The estrogen receptor (ER) plays a key role in predicting long-term outcomes in patients with ductal carcinoma in situ (DCIS), researchers from Queen Mary University of London reported in a study published online on 16 March in Clinical Cancer Research.
After analyzing samples from the randomized UK/ANZ DCIS trial, the authors led by Dr. Mangesh Thorat, PhD, found multiclonality in ER expression in 11% of ER-positive DCIS. What's more, they concluded that multiclonal ER had a greater than three-fold higher risk of ipsilateral recurrence than ER-negative DCIS. As a result, the researchers said that ER -- and its clonality -- should be routinely assessed in DCIS patients.
"Routine testing of ER in DCIS will help to avoid both overtreatment and undertreatment in this type of breast cancer," Thorat said in a statement from Queen Mary University of London. "The insights from this study will also help improve future breast cancer research through use of new scientific models, particularly in areas of drug resistance and the use of targeted therapies."
![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&dpr=2&fit=crop&h=167&q=70&w=250)
