Despite the encouraging results, more studies are needed in order to further evaluate these preliminary findings and to find to what extent radiomics and AI approaches can be integrated in clinical practice in a useful and reliable strategy [1]. I think that several issues reduce the application of radiomics approaches in clinical practice: the lack of knowledge of its basic concepts among radiologists, limited reproducibility, and limited external validation. However, radiomics analysis is non-invasive and could provide a lot of data directly from clinical medical images, so it is worthy of research efforts. Focus on better quality research studies with the potential to influence treatment, patient outcome, and social impact should be encouraged [2].
Key points
- The association between quantitative radiomic features from digital breast tomosynthesis and Ki-67 expression of breast cancer was investigated.
- A combination of five radiomic features yielded an area under the curve at receiver operating characteristics analysis of 0.676 to for high versus low Ki-67 expression.
- Thirty-four features were significantly correlated with Ki-67 expression.
Article: Breast cancer Ki-67 expression prediction by digital breast tomosynthesis radiomics features
Authors: Alberto Stefano Tagliafico, Bianca Bignotti, Federica Rossi, Joao Matos, Massimo Calabrese, Francesca Valdora, and Nehmat Houssami
