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Abstract：
Triple-negative breast cancer (TNBC) is often treated with neoadjuvant systemic therapy (NAST). We investigated if radiomic models based on multiparametric Magnetic Resonance Imaging (MRI) obtained early during NAST predict pathologic complete response (pCR). We included 163 patients with stage I-III TNBC with multiparametric MRI at baseline and after 2 (C2) and 4 cycles of NAST. Seventy-eight patients (48%) had pCR, and 85 (52%) had non-pCR. Thirty-six multivariate models combining radiomic features from dynamic contrast-enhanced MRI and diffusion-weighted imaging had an area under the receiver operating characteristics curve (AUC) > 0.7. The top-performing model combined 35 radiomic features of relative difference between C2 and baseline; had an AUC = 0.905 in the training and AUC = 0.802 in the testing set. There was high inter-reader agreement and very similar AUC values of the pCR prediction models for the 2 readers. Our data supports multiparametric MRI-based radiomic models for early prediction of NAST response in TNBC.
摘要：
三阴性乳腺癌（TNBC）通常采用新辅助系统治疗（NAST）。我们调查了在NAST早期获得的基于多参数磁共振成像（MRI）的影像组学模型是否可以预测病理完全缓解（pCR）。我们纳入了163例I-III期TNBC患者，在基线和2（C2）和4个NAST周期后进行了多参数MRI。78例患者（48%）有pCR，85（52％）患有非pCR。结合动态对比增强MRI和弥散加权成像的影像组学特征的36个多变量模型的受试者工作特征曲线下面积（AUC）>0.7。表现最好的模型组合了C2和基线之间的相对差异的35个放射学特征;在训练中具有AUC=0.905并且在测试集中具有AUC=0.802。对于2个读者,存在高的读者间一致性和pCR预测模型的非常相似的AUC值。我们的数据支持基于多参数MRI的影像组学模型，用于早期预测TNBC中的NAST反应。
