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J Med Imaging (Bellingham). 2018 Jan;5(1):011008. doi: 10.1117/1.JMI.5.1.011008. Epub 2017 Nov 2.

Assessing treatment response in triple-negative breast cancer from quantitative image analysis in perfusion magnetic resonance imaging.

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Stanford University, Department of Radiology, Stanford, California, United States.
Massachusetts Institute of Technology, Department of Mathematics, Cambridge, Massachusetts, United States.
Yale University, Department of Ecology and Evolutionary Biology, New Haven, Connecticut, United States.
University of Applied Sciences Western Switzerland (HES-SO), Department Institute of Information Systems, Sierre, Switzerland.
Stanford University, Department of Medicine (Oncology), Stanford, California, United States.
Arterys Inc., San Francisco, California, United States.


Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is sensitive but not specific to determining treatment response in early stage triple-negative breast cancer (TNBC) patients. We propose an efficient computerized technique for assessing treatment response, specifically the residual tumor (RT) status and pathological complete response (pCR), in response to neoadjuvant chemotherapy. The proposed approach is based on Riesz wavelet analysis of pharmacokinetic maps derived from noninvasive DCE-MRI scans, obtained before and after treatment. We compared the performance of Riesz features with the traditional gray level co-occurrence matrices and a comprehensive characterization of the lesion that includes a wide range of quantitative features (e.g., shape and boundary). We investigated a set of predictive models ([Formula: see text]) incorporating distinct combinations of quantitative characterizations and statistical models at different time points of the treatment and some area under the receiver operating characteristic curve (AUC) values we reported are above 0.8. The most efficient models are based on first-order statistics and Riesz wavelets, which predicted RT with an AUC value of 0.85 and pCR with an AUC value of 0.83, improving results reported in a previous study by [Formula: see text]. Our findings suggest that Riesz texture analysis of TNBC lesions can be considered a potential framework for optimizing TNBC patient care.


image features; machine learning; neoadjuvant chemotherapy; quantitative imaging; triple-negative breast cancer

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