ASCO: Modest Accuracy Seen for Existing Breast Cancer Risk Prediction Models

WEDNESDAY, June 3, 2026 (HealthDay News) -- For women with a family history of breast cancer, existing breast cancer risk prediction models show similar modest discriminatory accuracy, according to research published online June 1 in The Cochrane Database of Systematic Reviews to coincide with the annual meeting of the American Society of Clinical Oncology, held from May 29 to June 2 in Chicago.Sarah A. McGarrigle, Ph.D., from Trinity College Dublin, and colleagues identified, described, and appraised breast cancer risk prediction models developed or validated in women with a family history of breast cancer. Four models that had at least four external validation studies were included in a meta-analysis.The researchers found that the Gail/Breast Cancer Risk Assessment Tool was well calibrated. The pooled estimate for the C-statistic was 0.61 in the target population, and the 95 percent prediction interval (PI) was 0.47 to 0.74. The Tyrer-Cuzick/International Breast Cancer Intervention Study model overpredicted breast cancer risk; the pooled estimate for the C-statistic was 0.62 in the target population, with a 95 percent PI of 0.49 to 0.74. For version 8 of the Tyrer-Cuzick model, the C-statistic was 0.64 and the 95 percent PI was 0.46 to 0.79. The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm model was well calibrated, and the pooled estimate for the C-statistic was 0.65, with a 95 percent PI of 0.44 to 0.81. The BRCAPRO model underpredicted breast cancer risk; the pooled estimate for the C-statistic was 0.64, and the 95 percent prediction interval was 0.37 to 0.84."Our findings suggest that these tools have value in supporting risk assessment and that is encouraging, but we still have a long way to go," McGarrigle said in a statement.Abstract/Full Text (subscription or payment may be required)More Information.Sign up for our weekly HealthDay newsletter

ASCO: Modest Accuracy Seen for Existing Breast Cancer Risk Prediction Models

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