Banerjee Imon, Malladi Sadhika, Lee Daniela, Depeursinge Adrien, Telli Melinda, Lipson Jafi, Golden Daniel, Rubin Daniel L
Stanford University, Department of Radiology, Stanford, California, United States.
Massachusetts Institute of Technology, Department of Mathematics, Cambridge, Massachusetts, United States.
J Med Imaging (Bellingham). 2018 Jan;5(1):011008. doi: 10.1117/1.JMI.5.1.011008. Epub 2017 Nov 2.
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.
动态对比增强磁共振成像（DCE-MRI）在确定早期三阴性乳腺癌（TNBC）患者的治疗反应方面具有敏感性，但缺乏特异性。我们提出了一种有效的计算机技术，用于评估新辅助化疗后TNBC患者的治疗反应，特别是残余肿瘤（RT）状态和病理完全缓解（pCR）。所提出的方法基于对治疗前后获得的无创DCE-MRI扫描得出的药代动力学图谱进行里兹小波分析。我们将里兹特征的性能与传统的灰度共生矩阵以及包括广泛定量特征（如形状和边界）的病变综合特征进行了比较。我们研究了一组预测模型（[公式：见原文]），这些模型在治疗的不同时间点纳入了定量特征和统计模型的不同组合，并且我们报告的一些受试者操作特征曲线（AUC）下面积值高于0.8。最有效的模型基于一阶统计和里兹小波，预测RT的AUC值为0.85，预测pCR的AUC值为0.83，改进了[公式：见原文]先前研究中报告的结果。我们的研究结果表明，TNBC病变的里兹纹理分析可被视为优化TNBC患者护理的潜在框架。
