2 November 2017 Assessing treatment response in triple-negative breast cancer from quantitative image analysis in perfusion magnetic resonance imaging
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Abstract
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 ( 96 ) 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 13 % . Our findings suggest that Riesz texture analysis of TNBC lesions can be considered a potential framework for optimizing TNBC patient care.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Imon Banerjee, Imon Banerjee, Sadhika Malladi, Sadhika Malladi, Daniela Lee, Daniela Lee, Adrien Depeursinge, Adrien Depeursinge, Melinda Telli, Melinda Telli, Jafi Lipson, Jafi Lipson, Daniel Golden, Daniel Golden, Daniel L. Rubin, Daniel L. Rubin, } "Assessing treatment response in triple-negative breast cancer from quantitative image analysis in perfusion magnetic resonance imaging," Journal of Medical Imaging 5(1), 011008 (2 November 2017). https://doi.org/10.1117/1.JMI.5.1.011008 . Submission: Received: 23 June 2017; Accepted: 16 October 2017
Received: 23 June 2017; Accepted: 16 October 2017; Published: 2 November 2017
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