was made. Statistical methods: Microsoft Excel was used for data collection and statistical analysis, Results: 30 of the 60 scans were performed to confirm the diagnosis of early Parkinson’s disease, 13 were done to differentiate essential tremors from Parkinsonism, 6 were performed to exclude drug-induced Parkinsonism, 5 were done to look for alternative diagnosis as the patients were not responding to anti-Parkinson medication and 6 indications were outside the recommended guidelines. 55% of cases were confirmed with a diagnosis of Parkinson’s disease. 43.33% had Parkinson’s disease excluded. 33 of the 60 scans showed bilateral abnormalities and confirmed the clinical diagnosis of Parkinson’s disease. Conclusion: DAT scan provides valuable information in confirming Parkinson’s disease in 55% of patients along with excluding the diagnosis in 43.33% of patients aiding an alternative diagnosis.

Keywords: DATSCAN, Parkinson's disease, diagnosis, essential tremors

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Authors: Lavanya Madhuri Bollipo, K. V. Kadambari

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Objective: To discuss key issues in the diagnosis of Parkinson disease (PD), To discuss features influencing PD progression, To discuss importance of brain SPECT data in PD diagnosis, and To discuss the essentiality of machine learning techniques in early diagnosis of PD. An accurate and early diagnosis of PD is nowadays a challenge as clinical symptoms in PD arise only when there is more than 60% loss of dopaminergic neurons. So far there are no laboratory tests for the diagnosis of PD, causing a high rate of misdiagnosis especially when the disease is in the early stages. Recent neuroimaging studies with brain SPECT using 123I-Ioflupane (DaTSCAN) as radiotracer shown to be widely used to assist the diagnosis of PD even in its early stages. Machine learning techniques can be used in combination with image analysis procedures to develop computer-aided diagnosis (CAD) systems for PD. This paper addressed recent studies involving diagnosis of PD in its early stages using brain SPECT data with Machine Learning Techniques.

Keywords: Parkinson disease (PD), dopamine transporter, single-photon emission computed tomography (SPECT), support vector machine (SVM)

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Authors: Purnima Pandit

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Malaria remains one of the world’s most deadly infectious disease and arguably, the greatest menace to modern society in terms of morbidity and mortality. To choose the right treatment and to ensure a quality of life suitable for a specific patient condition, early and accurate diagnosis of malaria is essential. It reduces transmission of disease and prevents deaths. Our work focuses on designing an efficient, accurate fuzzy inference system for malaria diagnosis.

Keywords: fuzzy inference system, fuzzy logic, malaria disease, triangular fuzzy number

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Authors: Oluwaponmile David Alao

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Due to an increase in the death rate as a result of heart attack. There is need to develop a system that can be useful in the diagnosis of the disease at the medical centre. This system will help in preventing misdiagnosis that may occur from the medical practitioner or the physicians. In this research work, heart disease dataset obtained from UCI repository has been used to develop an intelligent prediction diagnosis system. The system is modeled on a feedforwad neural network and trained with back propagation neural network. A recognition rate of 86% is obtained from the testing of the network.

Keywords: heart disease, artificial neural network, diagnosis, prediction system

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Authors: Huong M. T. Nguyen, Hoa A. P. Nguyen, Chi V. Phan, Mai P. T. Nguyen, Ngoc D. Ngo, Van T. Ta, Hai T. Le

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Wilson’s disease is an autosomal recessive disorder of copper metabolism, which is caused by mutation in copper- transporting P-type ATPase (ATP7B). The mechanism of this disease is a failure of hepatic excretion of copper to the bile, and it leads to copper deposits in the liver and other organs. Most clinical symptoms of Wilson’s disease can present as liver disease and/or neurologic disease. Objective: The goal of the study is prenatal diagnosis for pregnant women at high risk of Wilson’s disease in Northern Vietnam. Material and method: Three probands with clinically diagnosed liver disease were detected in the mutations of 21 exons and exon-intron boundaries of the ATP7B gene by direct Sanger-sequencing. Prenatal diagnoses were performed by amniotic fluid sampling from pregnant women in the 16th-18th weeks of pregnancy after the genotypes of parents with the probands were identified. Result: A total of three different mutations of the probands, including of S105*, P1052L, P1273G, were detected. Among three fetuses which underwent prenatal genetic testing, one fetus was homozygote; two fetuses were carriers. Conclusion: Genetic testing provided a useful method for prenatal diagnosis, and is a basis for genetic counseling.

Keywords: ATP7B gene, genetic testing, prenatal diagnosis, pedigree, Wilson disease

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Authors: S. Hallal, Z. Chami, A. Hadji-Lehtihet, S. Sokhal-Boudella, A. Berhoune, L. Yargui

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Gaucher disease is the most common lysosomal storage in our population, it is due to a deficiency of β –glucosidase acid. The enzyme deficiency causes a pathological accumulation of undegraded substrate in lysosomes. This metabolic overload is responsible for a multisystemic disease with hepatosplenomegaly, anemia, thrombocytopenia, and bone involvement. Neurological involvement is rare. The laboratory diagnosis of Gaucher disease consists of phenotypic diagnosis by determining the enzymatic activity of β - glucosidase by fluorimetric method, a study by genotypic diagnosis in the GBA gene, limiting the search recurrent mutations (N370S, L444P, 84 GG); PCR followed by an enzymatic digestion. Abnormal profiles were verified by sequencing. Monitoring of treated patients is provided by the determination of chitotriosidase. Our experience spaning a period of 6 years (2007-2014) has enabled us to diagnose 78 patients out of a total of 328 requests from the various departments of pediatrics, internal medicine, neurology. Genotypic diagnosis focused on the entire family of 9 children treated at pediatric CHU Mustapha, which help define the clinical form; or 5 of them had type III disease, carrying the L444P mutation in the homozygous state. Three others were composite (N370/L444P) (N370S/other unintended mutation in our study), and only in one family no recurrent mutation has been found. This molecular study permits screening of heterozygous essential for genetic counseling.

Keywords: Gaucher disease, mutations, N370S, L444P

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Authors: Oluwaponmile D. Alao

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In this paper, two models have been developed to ascertain the best network needed for diagnosis of breast cancer. Breast cancer has been a disease that required the attention of the medical practitioner. Experience has shown that misdiagnose of the disease has been a major challenge in the medical field. Therefore, designing a system with adequate performance for will help in making diagnosis of the disease faster and accurate. In this paper, two models: backpropagation neural network and support vector machine has been developed. The performance obtained is also compared with other previously obtained algorithms to ascertain the best algorithms.

Keywords: breast cancer, data mining, neural network, support vector machine

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Authors: Teja Koduru

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Untreated Lyme disease can lead to neurological, cardiac, and dermatological complications. Rapid diagnosis of the erythema migrans (EM) rash, a characteristic symptom of Lyme disease is therefore crucial to early diagnosis and treatment. In this study, we aim to utilize deep learning frameworks including Tensorflow and Keras to create deep convolutional neural networks (DCNN) to detect images of acute Lyme Disease from images of erythema migrans. This study uses a custom database of erythema migrans images of varying quality to train a DCNN capable of classifying images of EM rashes vs. non-EM rashes. Images from publicly available sources were mined to create an initial database. Machine-based removal of duplicate images was then performed, followed by a thorough examination of all images by a clinician. The resulting database was combined with images of confounding rashes and regular skin, resulting in a total of 683 images. This database was then used to create a DCNN with an accuracy of 93% when classifying images of rashes as EM vs. non EM. Finally, this model was converted into a web and mobile application to allow for rapid diagnosis of EM rashes by both patients and clinicians. This tool could be used for patient prescreening prior to treatment and lead to a lower mortality rate from Lyme disease.

Keywords: Lyme, untreated Lyme, erythema migrans rash, EM rash

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Authors: Faith-Michael E. Uzoka, Boluwaji A. Akinnuwesi, Taiwo Amoo, Flora Aladi, Stephen Fashoto, Moses Olaniyan, Joseph Osuji

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The overarching aim of this study is to develop a soft-computing system for the differential diagnosis of tropical diseases. These conditions are of concern to health bodies, physicians, and the community at large because of their mortality rates, and difficulties in early diagnosis due to the fact that they present with symptoms that overlap, and thus become ‘confusable’. We report on the first phase of our study, which focuses on the development of a fuzzy cognitive map model for early differential diagnosis of tropical diseases. We used malaria as a case disease to show the effectiveness of the FCM technology as an aid to the medical practitioner in the diagnosis of tropical diseases. Our model takes cognizance of manifested symptoms and other non-clinical factors that could contribute to symptoms manifestations. Our model showed 85% accuracy in diagnosis, as against the physicians’ initial hypothesis, which stood at 55% accuracy. It is expected that the next stage of our study will provide a multi-disease, multi-symptom model that also improves efficiency by utilizing a decision support filter that works on an algorithm, which mimics the physician’s diagnosis process.

Keywords: medical diagnosis, tropical diseases, fuzzy cognitive map, decision support filters, malaria differential diagnosis

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Authors: Piyush Samant, Ravinder Agarwal

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Complementary and Alternative Medicine (CAM) techniques are quite popular and effective for chronic diseases. Iridology is more than 150 years old CAM technique which analyzes the patterns, tissue weakness, color, shape, structure, etc. for disease diagnosis. The objective of this paper is to validate the use of iridology for the diagnosis of the diabetes. The suggested model was applied in a systemic disease with ocular effects. 200 subject data of 100 each diabetic and non-diabetic were evaluated. Complete procedure was kept very simple and free from the involvement of any iridologist. From the normalized iris, the region of interest was cropped. All 63 features were extracted using statistical, texture analysis, and two-dimensional discrete wavelet transformation. A comparison of accuracies of six different classifiers has been presented. The result shows 89.66% accuracy by the random forest classifier.

Keywords: complementary and alternative medicine, classification, iridology, iris, feature extraction, disease prediction

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Authors: E. Irankhah, M. Zarif, E. Mazrooei Rad, K. Ghandehari

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Alzheimer's prevalence is on the rise, and the disease comes with problems like cessation of treatment, high cost of treatment, and the lack of early detection methods. The pathology of this disease causes the formation of protein deposits in the brain of patients called plaque amyloid. Generally, the diagnosis of this disease is done by performing tests such as a cerebrospinal fluid, CT scan, MRI, and spinal cord fluid testing, or mental testing tests and eye tracing tests. In this paper, we tried to use the Medial Temporal Atrophy (MTA) method and the Leave One Out (LOO) cycle to extract the statistical properties of the three Fz, Pz, and Cz channels of ERP signals for early diagnosis of this disease. In the process of CT scan images, the accuracy of the results is 81% for the healthy person and 88% for the severe patient. After the process of ERP signaling, the accuracy of the results for a healthy person in the delta band in the Cz channel is 81% and in the alpha band the Pz channel is 90%. In the results obtained from the signal processing, the results of the severe patient in the delta band of the Cz channel were 89% and in the alpha band Pz channel 92%.

Keywords: Alzheimer's disease, image and signal processing, LOO cycle, medial temporal atrophy

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Authors: Murat Durucu

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Glaucoma is a condition of irreversible blindness, early diagnosis and appropriate interventions to make the patients able to see longer time. In this study, it addressed that the importance of developing a decision support system for glaucoma diagnosis. Glaucoma occurs when pressure happens around the eyes it causes some damage to the optic nerves and deterioration of vision. There are different levels ranging blindness of glaucoma disease. The diagnosis at an early stage allows a chance for therapies that slows the progression of the disease. In recent years, imaging technology from Heidelberg Retinal Tomography (HRT), Stereoscopic Disc Photo (SDP) and Optical Coherence Tomography (OCT) have been used for the diagnosis of glaucoma. This better accuracy and faster imaging techniques in response technique of OCT have become the most common method used by experts. Although OCT images or HRT precision and quickness, especially in the early stages, there are still difficulties and mistakes are occurred in diagnosis of glaucoma. It is difficult to obtain objective results on diagnosis and placement process of the doctor's. It seems very important to develop an objective decision support system for diagnosis and level the glaucoma disease for patients. By using OCT images and pattern recognition systems, it is possible to develop a support system for doctors to make their decisions on glaucoma. Thus, in this recent study, we develop an evaluation and support system to the usage of doctors. Pattern recognition system based computer software would help the doctors to make an objective evaluation for their patients. It is intended that after development and evaluation processes of the software, the system is planning to be serve for the usage of doctors in different hospitals.

Keywords: decision support system, glaucoma, image processing, pattern recognition

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Authors: Recep Keşli, Onur Türkyılmaz, Hayriye Tokay, Kasım Demir

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Objective: Coeliac disease (CD) is a primary small intestine disorder caused by high sensitivity to gluten which is present in the crops, characterized by inflammation in the small intestine mucosa. The goal of this study was to determine and to compare the sensitivity and specificity values of anti-endomysial IgA (EMA IgA) (IFA) and anti-tissue transglutaminase IgA (anti-tTG IgA) (ELISA) antibodies in the diagnosis of patients suspected with the CD. Methods: One thousand two hundred seventy three patients, who have applied to gastroenterology and pediatric disease polyclinics of Afyon Kocatepe University ANS Research and Practice Hospital were included into the study between 23.09.2010 and 30.05.2016. Sera samples were investigated by immunofluorescence method for EMA positiveness (Euroimmun, Luebeck, Germany). In order to determine quantitative value of Anti-tTG IgA (EIA) (Orgentec Mainz, Germany) fully automated ELISA device (Alisei, Seac, Firenze, Italy) were used. Results: Out of 1273 patients, 160 were diagnosed with coeliac disease according to ESPGHAN 2012 diagnosis criteria. Out of 160 CD patients, 120 were female, 40 were male. The EMA specificity and sensitivity were calculated as 98% and 80% respectively. Specificity and sensitivity of Anti-tTG IgA were determined as 99% and 96% respectively. Conclusion: The specificity of EMA for CD was excellent because all EMA-positive patients (n = 144) were diagnosed with CD. The presence of human anti-tTG IgA was found as a reliable marker for diagnosis and follow-up the CD. Diagnosis of CD should be established on both the clinical and serologic profiles together.

Keywords: anti-endomysial antibody, anti-tTG IgA, coeliac disease, immunofluorescence assay (IFA)

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Authors: S. P. Chintan, A. M. Khoja, M. Modi, R. K. Chopra, S. Garde, D. Jain, O. Kajale

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Pulmonary Hypertension is a rare but debilitating disease that affects individuals of all ages and walks of life. As recent as 15 years ago, a patient diagnosed with PH was given an average survival rate of 2.8 years. Recent advances in treatment options have allowed patients to improve quality o and quantity of life. Initial screening for PH is through echocardiography with final diagnosis confirmed through right heart catheterization. PH is now considered to have five major classifications with subgroups among each. The mild to moderate PH is common in chronic lung diseases like Chronic obstructive pulmonary diseases and Interstitial lung disease. But very severe PH is noted in few cases. In COPD patients, PH is associated with an increased risk of severe exacerbations and a reduced life expectancy. Similarly, in patients with ILD, the presence of PH correlates with a poor prognosis. Early diagnosis is essential to slow disease progression. We report here five cases of severe PH (Out of Proportion) of which four cases were of COPD and another one of IPF (UIP pattern). There echocardiography showed gross RA/RV dilatation, interventricular septum bulging to the left and mPAP of more than 100 mmHg in all the five cases. These patients were put on LTOT, pulmonary rehabilitation, combination pharmacotherapy of vasodilators and diuretics in continuation to the treatment of underlying disease. As these patients have grave prognosis close monitoring and follow up is required. Physicians associated with respiratory care and treating chronic lung disease should have knowledge in the diagnosis and management of patients with PH.

Keywords: COPD, pulmonary hypertension, chronic lung disease, India

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Authors: Jay L. Fu

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Patients with Alzheimer's disease progressively lose their memory and thinking skills and, eventually, the ability to carry out simple daily tasks. The disease is irreversible, but early detection and treatment can slow down the disease progression. In this research, publicly available MRI data and demographic data from 373 MRI imaging sessions were utilized to build models to predict dementia. Various machine learning models, including logistic regression, k-nearest neighbor, support vector machine, random forest, and neural network, were developed. Data were divided into training and testing sets, where training sets were used to build the predictive model, and testing sets were used to assess the accuracy of prediction. Key risk factors were identified, and various models were compared to come forward with the best prediction model. Among these models, the random forest model appeared to be the best model with an accuracy of 90.34%. MMSE, nWBV, and gender were the three most important contributing factors to the detection of Alzheimer’s. Among all the models used, the percent in which at least 4 of the 5 models shared the same diagnosis for a testing input was 90.42%. These machine learning models allow early detection of Alzheimer’s with good accuracy, which ultimately leads to early treatment of these patients.

Keywords: Alzheimer's disease, clinical diagnosis, magnetic resonance imaging, machine learning prediction

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Authors: P. S. Jagadeesh Kumar, Tracy Lin Huan, S. Meenakshi Sundaram

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Alzheimer’s disease is one of the prevalent kind of ailment, expected for impudent reconciliation or an effectual therapy is to be accredited hitherto. Probable detonation of patients in the upcoming years, and consequently an enormous deal of apprehension in early discovery of the disorder, this will conceivably chaperon to enhanced healing outcomes. Complex impetuosity of the brain is an observant symbolic of the disease and a unique recognition of genetic sign of the disease. Machine learning alongside deep learning and decision tree reinforces the aptitude to absorb characteristics from multi-dimensional data’s and thus simplifies automatic classification of Alzheimer’s disease. Susceptible testing was prophesied and realized in training the prospect of Alzheimer’s disease classification built on machine learning advances. It was shrewd that the decision trees trained with deep neural network fashioned the excellent results parallel to related pattern classification.

Keywords: Alzheimer's diagnosis, decision trees, deep neural network, machine learning, pattern classification

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Authors: Amira Ben Rabeh, Faouzi Benzarti, Hamid Amiri, Mouna Bouaziz

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Alzheimer is a disease that affects the brain. It causes degeneration of nerve cells (neurons) and in particular cells involved in memory and intellectual functions. Early diagnosis of Alzheimer Diseases (AD) raises ethical questions, since there is, at present, no cure to offer to patients and medicines from therapeutic trials appear to slow the progression of the disease as moderate, accompanying side effects sometimes severe. In this context, analysis of medical images became, for clinical applications, an essential tool because it provides effective assistance both at diagnosis therapeutic follow-up. Computer Assisted Diagnostic systems (CAD) is one of the possible solutions to efficiently manage these images. In our work; we proposed an application to detect Alzheimer’s diseases. For detecting the disease in early stage we used the three sections: frontal to extract the Hippocampus (H), Sagittal to analysis the Corpus Callosum (CC) and axial to work with the variation features of the Cortex(C). Our method of classification is based on Support Vector Machine (SVM). The proposed system yields a 90.66% accuracy in the early diagnosis of the AD.

Keywords: Alzheimer Diseases (AD), Computer Assisted Diagnostic(CAD), hippocampus, Corpus Callosum (CC), cortex, Support Vector Machine (SVM)

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Authors: Oluwaponmile David Alao

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Misdiagnosis has been the major problem in health sector. Heart attack has been one of diseases that have high level of misdiagnosis recorded on the part of physicians. In this paper, an intelligent system has been developed for diagnosis of heart attack in the health sector. Dataset of heart attack obtained from UCI repository has been used. This dataset is made up of thirteen attributes which are very vital in diagnosis of heart disease. The system is developed on the multilayer perceptron trained with back propagation neural network then simulated with feed forward neural network and a recognition rate of 87% was obtained which is a good result for diagnosis of heart attack in medical field.

Keywords: heart attack, artificial neural network, diagnosis, intelligent system

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Authors: Hiranmoyee Das

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Aim. Due to late presentation and delayed diagnosis mortality rate of retinoblastoma is more than 50% in developing counties. So to facilitate the diagnosis, to decrease the disease and treatment burden and to increase the disease survival rate, an attempt was made for early diagnosis of Retinoblastoma by including fundus examination in routine immunization programs. Methods- A unique immunization card is followed in a tertiary health care center where examination of pupillary reflex is made mandatory in each visit of the child for routine immunization. In case of any abnormality, the child is referred to the ophthalmology department. Conclusion- Early detection is the key in the management of retinoblastoma. Every child is brought to the health care system at least five times before the age of 2 years for routine immunization. We should not miss this golden opportunity for early detection of retinoblastoma.

Keywords: retinoblastoma, immunization, unique, early

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Authors: Sabrina Azzi, Michal Iglewski, Véronique Nabelsi

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Diagnosis error problem is frequent and one of the most important safety problems today. One of the main objectives of our work is to propose an ontological representation that takes into account the diagnostic criteria in order to improve the diagnostic. We choose pneumonia disease since it is one of the frequent diseases affected by diagnosis errors and have harmful effects on patients. To achieve our aim, we use a semi-automated method to integrate diverse knowledge sources that include publically available pneumonia disease guidelines from international repositories, biomedical ontologies and electronic health records. We follow the principles of the Open Biomedical Ontologies (OBO) Foundry. The resulting ontology covers symptoms and signs, all the types of pneumonia, antecedents, pathogens, and diagnostic testing. The first evaluation results show that most of the terms are covered by the ontology. This work is still in progress and represents a first and major step toward a development of a diagnosis decision support system for pneumonia.

Keywords: Clinical decision support system, Diagnostic errors, Ontology, Pneumonia

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Authors: Biplab Singha, Mausumi Sen, Nidul Sinha

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In order to deal with the impreciseness and uncertainty of a system, D. Molodtsov has introduced the concept of ‘Soft Set’ in the year 1999. Since then, a number of related definitions have been conceptualized. This paper includes a study on various forms of Soft Sets with examples. The paper contains the concepts of domain and co-domain of a soft set, conversion to one-one and onto function, matrix representation of a soft set and its relation with one-one function, upper and lower triangular matrix, transpose and Kernel of a soft set. This paper also gives the idea of the extension of soft sets in medical diagnosis. Here, two soft sets related to disease and symptoms are considered and using AND operation and OR operation, diagnosis of the disease is calculated through appropriate examples.

Keywords: kernel of a soft set, soft set, transpose of a soft set, upper and lower triangular matrix of a soft set

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Authors: Magdalena Barbara Kaziuk, Ilona Hubner, Jacek Hubner, Slawomir Kroczka

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Background: The motor neuron disease is a progressive neurodegenerative disease causing malfunction. Irrespective of the form of the disease and its onset always leads to the worsening of the quality of life, with patients usually depending on the family. Materials and methods: The study included 20 persons (5 females, 15 males, aged 65,5 ± 20 years) with clinically certain or probable diagnosis of the motor neuron disease. Patients were examined three times in the period of six months. The diagnosis was established based on the criteria of El Escorial. Manual dexterity was assessed using the test of the card Rene Zazzo and the test of shading in with lines Mira Stambak. Results: All patients achieved unsatisfactory results in Rene Zazzo’s test of the card and most of the patients (60%) in Mira Stambak’s test of shading with lines. Significantly higher test results were achieved for Rene Zazzo’s test and lower test results for Mira Stambak’s test in consecutive measurements. Conclusions: Impairment of manual dexterity is present already at the moment of diagnosing the disease and is growing significantly during its course. The quality of life for MND patients undergoes gradual deterioration as a result of the malfunction.

Keywords: manual dexterity, motor neuron disease, quality of life, malfunction

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Authors: Kim Kennedy, Daren Gibson, Stephanie Flukes, Chandra Diwakarla, Lisa Spalding, Leanne Pilkington, Andrew Redfern

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Introduction: The mortality gap in Aboriginal Head and Neck Cancer is well known, but the reasons for poorer survival are not well established. Aim: We aimed to evaluate the locoregional and metastatic involvement, and stage at diagnosis, in Aboriginal compared with non-Aboriginal patients. Methods: We performed a retrospective cohort analysis of 320 HNC patients from a single centre in Western Australia, identifying 80 Aboriginal patients and 240 non-Aboriginal patients matched on a 1:3 ratio by sites, histology, rurality, and age. We collected data on the patient characteristics, tumour features, regions involved, stage at diagnosis, treatment history, and survival and relapse patterns, including sites of metastatic and locoregional involvement. Results: Aboriginal patients had a significantly higher incidence of lung metastases (26.3% versus 13.7%, p=0.009). Aboriginal patients also had a numerically but non-statistically significant higher incidence of thoracic nodal involvement (10% vs 5.8%) and malignant pleural effusions (3.8% vs 2.5%). Aboriginal patients also had a numerically but not statistically significantly higher incidence of adrenal and bony involvement. Interestingly, non-Aboriginal patients had an increased rate of cutaneous (2.1% vs 0%) and liver metastases (4.6% vs 2.5%) compared with Aboriginal patients. In terms of locoregional involvement, Aboriginal patients were more than twice as likely to have contralateral neck involvement (58.8% vs 24.2%, p<0.00001), and 30% more likely to have ipsilateral neck lymph node involvement (78.8% vs 60%, p=0.002) than non-Aboriginal patients. Aboriginal patients had significantly more advanced disease at diagnosis (p=0.008). Aboriginal compared with non-Aboriginal patients were less likely to present with stage I (7.5% vs 22.5%), stage II (11.3% vs 13.8%), or stage III disease (13.8% vs 17.1%), and more likely to present with more advanced stage IVA (42.5% vs 34.6%), stage IVB (15% vs 7.1%), or stage IVC (10% vs 5%) disease (p=0.008). Number of regions of disease involvement was higher in Aboriginal patients (median 3, mean 3.64, range 1-10) compared with non-Aboriginal patients (median 2, mean 2.80, range 1-12). Conclusion: Aboriginal patients had a significantly higher incidence of lung metastases, and significantly more frequent involvement of ipsilateral and contralateral neck lymph nodes. Aboriginal patients also had significantly more advanced disease at presentation with a higher stage at diagnosis. We are performing further analyses to investigate explanations for these findings.

Keywords: head and neck cancer, Aboriginal, metastases, locoregional, pattern of relapse, sites of disease

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Authors: Frederick Huie, Lauren Fusfeld, William Burchenal, Scott Howell, Alyssa McVey, Thomas F. Goss

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Alzheimer’s Disease (AD) is a degenerative brain disease characterized by memory loss and cognitive decline that presents a substantial economic burden for patients and health insurers in the US. This study evaluates the payer budget impact of the DISCERN™ test in the diagnosis and management of patients with symptoms of dementia evaluated for AD. DISCERN™ comprises three assays that assess critical factors related to AD that regulate memory, formation of synaptic connections among neurons, and levels of amyloid plaques and neurofibrillary tangles in the brain and can provide a quicker, more accurate diagnosis than tests in the current diagnostic pathway (CDP). An Excel-based model with a three-year horizon was developed to assess the budget impact of DISCERN™ compared with CDP in a Medicare Advantage plan with 1M beneficiaries. Model parameters were identified through a literature review and were verified through consultation with clinicians experienced in diagnosis and management of AD. The model assesses direct medical costs/savings for patients based on the following categories: •Diagnosis: costs of diagnosis using DISCERN™ and CDP. •False Negative (FN) diagnosis: incremental cost of care avoidable with a correct AD diagnosis and appropriately directed medication. •True Positive (TP) diagnosis: AD medication costs; cost from a later TP diagnosis with the CDP versus DISCERN™ in the year of diagnosis, and savings from the delay in AD progression due to appropriate AD medication in patients who are correctly diagnosed after a FN diagnosis.•False Positive (FP) diagnosis: cost of AD medication for patients who do not have AD. A one-way sensitivity analysis was conducted to assess the effect of varying key clinical and cost parameters ±10%. An additional scenario analysis was developed to evaluate the impact of individual inputs. In the base scenario, DISCERN™ is estimated to decrease costs by $4.75M over three years, equating to approximately $63.11 saved per test per year for a cohort followed over three years. While the diagnosis cost is higher with DISCERN™ than with CDP modalities, this cost is offset by the higher overall costs associated with CDP due to the longer time needed to receive a TP diagnosis and the larger number of patients who receive a FN diagnosis and progress more rapidly than if they had received appropriate AD medication. The sensitivity analysis shows that the three parameters with the greatest impact on savings are: reduced sensitivity of DISCERN™, improved sensitivity of the CDP, and a reduction in the percentage of disease progression that is avoided with appropriate AD medication. A scenario analysis in which DISCERN™ reduces the utilization for patients of computed tomography from 21% in the base case to 16%, magnetic resonance imaging from 37% to 27% and cerebrospinal fluid biomarker testing, positive emission tomography, electroencephalograms, and polysomnography testing from 4%, 5%, 10%, and 8%, respectively, in the base case to 0%, results in an overall three-year net savings of $14.5M. DISCERN™ improves the rate of accurate, definitive diagnosis of AD earlier in the disease and may generate savings for Medicare Advantage plans.

Keywords: Alzheimer’s disease, budget, dementia, diagnosis.

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Authors: Huong M. T. Nguyen, Hoa A. P. Nguyen, Mai P. T. Nguyen, Ngoc D. Ngo, Van T. Ta, Hai T. Le, Chi V. Phan

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Wilson’s disease (WD) is an autosomal recessive disorder of the copper metabolism, which is caused by a mutation in the copper-transporting P-type ATPase (ATP7B). The mechanism of this disease is the failure of hepatic excretion of copper to bile, and leads to copper deposits in the liver and other organs. The ATP7B gene is located on the long arm of chromosome 13 (13q14.3). This study aimed to investigate the gene mutation in the Vietnamese patients with WD, and make a presymptomatic diagnosis for their familial members. Forty-three WD patients and their 65 siblings were identified as having ATP7B gene mutations. Genomic DNA was extracted from peripheral blood samples; 21 exons and exon-intron boundaries of the ATP7B gene were analyzed by direct sequencing. We recognized four mutations ([R723=; H724Tfs*34], V1042Cfs*79, D1027H, and IVS6+3A>G) in the sum of 20 detectable mutations, accounting for 87.2% of the total. Mutation S105* was determined to have a high rate (32.6%) in this study. The hotspot regions of ATP7B were found at exons 2, 16, and 8, and intron 14, in 39.6 %, 11.6 %, 9.3%, and 7 % of patients, respectively. Among nine homozygote/compound heterozygote siblings of the patients with WD, three individuals were determined as asymptomatic by screening mutations of the probands. They would begin treatment after diagnosis. In conclusion, 20 different mutations were detected in 43 WD patients. Of this number, four novel mutations were explored, including [R723=; H724Tfs*34], V1042Cfs*79, D1027H, and IVS6+3A>G. The mutation S105* is the most prevalent and has been considered as a biomarker that can be used in a rapid detection assay for diagnosis of WD patients. Exons 2, 8, and 16, and intron 14 should be screened initially for WD patients in Vietnam. Based on risk profile for WD, genetic testing for presymptomatic patients is also useful in diagnosis and treatment.

Keywords: ATP7B gene, mutation detection, presymptomatic diagnosis, Vietnamese Wilson’s disease

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Authors: Ghada E. Esheba, Ghadeer F. Alharthi, Duaa A. Alhejaili, Rawan E. Hudairy, Wafaa A. Altaezi, Raghad M. Alhejaili

Abstract:

Background: Inflammatory bowel disease (IBD) consists of two specific gastrointestinal disorders: ulcerative colitis (UC) and Crohn's disease (CD). Despite their distinct natures, these two diseases share many similar etiologic, clinical and pathological features, as a result, their accurate differential diagnosis may sometimes be difficult. Correct diagnosis is important because surgical treatment and long-term prognosis differ from UC and CD. Aim: This study aims to study the characteristic clinicopathological features which help in the differential diagnosis between UC and CD, and assess the disease activity in ulcerative colitis. Materials and methods: This study was carried out on 50 selected cases. The cases included 27 cases of UC and 23 cases of CD. All the cases were examined using H& E and immunohistochemically for bcl-2 expression. Results: Characteristic features of UC include: decrease in mucous content, irregular or villous surface, crypt distortion, and cryptitis, whereas the main cardinal histopathological features seen in CD were: epitheloid granuloma, transmural chronic inflammation, absence of mucin depletion, irregular surface, or crypt distortion. 3 cases of UC were found to be associated with dysplasia. UC mucosa contains fewer Bcl-2+ cells compared with CD mucosa. Conclusion: This study using multiple parameters such clinicopathological features and Bcl-2 expression as studied by immunohistochemical stain, helped to gain an accurate differentiation between UC and CD. Furthermore, this work spotted the light on the activity and different grades of UC which could be important for the prediction of relapse.

Keywords: Crohn's disease, dysplasia, inflammatory bowel disease, ulcerative colitis

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Authors: A. Al Yousef, A. Toulon, L. Petit, S. Fraitag, F. Ruemmele, S. Hadj-Rabia, C. Bodemer

Abstract:

Cutaneous Crohn’s disease (CCD) refers to an extremely rare granulomatous inflammation of the skin that is non-contiguous to the bowel tract. These cutaneous lesions can occur prior to, concurrent with, or after the gastrointestinal manifestations. In adults, CCD most frequently occurs in the setting of well-documented intestinal disease. Only 20% of cases occur prior to its development. Review of CCD in children, reveals that 86% of cases (24 of 28) occurring in patients without a known diagnosis of intestinal Crohn’s disease. Overall, the genitalia was the most commonly involved location, representing 21 of the 28 cases with 16 vulvar and 5 penile/scrotal lesions.

Keywords: Crohn’s disease, cutaneous manifestations, children, atypical axillary involvement

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Authors: Mohamed Shafi Mahboob Ali

Abstract:

Male breast cancer (MBC) is a rare entity with overall cases reported less than 1%. However, the incidence of MBC is regularly rising every year. Due to the lack of data on MBC, diagnosis and treatment are tailored to female breast cancer. MBC risk increases with age and is usually diagnosed ten years late as the disease progression is slow compared to female breast cancer (FBC). The most common feature of MBC is an intra-ductal variant, and often, upon diagnosis, the stage of the disease is already advanced. The Prognosis of MBC is often flawed, but new treatment modalities are emerging with the current knowledge and advancement. We presented a series of male breast cancer in our center, highlighting the clinicopathological, radiological and treatment options.

Keywords: male, breast, cancer, clinicopathology, ultrasound, CT scan

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Authors: Mualla McManus, Jenna Luche Thaye

Abstract:

World Health Organisation International Classification of Disease codes were created to define disease including infections in order to guide and educate diagnosticians. Most infectious diseases such as syphilis are clearly defined by their ICD 10 codes and aid/help to educate the clinicians in syphilis diagnosis and treatment globally. However, current ICD 10 codes for relapsing fever Borreliosis and Lyme disease are less clearly defined and can impede appropriate diagnosis especially if the clinician is not familiar with the symptoms of these infectious diseases. This is despite substantial number of scientific articles published in peer-reviewed journals about relapsing fever and Lyme disease. In the USA there are estimated 380,000 people annually contacting Lyme disease, more cases than breast cancer and 6x HIV/AIDS cases. This represents estimated 0.09% of the USA population. If extrapolated to the global population (7billion), 0.09% equates to 63 million people contracting relapsing fever or Lyme disease. In many regions, the rate of contracting some form of infection from tick bite may be even higher. Without accurate and appropriate diagnostic codes, physicians are impeded in their ability to properly care for their patients, leaving those patients invisible and marginalized within the medical system and to those guiding public policy. This results in great personal hardship, pain, disability, and expense. This unnecessarily burdens health care systems, governments, families, and society as a whole. With accurate diagnostic codes in place, robust data can guide medical and public health research, health policy, track mortality and save health care dollars. Better defined ICD codes are the way forward in educating the diagnosticians about relapsing fever and Lyme diseases.

Keywords: WHO ICD codes, relapsing fever, Lyme diseases, World Health Organisation

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Authors: Rachel Shukrun, Szilvia Baron, Victoria Fidel, Anna Shusterman, Osnat Sher, Netanya Kollender, Dror Levin, Yair Peled, Yair Gortzak, Yoav Ben-Shahar, Revital Caspi, Sagi Gordon, Michal Manisterski, Ronit Elhasid

Abstract:

Ewing sarcoma (EWS) is a highly aggressive cancer with a survival rate of 70–80% for patients with localized disease and under 30% for those with metastatic disease. Tumor-infiltrating neutrophils (TIN) can generate extracellular net-like DNA structures known as neutrophil extracellular traps (NETs). However, little is known about the presence and prognostic significance of tumor-infiltrating NETs in EWS. Herein, we investigated 46 patients diagnosed with EWS and treated in the Tel Aviv Medical Center between 2010 and 2021. TINs and NETs were identified in diagnostic biopsies of EWS by immunofluorescent. In addition, NETs were investigated in neutrophils isolated from peripheral blood samples of EWS patients at diagnosis and following neoadjuvant chemotherapy. The relationships between the presence of TINs and NETs, pathological and clinical features, and outcomes were analyzed. Our results demonstrate that TIN and NETs at diagnosis were higher in EWS patients with metastatic disease compared to those with local disease. High NETs formation at diagnosis predicted poor response to neo-adjuvant chemotherapy, relapse, and death from disease (P < .05). NETs formation in peripheral blood samples at diagnosis was significantly elevated among patients with EWS compared to pediatric controls and decreased significantly following neoadjuvant chemotherapy. In conclusion, NETs formation seems to have a role in the EWS immune microenvironment. Their presence can refine risk stratification, predict chemotherapy resistance and survival, and serve as a therapeutic target in patients with EWS.

Keywords: Ewing sarcoma, tumor microenvironment, neutrophil, neutrophil extracellular traps (NETs), prognosis

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