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Search Results (4)

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Keywords = Cirrocumulus

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11 pages, 291 KB  
Article
Genetic Aspects of Mammographic Density Measures Associated with Breast Cancer Risk
by Shuai Li, Tuong L. Nguyen, Tu Nguyen-Dumont, James G. Dowty, Gillian S. Dite, Zhoufeng Ye, Ho N. Trinh, Christopher F. Evans, Maxine Tan, Joohon Sung, Mark A. Jenkins, Graham G. Giles, John L. Hopper and Melissa C. Southey
Cancers 2022, 14(11), 2767; https://doi.org/10.3390/cancers14112767 - 2 Jun 2022
Cited by 8 | Viewed by 2901
Abstract
Cumulus, Altocumulus, and Cirrocumulus are measures of mammographic density defined at increasing pixel brightness thresholds, which, when converted to mammogram risk scores (MRSs), predict breast cancer risk. Twin and family studies suggest substantial variance in the MRSs could be explained by genetic factors. [...] Read more.
Cumulus, Altocumulus, and Cirrocumulus are measures of mammographic density defined at increasing pixel brightness thresholds, which, when converted to mammogram risk scores (MRSs), predict breast cancer risk. Twin and family studies suggest substantial variance in the MRSs could be explained by genetic factors. For 2559 women aged 30 to 80 years (mean 54 years), we measured the MRSs from digitized film mammograms and estimated the associations of the MRSs with a 313-SNP breast cancer polygenic risk score (PRS) and 202 individual SNPs associated with breast cancer risk. The PRS was weakly positively correlated (correlation coefficients ranged 0.05–0.08; all p < 0.04) with all the MRSs except the Cumulus-white MRS based on the “white but not bright area” (correlation coefficient = 0.04; p = 0.06). After adjusting for its association with the Altocumulus MRS, the PRS was not associated with the Cumulus MRS. There were MRS associations (Bonferroni-adjusted p < 0.04) with one SNP in the ATXN1 gene and nominally with some ESR1 SNPs. Less than 1% of the variance of the MRSs is explained by the genetic markers currently known to be associated with breast cancer risk. Discovering the genetic determinants of the bright, not white, regions of the mammogram could reveal substantial new genetic causes of breast cancer. Full article
(This article belongs to the Special Issue The Genetic Epidemiology of Breast Density: A Strong and Heritable Breast Cancer Risk Factor)
10 pages, 251 KB  
Article
Familial Aspects of Mammographic Density Measures Associated with Breast Cancer Risk
by Tuong L. Nguyen, Shuai Li, James G. Dowty, Gillian S. Dite, Zhoufeng Ye, Tu Nguyen-Dumont, Ho N. Trinh, Christopher F. Evans, Maxine Tan, Joohon Sung, Mark A. Jenkins, Graham G. Giles, Melissa C. Southey and John L. Hopper
Cancers 2022, 14(6), 1483; https://doi.org/10.3390/cancers14061483 - 14 Mar 2022
Cited by 6 | Viewed by 2922
Abstract
Cumulus, Cumulus-percent, Altocumulus, Cirrocumulus, and Cumulus-white are mammogram risk scores (MRSs) for breast cancer based on mammographic density defined in effect by different levels of pixel brightness and adjusted for age and body mass index. We measured these MRS from [...] Read more.
Cumulus, Cumulus-percent, Altocumulus, Cirrocumulus, and Cumulus-white are mammogram risk scores (MRSs) for breast cancer based on mammographic density defined in effect by different levels of pixel brightness and adjusted for age and body mass index. We measured these MRS from digitized film mammograms for 593 monozygotic (MZ) and 326 dizygotic (DZ) female twin pairs and 1592 of their sisters. We estimated the correlations in relatives (r) and the proportion of variance due to genetic factors (heritability) using the software FISHER and predicted the familial risk ratio (FRR) associated with each MRS. The ρ estimates ranged from: 0.41 to 0.60 (standard error [SE] 0.02) for MZ pairs, 0.16 to 0.26 (SE 0.05) for DZ pairs, and 0.19 to 0.29 (SE 0.02) for sister pairs (including pairs of a twin and her non-twin sister), respectively. Heritability estimates were 39% to 69% under the classic twin model and 36% to 56% when allowing for shared non-genetic factors specific to MZ pairs. The FRRs were 1.08 to 1.17. These MRSs are substantially familial, due mostly to genetic factors that explain one-quarter to one-half as much of the familial aggregation of breast cancer that is explained by the current best polygenic risk score. Full article
(This article belongs to the Special Issue The Genetic Epidemiology of Breast Density: A Strong and Heritable Breast Cancer Risk Factor)
14 pages, 14259 KB  
Article
Adaptive Contrast Enhancement of Optical Imagery Based on Level of Detail (LOD)
by Cheng-Chien Liu
Remote Sens. 2020, 12(10), 1555; https://doi.org/10.3390/rs12101555 - 14 May 2020
Cited by 3 | Viewed by 4420
Abstract
The viewing and sharing of remote sensing optical imagery through the World Wide Web is an efficient means for providing information to the general public and decision makers. Since clouds and hazes inevitably limit the contrast and deteriorate visual effects, only cloudless scenes [...] Read more.
The viewing and sharing of remote sensing optical imagery through the World Wide Web is an efficient means for providing information to the general public and decision makers. Since clouds and hazes inevitably limit the contrast and deteriorate visual effects, only cloudless scenes are usually included and presented in existing web mapping services. This work proposes a level-of-detail (LOD) based enhancement approach to present satellite imagery with an adaptively enhanced contrast determined by its viewing LOD. Compared to existing web mapping services, this new approach provides a better visual effect as well as spectral details of satellite imagery for cases partially covered with clouds or cirrocumulus clouds. The full archive of global satellite imagery, either the existing one or the one collected in the future, can be utilized and shared through the Web with the processing proposed in this new approach. Full article
(This article belongs to the Special Issue The Quality of Remote Sensing Optical Images from Acquisition to Users)
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13 pages, 740 KB  
Commentary
Going Beyond Conventional Mammographic Density to Discover Novel Mammogram-Based Predictors of Breast Cancer Risk
by John L Hopper, Tuong L Nguyen, Daniel F Schmidt, Enes Makalic, Yun-Mi Song, Joohon Sung, Gillian S Dite, James G Dowty and Shuai Li
J. Clin. Med. 2020, 9(3), 627; https://doi.org/10.3390/jcm9030627 - 26 Feb 2020
Cited by 22 | Viewed by 3964
Abstract
This commentary is about predicting a woman’s breast cancer risk from her mammogram, building on the work of Wolfe, Boyd and Yaffe on mammographic density. We summarise our efforts at finding new mammogram-based risk predictors, and how they combine with the conventional mammographic [...] Read more.
This commentary is about predicting a woman’s breast cancer risk from her mammogram, building on the work of Wolfe, Boyd and Yaffe on mammographic density. We summarise our efforts at finding new mammogram-based risk predictors, and how they combine with the conventional mammographic density, in predicting risk for interval cancers and screen-detected breast cancers across different ages at diagnosis and for both Caucasian and Asian women. Using the OPERA (odds ratio per adjusted standard deviation) concept, in which the risk gradient is measured on an appropriate scale that takes into account other factors adjusted for by design or analysis, we show that our new mammogram-based measures are the strongest of all currently known breast cancer risk factors in terms of risk discrimination on a population-basis. We summarise our findings graphically using a path diagram in which conventional mammographic density predicts interval cancer due to its role in masking, while the new mammogram-based risk measures could have a causal effect on both interval and screen-detected breast cancer. We discuss attempts by others to pursue this line of investigation, the measurement challenge that allows different measures to be compared in an open and transparent manner on the same datasets, as well as the biological and public health consequences. Full article
(This article belongs to the Special Issue Mammographic Density: Biology and Clinical Applications)
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