Panthi Bikash, Mohamed Rania M, Adrada Beatriz E, Boge Medine, Candelaria Rosalind P, Chen Huiqin, Hunt Kelly K, Huo Lei, Hwang Ken-Pin, Korkut Anil, Lane Deanna L, Le-Petross Huong C, Leung Jessica W T, Litton Jennifer K, Pashapoor Sanaz, Perez Frances, Son Jong Bum, Sun Jia, Thompson Alastair, Tripathy Debu, Valero Vicente, Wei Peng, White Jason, Xu Zhan, Yang Wei, Zhou Zijian, Yam Clinton, Rauch Gaiane M, Ma Jingfei
Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Front Oncol. 2023 Oct 24;13:1264259. doi: 10.3389/fonc.2023.1264259. eCollection 2023.
Early prediction of neoadjuvant systemic therapy (NAST) response for triple-negative breast cancer (TNBC) patients could help oncologists select individualized treatment and avoid toxic effects associated with ineffective therapy in patients unlikely to achieve pathologic complete response (pCR). The objective of this study is to evaluate the performance of radiomic features of the peritumoral and tumoral regions from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquired at different time points of NAST for early treatment response prediction in TNBC. This study included 163 Stage I-III patients with TNBC undergoing NAST as part of a prospective clinical trial (NCT02276443). Peritumoral and tumoral regions of interest were segmented on DCE images at baseline (BL) and after two (C2) and four (C4) cycles of NAST. Ten first-order (FO) radiomic features and 300 gray-level-co-occurrence matrix (GLCM) features were calculated. Area under the receiver operating characteristic curve (AUC) and Wilcoxon rank sum test were used to determine the most predictive features. Multivariate logistic regression models were used for performance assessment. Pearson correlation was used to assess intrareader and interreader variability. Seventy-eight patients (48%) had pCR (52 training, 26 testing), and 85 (52%) had non-pCR (57 training, 28 testing). Forty-six radiomic features had AUC at least 0.70, and 13 multivariate models had AUC at least 0.75 for training and testing sets. The Pearson correlation showed significant correlation between readers. In conclusion, Radiomic features from DCE-MRI are useful for differentiating pCR and non-pCR. Similarly, predictive radiomic models based on these features can improve early noninvasive treatment response prediction in TNBC patients undergoing NAST.
新辅助全身治疗（NAST）对三阴性乳腺癌（TNBC）患者反应的早期预测，有助于肿瘤学家选择个体化治疗方案，并避免在不太可能实现病理完全缓解（pCR）的患者中使用无效治疗带来的毒副作用。本研究的目的是评估在NAST不同时间点采集的动态对比增强磁共振成像（DCE-MRI）中肿瘤周围和肿瘤区域的影像组学特征，用于TNBC早期治疗反应预测的性能。本研究纳入了163例接受NAST的I-III期TNBC患者，作为一项前瞻性临床试验（NCT02276443）的一部分。在基线（BL）以及NAST两个周期（C2）和四个周期（C4）后，在DCE图像上分割出肿瘤周围和肿瘤感兴趣区域。计算了10个一阶（FO）影像组学特征和300个灰度共生矩阵（GLCM）特征。采用受试者操作特征曲线下面积（AUC）和Wilcoxon秩和检验来确定最具预测性的特征。使用多变量逻辑回归模型进行性能评估。采用Pearson相关性评估阅片者内和阅片者间的变异性。78例患者（48%）达到pCR（52例用于训练，26例用于测试），85例（52%）未达到pCR（57例用于训练，28例用于测试）。46个影像组学特征的AUC至少为0.70，13个多变量模型在训练集和测试集上的AUC至少为0.75。Pearson相关性显示阅片者之间存在显著相关性。总之，DCE-MRI的影像组学特征有助于区分pCR和非pCR。同样，基于这些特征的预测性影像组学模型可以改善接受NAST的TNBC患者的早期非侵入性治疗反应预测。
