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| http://dx.doi.org/10.1148/ryai.2020200103 | DOI Listing |
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Decoding Breast Cancer: Using Radiomics to Non-Invasively Unveil Molecular Subtypes Directly from Mammographic Images.
J Imaging
September 2024
Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, 1649-004 Lisbon, Portugal.
Breast cancer is the most commonly diagnosed cancer worldwide. The therapy used and its success depend highly on the histology of the tumor. This study aimed to explore the potential of predicting the molecular subtype of breast cancer using radiomic features extracted from screening digital mammography (DM) images.
View Article and Find Full Text PDFBreast Cancer Molecular Subtype Prediction: A Mammography-Based AI Approach.
Biomedicines
June 2024
Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisbon, Portugal.
Breast cancer remains a leading cause of mortality among women, with molecular subtypes significantly influencing prognosis and treatment strategies. Currently, identifying the molecular subtype of cancer requires a biopsy-a specialized, expensive, and time-consuming procedure, often yielding to results that must be supported with additional biopsies due to technique errors or tumor heterogeneity. This study introduces a novel approach for predicting breast cancer molecular subtypes using mammography images and advanced artificial intelligence (AI) methodologies.
View Article and Find Full Text PDFDeep Learning for Breast Cancer Risk Prediction: Application to a Large Representative UK Screening Cohort.
Radiol Artif Intell
July 2024
From the Department of Scientific Computing (S.E., S.G., M.T., M.D.H.B., N.S.C., P.H., L.M.W.) and National Co-ordinating Centre for the Physics of Mammography (K.C.Y.), Royal Surrey NHS Foundation Trust, Egerton Road, Guildford GU2 7XX, England; and Centre for Vision, Speech and Signal Processing (M.D.H.B.) and Department of Physics (K.C.Y.), University of Surrey, Guildford, England.
Purpose To develop an artificial intelligence (AI) deep learning tool capable of predicting future breast cancer risk from a current negative screening mammographic examination and to evaluate the model on data from the UK National Health Service Breast Screening Program. Materials and Methods The OPTIMAM Mammography Imaging Database contains screening data, including mammograms and information on interval cancers, for more than 300 000 female patients who attended screening at three different sites in the United Kingdom from 2012 onward. Cancer-free screening examinations from women aged 50-70 years were performed and classified as risk-positive or risk-negative based on the occurrence of cancer within 3 years of the original examination.
View Article and Find Full Text PDFAre better AI algorithms for breast cancer detection also better at predicting risk? A paired case-control study.
Breast Cancer Res
February 2024
Wolfson Institute of Population Health, Queen Mary University of London, Charterhouse square, London, EC1M 6BQ, England, UK.
Background: There is increasing evidence that artificial intelligence (AI) breast cancer risk evaluation tools using digital mammograms are highly informative for 1-6 years following a negative screening examination. We hypothesized that algorithms that have previously been shown to work well for cancer detection will also work well for risk assessment and that performance of algorithms for detection and risk assessment is correlated.
Methods: To evaluate our hypothesis, we designed a case-control study using paired mammograms at diagnosis and at the previous screening visit.
Predicting Breast Cancer Risk Using Radiomics Features of Mammography Images.
J Pers Med
October 2023
Department of Breast and Endocrine Surgery, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
Mammography images contain a lot of information about not only the mammary glands but also the skin, adipose tissue, and stroma, which may reflect the risk of developing breast cancer. We aimed to establish a method to predict breast cancer risk using radiomics features of mammography images and to enable further examinations and prophylactic treatment to reduce breast cancer mortality. We used mammography images of 4000 women with breast cancer and 1000 healthy women from the 'starting point set' of the OPTIMAM dataset, a public dataset.
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