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We developed a four-dimensional (4D) radiomics approach for the analysis of breast cancer on dynamic contrast-enhanced (DCE) MRI scans. This approach quantifies 348 features related to kinetics, enhancement heterogeneity, and timedependent textural variation in 4D (3D over time) from the tumors and the peritumoral regions, leveraging both spatial and temporal image information. The potential of these features was studied for two clinical applications: the prediction of pathological complete response (pCR) to neoadjuvant chemotherapy (NAC), and of systemic recurrence (SR) in triplenegative (TN) breast cancers. For this, 72 pretreatment images of TN cancers (19 achieving pCR, 14 recurrence events), retrieved from a publicly available dataset (The Cancer Imaging Archive, Duke-Breast-Cancer-MRI dataset), were used. For both clinical problems, radiomic features were extracted from each case and used to develop a machine learning logistic regression model for outcome prediction. The model was trained and validated in a supervised leave-one-out cross validation fashion, with the input feature space reduced through statistical analysis and forward selection for overfitting prevention. The model was tested using the area under the receiver operating characteristics (ROC) curve (AUC), and statistical significance was assessed using the associated 95% confidence interval estimated through bootstrapping. The model achieved an AUC of 0.80 and 0.86, respectively for pCR and SR prediction. Both AUC values were statistically significant (p<0.05, adjusted for repeated testing). In conclusion, the developed approach could quantify relevant imaging biomarkers from TN breast cancers in pretreatment DCE-MRI images. These biomarkers were promising in the prediction of pCR to NAC and SR.
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