There was the launch of TMIST – the Tomosynthesis Mammography Imaging Screening Trial – which will be funded by the National Cancer Institute. The trial begins in mid-2017 and will enroll 165,000 asymptomatic women in the USA and Canada, who are between the ages of 45 and 74. The purpose of TMIST will be to compare the incidence of advanced cancers in those screened for four years with digital breast tomosynthesis (DBT) versus standard full-field digital mammography (FFDM). Importantly, the study will provide the scientific basis for the continued use of FFDM for screening.
In a paper presented on Monday morning, Cindy Lee of San Francisco (seen left) reported there was no evidence that women over age 75 should discontinue regular mammography screening. Her group evaluated almost 6 million screening FFDM studies from a national database and concluded that deciding when to stop breast cancer screening should take into account each woman’s personal preferences and health history. She said: “We know the risk of breast cancer increases with age and, with the uncertainty and controversy about what age to stop screening, we wanted to address this gap in knowledge.”
Another paper in this session on hot topics in breast imaging was entitled “High breast compression in mammography may reduce sensitivity.” Nico Karssemeijer and colleagues from the Netherlands studied over 100,000 FFDM examinations and what they found will be good news for all women who have experienced the discomfort – and sometimes pain – associated with a mammogram: if too much pressure is applied during the procedure this may increase cancer interval rates and decrease positive predicted value. So, by carefully controlling pressure, the outcome can be optimized.
Yesterday Maryellen Giger of Chicago, who previously reported on the benefits of adding automated breast ultrasound (ABUS) to FFDM, presented a keynote address on the applications of deep learning in precision medicine. In concert with RSNA’s theme for 2016, she illustrated how “The goal is to individually detect disease, and then give the right person the right treatment at the right time.”