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Hernández L, Estévez-Priego E, López-Pérez L, Fernanda Cabrera-Umpiérrez M, Arredondo MT, Fico G. HeNeCOn: An ontology for integrative research in Head and Neck cancer. Int J Med Inform 2024; 181:105284. [PMID: 37981440 DOI: 10.1016/j.ijmedinf.2023.105284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 07/14/2023] [Accepted: 11/01/2023] [Indexed: 11/21/2023]
Abstract
BACKGROUND Head and Neck Cancer (HNC) has a high incidence and prevalence in the worldwide population. The broad terminology associated with these diseases and their multimodality treatments generates large amounts of heterogeneous clinical data, which motivates the construction of a high-quality harmonization model to standardize this multi-source clinical data in terms of format and semantics. The use of ontologies and semantic techniques is a well-known approach to face this challenge. OBJECTIVE This work aims to provide a clinically reliable data model for HNC processes during all phases of the disease: prognosis, treatment, and follow-up. Therefore, we built the first ontology specifically focused on the HNC domain, named HeNeCOn (Head and Neck Cancer Ontology). METHODS First, an annotated dataset was established to provide a formal reference description of HNC. Then, 170 clinical variables were organized into a taxonomy, and later expanded and mapped to formalize and integrate multiple databases into the HeNeCOn ontology. The outcomes of this iterative process were reviewed and validated by clinicians and statisticians. RESULTS HeNeCOn is an ontology consisting of 502 classes, a taxonomy with a hierarchical structure, semantic definitions of 283 medical terms and detailed relations between them, which can be used as a tool for information extraction and knowledge management. CONCLUSION HeNeCOn is a reusable, extendible and standardized ontology which establishes a reference data model for terminology structure and standard definitions in the Head and Neck Cancer domain. This ontology allows handling both current and newly generated knowledge in Head and Neck cancer research, by means of data linking and mapping with other public ontologies.
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Affiliation(s)
- Liss Hernández
- Universidad Politécnica de Madrid-Life Supporting Technologies Research Group, ETSIT, 28040 Madrid, Spain
| | - Estefanía Estévez-Priego
- Universidad Politécnica de Madrid-Life Supporting Technologies Research Group, ETSIT, 28040 Madrid, Spain
| | - Laura López-Pérez
- Universidad Politécnica de Madrid-Life Supporting Technologies Research Group, ETSIT, 28040 Madrid, Spain
| | | | - María Teresa Arredondo
- Universidad Politécnica de Madrid-Life Supporting Technologies Research Group, ETSIT, 28040 Madrid, Spain
| | - Giuseppe Fico
- Universidad Politécnica de Madrid-Life Supporting Technologies Research Group, ETSIT, 28040 Madrid, Spain.
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Effects of Berberine on Liver Cancer. Nat Prod Commun 2022. [DOI: 10.1177/1934578x221102032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Liver cancer, otherwise known as hepatocellular carcinoma, is a chronic disease condition with an excessive deposition and growth of malignant cells in the body. The high incidence and prevalence rates of liver cancer continue to be problems, as well as its poor prognosis and therapeutic limitations involving severe drug adverse reactions linked to the use of synthetic chemotherapeutic compounds. Continuous experimental studies, as well as utilization of pure herbal-based compounds, are essential towards finding more potent cures for liver cancer. Natural bioactive compounds, particularly alkaloids (eg, berberine), have been shown to be highly beneficial in the treatment of various diseases. Berberine (BBR), an isoquinoline alkaloid, is obtained from stem, bark, roots, rhizomes, and leaves of several medicinal plants, including Berberis species. It is commonly synthesized from the benzyltetrahydroisoquinoline system with the incorporation of an additional carbon atom as a bridge. The multiple attributes of BBR involving effective inhibitory and cytotoxic actions against the proliferation of cancer cells have been demonstrated. The use of BBR in experimental studies (in vivo and in vitro) for over a decade for liver cancer treatment has proven to be highly effective, safe, and potent. Until now, the poor solubility of BBR remains one of the contributing factors leading to its minimal clinical bioavailability. Therefore, BBR could serve as a prospective drug candidate in the future towards drug formulation for liver cancer treatment. The relevant information regarding this review was obtained electronically through the use of databases such as PubMed, Google Scholar, Springer, Hindawi, Embase, Web of Science, and China National Knowledge Infrastructure. All the aforementioned databases were searched from 1981 to 2020. This literature represents an update of previous review papers discussing the various positive pharmacological and mechanistic effects (oxidative stress regulation, inflammation reduction, apoptosis activation, overcoming drug resistance, and metastasis inhibition) of BBR for liver cancer treatment, which would be of great significance to drug development and clinical research.
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Silva MC, Eugénio P, Faria D, Pesquita C. Ontologies and Knowledge Graphs in Oncology Research. Cancers (Basel) 2022; 14:cancers14081906. [PMID: 35454813 PMCID: PMC9029532 DOI: 10.3390/cancers14081906] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 03/25/2022] [Accepted: 04/07/2022] [Indexed: 11/16/2022] Open
Abstract
The complexity of cancer research stems from leaning on several biomedical disciplines for relevant sources of data, many of which are complex in their own right. A holistic view of cancer—which is critical for precision medicine approaches—hinges on integrating a variety of heterogeneous data sources under a cohesive knowledge model, a role which biomedical ontologies can fill. This study reviews the application of ontologies and knowledge graphs in cancer research. In total, our review encompasses 141 published works, which we categorized under 14 hierarchical categories according to their usage of ontologies and knowledge graphs. We also review the most commonly used ontologies and newly developed ones. Our review highlights the growing traction of ontologies in biomedical research in general, and cancer research in particular. Ontologies enable data accessibility, interoperability and integration, support data analysis, facilitate data interpretation and data mining, and more recently, with the emergence of the knowledge graph paradigm, support the application of Artificial Intelligence methods to unlock new knowledge from a holistic view of the available large volumes of heterogeneous data.
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Messaoudi R, Jaziri F, Mtibaa A, Gargouri F, Vacavant A. Ontology-Driven Approach for Liver MRI Classification and HCC Detection. INT J PATTERN RECOGN 2021. [DOI: 10.1142/s0218001421600077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Reading and interpreting the medical image still remains the most challenging task in radiology. Through the important achievement of deep Convolutional Neural Networks (CNN) in the context of medical image classification, various clinical applications have been provided to detect lesions from Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans. In the diagnosis process for the liver cancer from Dynamic Contrast-Enhanced MRI (DCE-MRI), radiologists consider three phases during contrast injection: before injection, arterial phase, and portal phase for instance. Even if the contrast agent helps in enhancing the tumoral tissues, the diagnosis may be very difficult due to the possible low contrast and pathological tissues surrounding the tumors (cirrhosis). Alongside, in the medical field, ontologies have proven their effectiveness to solve several clinical problems such as offering shareable terminologies, vocabularies, and databases. In this article, we propose a multi-label CNN classification approach based on a parallel preprocessing algorithm. This algorithm is an extension of our previous work cited in the International Conference on Pattern Recognition and Artificial Intelligence (ICPRAI) 2020. The aim of our approach is to ameliorate the detection of HCC lesions and to extract more information about the detected tumor such as the stage, the localization, the size, and the type thanks to the use of ontologies. Moreover, the integration of such information has improved the detection process. In fact, experiments conducted by testing with real patient cases have shown that the proposed approach reached an accuracy of 93% using MRI patches of [Formula: see text] pixels, which is an improvement compared with our previous works.
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Affiliation(s)
- Rim Messaoudi
- MIRACL Laboratory, University of Sfax, Sfax, Tunisia
- Institut Pascal, Université Clermont Auvergne, UMR6602, CNRS/UCA/SIGMA, 63171 Aubière, France
| | - Faouzi Jaziri
- Institut Pascal, Université Clermont Auvergne, UMR6602, CNRS/UCA/SIGMA, 63171 Aubière, France
| | - Achraf Mtibaa
- MIRACL Laboratory, University of Sfax, Sfax, Tunisia
- National School of Electronic and Telecommunications, University of Sfax, Sfax, Tunisia
| | - Faïez Gargouri
- MIRACL Laboratory, University of Sfax, Sfax, Tunisia
- Higher Institute of Computer Science and Multimedia, University of Sfax, Sfax, Tunisia
| | - Antoine Vacavant
- Institut Pascal, Université Clermont Auvergne, UMR6602, CNRS/UCA/SIGMA, 63171 Aubière, France
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Prediction of Bladder Cancer Treatment Side Effects Using an Ontology-Based Reasoning for Enhanced Patient Health Safety. INFORMATICS 2021. [DOI: 10.3390/informatics8030055] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Predicting potential cancer treatment side effects at time of prescription could decrease potential health risks and achieve better patient satisfaction. This paper presents a new approach, founded on evidence-based medical knowledge, using as much information and proof as possible to help a computer program to predict bladder cancer treatment side effects and support the oncologist’s decision. This will help in deciding treatment options for patients with bladder malignancies. Bladder cancer knowledge is complex and requires simplification before any attempt to represent it in a formal or computerized manner. In this work we rely on the capabilities of OWL ontologies to seamlessly capture and conceptualize the required knowledge about this type of cancer and the underlying patient treatment process. Our ontology allows case-based reasoning to effectively predict treatment side effects for a given set of contextual information related to a specific medical case. The ontology is enriched with proofs and evidence collected from online biomedical research databases using “web crawlers”. We have exclusively designed the crawler algorithm to search for the required knowledge based on a set of specified keywords. Results from the study presented 80.3% of real reported bladder cancer treatment side-effects prediction and were close to really occurring adverse events recorded within the collected test samples when applying the approach. Evidence-based medicine combined with semantic knowledge-based models is prominent in generating predictions related to possible health concerns. The integration of a diversity of knowledge and evidence into one single integrated knowledge-base could dramatically enhance the process of predicting treatment risks and side effects applied to bladder cancer oncotherapy.
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Bayoudhi L, Sassi N, Jaziri W. An Overview of Biomedical Ontologies for Pandemics and Infectious Diseases Representation. PROCEDIA COMPUTER SCIENCE 2021; 192:4249-4258. [PMID: 34868401 PMCID: PMC8629358 DOI: 10.1016/j.procs.2021.09.201] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Several infectious diseases and pandemics have so far emerged. Pandemics are by nature rapidly evolving. In this context, COVID-19 cases, seen recently in a growing number of countries around the world, have been increasing exponentially. So, researchers and responsible actors should take quick decisions to mitigate the spread of such diseases. To do so, several computer science solutions, including ontologies, have been proposed to cope with these issues and save humanity. The ontology is the key formalism which allows modelling knowledge along with its semantics in a formal way. Indeed, the ontology provides unambiguous definitions of a discourse’s domain terms in a machine understandable way. Particularly, biomedical ontologies have ever been developed to capture and represent pandemics and infectious diseases. In this context, this paper aims to scrutinize and study these state-of-the-art ontologies.
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Affiliation(s)
| | - Najla Sassi
- Miracl Laboratory, Universiy of Sfax, Sfax, Tunisia
- CCSE, Taibah University, Medina, Saudi Arabia
| | - Wassim Jaziri
- Miracl Laboratory, Universiy of Sfax, Sfax, Tunisia
- CCSE, Taibah University, Medina, Saudi Arabia
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Healthcare Applications of Artificial Intelligence and Analytics: A Review and Proposed Framework. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10186553] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Healthcare is considered as one of the most promising application areas for artificial intelligence and analytics (AIA) just after the emergence of the latter. AI combined to analytics technologies is increasingly changing medical practice and healthcare in an impressive way using efficient algorithms from various branches of information technology (IT). Indeed, numerous works are published every year in several universities and innovation centers worldwide, but there are concerns about progress in their effective success. There are growing examples of AIA being implemented in healthcare with promising results. This review paper summarizes the past 5 years of healthcare applications of AIA, across different techniques and medical specialties, and discusses the current issues and challenges, related to this revolutionary technology. A total of 24,782 articles were identified. The aim of this paper is to provide the research community with the necessary background to push this field even further and propose a framework that will help integrate diverse AIA technologies around patient needs in various healthcare contexts, especially for chronic care patients, who present the most complex comorbidities and care needs.
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Chaki J, Dey N. Data Tagging in Medical Images: A Survey of the State-of-Art. Curr Med Imaging 2020; 16:1214-1228. [PMID: 32108002 DOI: 10.2174/1573405616666200218130043] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 12/02/2019] [Accepted: 12/16/2019] [Indexed: 11/22/2022]
Abstract
A huge amount of medical data is generated every second, and a significant percentage of the data are images that need to be analyzed and processed. One of the key challenges in this regard is the recovery of the data of medical images. The medical image recovery procedure should be done automatically by the computers that are the method of identifying object concepts and assigning homologous tags to them. To discover the hidden concepts in the medical images, the lowlevel characteristics should be used to achieve high-level concepts and that is a challenging task. In any specific case, it requires human involvement to determine the significance of the image. To allow machine-based reasoning on the medical evidence collected, the data must be accompanied by additional interpretive semantics; a change from a pure data-intensive methodology to a model of evidence rich in semantics. In this state-of-art, data tagging methods related to medical images are surveyed which is an important aspect for the recognition of a huge number of medical images. Different types of tags related to the medical image, prerequisites of medical data tagging, different techniques to develop medical image tags, different medical image tagging algorithms and different tools that are used to create the tags are discussed in this paper. The aim of this state-of-art paper is to produce a summary and a set of guidelines for using the tags for the identification of medical images and to identify the challenges and future research directions of tagging medical images.
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Affiliation(s)
- Jyotismita Chaki
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - Nilanjan Dey
- Department of Information Technology, Techno India College of Technology, West Bengal, India
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Messaoudi R, Mtibaa A, Vacavant A, Gargouri F, Jaziri F. Ontologies for Liver Diseases Representation: A Systematic Literature Review. J Digit Imaging 2019; 33:563-573. [PMID: 31848894 DOI: 10.1007/s10278-019-00303-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Ontology, as a useful knowledge engineering technique, has been widely used for reducing ambiguity and helping with information sharing. It is considered originally to be clear, comprehensive, and with well-defined format. It characterizes several domains purposes description through structured and formalized languages. In various areas of research, it has become a significant way to realize successful and powerful accomplishments. Actually, medical ontologies were turned into an efficient application in medical domains. They also become a relevant approach to process large medical data volumes. Consequently, they are behaving as a support decision system in some cases. Also, they ensure diagnosis process acceleration and assistance. Additionally, they have been integrated especially to represent human healthcare concepts. For that reason, plenty of research works applied ontologies to design and treat liver diseases. In this article, we present a general overview of medical ontologies to stand for this type of disease. We expose and discuss these works in details by a complete comparison. Also, we show their performance to arrange clinical data and extract results.
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Affiliation(s)
- Rim Messaoudi
- MIRACL Laboratory, University of Sfax, Sfax, Tunisia.
- Institut Pascal, Université Clermont Auvergne, UMR6602 CNRS/UCA/SIGMA, 63171, Aubière, France.
| | - Achraf Mtibaa
- MIRACL Laboratory, University of Sfax, Sfax, Tunisia
- National School of Electronic and Telecommunications, University of Sfax, Sfax, Tunisia
| | - Antoine Vacavant
- Institut Pascal, Université Clermont Auvergne, UMR6602 CNRS/UCA/SIGMA, 63171, Aubière, France
| | - Faïez Gargouri
- MIRACL Laboratory, University of Sfax, Sfax, Tunisia
- Higher Institute of Computer Science and Multimedia, University of Sfax, Sfax, Tunisia
| | - Faouzi Jaziri
- Institut Pascal, Université Clermont Auvergne, UMR6602 CNRS/UCA/SIGMA, 63171, Aubière, France
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