Eugenio Del Prete1,2 and Angelo Facchiano1*
1Institute of Food Science, Italy
2Institute for Applied Mathematics “Mauro Picone”, Italy
*Corresponding author: Angelo Facchiano, Institute of Food Science, Italy
Submission: March 4, 2020; Published: November 09, 2020
ISSN: 2689-2707 Volume 2 Issue 4
A radiology report serves as an intermediary between a radiologist and referring clinician for suggesting appropriate treatment to the patients, aimed at better healthcare management. It is essentially a tool that assists radiologists in conveying their input to the patients and clinicians regarding positive or negative findings on a case. The objective of this paper is to discuss and propose Radiology Information & Reporting System (RIRS), highlight challenges governing its implementation and suggest way forwards towards its effective implementation across the public sector tertiary care institutions of Pakistan. In the end, it is concluded that the proposed RIRS would potentially offer enormous benefits in terms of cost savings, reporting accuracy, faster processing and operational efficiency as opposed to the conventionally available manual radiology reporting procedures and systems.
Keywords: Radiology Information & Reporting System (RIRS); Radiological Diagnostics & Imaging; Radiology Reporting; Developing Countries
Emerging techniques in molecular biology open grand challenges in the development
of new diagnostic procedures. Omics techniques include the capability of analysing a large
number of molecules with a single analysis, and continuous development of technology offers
the chance to perform the analysis at low cost, so that it becomes possible the screening
of large number of patients with a given pathology, looking for novel biomarkers [1-4].
Integration of experimental studies, computational approaches, and bioinformatics tools
represents an effective processing of the omics information, especially for multi-omics
studies, to investigate complex pathologies . Further integration with clinical data opens
to the precision medicine perspective. In this mini review, we describe how the integration of
computational and bioinformatics methods can support the investigation on human diseases
and drive to the most effective clinical research.
Clinical bioinformatics take advantage of bioinformatics methods and technologies for the elaboration of clinical data. Physicians, clinicians, and other researchers with expertise in the management of patients collect heterogeneous data, with the aim of achieving underlying information about a disease, usually not trivial or easy to obtain with standard analyses. Such structured information can help in studying not only a single pathology, but also other diseases (comorbidities or not) connected with the starting one . The evolution of clinical research involves also the application of bioinformatics and computational biochemistry for drug discovery and development . Virtual screening is a computational approach aimed to screen a database of molecules searching for potential biological activity  and it is object of interest in bioinformatics for the development of appropriate tools , as well as it represents an active field for the search of molecules of interest for drug development [10-13]. Machine learning approaches have been developed for novel drug discovery . Molecular simulations are strongly based on computational approaches and offer many examples of success in supporting the drug development steps [15,16]. Molecular simulations are also useful in investigating molecular mechanisms underlying pathologies, also in the case of rare diseases, that is of particular interest in our laboratory [17-24]. The low number of patients make difficult to find resources to support studies, so that in many cases rare diseases are also indicated as orphan diseases. In these cases, the bioinformatics and bio-computational approach offers the opportunity to investigate disease as well clinical cases [20,21,23] with an approach that is an example of personalized medicine. Clinical bioinformatics represents an interface between healthcare data and the disciplines suitable for their analysis (statistics, mathematics, informatics, molecular biology, biochemistry, and so on), essential to cope with the goal of personalized medicine. From a biological point of view, omics data and technologies, together with research strategies (such as cancer research and system biology), are included in the field of clinical bioinformatics . The increasing importance of clinical bioinformatics in medical laboratories is related to the diagnosis of complex disease: the integration of omics data with patients’ Electronic Health Record (EHR) helps physicians in improving diagnoses and designing suitable therapies .
Many factors contribute in extracting knowledge from stored data, useful in finding new evidences on a given pathology. Highthroughput experimental techniques (e.g. Next Generation Sequencing, NGS) provide a huge amount of data [27,28] to be integrated with the clinical information. Discovering new molecular biomarkers complete the knowledge and strengthen the strategy of approaching a particular disease [3-5]. Scientific and structured bibliography reassesses the importance of semantic algorithms for the automated extraction of information from dedicated documents [29,30]. The decrease in costs of technologies, making protocols and machines available for many clinical and research centres. An example of a complete clinical bioinformatics pipeline  starts from the collection of the patients by clinical trials, the extraction of biological samples and the conversion in omics data (by means of microarray technologies) provide the material for the in silico postprocessing, by the study of the differential expressed genes (DEG), necessary for the discovery of new biomarkers related to a disease.
Translational bioinformatics is strictly connected to clinical bioinformatics, with a particular reference on storage, analysis and interpretation of biomedical data from an informatics point of view, in order to ease all the health management . The cooperation of both fields is targeted to the personalized medicine. Many challenges of the personalized medicine are still under fixing: treating largescale genomic data, interpreting the effect of variations and the differences in biological functions, creating robust models for complex systems, converting evidences in medical practice, and so on . The ‘fourth paradigm’, data-intensive science, is oriented towards personalized medicine, since nowadays in silico analyses are feasible all over the world, with affordable computational power and suitable infrastructures, together with the possibility of collaboration among the scientists, and the availability of data and results from public online repositories .
From these perspectives, personalized medicine can be encouraged by integrating different kinds of omics data, helping in the prediction of phenotypic outcomes. The main omics areas are genomics, transcriptomics, proteomics and metabolomics. Genomics studies genome biological function, genes distribution on the genome, modifications in their expression, and relationships with biological pathways, towards the increase of the therapeutic efficiency. Clusterization of cells and tissues by expression profiles is based on transcriptomics, such as in single-cell experiments, in order to classify diseases by their similarity (e.g. by means of microarray technology and Polymerase Chain Reaction protocol). Proteomics studies the proteins with their relationships in biological pathways, the modifications between structure and functionality, and the interactions among them, with goals such as drug discovery or discrimination of patients by mass spectrometry data. Metabolomics study the set of metabolites, key regulators in system homeostasis, in specific conditions, with emphasis on changes caused by genetic or environmental variations, analysing the profiles by technologies such as Gas Chromatography Mass Spectrometry (GC-MS) or Nuclear Magnetic Resonance (NMR) spectroscopy [35,36].
Cleaning, integrating and analysing multi-omics datasets are important tasks in improving the personalized medicine and need more and more updated tools and algorithms, to find intra-layer and inter-layer connections among different omics, with references to biological systems and, consequentially, to clinical evidences. Currently, in silico integration methods are mainly divided into unsupervised and supervised methods, with techniques focused on dimensionality reduction, classification, clustering, variable selection and network representation [37-39]. The integration of omics data is helpful in cancer analysis [40,41] and in tissue analysis, even considering imaging data . From a statistical, mathematical and informatics point of view, the work in  well explains how the concept of integration can be enlarged in different directions, in terms of omics data (P-integration) or in terms of patients (N-integration), highlighted how the same problem can be afforded by considering different features or the availability of the data. From this perspective, the patient can be an active part of the integration process, with all the information stored in the form of metadata, that is available for a deeper post-processing analysis of the obtained results. It is obvious how the metadata should be standardized, creating an ontology where possible [44,45], not only to ease the availability for the physicians and clinicians, but also for the researchers that need these features, which are important in studies as clustering, classification and outliers discovery. This process has important implications also in the research reproducibility .
A clinical bioinformatics pipeline is useful in the analysis of a complex disease. The definition of a complex disease is not trivial, especially because many chronic diseases have not Mendelian behaviour. A representative list for describing a complex disease can be caused by a combination of different factors (genetic, environmental and lifestyle); not simple patterns of inheritance; difficulty in predicting insurgence and transmission; complicated treatment. For example, two patients can have different mutations in their genomes, thus the challenge is to extract the phenotypes and unravel specific casual mutations with association studies . An example of clinical bioinformatics pipeline applied to a complex disease is reported in , with the analysis of celiac disease together with some specific comorbidities. In this study, microarray data available online are selected in order to extract DEGs from transcriptomics data, and Gene Set Enrichment Analysis (GSEA) is performed to connect the most important DEGs to Gene Ontology (GO) terms, extracting the biological process (BP) mainly related to the pathology. Moreover, the GO and Disease Ontology (DO) trees are compared by semantic similarity, to show which datasets (celiac disease or other autoimmune diseases) are more correlated. Finally, the pathways analysis is used to correlated DEGs to Kyoto Encyclopaedia of Genes and Genomes (KEGG) database, to search for biological pathways with strong relationships between celiac disease and its comorbidities.
Novel efforts are applied to omics (and integration) in reducing computational time, providing and storing big data, through modern workflows and pipelines, which cover all the study from the in vivo/in vitro experiment to the in-silico prediction and analysis [49,50]. The standardization of different approaches for the insilico elaboration is important in each subsequent step (getting and cleaning data, aggregation of data, statistical analysis and validation, presentation of result) . Nevertheless, a cooperation between the ‘bio’ area (biology, medicine, chemistry) and the ‘info’ area (mathematics, physics, statistics, informatics, engineering) is compulsory, in terms of knowledge and collaboration among the researchers. Fortunately, many programming languages are clinical/biological data-oriented, providing ad hoc libraries for specific problems: a valuable example is R (and the related Bioconductor repository) [52-54], with lots of libraries conceived for the sake of the reproducible research. For example, the class of data ‘Summarized Experiment’  was conceived to carry both expression values and patients’ metadata, and library such as ‘TCGAbiolinks’  provides the possibility to download, arrange, analyze, and integrate with clinical information, cancer data from the Genomic Data Commons (GDC) Data Portal online repository .
In conclusion, clinical bioinformatics connect bioinformatics approaches to clinical data from patients (and healthy control) in order to extract principal features (e.g. biomarkers) that represent a sort of fingerprint for the subject. Such features are important for the prediction, diagnosis and treatment of the disease, with a view to personalized medicine, towards the last goal of treating every single patient on the base of the abovementioned specific evidences. Integration of this area with telemedicine and e-health services can represent an effective step towards personalized medicine approaches, especially taking into account the possibility to reduce the time in curing the patient, and to help physicians and clinicians in taking decisions using information, which has an automated extraction and a fast availability on electronical devices.
© 2020 and Angelo Facchiano. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and build upon your work non-commercially.