A larger, weekly dose of radiation therapy has side effects that are comparable with those from conventional daily treatments for early-stage breast cancer, said U.K. researchers, who shared their findings on 21 October at the American Society for Radiation Oncology (ASTRO) meeting in San Antonio, Texas.
The group, led by Dr. Murray Brunt of the University Hospitals of North Midlands and Keele University, evaluated the condition of 915 women who had undergone breast conservation surgery and subsequently opted for radiation therapy at one of 18 centers across the U.K.
An initial report on the two-year results of this Faster Radiotherapy for Breast Cancer Patients (FAST) trial suggested that larger, weekly doses -- known as hypofractionation -- led to few adverse effects on breast tissue. Patients in the study were divided to receive several different protocols:
- A conventional protocol of 50 Gy in 25 fractions of 2 Gy per day
- A hypofractionated protocol of 30 Gy in five fractions of 6 Gy once a week
- A hypofractionated protocol of 28.5 Gy in five fractions of 5.7 Gy once a week during a five-week period
The study presented at ASTRO 2018 was a 10-year follow-up report, in which Brunt and colleagues confirmed their earlier findings: that the incidence of side effects was similarly low among the three groups, with minimal changes to normal tissue in 86% of the women after 10 years.
However, the 30-Gy weekly protocol did trigger statistically significant increases in long-term moderate to severe side effects such as breast shrinkage and fluid buildup. After accounting for fractionation sensitivity, the 30-Gy protocol (equivalent to 57.3 Gy over the five-week period) delivered a higher dose than the other two protocols, which may have contributed to this increase in adverse effects, Brunt noted.
"These results support treatment options that are more convenient for patients, resulting in fewer hospital visits and less expensive health services, without increasing the risk of long-term side effects," co-author Joanne Haviland said in a statement.
![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)
