Prediction of contralateral breast cancer: statistical aspects and prediction performance

Giardiello, D.

Giardiello, D. (2022)

Prediction of contralateral breast cancer: statistical aspects and prediction performance

Doctoral Thesis

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. 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.

Contralateral breast cancer

Germline variants

Model validation

Performance assessment

Risk prediction

Clinical utility

Clinical decision making