Proposal and Definition of an Intelligent Clinical Decision Support System Applied to the Screening and Early Diagnosis of Breast Cancer.
Simple Summary: Designing systems that optimize the process of evaluating mammogram images with the goal of improving the diagnostic process of breast cancer is an active field of research due to the large health and social impact of this disease. This paper presents a new intelligent clinical decis...
| Publicado en: | Cancers Vol. 15; no. 6; pp. 1711 - 1733 |
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| Autores principales: | , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
MDPI
Mar2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=162751110&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 162751110 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726694 B74B jtl: Cancers issn: 20726694 maglogo: N pubinfo: dt: Mar2023 vid: 15 iid: 6 pid: 97109 pub: MDPI artinfo: ui: 162751110 162751110 162751110 10.3390/cancers15061711 162751110 ppf: 1711 ppct: 22 formats: tig: atl: Proposal and Definition of an Intelligent Clinical Decision Support System Applied to the Screening and Early Diagnosis of Breast Cancer. aug: au: Casal-Guisande, Manuel Álvarez-Pazó, Antía Cerqueiro-Pequeño, Jorge Bouza-Rodríguez, José-Benito Peláez-Lourido, Gustavo Comesaña-Campos, Alberto affil: Department of Design in Engineering, University of Vigo, 36208 Vigo, Spain sug: subj: Breast Neoplasms Diagnosis Decision Support Systems, Clinical Early Detection of Cancer Cancer Screening Human Female Mammography Female ab: Simple Summary: Designing systems that optimize the process of evaluating mammogram images with the goal of improving the diagnostic process of breast cancer is an active field of research due to the large health and social impact of this disease. This paper presents a new intelligent clinical decision support system that, through the concurrence of inferential models, allows the definition of various risk metrics for patients. Those metrics are weighted and combined into a Global Risk value to be finally corrected by means of an empirical weighting factor derived from the BI-RADS analysis and condition associated with the patient's mammogram images. The validation results have shown meaningful disease detection rates within the study group used, which makes it possible to estimate the potential for a diagnostic use of the developed system. Breast cancer is the most frequently diagnosed tumor pathology on a global scale, being the leading cause of mortality in women. In light of this problem, screening programs have been implemented on the population at risk in the form of mammograms, starting in the 20th century. This has considerably reduced the associated deaths, as well as improved the prognosis of the patients who suffer from this disease. In spite of this, the evaluation of mammograms is not without certain variability and depends, to a large extent, on the experience and training of the medical team carrying out the assessment. With the aim of supporting the evaluation process of mammogram images and improving the diagnosis process, this work presents the design, development and proof of concept of a novel intelligent clinical decision support system, grounded on two predictive approaches that work concurrently. The first of them applies a series of expert systems based on fuzzy inferential engines, geared towards the treatment of the characteristics associated with the main findings present in mammograms. This allows the determination of a series of risk indicators, the Symbolic Risks, related to the risk of developing breast cancer according to the different findings. The second one implements a classification machine learning algorithm, which using data related to mammography findings as well as general patient information determines another metric, the Statistical Risk, also linked to the risk of developing breast cancer. These risk indicators are then combined, resulting in a new indicator, the Global Risk. This could then be corrected using a weighting factor according to the BI-RADS category, allocated to each patient by the medical team in charge. Thus, the Corrected Global Risk is obtained, which after interpretation can be used to establish the patient's status as well as generate personalized recommendations. The proof of concept and software implementation of the system were carried out using a data set with 130 patients from a database from the School of Medicine and Public Health of the University of Wisconsin-Madison. The results obtained were encouraging, highlighting the potential use of the application, albeit pending intensive clinical validation in real environments. Moreover, its possible integration in hospital computer systems is expected to improve diagnostic processes as well as patient prognosis. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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