A New York Times article this week on a debate over the value of mammography in the U.K. includes statements about breast screening that are incorrect or greatly exaggerated, according to the American College of Radiology (ACR) of Reston, VA.
In a March 31 story, Roni Caryn Rabin of the New York Times wrote that "the conventional wisdom about breast cancer screening is coming under sharp attack in Britain, and health officials there are taking notice ... after advocates and experts complained in a letter to The Times of London that none of [patient informational mammography] handouts 'come close to telling the truth' -- overstating the benefits of screening and leaving out critical information about the harms."
The story ignores the positive role that mammography has had in reducing the incidence of breast cancer, according to Dr. Carol Lee, chair of ACR's Breast Imaging Commission, who wrote a letter to the paper countering statements in the story.
"All medical tests can result in false positives or false negatives. The question is, does mammography save lives? The answer is a resounding Yes," Lee wrote. "Scientific evidence clearly shows that in the United States, the death rate from breast cancer, unchanged for the 50 years prior to 1990, is down nearly 30% since 1990, primarily due to mammography screening."
Related Reading
Research assesses mammography's value, April 2, 2009
Mammography procedure volume drops 16% since 2000, March 17, 2009
Radiologists overestimate breast imaging malpractice risk, February 9, 2009
Breast centers can manage malpractice risk, January 13, 2009
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![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&fit=crop&h=112&q=70&w=112)





