Novel mammogram-based measures improve breast cancer risk prediction beyond an established measure of mammographic density
Novel mammogram-based measures improve breast cancer risk prediction beyond an established measure of mammographic density
ABSTRACT BackgroundMammograms contain information that predicts breast cancer risk. We recently discovered two novel mammogram-based breast cancer risk measures based on image brightness (Cirrocumulus) and texture (Cirrus). It is not known whether these measures improve risk prediction when fitted together, and with an established measure of mammographic density (Cumulus). MethodsWe used three studies consisting of: 168 interval cases and 498 matched controls; 422 screen-detected cases and 1,197 matched controls; and 354 younger-diagnosis cases and 944 frequency-matched controls. We conducted conditional and unconditional logistic regression analyses of individually-and frequency-matched studies, respectively. We reported risk gradients as change in odds ratio per standard deviation of controls after adjusting for age and body mass index (OPERA). For models involving multiple measures, we calculated the OPERA equivalent to the area under the receiver operating characteristic curve. ResultsFor interval, screen-detected and younger-diagnosis cancer, the best fitting models (OPERAs [95% confidence intervals]) were: Cumulus (1.81 [1.41 to 2.31]) and Cirrus (1.7 [1.38 to 2.14]); Cirrus (1.49 [1.32 to 1.67]) and Cirrocumulus (1.16 [1.03 to 1.31]); and Cirrus (1.70 [1.48 to 1.94]) and Cirrocumulus (1.46 [1.27 to 1.68]), respectively. Their OPERA equivalents were: 2.35, 1.58, and 2.28, respectively. ConclusionsOur mammogram-based measures improved risk prediction beyond and, except for interval cancers, negated the influence of conventional mammographic density. Combined, these new mammogram-based risk measures are at least as accurate as the current polygenetic risk scores (OPERA ~ 1.6) in predicting, on a population basis, women who will be diagnosed with breast cancer.
Nguyen Tuong L.、Hopper John L.、Makalic Enes、Schmidt Daniel F.、Milne Roger L.、Maskarinec Gertraud、Aung Ye K.、Evans Christopher F.、Stone Jennifer、Song Yun-Mi、MacInnis Robert J.、Baglietto Laura、Dugu¨| Pierre-Antoine、Trinh Ho N.、Giles Graham G.、Dite Gillian、Dowty James G.、Li Shuai、Jenkins Mark A.、Sung Joohon、Southey Melissa C.
Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of MelbourneCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of MelbourneCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of MelbourneFaculty of Information Technology, Monash UniversityCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne||Cancer Epidemiology Division, Cancer Council Victoria||Precision Medicine, School of Clinical Sciences at Monash Health, Monash UniversityUniversity of Hawaii Cancer CenterCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of MelbourneCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of MelbourneCurtin UWA Centre for Genetic Origins of Health and Disease, Curtin University and the University of Western AustraliaDepartment of Family Medicine, Samsung Medical Center, Sungkyunkwan University School of MedicineCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne||Cancer Epidemiology Division, Cancer Council VictoriaDepartment of Clinical and Experimental Medicine, University of PisaCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne||Cancer Epidemiology Division, Cancer Council Victoria||Precision Medicine, School of Clinical Sciences at Monash Health, Monash UniversityCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of MelbourneCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne||Cancer Epidemiology Division, Cancer Council Victoria||Precision Medicine, School of Clinical Sciences at Monash Health, Monash UniversityCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of MelbourneCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of MelbourneCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of MelbourneCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of MelbourneDepartment of Epidemiology School of Public Health, Seoul National University||Institute of Health and Environment, Seoul National UniversityCancer Epidemiology Division, Cancer Council Victoria||Precision Medicine, School of Clinical Sciences at Monash Health, Monash University
医学研究方法肿瘤学预防医学
breast cancerCirrusCirrocumulusinterval breast cancermammographic densityscreen-detected breast cancer
Nguyen Tuong L.,Hopper John L.,Makalic Enes,Schmidt Daniel F.,Milne Roger L.,Maskarinec Gertraud,Aung Ye K.,Evans Christopher F.,Stone Jennifer,Song Yun-Mi,MacInnis Robert J.,Baglietto Laura,Dugu¨| Pierre-Antoine,Trinh Ho N.,Giles Graham G.,Dite Gillian,Dowty James G.,Li Shuai,Jenkins Mark A.,Sung Joohon,Southey Melissa C..Novel mammogram-based measures improve breast cancer risk prediction beyond an established measure of mammographic density[EB/OL].(2025-03-28)[2025-05-12].https://www.medrxiv.org/content/10.1101/2020.05.24.20111815.点此复制
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