{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T09:55:13Z","timestamp":1788861313842,"version":"build-2803163510"},"reference-count":51,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,10,25]],"date-time":"2023-10-25T00:00:00Z","timestamp":1698192000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JPM"],"abstract":"<jats:p>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 \u2018starting point set\u2019 of the OPTIMAM dataset, a public dataset. We trained a Light Gradient Boosting Machine using radiomics features extracted from mammography images of women with breast cancer (only the healthy side) and healthy women. This model was a binary classifier that could discriminate whether a given mammography image was of the contralateral side of women with breast cancer or not, and its performance was evaluated using five-fold cross-validation. The average area under the curve for five folds was 0.60122. Some radiomics features, such as \u2018wavelet-H_glcm_Correlation\u2019 and \u2018wavelet-H_firstorder_Maximum\u2019, showed distribution differences between the malignant and normal groups. Therefore, a single radiomics feature might reflect the breast cancer risk. The odds ratio of breast cancer incidence was 7.38 in women whose estimated malignancy probability was \u22650.95. Radiomics features from mammography images can help predict breast cancer risk.<\/jats:p>","DOI":"10.3390\/jpm13111528","type":"journal-article","created":{"date-parts":[[2023,10,25]],"date-time":"2023-10-25T04:14:47Z","timestamp":1698207287000},"page":"1528","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Predicting Breast Cancer Risk Using Radiomics Features of Mammography Images"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2617-9614","authenticated-orcid":false,"given":"Yusuke","family":"Suzuki","sequence":"first","affiliation":[{"name":"Department of Breast and Endocrine Surgery, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shouhei","family":"Hanaoka","sequence":"additional","affiliation":[{"name":"Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6558-4399","authenticated-orcid":false,"given":"Masahiko","family":"Tanabe","sequence":"additional","affiliation":[{"name":"Department of Breast and Endocrine Surgery, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takeharu","family":"Yoshikawa","sequence":"additional","affiliation":[{"name":"Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6953-8752","authenticated-orcid":false,"given":"Yasuyuki","family":"Seto","sequence":"additional","affiliation":[{"name":"Department of Breast and Endocrine Surgery, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"209","DOI":"10.3322\/caac.21660","article-title":"Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J. Clin."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"909","DOI":"10.1016\/S0140-6736(02)08020-0","article-title":"Long-Term Effects of Mammography Screening: Updated Overview of the Swedish Randomised Trials","volume":"359","author":"Andersson","year":"2002","journal-title":"Lancet"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1093\/jncimono\/1997.22.63","article-title":"Reduced Breast Cancer Mortality in Women under Age 50: Updated Results from the Malm\u00f6 Mammographic Screening Program","volume":"1997","author":"Andersson","year":"1997","journal-title":"J. Natl. Cancer Inst. Monogr."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1258\/jms.2009.009094","article-title":"Absolute Numbers of Lives Saved and Overdiagnosis in Breast Cancer Screening, from a Randomized Trial and from the Breast Screening Programme in England","volume":"17","author":"Duffy","year":"2010","journal-title":"J. Med. Screen."},{"key":"ref_5","first-page":"829","article-title":"Reduction in Mortality from Breast Cancer after Mass Screening with Mammography. Randomised Trial from the Breast Cancer Screening Working Group of the Swedish National Board of Health and Welfare","volume":"1","author":"Fagerberg","year":"1985","journal-title":"Lancet"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1023\/A:1005872617944","article-title":"Followup after 11 Years--Update of Mortality Results in the Stockholm Mammographic Screening Trial","volume":"45","author":"Frisell","year":"1997","journal-title":"Breast Cancer Res. Treat."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1259\/bjr\/80482243","article-title":"Image Features of True Positive and False Negative Cancers in Screening Mammograms","volume":"76","author":"Meeson","year":"2003","journal-title":"Br. J. Radiol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1148\/radiology.204.1.9205234","article-title":"Characteristics of Breast Carcinomas Missed by Screening Radiologists","volume":"204","author":"Goergen","year":"1997","journal-title":"Radiology"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1148\/radiology.184.3.1509041","article-title":"Analysis of Cancers Missed at Screening Mammography","volume":"184","author":"Bird","year":"1992","journal-title":"Radiology"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.ejrad.2018.11.012","article-title":"Mammographic Breast Density: How It Affects Performance Indicators in Screening Programmes?","volume":"110","author":"Posso","year":"2019","journal-title":"Eur. J. Radiol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.carj.2018.10.007","article-title":"Screening Sensitivity According to Breast Cancer Location","volume":"70","author":"Guertin","year":"2019","journal-title":"Can. Assoc. Radiol. J."},{"key":"ref_12","first-page":"3","article-title":"Breast tomosynthesis: A new tool for diagnosing breast cancer","volume":"57","author":"Etxano","year":"2015","journal-title":"Radiologia"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.ajog.2014.06.048","article-title":"Screening Ultrasound as an Adjunct to Mammography in Women with Mammographically Dense Breasts","volume":"212","author":"Scheel","year":"2015","journal-title":"Am. J. Obstet. Gynecol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1016\/j.acra.2007.11.006","article-title":"Breast MRI for Cancer Detection and Characterization: A Review of Evidence-Based Clinical Applications","volume":"15","author":"DeMartini","year":"2008","journal-title":"Acad. Radiol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"75","DOI":"10.3322\/canjclin.57.2.75","article-title":"American Cancer Society Guidelines for Breast Screening with MRI as an Adjunct to Mammography","volume":"57","author":"Saslow","year":"2007","journal-title":"CA Cancer J. Clin."},{"key":"ref_16","unstructured":"National Comprehensive Cancer Network (2023, September 09). Genetic\/Familial High-Risk Assessment: Breast, Ovarian, and Pancreatic Version 1.2024\u201428 August 2023. Available online: https:\/\/www.nccn.org\/professionals\/physician_gls\/pdf\/genetics_bop.pdf."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/S0140-6736(15)00774-6","article-title":"Sensitivity and Specificity of Mammography and Adjunctive Ultrasonography to Screen for Breast Cancer in the Japan Strategic Anti-Cancer Randomized Trial (J-START): A Randomised Controlled Trial","volume":"387","author":"Ohuchi","year":"2016","journal-title":"Lancet"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1879","DOI":"10.1093\/jnci\/81.24.1879","article-title":"Projecting Individualized Probabilities of Developing Breast Cancer for White Females Who Are Being Examined Annually","volume":"81","author":"Gail","year":"1989","journal-title":"J. Natl. Cancer Inst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.ajhg.2018.11.002","article-title":"Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes","volume":"104","author":"Mavaddat","year":"2019","journal-title":"Am. J. Hum. Genet."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1007\/s10549-022-06834-7","article-title":"Validation of a Breast Cancer Risk Prediction Model Based on the Key Risk Factors: Family History, Mammographic Density and Polygenic Risk","volume":"198","author":"Allman","year":"2023","journal-title":"Breast Cancer Res. Treat."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.breast.2022.09.007","article-title":"Mammographic Breast Density and the Risk of Breast Cancer: A Systematic Review and Meta-Analysis","volume":"66","author":"Bodewes","year":"2022","journal-title":"Breast"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1148\/radiol.11101266","article-title":"Association of Computerized Mammographic Parenchymal Pattern Measure with Breast Cancer Risk: A Pilot Case-Control Study","volume":"260","author":"Wei","year":"2011","journal-title":"Radiology"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/S1470-2045(05)01736-5","article-title":"Diabetes Mellitus and Breast Cancer","volume":"6","author":"Wolf","year":"2005","journal-title":"Lancet Oncol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/S0140-6736(84)92114-7","article-title":"Fibrous Disease Of The Breast, Thyroiditis, And Cheiroarthropathy In Type I Diabetes Mellitus","volume":"323","author":"Soler","year":"1984","journal-title":"Lancet"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1007\/s11892-003-0054-5","article-title":"Mastopathy and Diabetes","volume":"3","author":"Kudva","year":"2003","journal-title":"Curr. Diab. Rep."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Vasileiou, G., Costa, M.J., Long, C., Wetzler, I.R., Hoyer, J., Kraus, C., Popp, B., Emons, J., Wunderle, M., and Wenkel, E. (2020). Breast MRI Texture Analysis for Prediction of BRCA-Associated Genetic Risk. BMC Med. Imaging, 20.","DOI":"10.1186\/s12880-020-00483-2"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.ejca.2011.11.036","article-title":"Radiomics: Extracting More Information from Medical Images Using Advanced Feature Analysis","volume":"48","author":"Lambin","year":"2012","journal-title":"Eur. J. Cancer"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.semcancer.2020.04.002","article-title":"Radiomics in Breast Cancer Classification and Prediction","volume":"72","author":"Conti","year":"2021","journal-title":"Semin. Cancer Biol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1228","DOI":"10.1016\/j.acra.2021.09.025","article-title":"Mammography-Based Radiomics in Breast Cancer: A Scoping Review of Current Knowledge and Future Needs","volume":"29","author":"Siviengphanom","year":"2022","journal-title":"Acad. Radiol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"843436","DOI":"10.3389\/fonc.2022.843436","article-title":"Radiomics Based on Digital Mammography Helps to Identify Mammographic Masses Suspicious for Cancer","volume":"12","author":"Wang","year":"2022","journal-title":"Front. Oncol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2357","DOI":"10.1007\/s10238-022-00944-8","article-title":"Improving the Malignancy Prediction of Breast Cancer Based on the Integration of Radiomics Features from Dual-View Mammography and Clinical Parameters","volume":"23","author":"Zhou","year":"2022","journal-title":"Clin. Exp. Med."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"21566","DOI":"10.1038\/s41598-020-78681-9","article-title":"Prediction of Breast Cancer Molecular Subtypes Using Radiomics Signatures of Synthetic Mammography from Digital Breast Tomosynthesis","volume":"10","author":"Son","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.acra.2018.01.023","article-title":"Breast Cancer Molecular Subtype Prediction by Mammographic Radiomic Features","volume":"26","author":"Ma","year":"2019","journal-title":"Acad. Radiol."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Tamez-Pe\u00f1a, J.-G., Rodriguez-Rojas, J.-A., Gomez-Rueda, H., Celaya-Padilla, J.-M., Rivera-Prieto, R.-A., Palacios-Corona, R., Garza-Montemayor, M., Cardona-Huerta, S., and Trevi\u00f1o, V. (2018). Radiogenomics Analysis Identifies Correlations of Digital Mammography with Clinical Molecular Signatures in Breast Cancer. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0193871"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5418364","DOI":"10.1155\/2020\/5418364","article-title":"A Nomogram Based on Radiomics with Mammography Texture Analysis for the Prognostic Prediction in Patients with Triple-Negative Breast Cancer","volume":"2020","author":"Jiang","year":"2020","journal-title":"Contrast Media Mol. Imaging"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2955","DOI":"10.1002\/cncr.28771","article-title":"Breast Cancer Screening in an Era of Personalized Regimens: A Conceptual Model and National Cancer Institute Initiative for Risk-Based and Preference-Based Approaches at a Population Level","volume":"120","author":"Onega","year":"2014","journal-title":"Cancer"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4149","DOI":"10.1118\/1.4921996","article-title":"Parenchymal Texture Analysis in Digital Mammography: A Fully Automated Pipeline for Breast Cancer Risk Assessment","volume":"42","author":"Zheng","year":"2015","journal-title":"Med. Phys."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Breast Cancer Association Consortium, Dorling, L., Carvalho, S., Allen, J., Gonz\u00e1lez-Neira, A., Luccarini, C., Wahlstr\u00f6m, C., Pooley, K.A., Parsons, M.T., and Fortuno, C. (2021). Breast Cancer Risk Genes\u2014Association Analysis in More than 113,000 Women. N. Engl. J. Med., 384, 428\u2013439.","DOI":"10.1056\/NEJMoa1913948"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Collaborative Group on Hormonal Factors in Breast Cancer (2012). Menarche, Menopause, and Breast Cancer Risk: Individual Participant Meta-Analysis, Including 118 964 Women with Breast Cancer from 117 Epidemiological Studies. Lancet Oncol., 13, 1141\u20131151.","DOI":"10.1016\/S1470-2045(12)70425-4"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1002\/ijc.10799","article-title":"A Prospective Study of Adiposity and Postmenopausal Breast Cancer Risk: The Malm\u00f6 Diet and Cancer Study","volume":"103","author":"Lahmann","year":"2003","journal-title":"Int. J. Cancer"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1007\/s10549-015-3628-4","article-title":"Active and Passive Smoking and Risk of Breast Cancer: A Meta-Analysis","volume":"154","author":"Macacu","year":"2015","journal-title":"Breast Cancer Res. Treat."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"e221922","DOI":"10.1148\/radiol.221922","article-title":"Long-Term Survival in Breast Cancer Patients Is Associated with Contralateral Parenchymal Enhancement at MRI: Outcomes of the SELECT Study","volume":"307","author":"Ragusi","year":"2023","journal-title":"Radiology"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"e200103","DOI":"10.1148\/ryai.2020200103","article-title":"OPTIMAM Mammography Image Database: A Large-Scale Resource of Mammography Images and Clinical Data","volume":"3","author":"Warren","year":"2021","journal-title":"Radiol. Artif. Intell."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A Threshold Selection Method from Gray-Level Histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1081","DOI":"10.1007\/s10278-022-00770-0","article-title":"Prognostic Prediction of Cancer Based on Radiomics Features of Diagnostic Imaging: The Performance of Machine Learning Strategies","volume":"36","author":"Tang","year":"2023","journal-title":"J. Digit. Imaging"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s10555-012-9415-3","article-title":"Stromal Cells in Tumor Microenvironment and Breast Cancer","volume":"32","author":"Mao","year":"2013","journal-title":"Cancer Metastasis Rev."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1148\/radiol.2019190872","article-title":"Comparison of a Deep Learning Risk Score and Standard Mammographic Density Score for Breast Cancer Risk Prediction","volume":"294","author":"Dembrower","year":"2020","journal-title":"Radiology"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybernitics"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/S0140-6736(19)32955-1","article-title":"Use of Anastrozole for Breast Cancer Prevention (IBIS-II): Long-Term Results of a Randomised Controlled Trial","volume":"395","author":"Cuzick","year":"2020","journal-title":"Lancet"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3971","DOI":"10.1158\/1078-0432.CCR-15-1465","article-title":"Effectiveness of Prophylactic Surgeries in BRCA1 or BRCA2 Mutation Carriers: A Meta-Analysis and Systematic Review","volume":"22","author":"Li","year":"2016","journal-title":"Clin. Cancer Res."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1186\/s13058-015-0653-5","article-title":"Mammographic Density Adds Accuracy to Both the Tyrer-Cuzick and Gail Breast Cancer Risk Models in a Prospective UK Screening Cohort","volume":"17","author":"Brentnall","year":"2015","journal-title":"Breast Cancer Res."}],"container-title":["Journal of Personalized Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4426\/13\/11\/1528\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:11:25Z","timestamp":1760130685000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4426\/13\/11\/1528"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,25]]},"references-count":51,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["jpm13111528"],"URL":"https:\/\/doi.org\/10.3390\/jpm13111528","relation":{},"ISSN":["2075-4426"],"issn-type":[{"value":"2075-4426","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,25]]}}}