{"id":6622,"date":"2020-12-30T00:00:00","date_gmt":"2020-12-30T00:00:00","guid":{"rendered":"http:\/\/ec2-34-224-182-223.compute-1.amazonaws.com\/dermatology.healthcare.pro\/index.php\/2020\/12\/30\/new-mammogram-based-measures-improve-breast-cancer-prediction\/"},"modified":"2021-01-05T16:10:23","modified_gmt":"2021-01-05T16:10:23","slug":"new-mammogram-based-measures-improve-breast-cancer-prediction","status":"publish","type":"post","link":"http:\/\/ec2-34-224-182-223.compute-1.amazonaws.com\/dermatology.healthcare.pro\/index.php\/2020\/12\/30\/new-mammogram-based-measures-improve-breast-cancer-prediction\/","title":{"rendered":"New Mammogram-Based Measures Improve Breast Cancer Prediction"},"content":{"rendered":"<h3>\n<p>For screen-detected, younger-diagnosis breast cancers, best-fitting models include new measures based on brightness, texture<\/p>\n<\/h3>\n<p><b><\/b><\/p>\n<p><b><\/b><\/p>\n<p>WEDNESDAY, Dec. 30, 2020 (HealthDay News) &#8212; New mammogram-based risk measures based on brightness (<em>Cirrocumulus<\/em>) and texture (<em>Cirrus<\/em>) improve breast cancer risk prediction beyond an established measure of mammographic density (<em>Cumulus<\/em>), according to a study recently published in the <em>International Journal of Cancer.<\/em><\/p>\n<p>Tuong L. Nguyen, Ph.D., from the University of Melbourne in Australia, and colleagues examined risk prediction with <em>Cirrocumulus<\/em> and <em>Cirrus<\/em> fitted together and with <em>Cumulus<\/em> using data from 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 controls frequency-matched for age at mammogram. Measure-specific risk gradients were estimated as the change in odds per standard deviation of controls after adjustment for age and body mass index (OPERA); the area under the receiver operating characteristic curve (AUC) was also calculated.<\/p>\n<p>The researchers found that for interval, screen-detected, and younger-diagnosis cancer risks, the best-fitting models involved <em>Cumulus <\/em>and <em>Cirrus<\/em> (OPERAs, 1.81 and 1.72, respectively), <em>Cirrus<\/em> and <em>Cirrocumulus<\/em> (OPERAs, 1.49 and 1.16, respectively), and <em>Cirrus<\/em> and <em>Cirrocumulus<\/em> (OPERAs, 1.70 and 1.46, respectively), with corresponding AUCs of 0.73, 0.63, and 0.72. <\/p>\n<p>&#8220;Our new measures appear to be more strongly correlated with such causal factors than conventional mammographic density,&#8221; the authors write. &#8220;Our findings also demonstrate the potential for much improved and more etiologically relevant breast cancer risk prediction, by discovering new ways of extracting information on breast cancer risk from a mammogram.&#8221;<\/p>\n<p>One author disclosed financial ties to Genetic Technologies.<\/p>\n<p><a href=\"https:\/\/onlinelibrary.wiley.com\/doi\/abs\/10.1002\/ijc.33396\" target=\"_blank\" rel=\"noopener noreferrer\">Abstract\/Full Text (subscription or payment may be required)<\/a><\/p>\n<p><i><\/i><\/p>\n<p><i>Copyright \u00a9 2020 <a href=\"http:\/\/www.healthday.com\/\" target=\"_new\" rel=\"noopener noreferrer\">HealthDay<\/a>. All rights reserved.<\/i><\/p>\n","protected":false},"excerpt":{"rendered":"<p>For screen-detected, younger-diagnosis breast cancers, best-fitting models include new measures based on brightness, texture<\/p>\n","protected":false},"author":4,"featured_media":7002,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[85],"tags":[96,382],"acf":[],"_links":{"self":[{"href":"http:\/\/ec2-34-224-182-223.compute-1.amazonaws.com\/dermatology.healthcare.pro\/index.php\/wp-json\/wp\/v2\/posts\/6622"}],"collection":[{"href":"http:\/\/ec2-34-224-182-223.compute-1.amazonaws.com\/dermatology.healthcare.pro\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/ec2-34-224-182-223.compute-1.amazonaws.com\/dermatology.healthcare.pro\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/ec2-34-224-182-223.compute-1.amazonaws.com\/dermatology.healthcare.pro\/index.php\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"http:\/\/ec2-34-224-182-223.compute-1.amazonaws.com\/dermatology.healthcare.pro\/index.php\/wp-json\/wp\/v2\/comments?post=6622"}],"version-history":[{"count":0,"href":"http:\/\/ec2-34-224-182-223.compute-1.amazonaws.com\/dermatology.healthcare.pro\/index.php\/wp-json\/wp\/v2\/posts\/6622\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"http:\/\/ec2-34-224-182-223.compute-1.amazonaws.com\/dermatology.healthcare.pro\/index.php\/wp-json\/wp\/v2\/media\/7002"}],"wp:attachment":[{"href":"http:\/\/ec2-34-224-182-223.compute-1.amazonaws.com\/dermatology.healthcare.pro\/index.php\/wp-json\/wp\/v2\/media?parent=6622"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/ec2-34-224-182-223.compute-1.amazonaws.com\/dermatology.healthcare.pro\/index.php\/wp-json\/wp\/v2\/categories?post=6622"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/ec2-34-224-182-223.compute-1.amazonaws.com\/dermatology.healthcare.pro\/index.php\/wp-json\/wp\/v2\/tags?post=6622"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}