Background: The validity of the PREDICT breast cancer prognostic model is unclear for young patients without adjuvant systemic treatment. This study aimed to validate PREDICT and assess its... Show moreBackground: The validity of the PREDICT breast cancer prognostic model is unclear for young patients without adjuvant systemic treatment. This study aimed to validate PREDICT and assess its clinical utility in young women with node-negative breast cancer who did not receive systemic treatment.Methods: We selected all women from the Netherlands Cancer Registry who were diagnosed with node-negative breast cancer under age 40 between 1989 and 2000, a period when adjuvant systemic treatment was not standard practice for women with node-negative disease. We evaluated the calibration and discrimination of PREDICT using the observed/expected (O/E) mortality ratio, and the area under the receiver operating characteristic curve (AUC), respectively. Additionally, we compared the potential clinical utility of PREDICT for selectively administering chemotherapy to the chemotherapy-to-all strategy using decision curve analysis at predefined thresholds.Results: A total of 2264 women with a median age at diagnosis of 36 years were included. Of them, 71.2% had estrogen receptor (ER)-positive tumors and 44.0% had grade 3 tumors. Median tumor size was 16 mm. PREDICT v2.2 underestimated 10-year all-cause mortality by 33% in all women (O/E ratio:1.33, 95%CI:1.22-1.43). Model discrimination was moderate overall (AUC10-year:0.65, 95%CI:0.62-0.68), and poor for women with ER-negative tumors (AUC10-year:0.56, 95%CI:0.51-0.62). Compared to the chemotherapy-to-all strategy, PREDICT only showed a slightly higher net benefit in women with ER-positive tumors, but not in women with ER-negative tumors. Conclusions: PREDICT yields unreliable predictions for young women with node-negative breast cancer. Further model updates are needed before PREDICT can be routinely used in this patient subset. Show less
Women with breast cancer often wonder whether they should have their other breast removed as well, to prevent a potential tumor from developing there. The exact risks vary significantly per person.... Show moreWomen with breast cancer often wonder whether they should have their other breast removed as well, to prevent a potential tumor from developing there. The exact risks vary significantly per person. We used information about patients, breast cancer characteristics and treatments, and rare and common genetic variant correlated with a higher or lower risk of developing breast cancer in the other breast in large datasets to develop and validate statistical models to predict each patient’s risk of developing a tumor. We investigated whether and how these models might be clinically useful to better inform patients and physicians to tailor clinical decision making about potential strategies to prevent or early detect a tumor in the opposite breast. We discussed statistical aspects about model development and validation, and we provided frameworks about how to develop and assess prediction performance of risk prediction models using motivating examples in breast cancer. Show less
