BackgroundCADe and CADx systems for the detection and diagnosis of lung cancer have been important areas of research in recent decades. However, these areas are being worked on separately. CADe systems do not present the radiological characteristics of tumors, and CADx systems do not detect nodules and do not have good levels of automation. As a result, these systems are not yet widely used in clinical settings.MethodsThe purpose of this article is to develop a new system for detection and diagnosis of pulmonary nodules on CT images, grouping them into a single system for the identification and characterization of the nodules to improve the level of automation. The article also presents as contributions: the use of Watershed and Histogram of oriented Gradients (HOG) techniques for distinguishing the possible nodules from other structures and feature extraction for pulmonary nodules, respectively. For the diagnosis, it is based on the likelihood of malignancy allowing more aid in the decision making by the radiologists. A rule-based classifier and Support Vector Machine (SVM) have been used to eliminate false positives.ResultsThe database used in this research consisted of 420 cases obtained randomly from LIDC-IDRI. The segmentation method achieved an accuracy of 97 % and the detection system showed a sensitivity of 94.4 % with 7.04 false positives per case. Different types of nodules (isolated, juxtapleural, juxtavascular and ground-glass) with diameters between 3 mm and 30 mm have been detected. For the diagnosis of malignancy our system presented ROC curves with areas of: 0.91 for nodules highly unlikely of being malignant, 0.80 for nodules moderately unlikely of being malignant, 0.72 for nodules with indeterminate malignancy, 0.67 for nodules moderately suspicious of being malignant and 0.83 for nodules highly suspicious of being malignant.Conclusions From our preliminary results, we believe that our system is promising for clinical applications assisting radiologists in the detection and diagnosis of lung cancer.
IntroductionThe goal of this paper is to present a critical review of major Computer-Aided Detection systems (CADe) for lung cancer in order to identify challenges for future research. CADe systems must meet the following requirements: improve the performance of radiologists providing high sensitivity in the diagnosis, a low number of false positives (FP), have high processing speed, present high level of automation, low cost (of implementation, training, support and maintenance), the ability to detect different types and shapes of nodules, and software security assurance.MethodsThe relevant literature related to “CADe for lung cancer” was obtained from PubMed, IEEEXplore and Science Direct database. Articles published from 2009 to 2013, and some articles previously published, were used. A systemic analysis was made on these articles and the results were summarized.DiscussionBased on literature search, it was observed that many if not all systems described in this survey have the potential to be important in clinical practice. However, no significant improvement was observed in sensitivity, number of false positives, level of automation and ability to detect different types and shapes of nodules in the studied period. Challenges were presented for future research.ConclusionsFurther research is needed to improve existing systems and propose new solutions. For this, we believe that collaborative efforts through the creation of open source software communities are necessary to develop a CADe system with all the requirements mentioned and with a short development cycle. In addition, future CADe systems should improve the level of automation, through integration with picture archiving and communication systems (PACS) and the electronic record of the patient, decrease the number of false positives, measure the evolution of tumors, evaluate the evolution of the oncological treatment, and its possible prognosis.
Over the last decades, microRNAs (miRNAs) have emerged as important molecules associated with the regulation of gene expression in humans and other organisms, expanding the strategies available to diagnose and handle several diseases. This paper presents a systematic review of literature of miRNAs related to cancer development and explores the main techniques used to quantify these molecules and their limitations as screening strategy. The bibliographic research was conducted using the online databases, PubMed, Google Scholar, Web of Science, and Science Direct searching the terms “microRNA detection”, “miRNA detection”, “miRNA and prostate cancer”, “miRNA and cervical cancer”, “miRNA and cervix cancer”, “miRNA and breast cancer”, and “miRNA and early cancer diagnosis”. Along the systematic review over 26,000 published papers were reported, and 252 papers were returned after applying the inclusion and exclusion criteria, which were considered during this review. The aim of this study is to identify potential miRNAs related to cancer development that may be useful for early cancer diagnosis, notably in the breast, prostate, and cervical cancers. In addition, we suggest a preliminary top 20 miRNA panel according to their relevance during the respective cancer development. Considering the progressive number of new cancer cases every year worldwide, the development of new diagnostic tools is critical to refine the accuracy of screening tests, improving the life expectancy and allowing a better prognosis for the affected patients.
Main Electronic health record (EHR) applications are digital versions of paper-based patient health information. Traditionally, medical records are made on paper. However, nowadays, advances in information and communication technology have made it possible to change medical records from paper to EHR. Therefore, preserving user data privacy is extremely important in healthcare environments. The main challenges are providing ways to make EHR systems increasingly capable of ensuring data privacy and at the same time not compromising the performance and interoperability of these systems. Subject and methods This systematic mapping study intends to investigate the current research on security and privacy requirements in EHR systems and identify potential research gaps in the literature. The main challenges are providing ways to make EHR systems increasingly capable of ensuring data privacy, and at the same time, not compromising the performance and interoperability of these systems. Our research was carried out in the Scopus database, the largest database of abstracts and citations in the literature with peer review. Results We have collected 848 articles related to the area. After disambiguation and filtering, we selected 30 articles for analysis. The result of such an analysis provides a comprehensive view of current research. Conclusions We can highlight some relevant research possibilities. First, we noticed a growing interest in privacy in EHR research in the last 6 years. Second, blockchain has been used in many EHR systems as a solution to achieve data privacy. However, it is a challenge to maintain traceability by recording metadata that can be mapped to private data of the users applying a particular mapping function that can be hosted outside the blockchain. Finally, the lack of a systematic approach between EHR solutions and existing laws or policies leads to better strategies for developing a certification process for EHR systems.
RFID is a technology being adopted in many business fields, especially in the medical field. This work has the objective to present a system for automation of a hospital clinical analysis laboratory. This system initially uses contactless smart cards to store patient's data and for authentication of hospital employees in the system. The proposed system also uses RFID tags stuck to containers containing patient's collected samples for the correct identification of the patient who gave away the samples. This work depicts a hospital laboratory workflow, presents the system modeling and deals with security matters related to information stored in the smart cards.
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