Kefah Mokbel, Professor of Medicine (Honorary) at Cardiff University School of Medicine, shared a post on LinkedIn:
“Continuous Mammographic Density Outperforms Traditional Category Methods in Breast Cancer Risk Prediction.
A new study published in JCO Precision Oncology (Ficorella et al., September 2025) shows that using continuous mammographic density measurements provides superior breast cancer risk prediction compared with the traditional BI-RADS category system.
While BI-RADS relies on semi-quantitative, subjective readings, automated tools such as Volpara and STRATUS generate objective, continuous data that can be integrated into the BOADICEA v7.2 model.
This approach reclassified nearly 30% of women into more accurate risk categories and improved predictive accuracy by up to 4%, underscoring the value of continuous, automated density measures in advancing early detection and personalized prevention.”
Title: Incorporating Continuous Mammographic Density Into the BOADICEA Breast Cancer Risk Prediction Model
Authors: Lorenzo Ficorella, Mikael Eriksson, Kamila Czene, Goska Leslie, Xin Yang, Tim Carver, Adam E. Stokes, Douglas F. Easton, Per Hall, Antonis C. Antoniou
Read The Full Article at ASCO Pubs.
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