Classification of mammographic breast density and its correlation with BI-RADS in elder women using machine learning approach |
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| Institution: | 1. Université de Tunis El Manar, Laboratoire de recherche en Biophysique et Technologies Médicales (LRBTM), Institut Supérieur des Technologies Médicales de Tunis (ISTMT), 1006, Tunis, Tunisia;2. Université de Tunis El Manar, Faculté de Médecine de Tunis, Laboratoire de recherche en Biophysique et Technologies Médicales (LRBTM) ISTMT, Institut Salah AZAIEZ: Service de Médecine Nucléaire, 1006, Tunis, Tunisia;1. Radiography Department, School of Biomedical and Allied Health Sciences, College of Health Sciences, University of Ghana, P.O Box KB 143, Korle-Bu Campus, Accra, Ghana.;2. Department of Nuclear Safety and Security, School of Nuclear and Allied Sciences, University of Ghana, Atomic Campus, Accra, Ghana, Legon.;3. Medical Physics Department, School of Nuclear and Allied Sciences, University of Ghana, Atomic Campus, Accra, Ghana.;4. Radiation Protection Institute (RPI), Ghana Atomic Energy Commission, Accra, Ghana.;5. Radiological and Non-ionizing Radiation Directorate, Nuclear Regulatory Authority, Accra, Ghana.;1. Medical Imaging and Radiation Therapy, School of Medicine, UG Assert, Brookfield Health Sciences, University College Cork, T12 AK54 Ireland;2. Department of Radiology, Cork University Hospital, Wilton Road, Cork, T12 DFK4 Ireland;1. Nuclear Medicine Unit, Ente Ecclesiastico Ospedale Generale Regionale “F.Miulli”, Bari, Acquaviva delle Fonti, Italy;2. UOC Radiologia, Azienda Ospedaliera Regionale San Carlo, Potenza, Italy;3. Istituto di Clinica delle malattie infettive, Università Cattolica del Sacro Cuore, Roma, Italy;4. Dipartimento di Scienze di laboratorio e infettivologiche, Fondazione Policlinico Universitario A. Gemelli IRCCS, UOC di Malattie infettive, Roma, Italy;5. Dipartimento di Diagnostica per immagini, Fondazione Policlinico Universitario A. Gemelli IRCCS, UOC di Radiologia Diagnostica e Interventistica Generale, Radioterapia oncologica ed ematologia, Roma, Italy;1. Sunnybrook Health Sciences Centre, Canada;2. University of Toronto, Canada |
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| Abstract: | IntroductionMammographic breast density (MBD) is a known risk factor for breast cancer and older women have higher incidence rates of breast cancer occurrence. The Breast Imaging Reporting and Data System (BI-RADS) is a commonly used MBD classification tool for mammogram reporting. However, they have limitations since there are reading inconsistencies between different radiologists with the visual assessment of breast density.MethodsDigitised film-screen mammographic images were extracted from the Digital Database for Screening Mammography (DDSM). A machine learning project was developed using commercially available software with several predictive models applied to classify different amount of MBD on mammograms into different density groups. The effectiveness of different predictive models used in classifying the mammograms were tested by receiver operator characteristics (ROC) curve with comparison to the gold standard of BI-RADS classification.ResultsThree predictive models, Decision Tree (Tree), Support Vector Model (SVM) and k-Nearest Neighbour (kNN) showed high AUC values of 0.801, 0.805 and 0.810 respectively. High AUC values for the three predictive models indicates that the accuracy of the model is approaching that of the BI-RADS method.DiscussionOur machine learning project showed to have capabilities to be potentially used in the clinical settings to help categorise mammograms into extremely dense breasts (BI-RADS Group A) from entirely fatty breasts (BI-RADS Group D).ConclusionFindings from the present study suggest that the machine learning method is potentially useful to quantify the amount of MBD in mammograms. |
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