
London researchers have created a web-based calculator that can better predict the long-term risk of breast cancer returning in other areas of the body. The tool, Clinical Treatment Score post-5 years (CTS5), went live on 24 August.
Researchers from the Royal Marsden National Health Service (NHS) Foundation Trust and Queen Mary University of London gathered data from nearly 12,000 postmenopausal women to determine who developed metastasis five to 10 years after finishing endocrine therapy. Then they combined the data with information about the tumor to produce a risk equation.
Over the last 30 years, the rate of invasive breast cancer in Western countries has increased significantly. Approximately 85% of patients are diagnosed as estrogen receptor (ER)-positive, and almost all of these patients are prescribed five years of hormone therapy after having standard treatment of surgery, chemotherapy, and/or radiation therapy to lower the risk of the cancer returning, according to the researchers (Journal of Clinical Oncology, 1 July 2018, Vol. 26:19, pp. 1941-1948).
However, hormone therapy can have significant side effects, including weakness of bone tissue and exacerbation of menopausal symptoms, for some patients.
"Oncologists along with patients have to decide after five years of hormone treatment whether extending this type of therapy is worthwhile and appropriate," stated Dr. Mitch Dowsett, head of the Royal Marsden Ralph Lauren Centre for Breast Cancer Research and a professor of biochemical endocrinology at the Institute of Cancer Research, and his colleagues in a press release.
To aid in this process, they created CTS5. They pulled data from two previously published studies that provided information on 11,446 postmenopausal women with ER-positive breast cancer who had received five years of hormone therapy with tamoxifen, anastrozole, or letrozole. The researchers measured how many women developed metastasis five to 10 years after they finished endocrine therapy, which was then combined with information about the tumor to produce a risk equation.
To validate CTS5, the researchers tested it against data from the second study by inputting patient details, including age, tumor size, and tumor grade to give an estimated five- to 10-year risk of the cancer returning in another part of the woman's body, with an estimated benefit from extending their hormone therapy. CTS5 accurately separated women into groups of low, intermediate, or high risk of developing a late distant recurrence breast cancer after five years of hormone therapy. The test identified 42% of women who were at sufficiently low risk that extending hormone therapy would have been of very little value.
One caveat: The patient data the researchers culled from didn't include trastuzumab, so clinicians treating their patients with that drug should be cautious of using CTS5, the authors noted.
The online web-based calculator can be found here.












![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)





