AI for response prediction remains work in progress
Dr Ritse Mann from Radboud University Medical Center in the Netherlands examined the role of AI for response prediction and diagnosis. He said that AI using images may improve pCR (pathologic complete response) prediction over prediction based on clinical features alone – ‘but the effect is modest.’
He further suggested that AI may aid in selection of less toxic therapeutic regimens for patients, though while acknowledging that presents a ‘golden opportunity’, he noted that very few studies have looked into it. ‘In the post-treatment setting, AI might help us to identify patients that do not need surgery and potentially those that do not need radiotherapy. It may also select high-risk populations with poorer prognosis for further intervention.’
However, he concluded: ‘Everything here is very much work in progress.’
Profiles:
Mikael Eriksson, PhD, is an epidemiologist and Assistant Professor in the Department of Medical Epidemiology and Biostatistics at Karolinska Institutet, Sweden. Driven by improving mammography screening to reduce breast cancer mortality, he specialises in developing individualised risk assessment techniques for identifying women who have suboptimal benefit from screening.
Dr Ritse Mann is a breast and interventional radiologist at Radboud University Medical Center in Nijmegen and the Netherlands Cancer Institute in Amsterdam, where he leads clinical breast imaging research. His research focuses on the evaluation and implementation of novel breast imaging techniques, including AI applications. He is an executive board member of the European Society of Breast Imaging (EUSOBI) and chairs its scientific committee.
References:
- Eriksson M, Destounis S, Czene K et al.: A risk model for digital breast tomosynthesis to predict breast cancer and guide clinical care, Science Translational Medicine 2022
- Eriksson M, Czene K, Vachon C et al.: Long-Term Performance of an Image-Based Short-Term Risk Model for Breast Cancer; Journal of Clinical Oncology 2023
- Yala A, Mikhael PG, Strand F et al.: Multi-Institutional Validation of a Mammography-Based Breast Cancer Risk Model; Journal of Clinical Oncology 2022