Quantitative ultrasound radiomics in prediction of treatment response for breast cancer

The cover for Issue 42 of Oncotarget features Figure 4, "Generation of parametric and texture maps from radiofrequency data," recently published in "Quantitative ultrasound radiomics using texture derivatives in prediction of treatment response to neo-adjuvant chemotherapy for locally advanced breast cancer" by Dasgupta, et al. which reported that to investigate quantitative ultrasound based higher-order texture derivatives in predicting the response to neoadjuvant chemotherapy in patients with locally advanced breast cancer. Three machine learning algorithms based on linear discriminant, k-nearest-neighbors, and support vector machine were used for developing radiomic models of response prediction. The most helpful features in separating the two response groups were QUS-Tex1-Tex2 features. The 5-year recurrence-free survival calculated for KNN predicted responders and non-responders using QUS-Tex1-Tex2 model was comparable to RFS for the actual response groups. The Oncotarget authors report the first study demonstrating QUS texture-derivative methods in predicting NAC responses in LABC, which leads to better results compared to using texture features alone. Dr. Gregory J. Czarnota from The Sunnybrook Health Sciences Centre, The University of Toronto, The Sunnybrook Research Institute, as well as York University said, "Breast cancer is the second most common cancer globally in terms of incidence, comprising 11.6% of all new cancers and is the 5th leading cause of mortality attributed to 6.6% of all cancer deaths."